Artificial Intelligence

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[244] viXra:1612.0030 [pdf] submitted on 2016-12-02 12:28:36

Machine Learning Breakthroughs

Authors: George Rajna
Comments: 47 Pages.

Machine Learning Breakthroughs As machine learning breakthroughs abound, researchers look to democratize benefits. [27] Machine-learning system spontaneously reproduces aspects of human neurology. [26] Surviving breast cancer changed the course of Regina Barzilay's research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. [25] New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A 'nonlinear' effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC's LCLS. [19] Leiden physicists have manipulated light with large artificial atoms, so-called quantum dots. Before, this has only been accomplished with actual atoms. It is an important step toward light-based quantum technology. [18] In a tiny quantum prison, electrons behave quite differently as compared to their counterparts in free space. They can only occupy discrete energy levels, much like the electrons in an atom-for this reason, such electron prisons are often called "artificial atoms". [17]
Category: Artificial Intelligence

[243] viXra:1612.0022 [pdf] submitted on 2016-12-02 07:17:06

Machine-Learning and Human Neurology

Authors: George Rajna
Comments: 44 Pages.

Machine-learning system spontaneously reproduces aspects of human neurology. [26] Surviving breast cancer changed the course of Regina Barzilay's research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. [25] New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A 'nonlinear' effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC's LCLS. [19] Leiden physicists have manipulated light with large artificial atoms, so-called quantum dots. Before, this has only been accomplished with actual atoms. It is an important step toward light-based quantum technology. [18] In a tiny quantum prison, electrons behave quite differently as compared to their counterparts in free space. They can only occupy discrete energy levels, much like the electrons in an atom-for this reason, such electron prisons are often called "artificial atoms". [17]
Category: Artificial Intelligence

[242] viXra:1612.0009 [pdf] submitted on 2016-12-01 12:44:05

Computer Learns by Watching Video

Authors: George Rajna
Comments: 28 Pages.

In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[241] viXra:1611.0335 [pdf] submitted on 2016-11-24 10:44:26

Kannada Spell Checker with Sandhi Splitter

Authors: Akshatha A N, Chandana G Upadhyaya, Rajashekara Murthy S
Comments: Number of pages is 7

Spelling errors are introduced in text either during typing, or when the user does not know the correct phoneme or grapheme. If a language contains complex words like sandhi where two or more morphemes join based on some rules, spell checking becomes very tedious. In such situations, having a spell checker with sandhi splitter which alerts the user by flagging the errors and providing suggestions is very useful. A novel algorithm of sandhi splitting is proposed in this paper. The sandhi splitter can split about 7000 most common sandhi words in Kannada language used as test samples. The sandhi splitter was integrated with a Kannada spell checker and a mechanism for generating suggestions was added. A comprehensive, platform independent, standalone spell checker with sandhi splitter application software was thus developed and tested extensively for its efficiency and correctness. A comparative analysis of this spell checker with sandhi splitter was made and results concluded that the Kannada spell checker with sandhi splitter has an improved performance. It is twice as fast, 200 times more space efficient, and it is 90% accurate in case of complex nouns and 50% accurate for complex verbs. Such a spell checker with sandhi splitter will be of foremost significance in machine translation systems, voice processing, etc. This is the first sandhi splitter in Kannada and the advantage of the novel algorithm is that, it can be extended to all Indian languages.
Category: Artificial Intelligence

[240] viXra:1611.0316 [pdf] submitted on 2016-11-23 08:10:08

Minds for Machine Intelligence

Authors: George Rajna
Comments: 42 Pages.

Surviving breast cancer changed the course of Regina Barzilay's research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. [25] New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A 'nonlinear' effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC's LCLS. [19] Leiden physicists have manipulated light with large artificial atoms, so-called quantum dots. Before, this has only been accomplished with actual atoms. It is an important step toward light-based quantum technology. [18] In a tiny quantum prison, electrons behave quite differently as compared to their counterparts in free space. They can only occupy discrete energy levels, much like the electrons in an atom-for this reason, such electron prisons are often called "artificial atoms". [17] When two atoms are placed in a small chamber enclosed by mirrors, they can simultaneously absorb a single photon. [16] Optical quantum technologies are based on the interactions of atoms and photons at the single-particle level, and so require sources of single photons.
Category: Artificial Intelligence

[239] viXra:1611.0314 [pdf] submitted on 2016-11-23 08:47:27

New AI Algorithm Learns Beyond its Training

Authors: George Rajna
Comments: 27 Pages.

Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[238] viXra:1611.0260 [pdf] submitted on 2016-11-17 11:18:04

Deng Entropy in Hyper Power Set and Super Power Set

Authors: Bingyi Kang, Yong Deng
Comments: 18 Pages.

Deng entropy has been proposed to handle the uncertainty degree of belief function in Dempster-Shafer framework very recently. In this paper, two new belief entropies based on the frame of Deng entropy for hyper-power sets and super-power sets are respectively proposed to measure the uncertainty degree of more uncertain and more flexible information. Directly, the new entropies based on the frame of Deng entropy in hyper-power sets and super-power sets can be used in the application of DSmT.
Category: Artificial Intelligence

[237] viXra:1611.0211 [pdf] submitted on 2016-11-14 04:10:17

A Variable Order Hidden Markov Model with Dependence Jumps

Authors: Anastasios Petropoulos, Stelios Xanthopoulos, Sotirios P. Chatzis
Comments: 14 Pages.

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first- or moderate-order Markov chain. However, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. In this paper, we address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed data, where the patterns of the entailed temporal dynamics may change over time. We devise efficient training and inference algorithms for our model, following the expectation-maximization paradigm. We demonstrate the efficacy and usefulness of our approach considering several real-world datasets. As we show, our model allows for increased modeling and predictive performance compared to the alternative methods, while offering a good trade-off between the resulting increases in predictive performance and computational complexity.
Category: Artificial Intelligence

[236] viXra:1611.0181 [pdf] submitted on 2016-11-12 07:13:04

Finding Patterns in Corrupted Data

Authors: George Rajna
Comments: 29 Pages.

A team, including researchers from MIT's Computer Science and Artificial Intelligence Laboratory, has created a new set of algorithms that can efficiently fit probability distributions to high-dimensional data. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[235] viXra:1611.0177 [pdf] submitted on 2016-11-12 04:50:24

Machines Learn by Simply Observing

Authors: George Rajna
Comments: 28 Pages.

It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[234] viXra:1611.0174 [pdf] submitted on 2016-11-12 05:46:51

Social Emotions Test for Artificial Intelligence

Authors: George Rajna
Comments: 29 Pages.

New evidence from brain studies, including cognitive psychology and neurophysiology research, shows that the emotional assessment of every object, subject, action or event plays an important role in human mental processes. And that means that if we want to create human-like artificial intelligence, we must make it emotionally responsive. But how do we know that such intelligence actually experiences real, human-like emotions? [17] It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of “quantum artificial intelligence”. Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries - how a sliced up flatworm can regenerate into new organisms - has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[233] viXra:1611.0173 [pdf] submitted on 2016-11-12 06:46:28

AI System Surfs Web to Improve its Performance

Authors: George Rajna
Comments: 31 Pages.

Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions—about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results—may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming. [18] New evidence from brain studies, including cognitive psychology and neurophysiology research, shows that the emotional assessment of every object, subject, action or event plays an important role in human mental processes. And that means that if we want to create human-like artificial intelligence, we must make it emotionally responsive. But how do we know that such intelligence actually experiences real, human-like emotions? [17] It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of “quantum artificial intelligence”. Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries - how a sliced up flatworm can regenerate into new organisms - has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[232] viXra:1611.0169 [pdf] submitted on 2016-11-12 04:07:06

Brain-Inspired Device

Authors: George Rajna
Comments: 39 Pages.

New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A ‘nonlinear’ effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC’s LCLS. [19] Leiden physicists have manipulated light with large artificial atoms, so-called quantum dots. Before, this has only been accomplished with actual atoms. It is an important step toward light-based quantum technology. [18] In a tiny quantum prison, electrons behave quite differently as compared to their counterparts in free space. They can only occupy discrete energy levels, much like the electrons in an atom - for this reason, such electron prisons are often called "artificial atoms". [17] When two atoms are placed in a small chamber enclosed by mirrors, they can simultaneously absorb a single photon. [16] Optical quantum technologies are based on the interactions of atoms and photons at the single-particle level, and so require sources of single photons that are highly indistinguishable – that is, as identical as possible. Current single-photon sources using semiconductor quantum dots inserted into photonic structures produce photons that are ultrabright but have limited indistinguishability due to charge noise, which results in a fluctuating electric field. [14] A method to produce significant amounts of semiconducting nanoparticles for light-emitting displays, sensors, solar panels and biomedical applications has gained momentum with a demonstration by researchers at the Department of Energy's Oak Ridge National Laboratory. [13] A source of single photons that meets three important criteria for use in quantum-information systems has been unveiled in China by an international team of physicists. Based on a quantum dot, the device is an efficient source of photons that emerge as solo particles that are indistinguishable from each other. The researchers are now trying to use the source to create a quantum computer based on "boson sampling". [11] With the help of a semiconductor quantum dot, physicists at the University of Basel have developed a new type of light source that emits single photons. For the first time, the researchers have managed to create a stream of identical photons. [10] Optical photons would be ideal carriers to transfer quantum information over large distances. Researchers envisage a network where information is processed in certain nodes and transferred between them via photons. [9] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer using Quantum Information. In August 2013, the achievement of "fully deterministic" quantum teleportation, using a hybrid technique, was reported. On 29 May 2014, scientists announced a reliable way of transferring data by quantum teleportation. Quantum teleportation of data had been done before but with highly unreliable methods. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer with the help of Quantum Information.
Category: Artificial Intelligence

[231] viXra:1611.0095 [pdf] submitted on 2016-11-08 03:33:30

Quantitative Prediction of Electoral Vote for United States Presidential Election in 2016

Authors: Gang Xu
Comments: 8 Pages. This work was originally completed by October 22, 2016. The manuscript draft was prepared on November 7, 2016.

In this paper I am reporting the quantitative prediction of the electoral vote for United States presidential election in 2016. This quantitative prediction was based on the Google Trends (GT) data that is publicly available on the internet. A simple heuristic statistical model is applied to analyzing the GT data. This is intended to be an experiment for exploring the plausible dependency between the GT data and the electoral vote result of US presidential elections. The model's performance has also been tested by comparing the predicted results and the actual electoral votes in 2004, 2008 and 2012. For the year 2016, the Google Trends data projects that Mr. Trump will win the white house in landslide. This paper serves as a document to put this exploratory experiment in real test, since the actual election result can be compared to the prediction after tomorrow (November 8, 2016).
Category: Artificial Intelligence

[230] viXra:1611.0086 [pdf] submitted on 2016-11-07 06:27:49

Neuromorphic Processor

Authors: George Rajna
Comments: 22 Pages.

Toshiba advances deep learning with extremely low power neuromorphic processor. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[229] viXra:1611.0025 [pdf] submitted on 2016-11-02 08:20:12

Machine Learning for Cancer Treatment

Authors: George Rajna
Comments: 28 Pages.

Physicians have long used visual judgment of medical images to determine the course of cancer treatment. A new program package from Fraunhofer researchers reveals changes in images and facilitates this task using deep learning. The experts will demonstrate this software in Chicago from November 27 to December 2 at RSNA, the world's largest radiology meeting. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of “quantum artificial intelligence”. Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries - how a sliced up flatworm can regenerate into new organisms - has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[228] viXra:1611.0022 [pdf] submitted on 2016-11-02 06:49:11

Transforming, Self-Learning Software

Authors: George Rajna
Comments: 27 Pages.

Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[227] viXra:1610.0360 [pdf] submitted on 2016-10-30 02:33:09

Machine-Learning Decision Rationales

Authors: George Rajna
Comments: 28 Pages.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have devised a way to train neural networks so that they provide not only predictions and classifications but rationales for their decisions. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[226] viXra:1610.0359 [pdf] submitted on 2016-10-30 04:02:16

Machine Learning Understand Materials

Authors: George Rajna
Comments: 21 Pages.

Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[225] viXra:1610.0336 [pdf] submitted on 2016-10-27 21:31:21

Fuzzy Evidential Influence Diagram Evaluation Algorithm

Authors: Haoyang Zheng, Yong Deng
Comments: 38 Pages.

Fuzzy influence diagrams (FIDs) are one of the graphical models that combines the qualitative and quantitative analysis to solve decision-making problems. However, FIDs use an incomprehensive evaluation criteria to score nodes in complex systems, so that many different nodes got the same score, which can not reflect their differences. Based on fuzzy set and Dempster-Shafer (D-S) evidence theory, this paper changes the traditional evaluation system and modifies corresponding algorithm, in order that the influence diagram can more effectively reflect the true situation of the system, and get more practical results. Numerical examples and the real application in supply chain financial system are used to show the efficiency of the proposed influence diagram model.
Category: Artificial Intelligence

[224] viXra:1610.0314 [pdf] submitted on 2016-10-26 05:16:26

Artificial Intelligence Replaces Judges and Lawyers

Authors: George Rajna
Comments: 20 Pages.

An artificial intelligence method developed by University College London computer scientists and associates has predicted the judicial decisions of the European Court of Human Rights (ECtHR) with 79% accuracy, according to a paper published Monday, Oct. 24 in PeerJ Computer Science. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[223] viXra:1610.0281 [pdf] submitted on 2016-10-24 04:05:52

An Information Volume Measure

Authors: Yong Deng
Comments: 8 Pages.

How to measure the volume of uncertainty information is an open issue. Shannon entropy is used to represent the uncertainty degree of a probability distribution. Given a generalized probability distribution which means that the probability is not only assigned to the basis event space but also the power set of event space. At this time, a so called meta probability space is constructed. A new measure, named as Deng entropy, is presented. The results show that, compared with existing method, Deng entropy is not only better from the aspect of mathematic form, but also has the significant physical meaning.
Category: Artificial Intelligence

[222] viXra:1610.0249 [pdf] submitted on 2016-10-21 11:35:59

New Data Algorithms

Authors: George Rajna
Comments: 28 Pages.

Last year, MIT researchers presented a system that automated a crucial step in big-data analysis: the selection of a "feature set," or aspects of the data that are useful for making predictions. The researchers entered the system in several data science contests, where it outperformed most of the human competitors and took only hours instead of months to perform its analyses. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[221] viXra:1610.0169 [pdf] submitted on 2016-10-15 16:49:11

Band Gap Estimation Using Machine Learning Techniques

Authors: Anantha Natarajan S, R Varadhan, Ezhilvel ME
Comments: 3 Pages.

The purpose of this study is to build machine learning models to predict the band gap of binary compounds, using its known properties like molecular weight, electronegativity, atomic fraction and the group of the constituent elements in the periodic table. Regression techniques like Linear, Ridge regression and Random Forest were used to build the model. This model can be used by students and researchers in experiments involving unknown band gaps or new compounds.
Category: Artificial Intelligence

[220] viXra:1610.0142 [pdf] submitted on 2016-10-13 14:04:33

Google DeepMind Neural Networks

Authors: George Rajna
Comments: 24 Pages.

A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[219] viXra:1610.0110 [pdf] submitted on 2016-10-10 12:21:47

Neuro-Inspired Analog Computer

Authors: George Rajna
Comments: 23 Pages.

Researchers have developed a neuro-inspired analog computer that has the ability to train itself to become better at whatever tasks it performs. [13] A small, Santa Fe, New Mexico-based company called Knowm claims it will soon begin commercializing a state-of-the-art technique for building computing chips that learn. Other companies, including HP HPQ-3.45% and IBM IBM-2.10% , have already invested in developing these so-called brain-based chips, but Knowm says it has just achieved a major technological breakthrough that it should be able to push into production hopefully within a few years. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[218] viXra:1610.0074 [pdf] submitted on 2016-10-07 00:22:44

Belief Reliability Analysis and Its Application

Authors: Haoyang Zheng, Likang Yin, Tian Bian, Yong Deng
Comments: 24 Pages.

In reliability analysis, Fault Tree Analysis based on evidential networks is an important research topic. However, the existing EN approaches still remain two issues: one is the final results are expressed with interval numbers, which has a relatively high uncertainty to make a final decision. The other is the combination rule is not used to fuse uncertain information. These issues will greatly decrease the efficiency of EN to handle uncertain information. To address these open issues, a new methodology, called Belief Reliability Analysis, is presented in this paper. The combination methods to deal with series system, parallel system, series-parallel system as well as parallel-series system are proposed for reliability evaluation. Numerical examples and the real application in servo-actuation system are used to show the efficiency of the proposed Belief Reliability Analysis methodology.
Category: Artificial Intelligence

[217] viXra:1610.0029 [pdf] submitted on 2016-10-04 04:31:32

Associative Broadcast Neural Network

Authors: Aleksei Morozov
Comments: 3 Pages.

Associative broadcast neural network (ABNN) is an artificial neural network inspired by a hypothesis of broadcasting of neuron's output pattern in a biological neural network. Neuron has wire connections and ether connections. Ether connections are electrical. Wire connections provide a recognition functionality. Ether connections provide an association functionality.
Category: Artificial Intelligence

[216] viXra:1610.0028 [pdf] submitted on 2016-10-03 13:53:10

A New Belief Entropy: Possible Generalization of Deng Entropy, Tsallis Entropy and Shannon Entropy

Authors: Bingyi Kang, Yong Deng
Comments: 15 Pages.

Shannon entropy is the mathematical foundation of information theory, Tsallis entropy is the roots of nonextensive statistical mechanics, Deng entropy was proposed to measure the uncertainty degree of belief function very recently. In this paper, A new entropy H was proposed to generalize Deng entropy, Tsallis entropy and Shannon entropy. The new entropy H can be degenerated to Deng entropy, Tsallis entropy, and Shannon entropy under different conditions, and also can maintains the mathematical properity of Deng entropy, Tsallis entropy and Shannon entropy.
Category: Artificial Intelligence

[215] viXra:1609.0311 [pdf] submitted on 2016-09-21 07:22:05

Artificial Intelligence Discover New Materials

Authors: George Rajna
Comments: 20 Pages.

With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[214] viXra:1609.0238 [pdf] submitted on 2016-09-15 19:25:33

Revision on Fuzzy Artificial Potential Field for Humanoid Robot Path Planning in Unknown Environment

Authors: Mahdi Fakoor, Amirreza Kosari, Mohsen Jafarzadeh
Comments: 10 Pages.

Path planning in a completely known environment has been experienced various ways. However, in real world, most humanoid robots work in unknown environments. Robots' path planning by artificial potential field and fuzzy artificial potential field methods are very popular in the field of robotics navigation. However, by default humanoid robots lack range sensors; thus, traditional artificial potential field approaches needs to adopt themselves to these limitations. This paper investigates two different approaches for path planning of a humanoid robot in an unknown environment using fuzzy artificial potential (FAP) method. In the first approach, the direction of the moving robot is derived from fuzzified artificial potential field whereas in the second one, the direction of the robot is extracted from some linguistic rules that are inspired from artificial potential field. These two introduced trajectory design approaches are validated though some software and hardware in the loop simulations and the experimental results demonstrate the superiority of the proposed approaches in humanoid robot real-time trajectory planning problems.
Category: Artificial Intelligence

[213] viXra:1609.0213 [pdf] submitted on 2016-09-13 17:24:14

Compositional Adjacency Matrix Producing Networks: An Alternative Indirect Encoding

Authors: Stefano Palmieri
Comments: 6 Pages. (CC BY-NC 3.0 US) https://creativecommons.org/licenses/by-nc/3.0/us/

In the field of Artificial Evolution, Compositional Pattern Producing Networks (CPPNs) have proven to be an effective encoding for development that does not require iterative rewrite systems or cellular growth simulations. In this paper, a novel indirect encoding called Compositional Adjacency Matrix Producing Networks (CAMPNs) is proposed that keeps several characteristics of CPPNs. Experiments using human-guided artificial selection of directed graphs shows that CAMPNs are able to produce structural motifs also produced by CPPNs and other developmental abstractions.
Category: Artificial Intelligence

[212] viXra:1609.0134 [pdf] submitted on 2016-09-10 11:19:00

War Algorithm Accountability

Authors: Dustin A. Lewis, Gabriella Blum, Naz K. Modirzadeh
Comments: 244 Pages.

Compendium on Accountability issues as pertains to Lethal Autonomous Weapons
Category: Artificial Intelligence

[211] viXra:1609.0133 [pdf] submitted on 2016-09-10 12:00:18

Five Hundred Deep Learning Papers, Graphviz and Python

Authors: Daniele Ettore Ciriello
Comments: 13 Pages.

I invested days creating a graph with PyGraphviz to repre- sent the evolutionary process of deep learning’s state of the art for the last twenty-five years. Through this paper I want to show you how and what I obtained.
Category: Artificial Intelligence

[210] viXra:1609.0126 [pdf] submitted on 2016-09-10 04:54:56

Deep Neural Networks

Authors: George Rajna
Comments: 23 Pages.

Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[209] viXra:1608.0291 [pdf] submitted on 2016-08-24 01:48:59

A Comparison of the Generalized minC Combination and the Hybrid DSm Combination Rules

Authors: Milan Daniel
Comments: 18 Pages.

A generalization of the minC combination to DSm hyper-power sets is presented. Both the special formulas for static fusion or dynamic fusion without non-existential constraints and the quite general formulas for dynamic fusion with non-existential constraints are included. Examples of the minC combination on several different hybrid DSm models are presented. A comparison of the generalized minC combination with the hybrid DSm rule is discussed and explained on examples.
Category: Artificial Intelligence

[208] viXra:1608.0224 [pdf] submitted on 2016-08-20 12:45:56

Search for Dynamical Origin of Social Networks

Authors: Michail Zak
Comments: 33 Pages.

The challenge of this work is to re-define the concept of intelligent agent as a building block of social networks by presenting it as a physical particle with additional non-Newtonian properties. The proposed model of an intelligent agent described by a system of ODE coupled with their Liouville equation has been introduced and discussed. Following the Madelung equation that belongs to this class, non-Newtonian properties such as superposition, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that the proposed model can be linked to mathematical models of livings as well as to models of AI. The model is presented in two modifications. The first one is illustrated by the discovery of a stochastic attractor approached by the social network; as an application, it was demonstrated that any statistics can be represented by an attractor of the solution to the corresponding system of ODE coupled with its Liouville equation. It was emphasized that evolution to the attractor reveals possible micro-mechanisms driving random events to the final distribution of the corresponding statistical law. Special attention is concentrated upon the power law and its dynamical interpretation: it is demonstrated that the underlying micro- dynamics supports a “violent reputation” of the power-law statistics. The second modification of the model of social network associated with a decision-making process and applied to solution of NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing the location of this item.
Category: Artificial Intelligence

[207] viXra:1608.0187 [pdf] submitted on 2016-08-18 14:02:36

Neuromorphic Architecture

Authors: George Rajna
Comments: 28 Pages.

In the future, level-tuned neurons may help enable neuromorphic computing systems to perform tasks that traditional computers cannot, such as learning from their environment, pattern recognition, and knowledge extraction from big data sources. [19] IBM scientists have created randomly spiking neurons using phase-change materials to store and process data. This demonstration marks a significant step forward in the development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing. [18] An ion trap with four segmented blade electrodes used to trap a linear chain of atomic ions for quantum information processing. Each ion is addressed optically for individual control and readout using the high optical access of the trap. [17] To date, researchers have realised qubits in the form of individual electrons (aktuell.ruhr-uni-bochum.de/pm2012/pm00090.html.en). However, this led to interferences and rendered the information carriers difficult to programme and read. The group has solved this problem by utilising electron holes as qubits, rather than electrons. [16] Physicists from MIPT and the Russian Quantum Center have developed an easier method to create a universal quantum computer using multilevel quantum systems (qudits), each one of which is able to work with multiple "conventional" quantum elements – qubits. [15] Precise atom implants in silicon provide a first step toward practical quantum computers. [14] A method to produce significant amounts of semiconducting nanoparticles for light-emitting displays, sensors, solar panels and biomedical applications has gained momentum with a demonstration by researchers at the Department of Energy's Oak Ridge National Laboratory. [13] A source of single photons that meets three important criteria for use in quantum-information systems has been unveiled in China by an international team of physicists. Based on a quantum dot, the device is an efficient source of photons that emerge as solo particles that are indistinguishable from each other. The researchers are now trying to use the source to create a quantum computer based on "boson sampling". [11] With the help of a semiconductor quantum dot, physicists at the University of Basel have developed a new type of light source that emits single photons.
Category: Artificial Intelligence

[206] viXra:1608.0093 [pdf] submitted on 2016-08-08 22:08:41

Edge Based Grid Super-Imposition for Crowd Emotion Recognition

Authors: Amol S Patwardhan
Comments: 6 Pages.

