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检索条件"主题词=Cognitive informatics"

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Neighbouring Proximity - An Key Impact Factor of Deep Machine Learning

Neighbouring Proximity - An Key Impact Factor of Deep Machin...

引用

IEEE International Conference on cognitive informatics

作者： Hongyuan Shi Yunke Li Liang Chen Fan Jiang Department of Computer Science University of Northern British Columbia Prince George BC Canada

ISBN: (纸本)9781538633618

Deep Learning has become increasingly popular since Alexnet was proposed. It has been applied to many domains such as pattern recognition, computer vision, machine translation, and natural language processing. While the advantages of deep learning methods are wildly accepted, the limitations of them are not well researched. In this paper, we present our study and analysis of cases where deep learning methods lose their advantages over traditional methods. Our experiments show that, when the neighbouring proximity disappears, the accuracy of deep learning methods is at most as good as, if not worse than, that of advanced traditional shallow methods. As the resources that traditional shallow methods needed are always much less than deep learning methods. We conclude that, in situations where neighbouring elements of input samples do not have proximity, deep learning methods are significantly less powerful than traditional methods. Furthermore, this clearly indicates that deep structure methods cannot fully replace traditional shallow methods.

关键词： Manganese Machine-to-machine communications cognitive informatics cognitive systems

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Neural field model for perceptual learning

Neural field model for perceptual learning

引用

IEEE International Conference on cognitive informatics

作者： Zhongzhi Shi Youping Huang Jian Zhang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)0769521908

Perceptual learning happens at the perceptual level. We combine holism and reductionism to research perception learning. Based on information geometry the paper presents a neural field model which is used to understand the transformation mechanism, dynamical behavior, capability and limitation of neural network models, by the study of globally topological and geometrical structure on parameter spaces of neural networks. We discuss neural field representation, fractal learning principle, topology approximation correction learning and the dualistic correction learning algorithm. Finally the paper gives conclusions and points out future research topics.

关键词： Solid modeling Biological neural networks Information geometry Manifolds Information processing Fractals Network topology Approximation algorithms cognitive informatics Humans

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An Intelligent Analysis for Rural Settlement Distribution Based on Gaussian Mixture Models: A Case Study of Kengzi Village

An Intelligent Analysis for Rural Settlement Distribution Ba...

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IEEE International Conference on cognitive informatics

作者： Xi Yang Fuan Pu Guiming Luo Shenzhen Tourism College Jinan University Shenzhen China School of Sofnvare Tsinghua University Beijing China

ISBN: (纸本)9781538633618

In light of the booming rural construction industry in China, the recurring problem of the “village sameness” reflects the inadequate understanding of inherent characteristics of traditional settlements. To discern the internal logic of historic rural settlements from historical spatial data, this study proposed a machine learning method based on the Gaussian mixture model (GMM) to examine the settlement distribution sensitivity for every geographical variable, and to perform a multivariate regression analysis on the nonlinear non-monotonic relationship between variables and the land usage development potential. In accordance with the abstracted spatial rules, the model was also used for predicting the spatial trends to support regional planning activities.

关键词： Silicon Software Gaussian mixture model Machine learning Handheld computers cognitive informatics

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STOPA: a stochastic process algebra for the formal representation of cognitive systems

STOPA: a stochastic process algebra for the formal represent...

引用

IEEE International Conference on cognitive informatics

作者： N. Lopez M. Nunez F.L. Pelayo Dept. Sistemas Informáticos y Programación Universidad Complutense de Madrid C/. Juan del Rosal Madrid Spain Dept. Informática. Sección de Albacete Universidad de Castilla-La Mancha Campus Universitario Albacete Spain

We present a formal language to specify cognitive systems. In addition to the usual characteristics of these formalisms, our language features the possibility of including stochastic time. This kind of time is useful to represent systems where the delays are not controlled by fix amounts of time, but they are given by a probability distribution function.

