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Semantic computing and cognitive computing/informatics

Semantic computing and cognitive computing/informatics

引用

ieee international conference on cognitive Informatics

作者： Phillip Sheu University of California Irvine Irvine CA US

ISBN: (纸本)9781538607725

Semantic computing (SC) addresses the derivation, description, integration, and use of semantics (“meaning”, “context”, “intention”) for all types of resource including data, document, tool, device, process and people. A broader definition of Semantic computing includes the computing technologies (e.g., artificial intelligence, natural language, software engineering, data and knowledge engineering, computer systems, signal processing, etc.), and their interactions, that may be used to extract or process computational content. This connection between content and the user is made via Semantic Analysis, which analyzes content with the goal of converting it to machine processable descriptions (semantics); Semantic Integration, which integrates content and semantics from multiple sources; Semantic Applications, which utilize content and descriptions to solve problems; and Semantic Interface, which interprets users' intentions expressed in natural language or other communicative forms. The reverse connection converts the intentions of users to create content via analysis and synthesis techniques. This talk introduces Semantic computing based on its broader and narrower definitions, its relationship with Artificial Intelligence, cognitive computing and Informatics, and other branches of Computer Science. It also discusses several case studies showing how it can be used to solve more complex problems.

关键词： Electronic mail

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cognitive computing and its applications

Cognitive computing and its applications

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2017 international conference on Energy, Communication, data Analytics and Soft computing, ICECDS 2017

作者： Megha, C.R. Madhura, A. Sneha, Y.S. CSE JSS Academy of Technical Education Bengaluru India Dept of CSE JSS Academy of Technical Education Bengaluru India

ISBN: (纸本)9781538618868

cognitive computing is the imitation of the thought process of human beings using a sophisticated computerized model. cognitive systems are designed to work at human scales. cognitive systems reason with purpose and interact with humans instinctively. These systems are driven to understand concepts by decomposing expressions of an idea and then combining the results with context and the probability that certain terms in the passage are being used in a certain way. cognitive systems can learn through experiences, find correlations, create hypotheses, and remember, and learn from, the outcomes. It leverages deep content analysis and evidence-based reasoning to accelerate and improve decisions, reduce costs and optimize outcomes. cognitive computing allows wide variety of problems previously incapable of being solved by fixed program instructions to be calculable and estimable. It also takes into consideration various complex scenarios that are ambiguous and uncertain and provides probabilistic solutions to such problems which is accurate. This article presents an insight into the basics of cognitive computing and a survey on the recent applications and advancements in the field of cognitive computing. © 2017 ieee.

关键词： cognitive systems

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Searching in harsh environments

Searching in harsh environments

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ieee international conference on cognitive Informatics

作者： Ophir Frieder Robert L. McDevitt Catherine H. McDevitt Georgetown University Washington DC US

ISBN: (纸本)9781538607725

Many consider “searching” a solved problem, and for digital text processing, this belief is factually based. The problem is that many “real world” search applications involve “complex documents”, and such applications are far from solved. complex documents, or less formally, “real world documents”, comprise of a mixture of images, text, signatures, tables, etc., and are often available only in scanned hardcopy formats. Some of these documents are corrupted. Some of these documents, particularly of historical nature, contain multiple languages. Accurate search systems for such document collections are currently unavailable. We describe our efforts at building a complex document information-processing prototype. This prototype integrates “point solution” (mature) technologies, such as document readability enhancement, OCR capability, signature matching and handwritten word spotting techniques, search and mining approaches, among others, to yield a system capable of searching “real world documents”. The described prototype demonstrates the adage that “the whole is greater than the sum of its parts”. Our previous complex document benchmark development efforts are likewise presented. Having described “real world” search issues, we focus on spelling correction in adverse environments. Two environments are discussed: foreign name search and medical term search. In support of the Yizkor Books project of the Archives Section of the United States Holocaust Memorial Museum, novel foreign name search approaches that favorably compare against the state of the art are developed. By segmenting names, fusing individual results, and filtering via a threshold, our approach statistically significantly improves traditional Soundex and n-gram based search techniques used in the search of such texts. Thus, previously unsuccessful searches are now supported. Using a similar approach, within the medical domain, automated term corrections are made to reduce transcription errors. Finally, we

关键词： data mining information retrieval text analysis text processing information retrieval Searches data mining Harsh environments state of the art search systems search technique

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Study of complex-valued Learning algorithms for Post-surgery survival prediction 2

Study of Complex-valued Learning algorithms for Post-surgery...

