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International Conference of Advanced informatics: Concept, Theory and Application (ICAICTA)

作者： Lenny Putri Yulianti Agung Trisetyarso Judhi Santoso Kridanto Surendro School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta Indonesia

Ensemble learning integrates many learners to achieve better performance, however, it can increase the complexity dramatically. Redundant learners should be pruned to reduce ensemble complexity while allowing for increased accuracy. There are two common ways to achieve this, which include selecting the optimal training space and the optimal trained learners. This study contributes to reducing redundant learners by selecting the optimal training space using annealing-based optimization. The optimal training space was defined by 1) measuring the similarity between training spaces (clusters) and 2) searching for the best set of training spaces by minimizing the similarity between the selected training spaces. The proposed method was tested on 10 datasets from the UCI repository and compared to five state-of-the-art random subspace methods. The results showed that the proposed method could reduce the size of the ensemble while significantly increasing its accuracy.

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Cyber-Physical Steganography in Robotic Motion Control

arXiv

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arXiv 2025年

作者： Chang, Ching-Chun Lin, Yijie Echizen, Isao The Information and Society Research Division National Institute of Informatics Japan The Department of Information Engineering and Computer Science Feng Chia University Taiwan The Graduate School of Information Science and Technology University of Tokyo Japan

Steganography, the art of information hiding, has continually evolved across visual, auditory and linguistic domains, adapting to the ceaseless interplay between steganographic concealment and steganalytic revelation. This study seeks to extend the horizons of what constitutes a viable steganographic medium by introducing a steganographic paradigm in robotic motion control. Based on the observation of the robot's inherent sensitivity to changes in its environment, we propose a methodology to encode messages as environmental stimuli influencing the motions of the robotic agent and to decode messages from the resulting motion trajectory. The constraints of maximal robot integrity and minimal motion deviation are established as fundamental principles underlying secrecy. As a proof of concept, we conduct experiments in simulated environments across various manipulation tasks, incorporating robotic embodiments equipped with generalist multimodal policies. © 2025, CC BY.

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可信联邦学习进化优化算法综述

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Engineering 2024年第3期34卷 23-42页

作者： Qiqi Liu Yuping Yan Yaochu Jin Xilu Wang Peter Ligeti Guo Yu Xueming Yan School of Engineering Westlake UniversityHangzhou 310030China Faculty of Informatics Department of Computer AlgebraEötvös Loránd UniversityBudapest 1053Hungary Faculty of Technology Bielefeld UniversityBielefeld 33619Germany Institute of Intelligent Manufacturing Nanjing Tech UniversityNanjing 211816China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China

With the development of edge devices and cloud computing,the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past *** a privacy-preserving distributed machine learning method,federated learning(FL)has become popular in the last few ***,the data privacy issue also occurs when solving optimization problems,which has received little attention so *** survey paper is concerned with privacy-preserving optimization,with a focus on privacy-preserving data-driven evolutionary *** aims to provide a roadmap from secure privacy-preserving learning to secure privacy-preserving optimization by summarizing security mechanisms and privacy-preserving approaches that can be employed in machine learning and *** provide a formal definition of security and privacy in learning,followed by a comprehensive review of FL schemes and cryptographic privacy-preserving ***,we present ideas on the emerging area of privacy-preserving optimization,ranging from privacy-preserving distributed optimization to privacy-preserving evolutionary optimization and privacy-preserving Bayesian optimization(BO).We further provide a thorough security analysis of BO and evolutionary optimization methods from the perspective of inferring attacks and active *** the basis of the above,an in-depth discussion is given to analyze what FL and distributed optimization strategies can be used for the design of federated optimization and what additional requirements are needed for achieving these ***,we conclude the survey by outlining open questions and remaining challenges in federated data-driven *** hope this survey can provide insights into the relationship between FL and federated optimization and will promote research interest in secure federated optimization.

关键词： Federated learning Privacy-preservation Security Evolutionary optimization Data-driven optimization Bayesian optimization

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Depth and Pitch Control of Autonomous Underwater Glider Using Model Predictive Control with ARX Model

Depth and Pitch Control of Autonomous Underwater Glider Usin...

