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Classification of cognitive algorithms for managing services used in cloud computing

INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2020年第3期11卷 378-387页

作者： Ogiela, Lidia Takizawa, Makoto Ogiela, Urszula Pedag Univ Krakow Dept Cryptog & Cognit Informat Podchorazych 2 St Krakow Poland Hosei Univ Dept Adv Sci 3-7-2 Kajino Cho Koganei Tokyo 1848584 Japan Pedag Univ Krakow Podchorazych 2 St Krakow Poland

This paper presents a new idea of cognitive systems dedicated to cloud computing. Especially presents backgrounds, introduction and description of service management procedures and algorithms dedicated to cloud computing and infrastructure. cognitive methods based on semantic description and interpretation procedures. Described idea will be dedicated to secure service management procedures, especially in the cloud and fog stages. The proposed algorithms of cognitive service management will be presented and described by use of semantic aspects. Semantic analysis is used to extract meaning of analysed data. Also, in management processes is possible to analyse meaning aspects. These kinds of analysis can be used in different application areas. This paper presents services management protocols in the cloud and in the fog. In both of these stages is possible realised management procedure by application of secure methods and protocols. This paper presents the sharing techniques for data security in cloud computing.

关键词： cognitive algorithms fog and cloud computing service management protocols cognitive data security

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Visual cognitive algorithms for high-dimensional data and super-intelligence challenges

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cognitive SYSTEMS RESEARCH 2017年 45卷 95-108页

作者： Kovalerchuk, Boris Cent Washington Univ Dept Comp Sci Ellensburg WA 98926 USA

In the long run the cognitive algorithms intend to make super-intelligent machines and super-intelligent humans. This paper presents a technical process to reach specific aspects of super-intelligence that are out of the current human cognitive abilities. These aspects are inabilities to discover patterns in large numeric multidimensional data with a naked eye. This is a long-standing problem in Data Science and Modeling in general. The major obstacle is in human inability to see n-D data by a naked eye and our needs in visualization means to represent n-D data in 2-D losslessly. While these means exist their number and abilities are limited. This paper expands the class of such lossless visual methods, by further developing a new concept of Generalized Shifted Paired Coordinates. It shows the advantages of proposed reversible lossless technique by representing real data and by proving mathematical properties. (C) 2017 Elsevier B.V. All rights reserved.

关键词： cognitive algorithms High-dimensional data Visualization Machine learning generalized coordinates super-intelligence

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On the Role of Intelligent Power Management Strategies for Electrified Vehicles: A Review of Predictive and cognitive Methods

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2022年第1期8卷 368-383页

作者： Ali, Ahmed M. Moulik, Bedatri Mil Tech Coll Dept Automot Engn Cairo 11766 Egypt Amity Univ Dept Elect & Elect Engn Noida 201303 India

In light of increasing demands on decarbonized transportation systems, it became increasingly necessary to meet performance and environmental requirements for electromobility. Major challenges of electrified transportation can be pointed out as: rapid degradation of batteries, limited driving range and performance, and charging scheduling within infrastructural capabilities. Intelligent power management systems (I-PMSs), providing optimal energy handling decisions for electrified drivelines, have been increasingly the focus of electrification efforts, due to their significant potential to tackle the aforementioned challenges despite existing technological limitations. This contribution presents a comparative evaluation of recent advances in predictive and cognitive PMS with a particular focus on problem formulation, solving techniques, and practical examples of each method. Moreover, the evolving role of I-PMS in light of emerging intelligent transportation systems (ITSs) and connected vehicles technologies (CVTs) is discussed, to point out the ability of I-PMS to bridge the gap toward smart electromobility. This comprehensive overview of I-PMS aims at providing useful insights into the pros and cons of each method and puts forward potential outcomes for electrified transportation.

关键词： Transportation Power system management Real-time systems Predictive models Pattern recognition Optimal control Electric potential cognitive algorithms connected vehicles technology (CVT) electric vehicles intelligent power management intelligent transportation systems (ITSs) model predictive control (MPC)

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Justification and cognitive algorithms

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PHILOSOPHIA 2014年第2期42卷 503-515页

作者： Rosa, Luis Porto Alegre Brazil

In this paper, we offer an alternative interpretation for the claim that 'S is justified in believing that phi'. First, we present what seems to be a common way of interpreting this claim: as an attribution of propositional justification. According to this interpretation, being justified is just a matter of having confirming evidence. We present a type of case that does not fit well with the standard concept, where considerations about cognition are made relevant. The concept of cognitive algorithm is presented and explained. Finally, the new reading of 'S is justified in believing that phi' is fleshed out. According to this interpretation, being justified in believing that phi is not just a matter of having evidence in favor of phi, but also of having a cognitive algorithm available such that it allows one to form belief in phi on the basis of the relevant evidence.

关键词： Justification Cognition Epistemic rationality cognitive algorithms

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Operationalizing a Medical Intelligence Platform for Humanitarian Security in Protracted Crises 8th

Operationalizing a Medical Intelligence Platform for Humanit...