引用

2nd international conference on cognitive computing and Information Processing (CCIP)

作者： Muthusamy, Sivachitra Ramasamy, Savitha Kongu Engn Coll Dept EEE Perundurai India Inst Infocomm Reseach Data Analyt Dept Fusionopolis 1 Singapore Singapore

ISBN: (纸本)9781509010257

Prediction of post-surgery survival of breast cancer patients is critical for long term medical care. In this paper, we study the performances of several complex-valued classifiers in predicting the post-surgical survival, based on the real world Haber data set available in the UCI machine learning repository. The complex-valued classifiers used in the study include the Fully complex-valued Radial Basis Function (FC-RBF), Fully complex-valued Relaxation Network (FCRN),Meta-cognitive Fully complex-valued Relaxation Network (McFCRN), Fully complex-valued Fast Learning Classifier (FC-FLC), Meta-cognitive Fully complex-valued Fast Learning Classifier (McFCFLC), Fully complex-valued Functional Link Network (FCFLN), and Meta-cognitive Fully complex-valued Functional Link Network (McFCFLN). As the classification performance of the complex-valued classifiers is boosted by the presence of orthogonal decision boundaries, all these classifiers perform better than the state-of-the art real-valued classifiers. Performance results also show that the Mc-FCFLC and McFCRN outperform other classifiers used in the study. This can be attributed to the meta-cognition that helps in strategic learning in these classifiers.

关键词： Post surgical survival Meta-cognition complex-valued classifiers Orthogonal decision boundaries

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A New Multifunctional Neural Network with High Performance and Low Energy Consumption 15

A New Multifunctional Neural Network with High Performance a...

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15th ieee international conference on cognitive Informatics and cognitive computing (ICCI*CC)

作者： Zhang, Luna M. Soft Tech Consulting Inc Chantilly VA 20151 USA

ISBN: (纸本)9781509038466

A common artificial neural network (ANN) uses the same activation function for all hidden and output neurons. Therefore, it has an optimization limitation for complex big data analysis due to its single mathematical functionality. In addition, an ANN with a complicated activation function uses a very long training time and consumes a lot of energy. To address these issues, this paper presents a new energy-efficient "Multifunctional Neural Network" (MNN) that uses a variety of different activation functions to effectively improve performance and significantly reduce energy consumption. A generic training algorithm is designed to optimize the weights, biases, and function selections for improving performance while still achieving relatively fast computational time and reducing energy usage. A novel general learning algorithm is developed to train the new energy-efficient MNN. For performance analysis, a new "Genetic Deep Multifunctional Neural Network" (GDMNN) uses genetic algorithms to optimize the weights and biases, and selects the set of best-performing energy-efficient activation functions for all neurons. The results from sufficient simulations indicate that this optimized GDMNN can perform better than other GDMNNs in terms of achieving high performance (prediction accuracy), low energy consumption, and fast training time. Future works include (1) developing more effective energy-efficient learning algorithms for the MNN for data mining application problems, and (2) using parallel cloud computing methods to significantly speed up training the MNN.

关键词： Neural networks deep learning energy-efficient computing data mining activation functions genetic algorithms

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Limited Intervention Collaborative Decision Making of MAV-UAV Team based on VFCM

Limited Intervention Collaborative Decision Making of MAV-UA...

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13th ieee international conference on Services computing (SCC)

作者： Chen, Jun Xu, Jia Zhong, Linhui Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China Jiangxi Normal Univ Coll Comp & Informat Engn Nanchang Peoples R China

ISBN: (纸本)9781509026289

In view of complex battlefield environment and technological factors, unmanned aerial vehicle's (UAV) decision-making cannot accurately reflect battlefield situation and make reasonable decisions by itself. To solve this problem, this paper presents a limited intervention collaborative decision-making mechanism between manned aerial vehicle(MAV) and UAV based on visualising fuzzy cognitive map (VFCM). By use of limited intervention trigger strategy, high efficiency and feasibility of decision-making can be ensured. Simulation indicates that limited intervention collaborative decision making of the MAV/UAV team is reasonable and feasible.

关键词： unmanned aerial vehicle (UAV) manned aerial vehicle (MAV) limited intervention collaborative decision-making visualising fuzzy cognitive map (VFCM)

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Predicting learners' multi-question performance based on neural networks 4

Predicting learners' multi-question performance based on neu...