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International Conference on Electrical Engineering and informatics, ICEEI

作者： Jason Jeremy Egi Muhammad Idris Hidayat Simon Siregar Bambang Riyanto Trilaksono Natsir Habibullah School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department Computer Engineering Faculty of Applied Science Telkom University Bandung Indonesia

Autonomous Underwater Gliders (AUGs) are extensively developed vehicles capable of prolonged exploration and observation in complex marine environments. Control of the AUG is challenging due to its slow response system and its constraints. In this research, a linear model representation of AutoRegressive eXogenous input (ARX) will be constructed using input and output data from the AUG system, built Model Predictive Control (MPC), analyzed the performance, comparing with traditional Proportional-Integral-Derivative (PID). The objective is to enhance setpoint tracking accuracy and minimize energy to extend exploration time while faced with constraints. Simulation results reveal MPC exhibits potential setpoint tracking, low overshoot as low as 0.6% up to 0.3m/s maximum depth rate, has relatively low input changes indicating good efficiency compared to PID. MPC approach effectively addresses slow response systems, managing momentum, and handling actuator constraints commonly encountered in AUG.

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EHHGSO: Enhancing Hybrid Hugo Gas Solubility Optimization Algorithm Using Backtracking Technique 5

EHHGSO: Enhancing Hybrid Hugo Gas Solubility Optimization Al...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Mahanta, Ajoy Bin Nur, Md. Oshama Islam, Rakibul Azad, Saiful Wadith, Shadman Mahmud, Mufti Kaiser, M. Shamim Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Nottingham Trent University Department of Computer Science NottinghamNG118NS United Kingdom Nottingham Trent University Computing and Informatics Research Centre NottinghamNG118NS United Kingdom Nottingham Trent University Medical Technologies Innovation Facility NottinghamNG118NS United Kingdom Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh

ISBN: (纸本)9798350394290

Metaheuristic algorithms play a pivotal role in solving complex optimization problems. This study addresses a common challenge faced by such algorithms, i.e., getting trapped in local optima. This problem hampers the efficacy of metaheuristic algorithms in achieving globally optimal solutions. This limitation is also observed in Hybrid Henry Gas Solubility Optimization Algorithm (HHGSO)-a new metaheuristic based algorithm-which employs a cluster-based approach for population management. While this approach fosters exploration by diversifying the search process, it lacks adaptiveness in cluster assignments and the potentiality of hybridization with other algorithms. These shortcomings hinder the algorithm's ability to effectively escape local optima and harness the strengths of diverse optimization techniques. To mitigate these challenges, this study proposed an Enhanced HHGSO (EHHGSO) algorithm, an advanced hybridization of HHGSO. Here, a novel backtracking based technique is incorporated that can detect the trapping into local optima and can escape from it. Empirical findings substantiate the efficacy of EHHGSO in comparison to its ancestor, HHGSO for various benchmark functions. © 2023 IEEE.

关键词： Solubility

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Perspective-taking for promoting prosocial behaviors through robot-robot VR task

Perspective-taking for promoting prosocial behaviors through...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Hang Chenlin Ono Tetsuo Seiji Yamada Department of Informatics The Graduate University for Advanced Studies Sokendai Kanagawa Japan National Institute of Informatics Tokyo Japan Faculty of Information Science and Technology Division of Computer Science and Information Technology Hokkaido University Sapporo Japan

Perspective-taking, which enables individuals to consider the thoughts and objectives of another, is well established to be a successful strategy for encouraging pro-social behavior in human-computer interactions. Nowadays, perspective-taking is no longer limited to text; it is now more frequently used in virtual reality (VR). However, most previous research has focused on simulating human-human interactions in the real world in VR by providing participants with experiences connected to different moral tasks. In this study, we investigated whether participants’ prosocial behaviors toward robots would change if they experienced an altruistic VR task involving robots from the perspective of different robots. Our findings show that participants who had the help-receiver-view exhibited more altruistic behaviors toward a robot than those who had the help-provider-view one in a dictator game. We believe that this work is the first attempt to investigate the relationship between perspective-taking in a VR environment and changes in prosocial behavior in human-robot interaction.

