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arXiv 2024年

作者： Jia, Zihang Zhang, Zhen Pedrycz, Witold The Institute of Systems Engineering School of Economics and Management Dalian University of Technology Dalian116024 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada The Systems Research Institute Polish Academy of Sciences Warsaw Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey

Efficient and robust data clustering remains a challenging task in the field of data analysis. Recent efforts have explored the integration of granular-ball (GB) computing with clustering algorithms to address this challenge, yielding promising results. However, existing methods for generating GBs often rely on single indicators to measure GB quality and employ threshold-based or greedy strategies, potentially leading to GBs that do not accurately capture the underlying data distribution. To address these limitations, this article introduces a novel GB generation method. The originality of this method lies in leveraging the principle of justifiable granularity to measure the quality of a GB for clustering tasks. To be precise, we define the coverage and specificity of a GB and introduce a comprehensive measure for assessing GB quality. Utilizing this quality measure, the method incorporates a binary tree pruning-based strategy and an anomaly detection method to determine the best combination of sub-GBs for each GB and identify abnormal GBs, respectively. Compared to previous GB generation methods, the new method maximizes the overall quality of generated GBs while ensuring alignment with the data distribution, thereby enhancing the rationality of the generated GBs. Experimental results obtained from both synthetic and publicly available datasets underscore the effectiveness of the proposed GB generation method, showcasing improvements in clustering accuracy and normalized mutual information. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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Effects of Human Personal Space on the Robot Obstacle Avoidance Behavior: A Human-in-the-loop Assessment 65th

Effects of Human Personal Space on the Robot Obstacle Avoida...

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65th Human Factors and Ergonomics Society Annual Meeting, HFES 2021

作者： Chen, Yuhao Smith, Trevor Hewitt, Nathan Gu, Yu Hu, Boyi Department of Industrial and Systems Engineering University of Florida GainesvilleFL United States Department of Mechanical and Aerospace Engineering West Virginia University MorgantownWV United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC United States

ISBN: (纸本)9781713844129

To ensure both the physical and mental safety of humans during human-robot interaction (HRI), a rich body of literature has been accumulated, and the notion of socially acceptable robot behaviors has arisen. To be specific, it requires the motion of robots not only to be physically collision-free but also to consider and respect the social conventions developed and enforced in the human social contexts. Among these social conventions, personal space, or proxemics, is one of the most commonly considered in the robot behavioral design. Nevertheless, most previous research efforts assumed that robots could generate human-like motions by merely mimicking a human. Rarely are the robot’s behavioral algorithms assessed and verified by human participants. Therefore, to fill the research gap, a Turing-like simulation test, which contains the interaction of two agents (each agent could be a human or a robot) in a shared space was conducted. Participants (33 in total) were asked to identify and label the category of those agents followed by questionnaires. Results revealed that people who had different attitudes and prior expectations of appropriate robot behaviors responded to the algorithm differently, and their identification accuracy varied significantly. In general, by considering personal space in the robot obstacle avoidance algorithm, robots could demonstrate more humanlike motion behaviors which are confirmed by human experiments. © 2021 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Machine design

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A modified peer instruction protocol: peer versus teacher's instruction

arXiv

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arXiv 2022年

作者： Lan, Boon Leong Lim, Pooi Mee Ho, Patrick W.C. Electrical and Computer Systems Engineering School of Engineering Monash University 47500 Bandar Sunway Selangor Malaysia

Peer Instruction (PI) was introduced by Mazur [1] to help students learn physics concepts during lectures. Besides physics [2,3], PI has also been adopted in other STEM fields [4]. In this approach (Figure 1(a)), students answer a related question individually after a concept has been presented. Before they revote on the same question individually, they are asked to convince others their answer is correct during peer discussion. The percentage of correct answer typically increased after peer discussion [2,3]. However, Smith et al. [5] highlighted that the improvement may be due to copying, not because students actually learned how to reason correctly. To exclude copying, Smith et al. [5] modified Mazur’s PI protocol by adding a second question Q2 after the students revote on the first question Q1 (Figure 1(b)). Q2 is ‘isomorphic’ to Q1, meaning that it requires the application of the same concept but the ‘cover story’ is different [5]. Here, we simplify Smith et al.’s PI protocol by removing the revote on Q1 (Figure 1(c)). Moreover, our Q1 and Q2 are similar, i.e. the same but some information given is different. Our PI protocol is thus the same as Mazur’s, except the pre and post discussion questions are not exactly the same. We replace PI in our protocol by teacher’s instruction (TI) to compare the effectiveness of PI with TI for a pair of similar questions involving Lenz’s law, using Hake’s normalized gain [6] and a statistical test. © 2022, CC BY.

