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Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Random asynchronous iterations in distributed coordination algorithms

arXiv

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arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution

arXiv

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arXiv 2024年

作者： Ezugwu, Absalom E. Ho, Yuh-Shan Egwuche, Ojonukpe S. Ekundayo, Olufisayo S. Van Der Merwe, Annette Saha, Apu K. Pal, Jayanta Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Trend Research Centre Asia University No. 500 Lioufeng Road Taichung41354 Taiwan School of Computer Science and Information Systems North-West University 11 Hoffman Street Potchefstroom2520 South Africa Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Department of IT Tripura University Suryamaninagar Tripura 799022 India

Machine learning (ML) has transformed numerous fields, but understanding its foundational research is crucial for its continued progress. This paper presents an overview of the major classical ML algorithms and examines the state-of-the-art publications, spanning twelve decades, through an extensive bibliometric analysis study. We analyzed a dataset of highly cited papers from prominent ML conferences and journals, employing techniques such as citation and keyword analyses to uncover key insights. The study further identifies the most influential papers and authors, reveals the evolving collaborative networks within the ML community, and pinpoints prevailing research themes and emerging areas of focus. Additionally, we examine the geographic distribution of highly cited publications, highlighting the leading countries in ML research. This study provides a comprehensive overview of the evolution of traditional learning algorithms, and their impacts, and discusses challenges and opportunities for future development, with a particular focus on the Global South. The findings from this paper offer valuable insights for both ML experts and the broader research community, enhancing understanding of the field's trajectory and its significant influence on recent advances in learning algorithms. © 2024, CC BY.

关键词： Adversarial machine learning

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Clarifying Assumptions about Artificial Intelligence before Revolutionising Patent Law

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GRUR International 2022年第4期71卷 295-321页

作者： Kim, Daria Alber, Maximilian Kwok, Man Wai Mitrovic, Jelena Ramirez-Atencia, Cristian Perez, Jesus Alberto Rodriguez Zille, Heiner Max Planck Institute for Innovation and Competition Munich Germany Charite Institute of Pathology Berlin Germany Chinese University of Hong Kong Department of Physics (Formerly) Hong Kong BMBF Program for Women in AI Faculty of Computer Science and Mathematics Chair of Data Science University of Passau Germany Department of Computer Systems Universidad Politecnica de Madrid Spain School of Computing Science College of Science and Engineering University of Glasgow United Kingdom Faculty of Computer Science Otto von Guericke University Magdeburg Germany

This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of 'artificial ingenuity'. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where 'intelligent' systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited. © 2022 Published by OUP and CH Beck on behalf of GRUR e.V.

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Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts

arXiv

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arXiv 2024年

作者： Zhao, Changyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin Letaief, Khaled B. CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

AI technologies have become more widely adopted in wireless communications. As an emerging type of AI technologies, the generative artificial intelligence (GAI) gains lots of attention in communication security. Due to its powerful learning ability, GAI models have demonstrated superiority over conventional AI methods. However, GAI still has several limitations, including high computational complexity and limited adaptability. Mixture of Experts (MoE), which uses multiple expert models for prediction through a gate mechanism, proposes possible solutions. Firstly, we review GAI model’s applications in physical layer communication security, discuss limitations, and explore how MoE can help GAI overcome these limitations. Furthermore, we propose an MoE-enabled GAI framework for network optimization problems for communication security. To demonstrate the framework’s effectiveness, we provide a case study in a cooperative friendly jamming scenario. The experimental results show that the MoE-enabled framework effectively assists the GAI algorithm, solves its limitations, and enhances communication security. © 2024, CC BY.

关键词： Network security

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Robot Hand Interaction Using Plastic Deformation Control with Inner Position Loop

Robot Hand Interaction Using Plastic Deformation Control wit...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Kenichi Murakami Koki Ishimoto Taku Senoo Masatoshi Ishikawa Data Science Research Division Information Technology Center The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Department of Information Physics and Computing The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan Electrical Systems and Control Engineering Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima City Hiroshima Japan

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

In this study, the inner position loop addition for plastic deformation control of the error robustness of the model and actual parameters is proposed. The inner position loop is a type of position feedback control based on the estimation of model parameters and dynamics of robots. This position control system with proportional and derivative gains modifies the trajectory of the end effector close to the desired trajectory. The robustness of the proposed control scheme is evaluated by comparing it with plastic deformation control without an inner position loop in both simulation and actual environments. Finally, the stable reaction with the proposed control is demonstrated for the case where a human pushes the robot finger.

关键词： Strain Plastics Force Deformable models Robots Friction Trajectory

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Investigation of Environmental Monitoring Based on Three IoT Wireless Sensors

Investigation of Environmental Monitoring Based on Three IoT...