Numerous automatic continuous emotion detection system studies have examined mostly use of videos and images containing individual person expressing emotions. This study examines the detection of spontaneous emotions in a group and crowd settings. Edge detection was used with a grid of lines superimposition to extract the features. The feature movement in terms of movement from the reference point was used to track across sequences of images from the color channel. Additionally the video data capturing was done on spontaneous emotions invoked by watching sports events from group of participants. The method was view and occlusion independent and the results were not affected by presence of multiple people chaotically expressing various emotions. The edge thresholds of 0.2 and grid thresholds of 20 showed the best accuracy results. The overall accuracy of the group emotion classifier was 70.9%.
Category: Artificial Intelligence

[205] viXra:1608.0092 [pdf] submitted on 2016-08-08 22:15:06

Human Activity Recognition Using Temporal Frame Decision Rule Extraction

Authors: Amol Patwardhan
Comments: 4 Pages.

Activities of humans and their recognition has many practical and real world applications such as safety, security, surveillance, humanoid assistive robotics and intelligent simulation systems. Numerous human action and emotion recognition systems included analysis of position and geometric features and gesture based co-ordinates to detect actions. There exits additional data and information in the movement and motion based features and temporal and time-sequential series of image and video frames which can be leveraged to detect and extract a certain actions, postures, gestures and expressions. This paper uses dynamic, temporal, time-scale dependent data to compare with decision rules and templates for activity recognition. The human shape boundaries and silhouette is extracted using geometric co-ordinate and centroid model across multiple frames. The extracted shape boundary is transformed to binary state using eigen space mapping and parameter dependent canonical transformation in 3D space dimension. The image blob data frames are down sampled using activity templates to a single candidate reference frame. This candidate frame was compared with the decision rule driven model to associate with an activity class label. The decision rule driven and activity templates method produced 64% recognition accuracy indicating that the method was feasible for recognizing human activities.
Category: Artificial Intelligence

[204] viXra:1608.0076 [pdf] submitted on 2016-08-08 02:57:31

Watson Doctor

Authors: George Rajna
Comments: 19 Pages.

Watson correctly diagnoses woman after doctors were stumped. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[203] viXra:1608.0041 [pdf] submitted on 2016-08-04 08:00:47

Combining Infinity Number Of Neural Networks Into One

Authors: Bo Tian
Comments: 17 Pages.

One of the important aspects of a neural network is its generalization property, which is measured by its ability to make correct prediction on unseen samples. One option to improve generalization is to combine results from multiple networks, which is unfortunately a time-consuming process. In this paper, a new approach is presented to combine infinity number of neural networks in analytic way to produce a small, fast and reliable neural network.
Category: Artificial Intelligence

[202] viXra:1607.0484 [pdf] submitted on 2016-07-25 21:28:15

Active Appearance Model Construction: Implementation notes

Authors: Nikzad Babaii Rizvandi, Wilfried Philips, Aleksandra Pizurica
Comments: 7 Pages.

Active Appearance Model (AAM) is a powerful object modeling technique and one of the best available ones in computer vision and computer graphics. This approach is however quite complex and various parts of its implementation were addressed separately by different researchers in several recent works. In this paper, we present systematically a full implementation of the AAM model with pseudo codes for the crucial steps in the construction of this model.
Category: Artificial Intelligence

[201] viXra:1607.0483 [pdf] submitted on 2016-07-25 21:29:15

Active Appearance Model (Aam) from Theory to Implementation

Authors: Nikzad Babaii Rizvandi, Aleksandra Pizˇurica, Wilfried Philips
Comments: 4 Pages.

Active Appearance Model (AAM) is a kind of deformable shape descriptors which is widely used in computer vision and computer graphics. This approach utilizes statistical model obtained from some images in training set and gray-value information of the texture to fit on the boundaries of a new image. In this paper, we describe a brief implementation, apply the method on hand object and finally discuss its performance in compare to Active Shape Model(ASM). Our experiments shows this method is more sensitive to the initialization and slower than ASM.
Category: Artificial Intelligence

[200] viXra:1607.0459 [pdf] submitted on 2016-07-24 21:30:52

Pattern Recognition and Learning in Bistable Cam Networks

Authors: Vladimir Chinarov, Martin Dudziak, Yuri Kyrpach
Comments: 12 Pages.

The present study concerns the problem of learning, pattern recognition and computational abilities of a homogeneous network composed from coupled bistable units. New possibilities for pattern recognition may be realized due to the developed technique that permits a reconstruction of a dynamical system using the distributions of its attractors. In both cases the updating procedure for the coupling matrix uses the minimization of least-mean-square errors between the applied and desired patterns.
Category: Artificial Intelligence

[199] viXra:1607.0073 [pdf] submitted on 2016-07-07 02:49:14

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[198] viXra:1607.0014 [pdf] submitted on 2016-07-01 14:54:48

Interval-Valued Neutrosophic Oversets, Neutrosophic Undersets, and Neutrosophic Offsets

Authors: Florentin Smarandache
Comments: 4 Pages.

We have proposed since 1995 the existence of degrees of membership of an element with respect to a neutrosophic set to also be partially or totally above 1 (overmembership), and partially or totally below 0 (undermembership) in order to better describe our world problems [published in 2007].
Category: Artificial Intelligence

[197] viXra:1606.0343 [pdf] submitted on 2016-06-30 08:36:33

Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset. Similarly for Neutrosophic Over-/Under-/Off- Logic, Probability, and Statistics

Authors: Florentin Smarandache
Comments: 168 Pages.

Neutrosophic Over-/Under-/Off-Set and -Logic were defined for the first time by Smarandache in 1995 and published in 2007. They are totally different from other sets/logics/probabilities. He extended the neutrosophic set respectively to Neutrosophic Overset {when some neutrosophic component is > 1}, Neutrosophic Underset {when some neutrosophic component is < 0}, and to Neutrosophic Offset {when some neutrosophic components are off the interval [0, 1], i.e. some neutrosophic component > 1 and other neutrosophic component < 0}. This is no surprise with respect to the classical fuzzy set/logic, intuitionistic fuzzy set/logic, or classical/imprecise probability, where the values are not allowed outside the interval [0, 1], since our real-world has numerous examples and applications of over-/under-/off-neutrosophic components. Example of Neutrosophic Offset. In a given company a full-time employer works 40 hours per week. Let’s consider the last week period. Helen worked part-time, only 30 hours, and the other 10 hours she was absent without payment; hence, her membership degree was 30/40 = 0.75 < 1. John worked full-time, 40 hours, so he had the membership degree 40/40 = 1, with respect to this company. But George worked overtime 5 hours, so his membership degree was (40+5)/40 = 45/40 = 1.125 > 1. Thus, we need to make distinction between employees who work overtime, and those who work full-time or part-time. That’s why we need to associate a degree of membership strictly greater than 1 to the overtime workers. Now, another employee, Jane, was absent without pay for the whole week, so her degree of membership was 0/40 = 0. Yet, Richard, who was also hired as a full-time, not only didn’t come to work last week at all (0 worked hours), but he produced, by accidentally starting a devastating fire, much damage to the company, which was estimated at a value half of his salary (i.e. as he would have gotten for working 20 hours that week). Therefore, his membership degree has to be less that Jane’s (since Jane produced no damage). Whence, Richard’s degree of membership, with respect to this company, was - 20/40 = - 0.50 < 0. Consequently, we need to make distinction between employees who produce damage, and those who produce profit, or produce neither damage no profit to the company. Therefore, the membership degrees > 1 and < 0 are real in our world, so we have to take them into consideration. Then, similarly, the Neutrosophic Logic/Measure/Probability/Statistics etc. were extended to respectively Neutrosophic Over-/Under-/Off-Logic, -Measure, -Probability, -Statistics etc. [Smarandache, 2007].
Category: Artificial Intelligence

[196] viXra:1606.0341 [pdf] submitted on 2016-06-30 08:42:46

Operators on Single-Valued Neutrosophic Oversets, Neutrosophic Undersets, and Neutrosophic Offsets

Authors: Florentin Smarandache
Comments: 5 Pages.

We have defined Neutrosophic Over-/Under-/Off-Set and Logic for the first time in 1995 and published in 2007. During 1995-2016 we presented them to various national and international conferences and seminars. These new notions are totally different from other sets/logics/probabilities. We extended the neutrosophic set respectively to Neutrosophic Overset {when some neutrosophic component is > 1}, to Neutrosophic Underset {when some neutrosophic component is < 0}, and to Neutrosophic Offset {when some neutrosophic components are off the interval [0, 1], i.e. some neutrosophic component > 1 and other neutrosophic component < 0}. This is no surprise since our real-world has numerous examples and applications of over-/under-/off-neutrosophic components.
Category: Artificial Intelligence

[195] viXra:1606.0272 [pdf] submitted on 2016-06-25 19:29:46

Self-Controlled Dynamics

Authors: Michail Zak
Comments: 26 Pages.

A new class of dynamical system described by ODE coupled with their Liouville equation has been introduced and discussed. These systems called self-controlled, or self-supervised since the role of actuators is played by the probability produced by the Liouville equation. Following the Madelung equation that belongs to this class, non-Newtonian properties such as randomness, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that self-controlled dynamical systems can be linked to mathematical models of livings as well as to models of AI. The central point of this paper is the application of the self-controlled systems to NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing location of this item.
Category: Artificial Intelligence

[194] viXra:1606.0181 [pdf] submitted on 2016-06-17 22:41:17

Universal Natural Memory Embedding -3 (AI)

Authors: Ramesh Chandra Bagadi
Comments: 18 Pages.

In this research investigation, the author has presented a theory of ‘Universal Relative Metric That Generates A Field Super-Set To The Fields Generated By Various Distinct Relative Metrics’.
Category: Artificial Intelligence

[193] viXra:1606.0155 [pdf] submitted on 2016-06-15 07:30:51

Universal Natural Memory Embedding - Part Two

Authors: Ramesh Chandra Bagadi
Comments: 14 Pages.

In this research investigation, the author has presented a theory of ‘The Universal Irreducible Any Field Generating Metric’.
Category: Artificial Intelligence

[192] viXra:1606.0146 [pdf] submitted on 2016-06-15 00:17:24

Universal Natural Memory Embedding - I

Authors: Ramesh Chandra Bagadi
Comments: 14 Pages.

In this research investigation, the author has presented a theory of ‘Universal Natural Memory Embedding’.
Category: Artificial Intelligence

[191] viXra:1605.0288 [pdf] submitted on 2016-05-29 02:24:48

Syllabic Networks: Measuring the Redundancy of Associative Syntactic Patterns

Authors: Bradly Alicea
Comments: 6 Pages. 3 figures, 1 table

The self-organization and diversity inherent in natural and artificial language can be revealed using a technique called syllabic network decomposition. The topology of such networks are determined by a series of linguistic strings which are broken apart at critical points and then linked together in a non-linear fashion. Small proof-of-concept examples are given using words from the English language. A criterion for connectedness and two statistical parameters for measuring connectedness are applied to these examples. To conclude, we will discuss some applications of this technique, ranging from improving models of speech recognition to bioinformatic analysis and recreational games.
Category: Artificial Intelligence

[190] viXra:1605.0190 [pdf] submitted on 2016-05-18 06:12:00

The Algorithm of the Thinking Machine

Authors: Dimiter Dobrev
Comments: 13 Pages. Represented at 12 of May 2016 at Faculty of Mathematics and Informatics, University of Sofia.