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4th international conference on Behavioral, Economic, and Socio-Cultural computing, BESC 2017

作者： Liao, Pan Sun, Yuan Ye, Shiwei Li, Xin Su, Guiping Sun, Yi School of Computer and Control Engineering University of Chinese Academy of Sciences China Information and Society Research Division National Institute of Informatics Tokyo Japan Big Data Research Institute Hefei China

ISBN: (纸本)9781538623657

As massive open online courses (MOOCs) and online intelligent tutoring systems(ITS) have become increasingly widespread, the number of learners enrolled in online courses has shown explosive growth. However, these learners are likely to have acquired knowledge from diverse educational and vocational backgrounds. Therefore, it is unwise to apply the same criteria and assessment questions to assess all learners' abilities without differentiation. Therefore, the demand for the adaptive arrangement of questions for online learners is ever critical. Deep learning is a new increasingly popular approach for handling extraordinarily complex problems such as image recognition and natural language processing. In this research, we use neural networks to forecast learners' multi-question performance on new test questions and propose a new concept called predictable property for the first time to explain the reasons why neural networks can be applied to predict learners' multi-question performance based on their previous question responses. This approach means that fewer questions need to be answered by learners although more information can be gathered about them through the use of deep-learning-based techniques. Finally, we use both artificial datasets generated by cognitive models and three real-world datasets to validate the algorithm's performance. Experiments show a promising research result when using deep learning to predict learner performance in multi-question tasks and can ultimately provide more accurate adaptive tests for learners. © 2017 ieee.

关键词： Image recognition

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cognitive computing: From breakthroughs in the lab to applications on the field

Cognitive computing: From breakthroughs in the lab to applic...

引用

ieee international conference on Big data

作者： Guruduth S. Banavar Cognitive Computing IBM

Summary form only given. In the last decade, the availability of massive amounts of new data, the development of new machine learning technologies, and the availability of scalable computing infrastructure, have given rise to a new class of computing systems. These “cognitive Systems” learn from data, reason from models, and interact naturally with us, to perform complex tasks better than either humans or machines can do by themselves. These tasks range from answering questions conversationally to extracting knowledge for discovering insights to evaluating options for difficult decisions. These cognitive systems are designed to create new partnerships between people and machines to augment and scale human expertise in every industry, from healthcare to financial services to education. This talk will provide an overview of cognitive computing, the technology breakthroughs that are enabling this trend, and the practical applications of this technology that are transforming every industry.

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Disaster-aware smart routing scheme based on symbiotic computing for highly-available information storage systems

Disaster-aware smart routing scheme based on symbiotic compu...

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ieee international conference on cognitive Informatics

作者： Satoru Izumi Asato Edo Toru Abe Takuo Suganuma Cyberscience Center Tohoku University Graduate School of Information Sciences Tohoku University Sendai JAPAN

ISBN: (纸本)9781509038473

In this paper, we propose a disaster-aware smart routing scheme for highly-available information storage systems. Our proposed scheme is based on the concept of Symbiotic computing to recognize disaster status in Real Space, and provides appropriate routes form Digital Space dynamically. This realizes effective data transmission considering disaster situation and its time variation. We have designed architecture of our proposed scheme and conducted basic experimentation. In this paper, we extend its architecture based on the Symbiotic computing and evaluate its effectiveness through complex network environments.

关键词： Throughput data communication Symbiosis Routing Control systems Computer architecture Monitoring

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Active mining process for software quality estimation

Active mining process for software quality estimation

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ieee international conference on cognitive Informatics

作者： Shusaku Tsumoto Shoji Hirano Department of Medical Informatics Shimane University 89–1 Enya-cho Izumo Shimane Japan

ISBN: (纸本)9781509038473

Clinical environment is very complex, and flexible and adaptive service improvement is crucial in maintaining quality of medical care. Thus, incremental update of software service in a hospital information system (HIS) and its evaluation is important. This paper introduces an active mining process for development of a an embedded software in which service logs stored in HIS are used to calculate the test statistics for evaluation on the effect of introduction of a new alarming service for clinical practice. are used to measure the differences The results show that proposed method is useful to evaluate the system performance in a real clinical environment.

关键词： data mining Hospitals Biomedical imaging Information systems Software Blood

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