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We Are Sending You Back... to the Optimum! Fuzzy Time Travel Particle Swarm Optimization 28th

We Are Sending You Back... to the Optimum! Fuzzy Time Trav...

28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Papetti, Daniele M. Tangherloni, Andrea Coelho, Vasco Besozzi, Daniela Cazzaniga, Paolo Nobile, Marco S. Department of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Department of Computer Science Bocconi University Milan Italy Department of Human and Social Sciences University of Bergamo Bergamo Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Venice Italy Research Center Milan Italy

ISBN: (纸本)9783031900648

Particle Swarm Optimization (PSO) is a swarm intelligence meta-heuristics whose performance highly depends on the selection of its hyper-parameters, which control the particles’ exploration and exploitation capabilities during the search process. Since the tuning of the hyper-parameters is problem-dependent, settings-free methods are preferable. Fuzzy Self-Tuning PSO (FST-PSO) is a settings-free variant of PSO that exploits a Fuzzy Rule-Based System to determine the best hyper-parameter values for each particle dynamically. Despite this advantage, the optimization process might get stuck in local optima. Here, we propose a what-if strategy to generate and explore alternate swarm histories: what happens if we could go back in time and change something in the past so that one particle "was" different at the beginning of the optimization? Specifically, whenever the global best particle does not improve for a number of iterations, it is terminated and re-initialized, meanwhile sending the rest of the swarm back to the initial configuration. This approach, called "time travel" FST-PSO (FTT-PSO), works under the assumption that the convergence of any particle towards an optimum is strongly related to the influence of the global best particle. We compare the performance of FTT-PSO against FST-PSO on the benchmark suites used in IEEE CEC and GECCO competitions. Our results show that time traveling allows for outperforming both the standard and the multistart versions of FST-PSO. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Wireless Dual Mode Haptic Thimble based on Magnetoactive Rubber

Wireless Dual Mode Haptic Thimble based on Magnetoactive Rub...

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World Haptics Conference

作者： Yong Hea Heo Gyubin An Hyun-Jeong Kim Sang-Youn Kim Mohammad Shadman Hashem Seokhee Jeon Dong-Soo Choi Interaction Laboratory Korea University of Technology and Education Cheonan-si Republic of Korea Computer Science and Engineering Department Kyung Hee University Yongin Republic of Korea Smart Interface and eXtended Reality Laboratory Kumoh National Institute of Technology Gumi Republic of Korea

This paper proposes a wireless dual mode haptic thimble based on magneto-active rubber (MAR) for providing kinesthetic and vibrotactile feedback without any electric wire. When the external magnetic field is applied to the MAR, ferro-magnetic particles in the MAR polarize and attract each other, causing the magnetic attraction force between the MAR and the external magnetic field generator (e.g., a solenoid). We can easily provide both the force and vibration to a user wearing the MAR thimble by controlling the solenoid. To improve the wearability and maximize the haptic performance, the best ratio of ingredients for fabricating the MAR thimble is determined through extensive experiments. The haptic behavior of the fabricated MAR thimble is quantitatively investigated. The experimental results show that the haptic thimble can generate a sufficient magnitude of force or vibration to simulate the user’s finger for various haptic effects. Moreover, a transparent force rendering algorithm is proposed to compensate for non-linear force response. The potential of the system is further demonstrated in a distinctive virtual reality-based interaction scenario.

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Redundancy-aware unsupervised ranking based on game theory - application to gene enrichment analysis

arXiv

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arXiv 2022年

作者： Balestra, Chiara Maj, Carlo Müller, Emmanuel Mayr, Andreas Department of Computer Science TU Dortmund Germany University Hospital of Bonn Germany Center for Human Genetics University Hospital of Marburg Germany

Gene set collections are a common ground to study the enrichment of genes for specific phenotypic traits. Gene set enrichment analysis aims to identify genes that are over-represented in gene sets collections and might be associated with a specific phenotypic trait. However, as this involves a massive number of hypothesis testing, it is often questionable whether a pre-processing step to reduce gene sets collections' sizes is helpful. Moreover, the often highly overlapping gene sets and the consequent low interpretability of gene sets' collections demand for a reduction of the included gene sets. Inspired by this bioinformatics context, we propose a method to rank sets within a family of sets based on the distribution of the singletons and their size. We obtain sets' importance scores by computing Shapley values without incurring into the usual exponential number of evaluations of the value function. Moreover, we address the challenge of including a redundancy awareness in the rankings obtained where, in our case, sets are redundant if they show prominent intersections. We finally evaluate our approach for gene sets collections;the rankings obtained show low redundancy and high coverage of the genes. The unsupervised nature of the proposed ranking does not allow for an evident increase in the number of significant gene sets for specific phenotypic traits when reducing the size of the collections. However, we believe that the rankings proposed are of use in bioinformatics to increase interpretability of the gene sets collections and a step forward to include redundancy into Shapley values computations. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