Throughout my life I’ve tried to answer the question "What is AI?" and write the program that is AI. I’ve already known the answer to the question "What is AI?" for 16 years now but the AI algorithm has eluded me. I’ve come up with individual fragments but something has always been missing to put the puzzle together. Finally, I gathered all the missing pieces and I can introduce you to this algorithm. That is, in this article you will find a sufficiently detailed description of the algorithm of the AI. That sounds so audacious that probably you won’t believe me. Frankly, even I do not fully believe myself. I’ll believe it only when someone writes a program executing this algorithm and when I see that this program actually works. Even if you don’t manage to believe in the importance of this article, I hope that you will like it.
Category: Artificial Intelligence

[189] viXra:1605.0178 [pdf] submitted on 2016-05-16 10:25:46

Artificial Intelligence Replaces Physicists

Authors: George Rajna
Comments: 19 Pages.

The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[188] viXra:1605.0125 [pdf] submitted on 2016-05-12 08:13:31

Failure Mode and Effects Analysis Based on D Numbers and Topsis

Authors: Tian Bian, Haoyang Zheng, Likang Yin, Yong Deng, Sankaran Mahadevand
Comments: 39 Pages.

Failure mode and effects analysis (FMEA) is a widely used technique for assessing the risk of potential failure modes in designs, products, process, system or services. One of the main problems of FMEA is to deal with a variety of assessments given by FMEA team members and sequence the failure modes according to the degree of risk factors. The traditional FMEA using risk priority number (RPN) which is the product of occurrence (O), severity (S) and detection (D) of a failure to determine the risk priority ranking order of failure modes. However, it will become impractical when multiple experts give different risk assessments to one failure mode, which may be imprecise or incomplete or the weights of risk factors is inconsistent. In this paper, a new risk priority model based on D numbers, and technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate the risk in FMEA. In the proposed model, the assessments given by FMEA team members are represented by D numbers, a method can effectively handle uncertain information. TOPSIS method, a novel multi-criteria decision making (MCDM) method is presented to rank the preference of failure modes respect to risk factors. Finally, an application of the failure modes of rotor blades of an aircraft turbine is provided to illustrate the efficiency of the proposed method.
Category: Artificial Intelligence

[187] viXra:1605.0078 [pdf] submitted on 2016-05-07 23:23:50

An Improvement in Monotonicity of the Distance-Based Total Uncertainty Measure in Belief Function Theory

Authors: Xinyang Deng, Yong Deng
Comments: 18 Pages.

Measuring the uncertainty of evidences is an open issue in belief function theory. Recently, a distance-based total uncertainty measure for the belief function theory, indicated by ${TU}^I$, is presented. Some experiments show the efficiency of the ${TU}^I$ to measure uncertainty degree. In this paper, numerical example and theoretical analysis are illustrated that the monotonicity in ${TU}^I$ is not satisfied. To address this issue, an improved uncertainty measure ${TU}^I_E$ is proposed. The monotonicity for ${TU}^I_E$ is theoretically proved. Finally, through experimental comparison we show that ${TU}^I_E$ also has the desired high sensitivity to the evidence changes, which further indicates that the proposed ${TU}^I_E$ is better than ${TU}^I$.
Category: Artificial Intelligence

[186] viXra:1603.0378 [pdf] submitted on 2016-03-27 16:51:16

A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

Authors: Hussein A. Abbass, George Leu, Kathryn Merrick
Comments: 32 Pages.

Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus the paper on the `trust' component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three sub-topics: (1) reviewing and positioning the trust modelling literature for the purpose of trusted autonomy; (2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and (3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enables trusted autonomous systems to become a reality. The paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.
Category: Artificial Intelligence

[185] viXra:1603.0335 [pdf] submitted on 2016-03-23 10:04:45

Conditional Deng Entropy, Joint Deng Entropy and Generalized Mutual Information

Authors: Haoyang Zheng, Yong Deng
Comments: 16 Pages.

Shannon entropy, conditional entropy, joint entropy and mutual information, can estimate the chaotic level of information. However, these methods could only handle certain situations. Based on Deng entropy, this paper introduces multiple new entropy to estimate entropy under multiple interactive uncertain information: conditional Deng entropy is used to calculate entropy under conditional basic belief assignment; joint Deng entropy could calculate entropy by applying joint basic belief assignment distribution; generalized mutual information is applied to estimate the uncertainty of information under knowing another information. Numerical examples are used for illustrating the function of new entropy in the end.
Category: Artificial Intelligence

[184] viXra:1602.0345 [pdf] submitted on 2016-02-27 04:26:26

Biomolecular Parallel Computer

Authors: George Rajna
Comments: 16 Pages.

A study published this week in Proceedings of the National Academy of Sciences reports a new parallel-computing approach based on a combination of nanotechnology and biology that can solve combinatorial problems. [9] The substance that provides energy to all the cells in our bodies, Adenosine triphosphate (ATP), may also be able to power the next generation of supercomputers. The discovery opens doors to the creation of biological supercomputers that are about the size of a book. [8] The one thing everyone knows about quantum mechanics is its legendary weirdness, in which the basic tenets of the world it describes seem alien to the world we live in. Superposition, where things can be in two states simultaneously, a switch both on and off, a cat both dead and alive. Or entanglement, what Einstein called "spooky action-at-distance" in which objects are invisibly linked, even when separated by huge distances. [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[183] viXra:1602.0338 [pdf] submitted on 2016-02-27 02:11:05

Biological Supercomputers

Authors: George Rajna
Comments: 15 Pages.

The substance that provides energy to all the cells in our bodies, Adenosine triphosphate (ATP), may also be able to power the next generation of supercomputers. The discovery opens doors to the creation of biological supercomputers that are about the size of a book. [8] The one thing everyone knows about quantum mechanics is its legendary weirdness, in which the basic tenets of the world it describes seem alien to the world we live in. Superposition, where things can be in two states simultaneously, a switch both on and off, a cat both dead and alive. Or entanglement, what Einstein called "spooky action-at-distance" in which objects are invisibly linked, even when separated by huge distances. [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[182] viXra:1602.0299 [pdf] submitted on 2016-02-24 08:03:56

IBM Watson Emotion Analysis

Authors: George Rajna
Comments: 17 Pages.

IBM today announced new and expanded cognitive APIs for developers that enhance Watson's emotional and visual senses, further extending the capabilities of the industry's largest and most diverse set of cognitive technologies and tools. [8] The pursuit of an understanding of the base machinery of the mind led early researchers to anatomical exhaustion. With neuroscience now in the throes of molecular mayhem and a waning biochemical bliss, physics is spicing things up with a host of eclectic quantum, spin, and isotopic novelties. While increases in electron spin content have been linked to anesthetic effects, nuclear spins have recently been implicated in a more rarefied and subtle phenomenon— neural quantum processing. [7] The hypothesis that there may be something quantum-like about the human mental function was put forward with " Spooky Activation at Distance " formula which attempted to model the effect that when a word's associative network is activated during study in memory experiment; it behaves like a quantum-entangled system. The human body is a constant flux of thousands of chemical/biological interactions and processes connecting molecules, cells, organs, and fluids, throughout the brain, body, and nervous system. Up until recently it was thought that all these interactions operated in a linear sequence, passing on information much like a runner passing the baton to the next runner. However, the latest findings in quantum biology and biophysics have discovered that there is in fact a tremendous degree of coherence within all living systems. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to understand the Quantum Biology.
Category: Artificial Intelligence

[181] viXra:1602.0289 [pdf] submitted on 2016-02-23 01:09:01

Computer Quantum Experiments

Authors: George Rajna
Comments: 17 Pages.

Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[180] viXra:1602.0233 [pdf] submitted on 2016-02-18 19:28:27

Quantum Decision-Maker

Authors: Michail Zak
Comments: 24 Pages.

A QRN simulating human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and of classical neural nets describing evolution of probabilities of these processes which represent the mental dynamics. The autonomy of the decision making process is achieved by a feedback from mental to motor dynamics which changes the stochastic matrix based upon the probability distributions. This feedback replaces an unavailable external information by an internal knowledgebase stored in the mental model in the form of probability distributions. As a result, the coupled motor-mental dynamics is described by a nonlinear version of Markov chains which can decrease entropy without an external source of information. Applications to common sense based decisions as well as to evolutionary games are discussed.
Category: Artificial Intelligence

[179] viXra:1602.0232 [pdf] submitted on 2016-02-18 21:11:43

Quantum Model of Emerging Grammars

Authors: Michail Zak
Comments: 7 Pages.

A special class of quantum recurrent nets (QRNs) simulating Markov chains with absorbing states is introduced. The absorbing states are exploited for pattern recognition: each class of patterns is attracted to a unique absorbing state. Due to quantum interference of patterns, each combination of patterns acquires its own meaning: it is attracted to a certain combination of absorbing states which is dierent from those of individual attractions. This fundamentally new eect can be interpreted as formation of a grammar, i.e., a set of rules assigning certain meaning to dierent combinations of patterns. It appears that there exists a class of unitary operators in which each member gives rise to a dierent arti®cial language with associated grammar.
Category: Artificial Intelligence

[178] viXra:1602.0230 [pdf] submitted on 2016-02-18 22:38:51

Quantum-Inspired Teleportation.

Authors: Michail.Zak
Comments: 11 Pages.

Based upon quantum-inspired entanglement in quantum-classical hybrids, a simple algorithm for instantaneous transmissions of non-intentional messages (chosen at random) to remote distances is proposed. A special class of situations when such transmissions are useful is outlined. Application of such a quantum-inspired teleportation, i.e. instantaneous transmission of conditional information on remote distances for security of communications is discussed. Similarities and differences between quantum systems and quantum-classical hybrids are emphasized.
Category: Artificial Intelligence

[177] viXra:1602.0222 [pdf] submitted on 2016-02-18 04:36:09

Neuromorphic Computing

Authors: George Rajna
Comments: 16 Pages.

In the field of neuromorphic engineering, researchers study computing techniques that could someday mimic human cognition. Electrical engineers at the Georgia Institute of Technology recently published a "roadmap" that details innovative analog-based techniques that could make it possible to build a practical neuromorphic computer. [9] How does the brain-a lump of 'pinkish gray meat'-produce the richness of conscious experience, or any subjective experience at all? Scientists and philosophers have historically likened the brain to contemporary information technology, from the ancient Greeks comparing memory to a 'seal ring in wax,' to the 19th century brain as a 'telegraph switching circuit,' to Freud's subconscious desires 'boiling over like a steam engine,' to a hologram, and finally, the computer. [8] Discovery of quantum vibrations in 'microtubules' inside brain neurons supports controversial theory of consciousness. The human body is a constant flux of thousands of chemical/biological interactions and processes connecting molecules, cells, organs, and fluids, throughout the brain, body, and nervous system. Up until recently it was thought that all these interactions operated in a linear sequence, passing on information much like a runner passing the baton to the next runner. However, the latest findings in quantum biology and biophysics have discovered that there is in fact a tremendous degree of coherence within all living systems. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to understand the Quantum Biology.
Category: Artificial Intelligence

[176] viXra:1602.0077 [pdf] submitted on 2016-02-06 10:58:18

Critical Review, Vol. 11, 2015

Authors: Many Authors
Comments: 141 Pages.

Papers on neutrosophic set and logic
Category: Artificial Intelligence

[175] viXra:1602.0075 [pdf] submitted on 2016-02-06 11:02:58

Neutrosophic Sets and Systems, Vol. 10, 2015

Authors: Many Authors
Comments: 107 Pages.

Collection of papers on neutrosophics.
Category: Artificial Intelligence

[174] viXra:1601.0024 [pdf] submitted on 2016-01-04 06:27:06

Do People Leave in Matrix? Information, Entropy, Time and Cellular-Automata

Authors: Janis Belov
Comments: 6 Pages.