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8th International Conference on Modelling and Simulation for Autonomous Systems (MESAS)

作者： David, Walter King-Okoye, Michelle Sensidoni, Gianluca Capone, Alessandro Mugambwa, Irene Kraynova, Stanislava Garmendia-Doval, Beatriz Ronin Inst Montclair NJ 07043 USA Univ Edinburgh Edinburgh EH8 9YL Midlothian Scotland Expert Ai I-41123 Modena Italy Int Org Migrat CH-1211 Geneva Switzerland Crisis Management & Disaster Response Ctr Excelle Sofia 1606 Bulgaria Masa Grp SA F-75002 Paris France

ISBN: (纸本)9783030982607;9783030982591

Most of present humanitarian crises are protracted in nature and their average duration has increased. Climate change, environmental degradation, armed conflicts, terrorism, and migration are producing exponentially growing needs to whom humanitarian organizations are struggling to respond. Novel infectious diseases such as COVID-19 add complexity to protracted crises. Planning to respond to current and future medical threats should integrate terrorist risk assessment, to safeguard population and reduce risks to aid workers. Technologies such as Artificial Intelligence (AI), and Modelling and Simulation (M&S) can play a crucial role. The present research has included the conduct of the United Nations HNPW 2021 session on AI and Medical Intelligence and an exercise on a real scenario. Focusing on medical and terror threats in North East Nigeria operating environment, authors have successfully deployed and tested the *** Medical Intelligence Platform (MIP) jointly with the MASA SYNERGY constructive simulation, with the aim to improve situational awareness to support decision-making in the context of a humanitarian operation.

关键词： Protracted crises Humanitarian security Medical intelligence Artificial intelligence Machine learning Modelling & simulation Natural language understanding cognitive algorithms Digital disease detection Deep semantic analysis Social media Situational awareness Decision-making

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The cognitive and Mathematical Foundations of Analytic Epidemiology 19

The Cognitive and Mathematical Foundations of Analytic Epide...

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19th IEEE International Conference on cognitive Informatics and cognitive Computing (ICCI*CC)

作者： Wang, Yingxu Plataniotis, Kostas N. Wang, Jane Z. Hou, Ming Zhou, Menchu Howard, Newton Peng, Jun Huang, Runhe Patel, Shushma Zhang, Du Univ Calgary Schulich Sch Engn Int Inst Cognit Informat & Cognit Comp Calgary AB Canada Univ Calgary Hotchkiss Brain Inst Calgary AB Canada Univ Toronto Dept Elect & Comp Engn Toronto ON Canada Univ British Columbia Biomed Engn Sch Dept Elect & Comp Engn Vancouver BC Canada DRDC Ottawa ON Canada New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Univ Oxford Computat Neurosci Lab Oxford England Chongqing Univ Sci & Tech Sch Intelligent Tech & Engn Chongqing 401331 Peoples R China Hosei Univ Tokyo Japan London South Bank Univ Sch Engn London England Univ Sci & Technol Fac Informat Technol Macau Peoples R China

ISBN: (纸本)9781728195940

Analytic epidemiology is a transdisciplinary study on the cognitive, theoretical, and mathematical models of COVID-19 and other contagious diseases. It is recognized that analytic epidemiology may be better studied by big data explorations at the macro level rather than merely biological analyses at the micro level in order to not lose the forest for the trees. This paper presents a basic research on analytic epidemiology underpinned by sciences of cognition, computer, big data, information. AI, mathematics, epidemiology, and systems. It introduces a novel Causal Probability Theory (CPT) for explaining the Dynamic Pandemic Transmission Model (DPTM) of analytic epidemiology. It reveals how the fundamental reproductive rate (R-0) may he rigorously calibrated based on big data of COVID-19. A theoretical framework of analytic epidemiology is developed to elaborating the insights of pandemic mechanisms in general and COVID-19 in particular. Robust and accurate predictions on key attributes of COVID-19, including R-0(t), forecasted infectives/resources, and the expected date of pandemic termination, are derived via rigorous experiments on worldwide big data of epidemiology.

关键词： Analytic epidemiology COVID-19 cognitive pandemic models R-0 infectious transmission models cognitive informatics cognitive algorithms big data experiments

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Formal Software Requirement Elicitation based on Semantic Algebra and cognitive Computing 19

Formal Software Requirement Elicitation based on Semantic Al...