关键词： Students

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Improved Synthesis of Nondeterministic Publicly-Known Edit Functions for Opacity Enforcement

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IEEE Transactions on Automatic Control 2025年

作者： Liu, Rongjian Hadjicostis, Christoforos N. Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus

In this paper, we investigate the synthesis problem of edit functions for opacity enforcement in systems modelled as partially-observed finite-state automata. For better plausible deniability for the edit functions, in cases where the intruder may learn/know their existence, a synthesis problem for nondeterministic edit functions under a publicly-known setting was introduced in the literature, but not yet fully solved. In light of this, we revisit the publicly-known edit mechanism by proposing a sound and complete approach for solving the synthesis problem of nondeterministic publicly-known edit functions. For practical applications, the edit function is assumed to have limited erasure and insertion ability. Our approach is based on a new labelled finite-state structure, and results in wider applicability than previously-proposed in the literature. © 1963-2012 IEEE.

关键词： Discrete event simulation

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Empowering secure and sustainable healthcare through federated learning and blockchain synergies in a Medical Internet of Things

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International Journal of Machine Learning and Cybernetics 2025年 1-33页

作者： Wang, Jing Manna, Sarbajit Aksoy, Muammer Sarkar, Arindam Rahman, Md Arafatur Noorwali, Abdulfattah Othman, Kamal M. Alenazi, Mohammed J. F. School of Computer and Information Qiannan Normal University for Nationalities Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Malaysia Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Howrah India Cyber Security Department College of Sciences Al-Mustaqbal University Babylon Iraq Computer Information Systems Department Ahmed Bin Mohammed Military College Doha Qatar School of Mathematics and Computer Science University of Wolverhampton Wolverhampton UK Department of Electrical Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Electrical Engineering College of Engineering and Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Computer Engineering College of Computer Science King Saud University Riyadh Saudi Arabia

To promote a patient-centered, sustainable healthcare ecosystem, this paper explores how blockchain and Federated Learning (FL) might be integrated into the Medical Internet of Things (MIoT). A decentralized architecture for improving data security, privacy, and interoperability between healthcare systems is proposed in this article. The article handles the drawbacks of centralized Machine Learning (ML) techniques, which frequently jeopardize the privacy of sensitive medical data, by utilizing the collaborative character of FL and the security aspects of blockchain. ML models are jointly trained while protecting patient privacy by leveraging distributed MIoT data. To effectively anticipate diseases, the study uses a variety of approaches, including FL coupled with AdaBoost, Extra Tree Classifier, Decision Tree (DT), or Linear Discriminant Analysis (LDA). Extensive testing encompassing feature selection, data splitting, cross-validation, and hyperparameter tuning guarantees the effectiveness and confidentiality of the suggested methodology. To assess the efficacy of the technique, performance measures such as Accuracy, Balanced Accuracy (BA), Fowlkes-Mallows Index (FM), Matthews Correlation Coefficient (MCC), and Bookmaker Informedness (BI) are calculated. The findings demonstrate an improved level of accuracy in contrast with conventional techniques. The research represents a novel approach to medical data analysis by choosing the best algorithm using several evaluation metrics and then incorporating it into FL.

关键词： Federated learning

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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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computer systems Science & engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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Velocity-aided Channel Estimation for Spatially Selective mmWave Massive MIMO Communications

Velocity-aided Channel Estimation for Spatially Selective mm...

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European Signal Processing Conference (EUSIPCO)

作者： Evangelos Vlachos Christos Mavrokefalidis Kostas Berberidis Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited.

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Microcombs for ultradense optical communications

Microcombs for ultradense optical communications

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Signal Processing in Photonic Communications, SPPCom 2021 - Part of OSA Advanced Photonics Congress 2021

作者： Corcoran, Bill Department of Electrical and Computer Systems Engineering Photonic Communications Lab Monash University ClaytonVIC3800 Australia

Optical microcombs provide a compact and energy-efficient method to generate ultrahigh data rate signals from a single light source. We summarize some recent advances in this field, and some future prospectsv. © ...

ISBN: (纸本)9781557528209

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Shallow diffusion networks provably learn hidden low-dimensional structure

arXiv

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arXiv 2024年

作者： Boffi, Nicholas M. Jacot, Arthur Tu, Stephen Ziemann, Ingvar Carnegie Mellon University United States Courant Institute of Mathematical Sciences New York University United States Department of Electrical and Computer Engineering University of Southern California United States Department of Electrical and Systems Engineering University of Pennsylvania United States

Diffusion-based generative models provide a powerful framework for learning to sample from a complex target distribution. The remarkable empirical success of these models applied to high-dimensional signals, including images and video, stands in stark contrast to classical results highlighting the curse of dimensionality for distribution recovery. In this work, we take a step towards understanding this gap through a careful analysis of learning diffusion models over the Barron space of single layer neural networks. In particular, we show that these shallow models provably adapt to simple forms of low dimensional structure, thereby avoiding the curse of dimensionality. We combine our results with recent analyses of sampling with diffusion models to provide an end-to-end sample complexity bound for learning to sample from structured distributions. Importantly, our results do not require specialized architectures tailored to particular latent structures, and instead rely on the low-index structure of the Barron space to adapt to the underlying distribution. Copyright © 2024, The Authors. All rights reserved.

关键词： Multilayer neural networks

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