关键词： Stochastic systems Algebra Stochastic processes cognitive informatics Finishing Probability distribution Concurrent computing Formal languages Delay systems Delay effects

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Design of a cognitive Complexities Measurement and Analysis Tool

Design of a Cognitive Complexities Measurement and Analysis ...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Vincent Chiew Yingxu Wang Theoretical and Empirical Software Engineering Research Center Department of Electrical and Computer Engineering University of Calgary Calgary AB Canada

The complexities of software systems are more prominent now than ever, due to progression of simple software applications to large integrated systems, either centralized or distributed. The challenges to software engineer somehow to understand and manage such large software systems and deal with software cognitive complexities. Although there are research areas such as human machine interface or human factor engineering, there is still little research on how to quantitatively measure and analyze human cognitive complexities in comprehending and understanding complex software systems. This paper studies human interaction with software programs in the cognitive perspective on software complexity. Based on the findings, a pilot software cognitive complexity measurement and analyses tool (CCMAT) is presented

关键词： Software measurement Software engineering cognitive informatics Software systems Human factors Software tools Computer industry Robustness Brain modeling Electric variables measurement

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On abstract intelligence and its denotational mathematics foundations

On abstract intelligence and its denotational mathematics fo...

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IEEE International Conference on cognitive informatics

作者： Yingxu Wang International Center for Cognitive Informatics (ICfCI) Theoretical and Empirical Software Engineering Research Centre (TESERC) Department of Electrical and Computer Engineering Schulich School of Engineering University of Calgary Calgary AB Canada

ISBN: (纸本)9781424425389

Recent researches reveal that various paradigms of intelligence, such as natural, artificial, machinable, and computational intelligence, can be unified at the logical and functional levels known as abstract intelligence. This paper introduces abstract intelligence as a form of driving force that transfers information into knowledge and behaviors. An architectural framework of abstract intelligence and the generic abstract intelligence mode (GAIM) are formally developed that provide a unified theory for explaining the mechanisms of advanced intelligence. In order to deal with the highly complex and abstract objects in abstract intelligence, denotational mathematics is introduced as a category of expressive mathematical structures for modeling and manipulating high-level mathematical entities beyond numbers and sets, such as abstract objects, complex relations, behavioral information, abstract concepts, knowledge, processes, and systems. Applications of denotational mathematics in abstract intelligence, cognitive informatics, and computational intelligence are elaborated.

关键词： Abstract intelligence Intelligence science Natural intelligence Artificial intelligence cognitive informatics Denotational mathematics Concept algebra System algebra Process algebra RTPA cognitive science Computational intelligence Artificial Intelligence Computational intelligence cognitive informatics Intelligence process algebra cognitive Science mathematics

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Formal description of the cognitive comprehension process

Formal description of the cognitive comprehension process

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： D. Gafurov Yingxu Wang Department of Informatics and IT Technological University of Tajikistan Tajikistan Theoretical and Empirical Software Engineering Research Centre Department of Electrical and Computer Engineering University of Calgary Calgary AB Canada

The term of comprehension and the reveal of its mechanism and process are important research topics in cognitive informatics. Although knowledge and information are powerful, before any information can be possessed and processed, it should be comprehended properly. This paper intends to model one of the cognitive life functions, comprehension in cognitive informatics. The mechanism and process of comprehension are described based on the cognitive model of the brain developed by Wang. Then, real-time process algebra (RTPA) is used to formally describe the comprehension process. This work is considered as a fundamental facet of cognitive informatics towards the development of new theories and technologies in computing, software engineering, and life sciences.

关键词： cognitive informatics Brain modeling Humans Software engineering Algebra Knowledge engineering Artificial intelligence Psychology Drives Machine intelligence

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Deep reasoning and thinking beyond deep learning by cognitive robots and brain-inspired systems

Deep reasoning and thinking beyond deep learning by cognitiv...