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19th IEEE International Conference on cognitive Informatics and cognitive Computing (ICCI*CC)

作者： Xu, James Y. Wang, Yingxu Univ Calgary Hotchkiss Brain Inst HBO 2500 Univ Dr Calgary AB T2N 1N4 Canada

ISBN: (纸本)9781728195940

Autonomous software requirement analysis and generation are a persistent challenge to theories and technologies of software engineering. A cognitive system is demanded to automatically elicit and rigorously refine informal software requirements in natural language descriptions into formal specifications. This paper presents a novel software requirements elicitation methodology based on latest advances in software science and denotational mathematics such as semantic algebra and concept algebra. It is found that user requirements for a software system in natural language may he either expressed in to-be sentences for software structures or to-do sentences for software behaviors. Thus, formal software requirements may he elicited by two sets of structural and functional models. This approach is implemented by a tool for Formal Requirement Elicitation and Analysis (FREA). Experimental results demonstrate that the FREA tool may rigorously elicit and generate formal requirements for arbitrary software systems specified in real-time process algebra (RTPA) or equivalent notations. This technology paves a way towards autonomous code generation in software engineering.

关键词： Software science software engineering formal requirement analysis rigorous requirement generation RTPA concept algebra semantic algebra cognitive algorithms

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Affective computing in the modern workplace

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Business Information Review 2020年第2期37卷 78-85页

作者： Richardson, Sharon Independent Researcher Joining Dots Ltd United Kingdom

Affective computing refers to a class of computer-based solutions that analyse data about human behaviour, such as facial expressions, gestures, and language, for its emotional information. The term was first coined 25 years ago when the ability for computers to perform basic sensorimotor tasks such as object detection in images was in its infancy. This article revisits the subject and considers how it is being applied in real-world applications today. We look at research from cognitive science informing our understanding of emotions and how computing capabilities have advanced in recent years to produce cognitive algorithms capable of detecting human attention, emotion and health. Affective computing offers the potential to revolutionise how we incorporate emotion as information in communications and decision systems. However, much of the underlying research that forms the foundations for emotion detection is being challenged, raising concerns about the ethics, trustworthiness and viability of such platforms. This article presents a critique of the technologies and appraises their suitability for real-world applications as part of a modern workplace. © The Author(s) 2020.

关键词： Affective computing cognitive algorithms computer vision conversation agents emotional AI

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algorithms for Determining Semantic Relations of Formal Concepts by cognitive Machine Learning based on Concept Algebra 15

Algorithms for Determining Semantic Relations of Formal Conc...

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15th IEEE International Conference on cognitive Informatics and cognitive Computing (ICCI*CC)

作者： Valipour, Mehrdad Wang, Yingxu Zatarain, Omar A. Gavrilova, Marina L. Univ Calgary Int Inst Cognit Informat & Cognit Comp ICIC Lab Computat Intelligence Denotat Math Software S Dept Elect & Comp EngnSchulich Sch Engn 2500 Univ Dr NW Calgary AB T2N 1N4 Canada Univ Calgary Hotchkiss Brain Inst 2500 Univ Dr NW Calgary AB T2N 1N4 Canada Stanford Univ Packard Dept Elect Engn Informat Syst Lab Stanford CA 94305 USA

ISBN: (纸本)9781509038466

It is recognized that the semantic space of knowledge is a hierarchical concept network. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic relations via machine learning based on concept algebra. The equivalence between formal concepts are analyzed by an Algorithm of Concept Equivalence Analysis (ACEA), which quantitatively determines the semantic similarity of an arbitrary pair of formal concepts. This leads to the development of the Algorithm of Relational Semantic Classification (ARSC) for hierarchically classify any given concept in the semantic space of knowledge. Experiments applying algorithms ACEA and ARSC on 20 formal concepts are successfully conducted, which encouragingly demonstrate the deep machine understanding of semantic relations and their quantitative weights beyond human perspectives on knowledge learning and natural language processing.

关键词： cognitive machine learning formal concept cognitive algorithms concept algebra semantic analysis concept classification concept hierarchy concept similarity knowledge representation

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Super-intelligence Challenges and Lossless Visual Representation of High-Dimensional Data

Super-intelligence Challenges and Lossless Visual Representa...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kovalerchuk, Boris Cent Washington Univ Dept Comp Sci Ellensburg WA 98926 USA

ISBN: (纸本)9781509006199

Fundamental challenges and goals of the cognitive algorithms are moving super-intelligent machines and super-intelligent humans from dreams to reality. This paper is devoted to a technical way to reach some specific aspects of super-intelligence that are beyond the current human cognitive abilities. Specifically the proposed technique is to overcome inabilities to analyze a large amount of abstract numeric high-dimensional data and finding complex patterns in these data with a naked eye. Discovering patterns in multidimensional data using visual means is a long-standing problem in multiple fields and Data Science and Modeling in general. The major challenge is that we cannot see n-D data by a naked eye and need visualization tools to represent n-D data in 2-D losslessly. The number of available lossless methods is quite limited. The objective of this paper is expanding the class of such lossless methods, by proposing a new concept of Generalized Shifted Collocated Paired Coordinates. The paper shows the advantages of proposed lossless technique by proving mathematical properties and by demonstration on real data.

关键词： high-dimensional data high-dimensional patterns lossless representation generalized coordinates cognitive algorithms human cognitive abilities super-intelligence

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