The paper proves that we leave in Matrix. We show that Matrix was built by the creator. By this we solve the question how everything is built. We prove that the creator is infinite Turing machine or infinite Cellular-automaton. We show that Universe is Cellular-automaton or Turing machine too. And everything in the Universe is built as Cellular-automaton. We show that our Universe was created by “vegetative birth” from another Universe. In other words, there is an infinite Life Tree that is actually the creator itself. In other words, we show that everything is only Information, i.e. the creator. We show that there is no Time, moreover Time is the creator. So, the time is reversible. The arrow of time depends on Information entropy: if entropy increases – people leave in one world, if the entropy does not increase, in other words, no lost of information occurs – people step by step become closer to another world – the world where the creator is sensed directly. Someone can call the creator of Matrix – God. We would like to recall that Immanuel Kant tried to prove the existence of God but He did not succeed. The main reason for this is that he searched for a proof outside His own mind or His own conscience. Also, he did not take into consideration any evolutionary processes. The given paper tries to fill some gaps and show another way in understanding the reality. It is based on the existing science. In the paper we use mathematical methods in creating scientific concepts. The results are presented in the form Definition – Theorem to present them in a straightforward way. The immediate consequences of the obtained results are so that in studying the reality it is enough to develop Number Theory, Poetry, Art and other Sciences that are based on the Nature and Inner World of a person. We also give a simple solution for Yang-Mills Mass gap problem: there is no time and mass has no sense since it was invented artificially as an approximation of reality.
Category: Artificial Intelligence

[173] viXra:1512.0007 [pdf] submitted on 2015-12-02 01:27:54

Ontology, Evolving Under the Influence of the Facts

Authors: Aleksey A. Demidov
Comments: 34 Pages.

We propose an algebraic approach to building ontologies which capable of evolution under the influence of new facts and which have some internal mechanisms of validation. For this purpose we build a formal model of the interactions of objects based on cellular automata, and find out the limitations on transactions with objects imposed by this model. Then, in the context of the formal model, we define basic entities of the model of knowledge representation: concepts, samples, properties, and relationships. In this case the formal limitations are induced into the model of knowledge representation in a natural way.
Category: Artificial Intelligence

[172] viXra:1511.0145 [pdf] submitted on 2015-11-17 06:02:52

Which is the Best Belief Entropy?

Authors: Liguo Fei, Yong Deng, Sankaran Mahadevan
Comments: 4 Pages.

In this paper, many numerical examples are designed to compare the existing different belief functions with the new entropy, named Deng entropy. The results illustrate that, among the existing belief entropy functions,Deng entropy is the best alternative due to its reasonable properties.
Category: Artificial Intelligence

[171] viXra:1511.0144 [pdf] submitted on 2015-11-17 06:09:55

Measure Divergence Degree of Basic Probability Assignment Based on Deng Relative Entropy

Authors: Liguo Fei, Yong Deng
Comments: 15 Pages.

Dempster Shafer evidence theory (D-S theory) is more and more extensively applied to information fusion for the advantage dealing with uncertain information. However, the results opposite to common sense are often obtained when combining the different evidence using the Dempster’s combination rules. How to measure the divergence between different evidence is still an open issue. In this paper, a new relative entropy named as Deng relative entropy is proposed in order to measure the divergence between different basic probability assignments (BPAs). The Deng relative entropy is the generalization of Kullback-Leibler Divergence because when the BPA is degenerated as probability, Deng relative entropy is equal to Kullback-Leibler Divergence. Numerical examples are used to illustrate the effectiveness of the proposed Deng relative entropy.
Category: Artificial Intelligence

[170] viXra:1511.0095 [pdf] submitted on 2015-11-11 13:10:20

Robots and Computers 'Consciousness'

Authors: George Rajna
Comments: 22 Pages.

Imagine a world where "thinking" robots were able to care for the elderly and people with disabilities. This concept may seem futuristic, but exciting new research into consciousness could pave the way for the creation of intuitive artificial intelligence. [13] A small, Santa Fe, New Mexico-based company called Knowm claims it will soon begin commercializing a state-of-the-art technique for building computing chips that learn. Other companies, including HP HPQ -3.45% and IBM IBM -2.10% , have already invested in developing these so-called brain-based chips, but Knowm says it has just achieved a major technological breakthrough that it should be able to push into production hopefully within a few years. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[169] viXra:1511.0020 [pdf] submitted on 2015-11-02 18:50:19

Can a Mobile Game Teach Computer Users to Thwart Phishing Attacks?

Authors: Nalin Asanka Gamagedara Arachchilage, Steve Love, Carsten Maple
Comments: 11 Pages. Usable Security

Phishing is an online fraudulent technique, which aims to steal sensitive information such as usernames, passwords and online banking details from its victims. To prevent this, anti-phishing education needs to be considered. This research focuses on examining the effectiveness of mobile game based learning compared to traditional online learning to thwart phishing threats. Therefore, a mobile game prototype was developed based on the design introduced by Arachchilage and Cole [3]. The game design aimed to enhance avoidance behaviour through motivation to thwart phishing threats. A website developed by Anti-Phishing Work Group (APWG) for the public Anti-phishing education initiative was used as a traditional web based learning source. A think-aloud experiment along with a pre- and post-test was conducted through a user study. The study findings revealed that the participants who played the mobile game were better able to identify fraudulent web sites compared to the participants who read the website without any training.
Category: Artificial Intelligence

[168] viXra:1510.0486 [pdf] submitted on 2015-10-28 20:26:16

Embedded System for Waste Management using Fuzzy Logic

Authors: Sai Venkatesh Balasubramanian
Comments: 6 Pages.

The non-degradable wastes such as plastic are a big threat for the environment. Hence an embedded system for Automation in splitting up, disposal and recycling of wastes is the best solution. The entire process is done with Artificial intelligence. The A.I. is provided with the help of Fuzzy logic. In this paper, all the common wastes such as Ferrous & its compounds, Paper, Plastic, Polythene, E-wastes, Bio-degradable wastes, etc. are considered. The input wave is given for detecting the type of wastes. By using the IF …THEN… ELSE condition it is processed. The image processing has been implemented to check the material. This paper provides the entire design of the embedded system, the entire logic for the automation with the required IF…THEN….ELSE... codes. The automation used in this paper eliminates a significant amount of entire manual work.
Category: Artificial Intelligence

[167] viXra:1510.0022 [pdf] submitted on 2015-10-03 02:54:50

Nonextensive Deng Entropy

Authors: Yong Deng
Comments: 9 Pages.

In this paper, a generalized Tsallis entropy, named as Nonextensive Deng entropy, is presented. When the basic probability assignment is degenerated as probability, Nonextensive Deng entropy is identical to Tsallis entropy.
Category: Artificial Intelligence

[166] viXra:1509.0163 [pdf] submitted on 2015-09-18 04:00:41

Lie Detection System with Voice Using Bidirectional Associative Memory Algorithm

Authors: Bustami; Fadlisyah; Nurdania Delemunte
Comments: 07 Pages. Figures :04 Tables : 03, IJCAT.org, Volume 2, Issue 8, August 2015

Lie detection through voice can be detected using the algorithm bidirectional associative memory. This system is a branch of sound processing that can be used to identify the type of sound lies use some verbs like go, roads and move. This study uses an algorithm bidirectional associative memory for the process and the introduction of lie detection training through the sound use of bidirectional associative memory. The system was tested by simulating the training data and test data to generate a percentage of voice recognition and classification of these lies. Experiments performed with several changes in parameter values to obtain the best percentage of recognition and classification. The highest level of recognition contained in the verb "go" with up to 90%. Results of this research is a sound that indicated not indicated lies and deceit in the form of values are classified according to the type of sound that is known from the results of calculations of energy use bidirectional associative memory.
Category: Artificial Intelligence

[165] viXra:1509.0119 [pdf] submitted on 2015-09-13 20:01:24

The Maximum Deng Entropy

Authors: Bingyi Kang, Yong Deng
Comments: 17 Pages

Dempster Shafer evidence theory has widely used in many applications due to its advantages to handle uncertainty. Deng entropy, has been proposed to measure the uncertainty degree of basic probability assignment in evidence theory. It is the generalization of Shannon entropy since that the BPA is degenerated as probability, Deng entropy is identical to Shannon entropy. However, the maximal value of Deng entropy has not been disscussed until now. In this paper, the condition of the maximum of Deng entropy has been disscussed and proofed, which is usefull for the application of Deng entropy.
Category: Artificial Intelligence

[164] viXra:1509.0088 [pdf] submitted on 2015-09-07 18:59:16

A Cognitive Architecture for Human-Like and Personable ai

Authors: Arvind Chitra Rajasekaran
Comments: 4 Pages.

In this article we will introduce a cognitive architecture for creating a more human like and personable artificial intelligence. Recent works such as those by Marvin Minsky, Google DeepMind and cognitive models like AMBR, DUAL that aim to propose/discover an approach to commonsense AI have been promising, since they show that human intelligence can be emulated with a divide and conquer approach on a machine. These frameworks work with an universal model of the human mind and do not account for the variability between human beings. It is these differences between human beings that make communication possible and gives them a sense of identity. Thus, this work, despite being grounded in these methods, will differ in hypothesizing machines that are diverse in their behavior compared to each other and have the ability to express a dynamic personality like a human being. To achieve such individuality in machines, we characterize the various aspects that can be dynamically programmed onto a machine by its human owners. In order to ensure this on a scale parallel to how humans develop their individuality, we first assume a child-like intelligence in a machine that is more malleable and which then develops into a more concrete, mature version. By having a set of tunable inner parameters called aspects which respond to external stimuli from their human owners, machines can achieve personability. The result of this work would be that we will not only be able to bond with the intelligent machines and relate to them in a friendly way, we will also be able to perceive them as having a personality, and that they have their limitations. Just as each human being is unique, we will have machines that are unique and individualistic. We will see how they can achieve intuition, and a drive to find meaning in life, all of which are considered aspects unique to the human mind.
Category: Artificial Intelligence

[163] viXra:1509.0069 [pdf] submitted on 2015-09-05 10:41:32

Startup Breakthrough in Brain-like Computing

Authors: George Rajna
Comments: 21 Pages.

A small, Santa Fe, New Mexico-based company called Knowm claims it will soon begin commercializing a state-of-the-art technique for building computing chips that learn. Other companies, including HP HPQ -3.45% and IBM IBM -2.10% , have already invested in developing these so-called brain-based chips, but Knowm says it has just achieved a major technological breakthrough that it should be able to push into production hopefully within a few years. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[162] viXra:1508.0139 [pdf] submitted on 2015-08-17 14:52:18

Themes of Futurism and AI in the Novel BlindSight

Authors: Andrew Nassif
Comments: 5 Pages.

Andrew Nassif reviews the novel BlindSight and how it shows themes of modern Trans-humanism, Futurism, and Artificial Intelligence, as well as its symbolic references in terms of philosophy and human empathy. He reviews it through a philosopher, a physicist, a researcher, and a literary analyst point of view.
Category: Artificial Intelligence

[161] viXra:1507.0201 [pdf] submitted on 2015-07-27 13:47:44

Unification of Fusion Theories, Rules, Filters, Image Fusion and Target Tracking Methods (UFT)

Authors: Florentin Smarandache
Comments: 78 Pages.

The author has pledged in various papers, conference or seminar presentations, and scientific grant applications (between 2004-2015) for the unification of fusion theories, combinations of fusion rules, image fusion procedures, filter algorithms, and target tracking methods for more accurate applications to our real world problems - since neither fusion theory nor fusion rule fully satisfy all needed applications. For each particular application, one selects the most appropriate fusion space and fusion model, then the fusion rules, and the algorithms of implementation. He has worked in the Unification of the Fusion Theories (UFT), which looks like a cooking recipe, better one could say like a logical chart for a computer programmer, but one does not see another method to comprise/unify all things. The unification scenario presented herein, which is now in an incipient form, should periodically be updated incorporating new discoveries from the fusion and engineering research.
Category: Artificial Intelligence

[160] viXra:1507.0145 [pdf] submitted on 2015-07-19 01:46:47

Author Attribution in the Bitcoin Blocksize Debate on Reddit

Authors: Andre Haynes
Comments: 18 Pages.

The block size debate has been a contentious issue in the Bitcoin com- munity on the social media platform Reddit. Many members of the com- munity suspect there have been organized attempts to manipulate the debate, from people using multiple accounts to over-represent and mis- represent some sides of the debate. The following analysis uses techniques from authorship attribution and machine learning to determine whether comments from user accounts that are active in the debate are from the same author. The techniques used are able to recall over 90% of all in- stances of multiple account use and achieve up to 72% for the true positive rate.
Category: Artificial Intelligence

[159] viXra:1507.0121 [pdf] submitted on 2015-07-16 09:42:43

Extension Engineering

Authors: Yang Chunyan, Cai Wen
Comments: 352 Pages.