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Augmented Intelligence and Sustainable systems (ICAISS), International Conference on

作者： B Yamini Arthy M S Geetha Priya S Kathiresan Jeneetha Jebanazer J G S Uthayakumar Department of Networking and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Data Science& Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science Engineering R. M. D Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering St. Joseph's Institute of Technology Chennai Tamil Nadu India

Environmental monitoring has become more crucial as a result of the recent climatic changes, making it a hot issue and a very active study field. This field uses wireless sensor networks and remote sensing to collect data about the environment. Recent developments, including the Internet of Things (IoT) concept, the cloud computing paradigm, and cyber-physical systems, assist the administration and transmission of enormous volumes of data about changes in environmental parameters. The current work presents three different IoT-based wireless sensors for monitoring the environment and the surrounding area: one uses Wi-Fi User datagram Protocol (UDP)-based communication, one uses Wi-Fi and Hypertext Transfer Protocol (HTTP), and a third one uses Bluetooth Smart. The ability to capture data at distant places and see it from any device with an Internet connection is a feature shared by all of the systems that have been discussed. This makes it possible to monitor vast geographic regions. These systems' specific development details are discussed, along with their main distinctions and affinities. The three created systems were evaluated for their suitability for IoT-based solutions, taking into consideration their energy independence, usability, solution complexity, and Internet connectivity capability.

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Extracting human emotions at different places based on facial expressions and spatial clustering analysis

arXiv

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arXiv 2019年

作者： Kang, Yuhao Jia, Qingyuan Gao, Song Zeng, Xiaohuan Wang, Yueyao Angsuesser, Stephan Liu, Yu Ye, Xinyue Fei, Teng Geospatial Data Science Lab Department of Geography University of Wisconsin Madison United States School of Resources and Environmental Sciences Wuhan University Wuhan China College of Urban and Environment Sciences Peking University Beijing China Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China Urban Informatics and Spatial Computing Lab Department of Informatics New Jersey Institute of Technology Newark United States

The emergence of big data enables us to evaluate the various human emotions at places from a statistic perspective by applying affective computing. In this study, a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user generated footprints collected in social media websites, online cognitive services are utilized to extract human emotions from facial expressions using state-of-the-art computer vision techniques. And two happiness metrics are defined for measuring the human emotions at different places. To validate the feasibility of the framework, we take 80 tourist attractions around the world as an example and a happiness ranking list of places is generated based on human emotions calculated over 2 million faces detected out from over 6 million photos. Different kinds of geographical contexts are taken into consideration to find out the relationship between human emotions and environmental factors. Results show that much of the emotional variation at different places can be explained by a few factors such as openness. The research may offer insights on integrating human emotions to enrich the understanding of sense of place in geography and in place-based GIS. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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Multilevel delayed acceptance MCMC with an adaptive error model in PyMC3

arXiv

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arXiv 2020年

作者： Lykkegaard, Mikkel Bue Scheichl, Robert Mingas, Grigorios Fox, Colin Dodwell, Tim J. Centre for Water Systems Institute for Data Science Artificial Intelligence University of Exeter EX4 4QF United Kingdom Institute for Applied Mathematics Interdisciplinary Center for Scientific Computing Ruprecht-Karls-Universität Heidelberg Heidelberg69120 Germany Alan Turing Institute NW1 2DB United Kingdom Department of Physics University of Otago Dunedin9016 New Zealand Institute for Data Science and Artificial Intelligence University of Exeter EX4 4QF United Kingdom

Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance when the evaluation it involves solving a Partial Differential Equation (PDE), as is the case in a wide range of engineering applications. Multilevel Delayed Acceptance (MLDA) with an Adaptive Error Model (AEM) is a novel approach, which alleviates this problem by exploiting a hierarchy of models, with increasing complexity and cost, and correcting the inexpensive models on-the-fly. The method has been integrated within the open-source probabilistic programming package PyMC3 and is available in the latest development version. In this paper, the algorithm is presented along with an illustrative example. © 2020, CC BY.

关键词： Open source software

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Ballot-Polling Audits of Instant-Runoff Voting Elections with a Dirichlet-Tree Model

arXiv

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arXiv 2022年

作者： Everest, Floyd Blom, Michelle Stark, Philip B. Stuckey, Peter J. Teague, Vanessa Vukcevic, Damjan School of Mathematics and Statistics University of Melbourne Parkville Australia School of Computing and Information Systems University of Melbourne Parkville Australia Department of Statistics University of California BerkeleyCA United States Department of Data Science and AI Monash University Clayton Australia Thinking Cybersecurity Pty. Ltd. Melbourne Australia The Australian National University Canberra Australia Melbourne Integrative Genomics University of Melbourne Parkville Australia

Instant-runoff voting (IRV) is used in several countries around the world. It requires voters to rank candidates in order of preference, and uses a counting algorithm that is more complex than systems such as first-past-the-post or scoring rules. An even more complex system, the single transferable vote (STV), is used when multiple candidates need to be elected. The complexity of these systems has made it difficult to audit the election outcomes. There is currently no known risk-limiting audit (RLA) method for STV, other than a full manual count of the ballots. A new approach to auditing these systems was recently proposed, based on a Dirichlet-tree model. We present a detailed analysis of this approach for ballot-polling Bayesian audits of IRV elections. We compared several choices for the prior distribution, including some approaches using a Bayesian bootstrap (equivalent to an improper prior). Our findings include that the bootstrap-based approaches can be adapted to perform similarly to a full Bayesian model in practice, and that an overly informative prior can give counter-intuitive results. Via carefully chosen examples, we show why creating an RLA with this model is challenging, but we also suggest ways to overcome this. As well as providing a practical and computationally feasible implementation of a Bayesian IRV audit, our work is important in laying the foundation for an RLA for STV elections. Copyright © 2022, The Authors. All rights reserved.

关键词： Runoff

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