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Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

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IEEE International Conference on Big Data

作者： Bożena Małysiak-Mrozek Paulina Pawłowicz Vaidy Sunderam Che-Lun Hung Andrzej Kwiecień Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science Emory University Atlanta GA USA Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. Processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data.

关键词： Proteins Solid modeling Three-dimensional displays Pipelines Cluster computing Big Data Biology Organisms Decoding Sparks

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Design and implementation of obstacle detection system for powered wheelchairs 7

Design and implementation of obstacle detection system for p...

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7th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Tatsis, Giorgos Koritsoglou, Kyriakos Fudos, Ioannis Karvounis, Evaggelos Votis, Konstantinos Tzovaras, Dimitrios Information Technologies Institute Centre for Research and Technology Hellas Ioannina Greece Information Technologies Institute Centre for Research and Technology Hellas Thermi57001 Greece University of Ioannina Dept of Computer Science and Engineering Ioannina Greece University of Ioannina Campus of Arta Department of Informatics and Telecommunications Arta Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In this paper a detailed description of the design and implementation of a system capable of detecting various obstacles for wheelchairs is provided. This system is part of a larger project that its goal is to create intelligent applications for patients using powered wheelchairs. The system is implemented on an electric wheelchair using low-cost ultrasonic and infrared sensors. All tests took place in indoor environment inside our laboratories, and the performance of the system is found to be adequate for the purposes of our project. © 2022 IEEE.

关键词： Ultrasonic applications

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Target-Oriented WiFi Sensing for Respiratory Healthcare: from Indiscriminate Perception to In-Area Sensing

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IEEE Network 2024年

作者： Wang, Meng Huang, Jinyang Zhang, Xiang Liu, Zhi Li, Meng Zhao, Peng Yan, Huan Sun, Xiao Dong, Mianxiong Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligence Machine School of Computer and Information Hefei230601 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China Muroran Institute of Technology Department of Sciences and Informatics 0508585 Japan

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, i.e., beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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Accurate Cyberbullying Detection and Prevention on Social Media

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Procedia computer Science 2021年 181卷 605-611页

作者： Andrea Perera Pumudu Fernando AndreaPera Department of Computer Science Informatics Institute of Technology Colombo 06 Sri Lanka Pumudu Fernando Department of Computer Science Informatics Institute of Technology Colombo 06 Sri Lanka

The usage of digital/social media is increasing day by day with the advancement of technology. People in the twenty-first century are being raised in an internet-enabled world with social media. Communication has been just one button click. Even though there are plenty of opportunities with digital media people tend to misuse it. People spread hatred toward a person in social networking. Cyberbullying affects people in different aspects. It doesn’t affect only for health, there are more different aspects which will lead life to a threat. Cyberbullying is a worldwide modern phenomenon which humans cannot avoid hundred percent but can be prevented. Most existing solutions have shown techniques/approaches to detect cyberbullying, but they are not freely available for end-users to use. They haven’t considered the evolution of language which makes a big impact on cyberbullying text. This paper describes a system for automatic detection and prevention cyberbullying considering the main characteristics of cyberbullying such as Intention to harm an individual, Repeatedly and over time and using abusive curl language or hate speech using supervised machine learning. The system relies on the detection of cyberbullying text along with the themes/categories associated with cyberbullying such as racist, sexual, physical mean, swear and other, using support vector machines and Logistic regression. The author of this research presents a new hypothesis for cyberbullying detection that the circumstances and usage of texting and its language have changed by time. Most of the studies have considered calling someone stupid, ugly and idiot as cyberbullying. Things have changed, such words may or may not always be a bullying incident. If a person wants intentionally to harm an individual, they will use extreme words. In addition to traditional feature extraction techniques like Term Frequency–Inverse Document Frequency (TF-IDF), N-gram and profanity along with sentiment analysis increase

关键词： Cyberbullying Supervised machine learning Natural language processing Social media Health problems

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