This book is based on the revision and translation of the Chinese version of the Extension Engineering published in 2007 by China Science Press (second printing in 2010), combined with the latest research results of the recent years. The book is the research result of the National Natural Science Foundation Project (70671031), the Guangdong Natural Science Foundation Project (10151009001000044), and the Guangdong University of Technology Team Fostering Foundation Project (12ZK0056). The authors heartfelt thank the National Natural Science Foundation, the Guangdong Natural Science Foundation, and Guangdong University of Technology for their strong support of the Extenics research work. We heartfelt thank for the strong support of the China Science Press and Education Press of America. We heartfelt thank Professor Li Weihua, the part-time researcher with the Research Institute of Extension Engineering at Guangdong University of Technology and head of Computer Science Department of the School of Computers, Professor Florentin Smarandache, the head of Math and Sciences Department of the University of New Mexico for their hard work to proofread this book. Also, Guangdong University of Technology graduate students Cao Liyuan, Zhao Jie, Li Zhiming, and office staff Li Jianming of Research Institute of Extension Engineering at Guangdong University of Technology have participated in the book proofreading and auxiliary work. We express our thanks herein to all of them.
Category: Artificial Intelligence

[158] viXra:1507.0038 [pdf] submitted on 2015-07-06 12:04:22

Unreduced Complex Dynamics of Real Computer and Control Systems

Authors: Andrei P. Kirilyuk
Comments: 8 pages, 42 eqs, 23 refs; Proceedings of the 20th International Conference on Control Systems and Computer Science (27-29 May 2015, Bucharest, Romania), Ed. by I. Dumitrache et al. (IEEE Computer Society CPS, Los Alamitos, 2015), Vol. 2, pp. 557-564

The unreduced dynamic complexity of modern computer, production, communication and control systems has become essential and cannot be efficiently simulated any more by traditional, basically regular models. We propose the universal concept of dynamic complexity and chaoticity of any real interaction process based on the unreduced solution of the many-body problem by the generalised effective potential method. We show then how the obtained mathematically exact novelties of system behaviour can be applied to the development of qualitatively new, complex-dynamical kind of computer and control systems.
Category: Artificial Intelligence

[157] viXra:1506.0216 [pdf] submitted on 2015-06-30 19:46:35

Neutrosophic Axiomatic System

Authors: Florentin Smarandache
Comments: 24 Pages.

In this paper, we introduce for the first time the notions of Neutrosophic Axiom, Neutrosophic Axiomatic System, Neutrosophic Deducibility and Neutrosophic Inference, Neutrosophic Proof, Neutrosophic Tautologies, Neutrosophic Quantifiers, Neutrosophic Propositional Logic, Neutrosophic Axiomatic Space, Degree of Contradiction (Dissimilarity) of Two Neutrosophic Axioms, and Neutrosophic Model. A class of neutrosophic implications is also introduced. A comparison between these innovatory neutrosophic notions and their corresponding classical notions is made. Then, three concrete examples of neutrosophic axiomatic systems, describing the same neutrosophic geometrical model, are presented at the end of the paper.
Category: Artificial Intelligence

[156] viXra:1506.0205 [pdf] submitted on 2015-06-28 20:05:44

[155] viXra:1506.0168 [pdf] submitted on 2015-06-23 04:11:28

Games People Play Conflicts, Mechanisms and Collective Decision-Making in Expert Committees

Authors: Harris V. Georgiou
Comments: 106 Pages. game theory; pattern recognition; classifier combination

Game Theory is one of the most challenging and controversial fields of applied Mathematics. Based on a robust theoretical framework, its applications range from analyzing simple board games and conflict situations to modeling complex systems and evolutionary dynamics. This book is a short collection of introductory papers in the field, aimed primarily as reading material for graduate- and postgraduate-level lectures in Game Theory and/or Machine Learning. The four papers included here are all original works already published as open-access or conference publications, spanning a timeframe of several years apart and a wide range of topics. Hence, each paper is self-contained and can be studied on its own, without any prerequisite knowledge from the previous ones. However, their presentation order is consistent with going from the most elementary issues to the more advanced and experiment-rigorous topics.
Category: Artificial Intelligence

[154] viXra:1506.0120 [pdf] submitted on 2015-06-15 13:48:10

Quantum Artificial Intelligence

Authors: George Rajna
Comments: 24 Pages.

A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of “quantum artificial intelligence”. Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries - how a sliced up flatworm can regenerate into new organisms - has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[153] viXra:1506.0042 [pdf] submitted on 2015-06-06 03:24:51

Computer Invents new Scientific Theory

Authors: George Rajna
Comments: 21 Pages.

One of biology's biggest mysteries - how a sliced up flatworm can regenerate into new organisms - has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[152] viXra:1504.0221 [pdf] submitted on 2015-04-27 16:42:30

Constructing Sentient Androids

Authors: Richard L. Amoroso
Comments: 28 Pages.

Generally the simplistic distinction between a humanoid robot, a computerized machine capable of replicating a variety of complex human functions automatically, and an android is one of appearance; an android is meant to look and act like a human being even to the extent of being indistinguishable. While one day a yottaflop (1024 bits per second) hyper-supercomputer could have a sufficient holographic database and processing power to be truly indistinguishable from a human being, the issue of the applicability of sentience (self-awareness) to an android comes to the forefront. The currently dominant cognitive model of awareness, closely aligned to the AI model, states that mind equals brain and that once correct algorithms are known all of human intelligence could be replicated artificially. This is the so-called mechanistic view: ‘The laws of physics and chemistry are sufficient to describe all living systems; no additional life principle is required’. In this work we develop the point of view that the regime of Unified Field Mechanics (UFM) supplies an inherent action principle driving both the evolution of complex Self-Organized Living Systems (SOLS) and the physical processes of awareness. These UFM parameters in conjunction with ‘conscious quantum computing’ (class of quantum computer modeled with physical parameters of mind-body interaction) putatively leads directly to the construction of sentient (or sentient-like) Androids.
Category: Artificial Intelligence

[151] viXra:1504.0089 [pdf] submitted on 2015-04-11 08:08:04

MEMS Microcantilevers Sensor Modes of Operation and Transduction Principles

Authors: Gopinath.p.g., S. Aruna Mastaniy, V.R. Anithaz
Comments: 11 Pages.

MEMS based microcantilever is a microfabricated mostly rectangular bar shaped structure, longer as compared to width, and has a thickness much smaller than its length or width. Microfabricated silicon cantilever sensor arrays represent a powerful platform for sensing applications in physics, chemistry, material science, biology and medicine. Microcantielver senses even a few molecules or atoms. A small change in mass causes a greater displacement. It is important that due to micron size of cantilever, the cantilevers bend or displacement is due to small amount of mass but not weight. For application in biomedical diagnostics this device plays an important role in the identification of disease detection particles. In this paper we review the cantilever principle, modes of operation, transduction principle and application of cantilever as sensor. MEMS applications operate the cantilever in either a static mode of operation or a dynamic mode of operation. The concept of stress concentration region (SCR) is used to increase stress occurred in the cantilever.
Category: Artificial Intelligence

[150] viXra:1504.0037 [pdf] submitted on 2015-04-05 08:50:12

AI Chart of the Accelerating Universe

Authors: George Rajna
Comments: 9 Pages.

Astronomers in Germany have developed an artificial intelligence algorithm to help them chart and explain the structure and dynamics of the universe around us with unprecedented accuracy. The team, led by Francisco Kitaura of the Leibniz Institute for Astrophysics in Potsdam, report their results in the journal Monthly Notices of the Royal Astronomical Society. [6] This paper explains the Accelerating Universe, the Special and General Relativity from the observed effects of the accelerating electrons, causing naturally the experienced changes of the electric field potential along the moving electric charges. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the wave particle duality and the electron’s spin also, building the bridge between the Classical and Relativistic Quantum Theories. The Big Bang caused acceleration created the radial currents of the matter and since the matter composed of negative and positive charges, these currents are creating magnetic field and attracting forces between the parallel moving electric currents. This is the gravitational force experienced by the matter, and also the mass is result of the electromagnetic forces between the charged particles. The positive and negative charged currents attracts each other or by the magnetic forces or by the much stronger electrostatic forces. The gravitational force attracting the matter, causing concentration of the matter in a small space and leaving much space with low matter concentration: dark matter and energy.
Category: Artificial Intelligence

[149] viXra:1503.0199 [pdf] submitted on 2015-03-27 03:58:41

Implementing an Intelligent Version of the Classical Sliding-Puzzle Game for Unix Terminals Using Golang's Concurrency Primitives

Authors: Pravendra Singh
Comments: 8 Pages.

A smarter version of the sliding-puzzle game is developed using the Go programming language. The game runs in computer system's terminals. Mainly, it was developed for UNIX-type systems but because of cross-platform compatibility of the programming language used, it works very well in nearly all the operating systems. The game uses Go's concurrency primitives to simplify most of the hefty parts of the game. Real time notification functionality is also developed using language's built-in concurrency support.
Category: Artificial Intelligence

[148] viXra:1503.0172 [pdf] submitted on 2015-03-23 09:21:13

A Note on Quantum Entanglement in Dempster-Shafer Evidence Theory

Authors: Xinyang Deng, Yong Deng
Comments: 14 pages, 3 figures.

Dempster-Shafer evidence theory is an efficient mathematical tool to deal with uncertain information. In this theory, basic probability assignment (BPA) is the basic structure for the expression and inference of uncertainty. In this paper, quantum entanglement involved in Dempster-Shafer evidence theory is studied. A criterion is given to determine whether a BPA is in an entangled state or not. Based on that, the information volume involved in a BPA is discussed. The discussion shows that a non-quantum strategy (or observation) can not obtained all information contained in a BPA which is in an entangled state.
Category: Artificial Intelligence

[147] viXra:1503.0135 [pdf] submitted on 2015-03-17 03:59:09

Optic Eye in Sky Unmanned Aircraft for Identify Blemish and Conserving Crops in Cultivated Agricultural Lands

Authors: Gugainamasivayam S, Srinivasan M
Comments: 5 Pages.

In emerging era there is lot of innovation are made in agriculture field to enrich the production and maintenance of crops with reduced area and labor. We planned to introduce an optic in sky unmanned aircraft to fertilize, surveillance, nebulizer [1], growth monitoring and Yield analyzing purpose. This paper helps for crop feeding and treats pests. The OUA [2] has a special sprayer that can spread the minimum level of drugs over crops for the purpose of pest control during mid or late stages of development, food it in proper timing. In which used 64 channel GPS [3] for autonomous Driving by using Latitude, Longitude, Altitude and Speed it act as an tracker of OUA. The controllers are done in remote location using laptops/android devices when it is in autonomous mode. 2.4 GHz zigbee transceiver act as a communication channel between OUA and laptop/android devices .Pest identification is done by means of capturing the image of the crops using HD wireless camera if there is any variation in crop color then the spray pump will spray fertilizer or organic pesticides. The amount of pesticides sparing is determined by the infection rate of the crops, infection range is calculated through digital image processing by comparing and analyzing pixels of the crop image. Digital compass provides driving direction for OUA. Ultrasonic transducer determines the height of the optic unmanned aircraft. DSP processor is used for image processing in OUA. Growth of the crop keyed by changes in crop color and yield analyzing is done by capturing RGB of the crop means of continuous surveillance .It operates on solar power. The plane can prevent crop damage caused by traditional mechanical work and increase economic returns.
Category: Artificial Intelligence

[146] viXra:1503.0131 [pdf] submitted on 2015-03-16 09:57:17

A New Information Unit

Authors: Yong Deng
Comments: 13 Pages.