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Transforming HR in Finance by Leveraging IoT and AI for Strategic Talent Management

Transforming HR in Finance by Leveraging IoT and AI for Stra...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Indoria, Devadutta Dakshinamoorthy, Barani Karthik, M. Sharma, Manish Kaliappan, S. Manikandan, G. Vikram Dev University Department of Commerce Odisha Jeypore India Sharda School of Business Studies Sharda university Department of Human Resource Management &ob Uttar Pradesh Greater Noida India Shri Vishnu Engineering College for Women Department of Management Studies Andhra Pradesh Bhimavaram India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Phagwara Division of Research and Development Punjab India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331543617

Financial institutions have recognized the value of AI and the IoT in enhancing strategic human resource management. The paper presents a comprehensive integration framework that utilizes AI and IoT technologies. The focus is on key aspects of human resources operations, such as employee recruitment, retention, employee engagement, and performance evaluations. Productivity and efficiency can be maximized by 72% and 92%, respectively, through the application of AI algorithms and predictive analytics in IoT devices used in the collection of real-time data for HR operations. The proposed framework will be diverse in performance indicators, have appropriate strategies for change management, robust security measures, and pipelined data meant for long-term use. It consequently contributes to the design of a dynamic and personalized human resource management strategy based on insights derived from data. © 2024 IEEE.

关键词： Decentralized finance

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The Jade Gateway to Exergaming: How Socio-Cultural Factors Shape Exergaming Among East Asian Older Adults

arXiv

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arXiv 2024年

作者： Mogavi, Reza Hadi Son, Juhyung Yang, Simin Wang, Derrick M. Choong, Lydia Alhilal, Ahmad Zhou, Peng Yuan Hui, Pan Nacke, Lennart E. The Department of Systems Design Engineering University of Waterloo Canada The Cheriton School of Computer Science University of Waterloo Canada HCI Games Group Stratford School of Interaction Design and Business University of Waterloo ON Canada Guangzhou China Hong Kong University of Science and Technology Hong Kong Aarhus University Denmark Hong Kong University of Science and Technology University of Helsinki Hong Kong Helsinki Finland Finland

Exergaming, blending exercise and gaming, improves the physical and mental health of older adults. We currently do not fully know the factors that drive older adults to either engage in or abstain from exergaming. Large-scale studies investigating this are still scarce, particularly those studying East Asian older adults. To address this, we interviewed 64 older adults from China, Japan, and South Korea about their attitudes toward exergames. Most participants viewed exergames with a positive inquisitiveness. However, socio-cultural factors can obstruct this curiosity. Our study shows that perceptions of aging, lifestyle, the presence of support networks, and the cultural relevance of game mechanics are the crucial factors influencing their exergame engagement. Thus, we stress the value of socio-cultural sensitivity in game design and urge the HCI community to adopt more diverse design practices. We provide several design suggestions for creating more culturally approachable exergames. Copyright © 2024, The Authors. All rights reserved.

关键词： Blending

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Audio-visual multimodal speech recognition method fusing audio and video data 2

Audio-visual multimodal speech recognition method fusing aud...

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2nd International Conference on Digital Signal and computer Communications, DSCC 2022

作者： Zhao, Jian Zhang, Zhiwei Cui, Yuqing Qiang, Wenqian Dai, Weiqiang Kuang, Zhejun Shi, Lijuan Changchun University Department of Computer Science and Technology Changchun130022 China Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement Changchun130022 China Changchun University Department of Electronic Information Engineering Changchun130022 China Changchun University Cyberspace Security System Changchun130022 China

ISBN: (纸本)9781510656734

With the widespread application of deep learning methods, multimodal techniques have also achieved rapid development. Since single-modal speech recognition may affect the accuracy of recognition results in noisy environments, multimodal fusion recognition gradually replaces the traditional single-modal recognition methods. In this paper, we mainly strengthen and pre-process audio and video data first, and use LSTM recurrent neural network for deep feature extraction of audio and video streams, which effectively solves the problem of long-term forgetting of general neural networks. The audio and video feature vectors are then fused by a fully connected neural network with linear connections. Compared with the speech recognition technique alone, this audiovisual fusion recognition method has a better recognition effect in the case of noise interference. Compared with the traditional audiovisual recognition method, the model simplifies the recognition work. Recognition experiments on the LRS2-BBC dataset show that the recognition accuracy of this method improves to a certain extent over that of other methods in a clean environment and greatly improves in noisy conditions. © 2022 SPIE.