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IEEE International Conference on cognitive informatics

作者： Yingxu Wang Dept. of Electrical Engineering Stanford University Stanford CA USA Dept. of Electrical and Computer Engineering University of Calgary Alberta Canada

ISBN: (纸本)9781509038473

Summary form only given. Recent basic studies reveal that AI problems are deeply rooted in both the understanding of the natural intelligence and the adoption of suitable mathematical means for rigorously modeling the brain in machine understandable forms. Learning is a cognitive process of knowledge and behavior acquisition. Learning can be classified into five categories known as object identification, cluster classification, functional regression, behavior generation, and knowledge acquisition. A fundamental challenge to knowledge learning different from the deep and recurring neural network technologies has led to the emergence of the field of cognitive machine learning on the basis of recent breakthroughs in denotational mathematics and mathematical engineering. This keynote lecture presents latest advances in formal brain studies and cognitive systems for deep reasoning and deep learning. It is recognized that key technologies enabling cognitive robots mimicking the brain rely not only on deep learning, but also on deep reasoning and thinking towards machinable thoughts and cognitive knowledge bases built by a cognitive systems. A fundamental theory and novel technology for implementing deep thinking robots are demonstrated based on concept algebra, semantics algebra, and inference algebra.

关键词： mathematical engineering cognitive informatics cognitive computers cognitive robotics brain-inspired systems deep learning deep reasoning deep thinking knowledge learning denotational mathematics

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The Paradigm of Granular Rough Computing: Foundations and Applications

The Paradigm of Granular Rough Computing: Foundations and Ap...

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IEEE International Conference on cognitive informatics

作者： Lech Polkowski Polish Japanese Institute of Information Technology Warsaw Poland Department of Mathematics and Computer Science University of Warmia and Mazury Olsztyn Poland

Granular Rough Computing is a paradigm within Rough Computing which in turn can be set within the realm of cognitive informatics, i.e. machine intelligence tools which emulate cognitive processes in living organisms; its aim is to compute with granules of knowledge that are collective objects formed from individual objects by means of a similarity measure. In this work, we apply the formalism for granule formation proposed by us and studied over last few years which is based on similarity measures called Rough Inclusions. A proper study of rough inclusions has been done within Rough Mereology, a paradigm for approximate reasoning that is rooted in the theory of concepts called Mereology based on the notion of a part instead on the notion of an element like the naive concept theory. We give an outline of granulation theory based on rough inclusions; then, we discuss the most important consequence of similarity among objects in a granule, viz., the hypothesis that granules represent new objects, which preserve the most important features of objects in a granule. This leads to the notion of a granular decision system obtained by means of granulation from an original decision system. The hypothesis that granular decision systems reflect properties of original decision systems to a satisfactory degree, put forth by the author at 2005 and 2006 IEEE GrC conferences, has been tested with very good results. We include here some results of those tests.

关键词： Computer applications cognitive informatics Uncertainty Logic testing Set theory Application software Information technology Mathematics Computer science Machine intelligence

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An Approach to Representation Changes While Executing Problem Solver Intelligent Systems

An Approach to Representation Changes While Executing Proble...

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IEEE International Conference on cognitive informatics

作者： Antonio Hernando Luis de Ledesma Luis M. Laita Universidad Complutense de Madrid Spain Universidad Politécnica de Madrid Spain

The choice of an appropriate representation is generally assumed to play a decisive part in problem solving, both in human and computer performance. Some AI problem solvers have so far succeeded in performing automatic representation change, but none of them is actually able to do so once the resolution process is started. This inability proves to be a drawback so as to achieving a suitable representation is concerned. Also it should be observed that in human problem solving representation changes can actually occur in every stage of the resolution process. Taking all this into account, in the present paper we deal with a technique for changing representation while problem solving by making use of specific procedures which assign and modify a certain relevance value to every attribute involved in the problem representation, on behalf of their respective importance so as to actually solve the problem. This technique allows adapting representation to the peculiarities of each problem while the actual problem solving process takes place.

关键词： Intelligent systems Problem-solving Humans Psychology Computer performance Artificial intelligence Assembly cognitive informatics Algorithm design and analysis

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