It is well known that ”Bit” is the unit in information theory to measure information volume with Shannon entropy. However, one assumption to use bit as information unit is that each hypothesis is exclusive with each other. This assumption is also the basic assumption in probability theory which means that two events cannot happen synchronously. However, the assumption is violated such as the ”Entangled state”. A typical example is Schr?dinger’s cat where a cat may be simultaneously both alive and dead. At this situation, bit is not suitable to measure the information volume. To address this issue, a new information unit, called as ”Deng” and abbreviated as ”D”, is proposed based on Deng entropy. The proposed information unit may be used in entangle information processing and quantum information processing.
Category: Artificial Intelligence

[145] viXra:1503.0074 [pdf] submitted on 2015-03-10 17:21:38

Evidence Combination from an Evolutionary Game Theory Perspective

Authors: Xinyang Deng, Deqiang Han, Jean Dezert, Yong Deng, Yu Shyr
Comments: 31 pages, 8 figures

Dempster-Shafer evidence theory is a primary methodology for multi-source information fusion since it allows to deal with uncertain information. This theory is based on Dempster’s rule of combination to synthesize multiple evidences from various information sources. However, in some cases, counter-intuitive results may be obtained based on Dempster’s rule of combination. Lots of improved or new methods have been proposed to suppress the counter-intuitive results based on a physical perspective that minimizes the lost or deviation of original information. In this paper, inspired by evolutionary game theory, a biological and evolutionary perspective is considered to study the combination of evidences. An evolutionary combination rule (ECR) is proposed to mimick the evolution of propositions in a given population and finally find the biologically most supported proposition which is called as evolutionarily stable proposition (ESP) in this paper. Our proposed ECR provides new insight for the combination of multi-source information. Experimental results show that the proposed method is rational and effective.
Category: Artificial Intelligence

[144] viXra:1503.0024 [pdf] submitted on 2015-03-03 19:59:23

Switch or not ? the Simulation of Monty Hall Problem

Authors: Qi Zhang, Meizhu Li, Yong Deng
Comments: 3 Pages.

The Monty Hall problem is a brain teaser,The problem was originally posed in a letter by Steve Selvin to the American Statistician in 1975. To nd out the principle of this conclusion which given by Marilyn vos Savant, and to nd if there is always advantage to the contestants chose to switch their choice .we have make a simulation of this problem.
Category: Artificial Intelligence

[143] viXra:1502.0222 [pdf] submitted on 2015-02-24 23:15:37

Deng Entropy: a Generalized Shannon Entropy to Measure Uncertainty

Authors: Yong Deng
Comments: 20 Pages.

Shannnon entropy is an efficient tool to measure uncertain information. However, it cannot handle the more uncertain situation when the uncertainty is represented by basic probability assignment (BPA), instead of probability distribution, under the framework of Dempster Shafer evidence theory. To address this issue, a new entropy, named as Deng entropy, is proposed. The proposed Deng entropy is the generalization of Shannnon entropy. If uncertain information is represented by probability distribution, the uncertain degree measured by Deng entropy is the same as that of Shannnon’s entropy. Some numerical examples are illustrated to shown the efficiency of Deng entropy.
Category: Artificial Intelligence

[142] viXra:1502.0216 [pdf] submitted on 2015-02-23 23:45:06

A Novel Approach for Unique Address of the Computer in Network Security Environment

Authors: Sanjay Patel, Neha Parmar, Viral Patel
Comments: 4 Pages.

In computer networking, the term IP address spoofing or IP spoofing refers to the creation of Internet Protocol (IP) packets with a forged source IP address, called spoofing. This paper contains an overview of IP address and IP Spoofing and its background. It also shortly discusses various types of IP Spoofing, how they attack on communication system. This paper also describes some methods to detection and prevention methods of IP spoofing and also describes impacts on communication system by IP Spoofing.
Category: Artificial Intelligence

[141] viXra:1502.0204 [pdf] submitted on 2015-02-23 04:29:38

Personal Multithreading: Account Snippet Proposals and Missing Account Indications

Authors: Jan A. Bergstra
Comments: 25 Pages.

A modular way of making progress concerning personal multithreading is suggested: collecting account snippet proposals and missing account indications without an immediate need for integration into a coherent account. Six account snippets for personal multithreading are proposed and and four options for further contributions, that is missing account indications, on personal multi-threading are listed.
Category: Artificial Intelligence

[140] viXra:1501.0088 [pdf] submitted on 2015-01-07 14:29:19

Decision Taking Avoiding Agency

Authors: Jan A. Bergstra
Comments: 42 Pages.

In the setting of outcome oriented decision taking (OODT), decisions are scarce events occurring in between of many other events which are not decisions. Activities of and agency by an agent may occur without any decisions being taken by that agent, with choice and action determination as the only mechanisms for actively resolving uncertainty about future behavior. Such behaviour will be referred to as decision taking avoiding agency. A model, or rather a preliminary qualitative description, of decision taking avoiding agency is provided for systems consisting of or controlled by a solitary natural or artificial agent, as well as for a group of agents.
Category: Artificial Intelligence

[139] viXra:1412.0210 [pdf] submitted on 2014-12-20 04:36:42

Complex Fuzzy Abstraction: The Brain Logic

Authors: Subhajit Ganguly
Comments: 34 Pages.

Taking abstraction as the starting point, we build a complex, self-organizing fuzzy logic system. Such a system, being built on top of abstraction as the base, turns out to be just a special outcome of the laws of abstraction. As the system is self-organizing, it runs automatically towards optimization. Using such a system in neural networks, we may come as close as possible to the workings of the human brain. The abstract fuzzy optimization is seen to follow a Gaussian distribution.
Category: Artificial Intelligence

[138] viXra:1411.0006 [pdf] submitted on 2014-11-01 11:28:17

Weighted Neutrosophic Soft Sets Approach in a Multi-criteria Decision Making Problem

Authors: Pabitra Kumar Maji
Comments: 12 Pages.

The paramount importance of decision making problem in an imprecise environment is becoming very much significant in recent years. In this paper we have studied weighted neutrosophic soft sets which is a hybridization of neutrosophic sets with soft sets corresponding to weighted parameters. We have considered here a multicriteria decision making problem as an application of weighted neutrosophic soft sets.
Category: Artificial Intelligence

[137] viXra:1410.0210 [pdf] submitted on 2014-10-31 19:20:58

A Novel Scheme for Intelligent Recognition of Pornographic Images

Authors: Seyed Mostafa Kia, Hossein Rahmani, Reza Mortezaei, Mohsen Ebrahimi Moghaddam, Amer Namazi
Comments: 6 Pages.

Harmful contents are rising in internet day by day and this motivates the essence of more research in fast and reliable obscene and immoral material filtering. Pornographic image recognition is an important component in each filtering system. In this paper, a new approach for detecting pornographic images is introduced. In this approach, two new features are suggested. These two features in combination with other simple traditional features provide decent difference between porn and non-porn images. In addition, we applied fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron) and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of system was evaluated over 18354 download images from internet. The attained precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on test dataset. Achieved results verify the performance of proposed system versus other related works.
Category: Artificial Intelligence

[136] viXra:1408.0122 [pdf] submitted on 2014-08-18 13:10:12

Bird's-eye view on Noise-Based Logic

Authors: Laszlo B. Kish, Claes-Goran Granqvist, Tamas Horvath, Andreas Klappenecker, He Wen, Sergey M. Bezrukov
Comments: 5 Pages. In: Proceedings of the first conference on Hot Topics in Physical Informatics (HoTPI, 2013 November). Paper is in press at International Journal of Modern Physics: Conference Series (2014).

Noise-based logic is a practically deterministic logic scheme inspired by the randomness of neural spikes and uses a system of uncorrelated stochastic processes and their superposition to represent the logic state. We briefly discuss various questions such as (i) What does practical determinism mean? (ii) Is noise-based logic a Turing machine? (iii) Is there hope to beat (the dreams of) quantum computation by a classical physical noise-based processor, and what are the minimum hardware requirements for that? Finally, (iv) we address the problem of random number generators and show that the common belief that quantum number generators are superior to classical (thermal) noise-based generators is nothing but a myth.
Category: Artificial Intelligence

[135] viXra:1408.0121 [pdf] submitted on 2014-08-18 13:12:46

Brain: Biological Noise-Based Logic

Authors: Laszlo B. Kish, Claes-Goran Granqvist, Sergey M. Bezrukov, Tamas Horvath
Comments: 4 Pages. In press at: Advances in Cognitive Neurodynamics Vol. 4 - Proc. 4th International Conference on Cognitive Neurodynamics (Springer, 2014)

Neural spikes in the brain form stochastic sequences, i.e., belong to the class of pulse noises. This stochasticity is a counterintuitive feature because extracting information - such as the commonly supposed neural information of mean spike frequency - requires long times for reasonably low error probability. The mystery could be solved by noise-based logic, wherein randomness has an important function and allows large speed enhancements for special-purpose tasks, and the same mechanism is at work for the brain logic version of this concept.
Category: Artificial Intelligence

[134] viXra:1408.0017 [pdf] submitted on 2014-08-04 03:55:23

Mining Software Metrics from Jazz

Authors: Jacqui Finlay, Andy M. Connor, Russel Pears
Comments: 7 Pages. 9th International Conference on Software Engineering Research, Management and Applications

In this paper, we describe the extraction of source code metrics from the Jazz repository and the application of data mining techniques to identify the most useful of those metrics for predicting the success or failure of an attempt to construct a working instance of the software product. We present results from a systematic study using the J48 classification method. The results indicate that only a relatively small number of the available software metrics that we considered have any significance for predicting the outcome of a build. These significant metrics are discussed and implication of the results discussed, particularly the relative difficulty of being able to predict failed build attempts.
Category: Artificial Intelligence

[133] viXra:1408.0012 [pdf] submitted on 2014-08-04 02:42:53

Synthetic Minority ov er-Sampling Technique (Smote) for Predicting so Ftware Build Outcomes

Authors: Russel Pears, Jacqui Finlay, Andy M. Connor
Comments: 6 Pages. wenty-Sixth International Conference on Software Engineering and Knowledge Engineering (SEKE 2014) held at Hyatt Regency, Vancouver, Canada, 2014-07-01 to 2014-04-03

In this research we use a data stream approach to mining data and construct Decision Tree models that predict software build outcomes in terms of software metrics that are derived from source code used in the software construction process. The rationale for using the data stream approach was to track the evolution of the prediction model over time as builds are incrementally constructed from previous versions either to remedy errors or to enhance functionality. As the volume of data available for mining from the software repository that we used was limited, we synthesized new data instances through the application of the SMOTE oversampling algorithm. The results indicate that a small number of the available metrics have significance for prediction software build outcomes. It is observed that classification accuracy steadily improves after approximately 900 instances of builds have been fed to the classifier. At the end of the data streaming process classification accuracies of 80% were achieved, though some bias arises due to the distribution of data across the two classes over time.
Category: Artificial Intelligence

[132] viXra:1408.0008 [pdf] submitted on 2014-08-03 10:17:37

The Grow-Shrink Strategy for Learning Markov Network Structures Constrained by Context-Specific Independences

Authors: Alejandro Edera, Yanela Strappa, Facundo Bromberg
Comments: 12 Pages.

Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions. A key application for learning structures from data is to automatically discover knowledge. In practice, structure learning algorithms focused on "knowledge discovery" present a limitation: they use a coarse-grained representation of the structure. As a result, this representation cannot describe context-specific independences. Very recently, an algorithm called CSPC was designed to overcome this limitation, but it has a high computational complexity. This work tries to mitigate this downside presenting CSGS, an algorithm that uses the Grow-Shrink strategy for reducing unnecessary computations. On an empirical evaluation, the structures learned by CSGS achieve competitive accuracies and lower computational complexity with respect to those obtained by CSPC.
Category: Artificial Intelligence

[131] viXra:1406.0081 [pdf] submitted on 2014-06-13 20:26:37

Quantum Computing by Simulations.

Authors: Michail Zak
Comments: 28 Pages.

Quantum computing by simulations is based upon similarity between mathematical formalism of quantum mechanics and phenomena to be computed. It exploits a dynamical convergence of several competing phenomena to an attractor which can represent an extrenum of a function, an image, a solution to a system of ODE, or a stochastic process. In this chapter, a quantum version of recurrent nets (QRN) as an analog computing device is discussed. This concept is introduced by incorporating classical feedback loops into conventional quantum networks. It is shown that the dynamical evolution of such networks, which interleave quantum evolution with measurement and reset operations, exhibit novel dynamical properties. Moreover, decoherence in quantum recurrent networks is less problematic than in conventional quantum network architectures due to the modest phase coherence times needed for network operation. It is proven that a hypothetical quantum computer can implement an exponentially larger number of the degrees of freedom within the same
Category: Artificial Intelligence

[130] viXra:1405.0335 [pdf] submitted on 2014-05-27 15:31:17

Solving NP-Complete Problem by Simulations.