关键词： Speech recognition

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Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges

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Chemosphere 2024年 353卷 141474页

作者： Maurya, Brij Mohan Yadav, Nidhi T, Amudha J, Satheeshkumar A, Sangeetha V, Parthasarathy Iyer, Mahalaxmi Yadav, Mukesh Kumar Vellingiri, Balachandar Human Cytogenetics and Stem Cell Laboratory Department of Zoology School of Basic Sciences Central University of Punjab Punjab Bathinda151401 India Department of Computer Applications Bharathiar University Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Pollachi Main Road Eachanari Post Tamil Nadu Coimbatore641021 India Centre for Neuroscience Department of Biotechnology Karpagam Academy of Higher Education Tamil Nadu Coimbatore641021 India Department of Microbiology Central University of Punjab Punjab Bathinda151401 India

Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies. © 2024

关键词： Heavy metals

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Spatial Referencing for Anywhere, Anytime Augmented Reality

Spatial Referencing for Anywhere, Anytime Augmented Reality

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Yuan Li Center for Human-Computer Interaction and Dept. of Computer Science Virginia Tech

ISBN: (数字)9781728165325

ISBN: (纸本)9781728165332

Augmented Reality (AR) systems can effectively enhance collaboration in the real world by providing useful spatial cues through virtual overlays. In particular, AR can be used to facilitate referencing a location in space as required in many collaborative activities. However, the goal of supporting spatial referencing anywhere at any time is hindered by issues with current AR technology, such as the lack of a precise environment model, unreliable tracking, and difficulty in synchronizing among different devices. This paper proposes a research agenda that innovates in the domain of interaction techniques for spatial referencing for everywhere AR.

关键词： Biological system modeling Three-dimensional displays Collaboration Augmented reality Visualization Reliability Computational modeling

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L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection

arXiv

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arXiv 2024年

作者： Ortigoso-Narro, Jorge Diaz-De-Maria, Fernando Dehshibi, Mohammad Mahdi Tajadura-Jiménez, Ana Department of Signal Theory and Communications Universidad Carlos III de Madrid in Leganés Spain Department of Computer Science and Engineering Universidad Carlos III de Madrid in Leganés Spain Unconventional Computing Laboratory University of the West of England Bristol United Kingdom UCL Interaction Centre University College London London United Kingdom

Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' wellbeing and imposing economic burdens on healthcare systems. While artificial intelligence (AI) and deep learning offer promising avenues for analyzing pain-related behaviors to improve rehabilitation strategies, current models, including convolutional neural networks (CNNs), recurrent neural networks, and graph-based neural networks, have limitations. These approaches often focus singularly on the temporal dimension or require complex architectures to exploit spatial interrelationships within multivariate time series data. To address these limitations, we introduce L-SFAN, a lightweight CNN architecture incorporating 2D filters designed to meticulously capture the spatial-temporal interplay of data from motion capture and surface electromyography sensors. Our proposed model, enhanced with an oriented global pooling layer and multi-head selfattention mechanism, prioritizes critical features to better understand CLBP and achieves competitive classification accuracy. Experimental results on the EmoPain database demonstrate that our approach not only enhances performance metrics with significantly fewer parameters but also promotes model interpretability, offering valuable insights for clinicians in managing CLBP. This advancement underscores the potential of AI in transforming healthcare practices for chronic conditions like CLBP, providing a sophisticated framework for the nuanced analysis of complex biomedical data. © 2024, CC BY.

关键词： Recurrent neural networks

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Fostering Understanding: Bridging the Gap Between Black-Box Models and human Interpretability with Explainable Artificial Intelligence 1

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3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Sunil, G. Milton, T. Singh, Sarita Kumari Mubeen, Samiya Sharma, M.K. Dhanke, Jyoti School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Dean Tourism and Hospitality Management Bharath Institute of Higher Education and Research 173 Agaram Road Selaiyur Tamil Nadu Chennai600073 India School of Social Financial & Human Sciences KIIT Deemed to Be University Odisha Bhubaneswar India Department of Management Studies IIBS Bangalore India Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India Savitribai Phule Pune University Maharashtra Pune India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

The energy industry has benefited the most from the deployment of artificial intelligence (AI) techniques, particularly Machine Learning frameworks, for several purposes, such as assessing construction energy functionality. Nevertheless, the black-box models might end up in results that are difficult to human interpretability, making it impossible to use AI effectively in certain real-life circumstances. To improve the explanation of the system's findings, explainable AI (XAI) technologies may be successfully integrated into an AI-powered energy analysis approach. This study presents an XAI-based benchmarking system for predicting the membership to particular energy functioning categories of an extensive number of flattened energy-performance certifications (EPCs). Several black-box models with excellent accuracy are used to produce the categorization;however, their inference method is not accessible to humans. To further comprehend the model's conduct and the reasons behind both accurate and inaccurate categorizations, a universal XAI technique is used. This approach combines a regional explanation with a clustering technique for clarifying the outcomes of the model and the causal relationships between the predictions and the intended factor. The study offers a broad empirical strategy that can make utilization of a restricted number of examples to gather, clarify, and understand inference processes that the model has learned and finds valuable for the user. The system has been evaluated on around 100,000 EPCs of apartments in Italy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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