Authors: Michail Zak
Comments: 5 Pages.

Abstract. The mathematical formalism of quantum resonance combined with tensor product decomposability of unitary evolutions is mapped onto a class of NP-complete combinatorial problems. It has been demonstrated that nature has polynomial resources for solving NP-complete problems and that may help to develop a new strategy for artificial intelligence, as well as to re-evaluate the role of natural selection in biological evolution.
Category: Artificial Intelligence

[129] viXra:1405.0222 [pdf] submitted on 2014-05-12 17:28:46

Learning Markov Networks Structures Constrained by Context-Specific Independences

Authors: Alejandro Edera, Federico Schlüter, Facundo Bromberg
Comments: 41 Pages. This work is under revision in the IJAIT

This work focuses on learning the structure of Markov networks. Markov networks are parametric models for compactly representing complex probability distributions. These models are composed by: a structure and a set of numerical weights. The structure describes independences that hold in the distribution. Depending on the goal of learning intended by the user, structure learning algorithms can be divided into: density estimation algorithms, focusing on learning structures for answering inference queries; and knowledge discovery algorithms, focusing on learning structures for describing independences qualitatively. The latter algorithms present an important limitation for describing independences as they use a single graph, a coarse grain representation of the structure. However, many practical distributions present a flexible type of independences called context-specific independences, which cannot be described by a single graph. This work presents an approach for overcoming this limitation by proposing an alternative representation of the structure that named canonical model; and a novel knowledge discovery algorithm called CSPC for learning canonical models by using as constraints context-specific independences present in data. On an extensive empirical evaluation, CSPC learns more accurate structures than state-of-the-art density estimation and knowledge discovery algorithms. Moreover, for answering inference queries, our approach obtains competitive results against density estimation algorithms, significantly outperforming knowledge discovery algorithms.
Category: Artificial Intelligence

[128] viXra:1405.0214 [pdf] submitted on 2014-05-11 19:30:31

Communication Neutrosophic Routes

Authors: editors Florentin Smarandache, Stefan Vladutescu
Comments: 217 Pages.

The life of human beings is a place of communication. Consequently, any cognitive and cogitative manifestation presents a route of communication. People consume their lives relating by communicational. Some communicational relationships are contradictory, others are neutral, since within the manifestations of life there are found conflicts meanings and/or neutral meanings. Communicational relations always comprise a set of neutral, neutrosophic meanings. Particularly, we talk about scientific sculptural communication, esthetic communication and so on, as specific manifestations of life. It can be asserted that in any communication there are routes of access and neutrosophic routes. Any communication is traversed by neutrosophic routes of communication. [F. Smarandache & S. Vladutescu] The book has 10 chapters written by the following authors and co-authors: Florentin Smarandache, Stefan Vladutescu, Ioan Constantin Dima, Mariana Man, Alexandra Iorgulescu, Alina Tenescu, Madalina Strechie, Daniela Gifu, MIhaela Gabriela Paun, Maria Nowicka-Skowron, Sorin Mihai Radu, Janusz Grabara, Ion Cosmescu, and Bianca Teodorescu.
Category: Artificial Intelligence

[127] viXra:1404.0425 [pdf] submitted on 2014-04-19 02:45:15

Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis

Authors: Harris V Georgiou
Comments: 12 Pages.

Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.
Category: Artificial Intelligence

[126] viXra:1404.0050 [pdf] submitted on 2014-04-06 22:23:17

The Human Computer Science of 13,800,000,000 B.C.

Authors: Rodney Bartlett
Comments: 10 Pages.

The hardest part of writing this was selecting a category. It involves physics, maths, biology, philosophy. But it speaks of computer science and the AI of bits being responsible for all creation. So maybe this is the right category? This article is another version of my entry in FQXi’s 2014 contest – http://fqxi.org/community/forum/topic/1977. I was inspired to write this new version after reading "The Missing Universe" by Bob Berman (Astronomy magazine - April 2014) which says, "... MOND (MOdified Newtonian Dynamics) works well in predicting how galaxies rotate, but it doesn't work as well in predicting motions at much larger scales, such as those between galaxy groups (clusters and superclusters - DM, dark matter, seems compelling at the largest scales)." This sounds like a message from the universe that we don't have to choose between dark matter and modified gravity. The following article shows that they can be integrated. And if a Theory of Everything really does describe the universe, the strong force and electromagnetic force (p.28 of Bob Berman's article) must also be capable of integration with DM/MOND. To do this, I'll "Einsteinize" my article by reminding readers of a 1919 paper he wrote. Further Einsteinization will return us to the Theory of Everything by speaking of his Unified Field Theory. Then I’ll finish with “What is Gravity?” (thanks to Einstein’s General Relativity explaining that gravity is the warping of space-time, this could also be called “What is Space-time?”) and a new interpretation of infinity.
Category: Artificial Intelligence

Replacements of recent Submissions

[41] viXra:1611.0211 [pdf] replaced on 2016-12-01 04:59:33

A Variable Order Hidden Markov Model with Dependence Jumps

Authors: Anastasios Petropoulos, Stelios Xanthopoulos, Sotirios P. Chatzis
Comments: 33 Pages.

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first- or moderate-order Markov chain. However, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. In this paper, we address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed data, where the patterns of the entailed temporal dynamics may change over time. We devise efficient training and inference algorithms for our model, following the expectation-maximization paradigm. We demonstrate the efficacy and usefulness of our approach considering several real-world datasets. As we show, our model allows for increased modeling and predictive performance compared to the alternative methods, while offering a good trade-off between the resulting increases in predictive performance and computational complexity.
Category: Artificial Intelligence

[40] viXra:1611.0211 [pdf] replaced on 2016-11-14 08:01:26

A Variable Order Hidden Markov Model with Dependence Jumps

Authors: Anastasios Petropoulos, Stelios Xanthopoulos, Sotirios P. Chatzis
Comments: 15 Pages.

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first- or moderate-order Markov chain. However, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. In this paper, we address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed data, where the patterns of the entailed temporal dynamics may change over time. We devise efficient training and inference algorithms for our model, following the expectation-maximization paradigm. We demonstrate the efficacy and usefulness of our approach considering several real-world datasets. As we show, our model allows for increased modeling and predictive performance compared to the alternative methods, while offering a good trade-off between the resulting increases in predictive performance and computational complexity.
Category: Artificial Intelligence

[39] viXra:1611.0211 [pdf] replaced on 2016-11-14 04:26:58

A Variable Order Hidden Markov Model with Dependence Jumps

Authors: Anastasios Petropoulos, Stelios Xanthopoulos, Sotirios P. Chatzis
Comments: 15 Pages.

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first- or moderate-order Markov chain. However, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. In this paper, we address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed data, where the patterns of the entailed temporal dynamics may change over time. We devise efficient training and inference algorithms for our model, following the expectation-maximization paradigm. We demonstrate the efficacy and usefulness of our approach considering several real-world datasets. As we show, our model allows for increased modeling and predictive performance compared to the alternative methods, while offering a good trade-off between the resulting increases in predictive performance and computational complexity.
Category: Artificial Intelligence

[38] viXra:1610.0029 [pdf] replaced on 2016-10-16 08:51:52

Associative Broadcast Neural Network

Authors: Aleksei Morozov
Comments: 5 Pages.

Associative broadcast neural network (aka Ether Neural Network) is an artificial neural network inspired by a hypothesis of broadcasting of neuron's output pattern in a biological neural network. Neuron has wire connections and ether connections. Ether connections are electrical. Wire connections provide a recognition functionality. Ether connections provide an association functionality.
Category: Artificial Intelligence

[37] viXra:1607.0073 [pdf] replaced on 2016-07-08 06:55:14

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[36] viXra:1607.0073 [pdf] replaced on 2016-07-08 03:07:07

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[35] viXra:1607.0073 [pdf] replaced on 2016-07-07 10:41:19

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[34] viXra:1607.0073 [pdf] replaced on 2016-07-07 08:09:10

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[33] viXra:1607.0073 [pdf] replaced on 2016-07-07 07:08:00

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[32] viXra:1606.0272 [pdf] replaced on 2016-11-24 16:47:04

Self-Controlled Dynamics

Authors: Michail Zak
Comments: 26 Pages.

A new class of dynamical system described by ODE coupled with their Liouville equation has been introduced and discussed. These systems called self-controlled, or self-supervised since the role of actuators is played by the probability produced by the Liouville equation. Following the Madelung equation that belongs to this class, non- Newtonian properties such as randomness, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that self-controlled dynamical systems can be linked to mathematical models of livings as well as to models of AI. The central point of this paper is the application of the self-controlled systems to NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing location of this item.
Category: Artificial Intelligence

[31] viXra:1606.0272 [pdf] replaced on 2016-11-24 15:46:24

Self-Controlled Dynamics

Authors: Michail Zak
Comments: 26 Pages.

A new class of dynamical system described by ODE coupled with their Liouville equation has been introduced and discussed. These systems called self-controlled, or self-supervised since the role of actuators is played by the probability produced by the Liouville equation. Following the Madelung equation that belongs to this class, non- Newtonian properties such as randomness, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that self-controlled dynamical systems can be linked to mathematical models of livings as well as to models of AI. The central point of this paper is the application of the self-controlled systems to NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing location of this item.
Category: Artificial Intelligence

[30] viXra:1605.0190 [pdf] replaced on 2016-05-20 08:36:35

The Algorithm of the Thinking Machine

Authors: Dimiter Dobrev
Comments: 15 Pages. Represented at 12 of May, 2016 at Faculty of Mathematics and Informatics, University of Sofia.

In this article we consider the questions 'What is AI?' and 'How to write a program that satisfies the definition of AI?' It deals with the basic concepts and modules that must be at the heart of this program. The most interesting concept that is discussed here is the concept of abstract signals. Each of these signals is related to the result of a particular experiment. The abstract signal is a function that at any time point returns the probability the corresponding experiment to return true.
Category: Artificial Intelligence

[29] viXra:1509.0088 [pdf] replaced on 2015-09-08 18:58:24

Cognitive Architecture for Personable and Human-Like ai :A Perspective

Authors: Arvind Chitra Rajasekaran
Comments: 4 Pages.

In this article we will introduce a cognitive architecture for creating a more human like and personable artificial intelligence. Recent works such as those by Marvin Minsky, Google DeepMind and cognitive models like AMBR, DUAL that aim to propose/discover an approach to commonsense AI have been promising, since they show that human intelligence can be emulated with a divide and conquer approach on a machine. These frameworks work with an universal model of the human mind and do not account for the variability between human beings. It is these differences between human beings that make communication possible and gives them a sense of identity. Thus, this work, despite being grounded in these methods, will differ in hypothesizing machines that are diverse in their behavior compared to each other and have the ability to express a dynamic personality like a human being. To achieve such individuality in machines, we characterize the various aspects that can be dynamically programmed onto a machine by its human owners. In order to ensure this on a scale parallel to how humans develop their individuality, we first assume a child-like intelligence in a machine that is more malleable and which then develops into a more concrete, mature version. By having a set of tunable inner parameters called aspects which respond to external stimuli from their human owners, machines can achieve personability. The result of this work would be that we will not only be able to bond with the intelligent machines and relate to them in a friendly way, we will also be able to perceive them as having a personality, and that they have their limitations. Just as each human being is unique, we will have machines that are unique and individualistic. We will see how they can achieve intuition, and a drive to find meaning in life, all of which are considered aspects unique to the human mind.
Category: Artificial Intelligence

[28] viXra:1502.0216 [pdf] replaced on 2015-02-25 20:50:22

A Novel Approach for Unique Address of the Computer in Network Security Environment

Authors: Sanjay Patel, Neha Parmar, Viral Patel
Comments: 4 Pages.

In computer networking, the term IP address spoofing or IP spoofing refers to the creation of Internet Protocol (IP) packets with a forged source IP address, called spoofing. This paper contains an overview of IP address and IP Spoofing and its background. It also shortly discusses various types of IP Spoofing, MAC spoofing and how they attack on communication system. This paper also describes some methods to detection and prevention methods of IP spoofing and also describes impacts on communication system by IP Spoofing. Propose approach identifies the computer through defined unique identification address of computer.
Category: Artificial Intelligence