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IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Muhammad Sharshar Kareem Elzeky Menna Othman SeifAldin Khaled Hosam Moubarak Walid Gomaa Department of Computer Science Center for Secure Cyber-Physical Systems (C2PS) Khalifa University UAE Department of Natural Language Processing MBZUAI UAE Department of Computer Engineering Cyber-Physical Lab Egypt-Japan University of Science and Technology Egypt Accounting Department Egypt-Japan University of Science and Technology Egypt

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

This study examines blockchain technology's potential to enhance the stock market by offering a decentralized alternative to the traditional system. The study explores the challenges of the current traditional stock market, such as long settlement times, weak transparency, dependency on centralized clearing houses, and heightened fees. The proposed novel system leverages blockchain technology to create a fully decentralized stock market encompassing all fundamental operations from Initial Public Offerings (IPOs) to stock trading and extending to distributing profits or dividends. It connects all market participants through an Ethereum consortium blockchain fuelled by smart contracts. This approach replaces traditional intermediaries like brokers with automated regulations, ensuring more rapid transaction settlements, reduced operational errors and fees, and strengthened security measures. The proposed blockchain-based system, consisting of four smart contracts for IPO, stock exchange, dividend distribution, and participant management, demonstrates remarkable performance, showing promise in efficiently handling the current load of the largest stock markets like the London Stock Exchange (LSE). Through theoretical and practical testing, the system achieved a remarkable throughput of 120 transactions per second and latency or finality of just 4 seconds, underscoring its capability to significantly enhance the efficiency of stock market operations. 1 1 The implementation code is available via GitHub upon request.

关键词： Cloud computing Smart contracts Transforms Throughput Regulation Blockchains Security Stock markets Testing Software development management

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Role of cilia activity and surrounding viscous fluid in properties of metachronal waves

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Physical Review E 2024年第1期110卷 014409-014409页

作者： Supravat Dey Gladys Massiera Estelle Pitard Department of Physics and Department Computer Science and Engineering <a href="https://***/013vs5h31">SRM University-AP</a> Amaravati Andhra Pradesh 522240 India. <a href="https://***/02ftce284">Laboratoire Charles Coulomb</a> (L2C) <a href="https://***/051escj72">Université de Montpellier</a> CNRS 34095 Montpellier France.

Large groups of active cilia collectively beat in a fluid medium as metachronal waves, essential for some microorganisms motility and for flow generation in mucociliary clearance. Several models can predict the emergence of metachronal waves, but what controls the properties of metachronal waves is still unclear. Here, we numerically investigate the respective impacts of active beating and viscous dissipation on the properties of metachronal waves in a collection of oscillators, using a simple model for cilia in the presence of noise on regular lattices in one and two dimensions. We characterize the wave using spatial correlation and the frequency of collective beating. Our results clearly show that the viscosity of the fluid medium does not affect the wavelength; the activity of the cilia does. These numerical results are supported by a dimensional analysis, which shows that the result of wavelength invariance is robust against the model taken for sustained beating and the structure of hydrodynamic coupling. Interestingly, the enhancement of cilia activity increases the wavelength and decreases the beating frequency, keeping the wave velocity almost unchanged. These results might have significance in understanding paramecium locomotion and mucociliary clearance diseases.

关键词： Synchronization Cilia Hydrodynamic models Theories of collective dynamics & active matter

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Polarization Aware Movable Antenna

arXiv

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arXiv 2024年

作者： Zhang, Runxin Shao, Yulin Eldar, Yonina C. The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

This paper presents a polarization-aware movable antenna (PAMA) framework that integrates polarization effects into the design and optimization of movable antennas (MAs). While MAs have proven effective at boosting wireless communication performance, existing studies primarily focus on phase variations caused by different propagation paths and leverage antenna movements to maximize channel gains. This narrow focus limits the full potential of MAs. In this work, we introduce a polarization-aware channel model rooted in electromagnetic theory, unveiling a defining advantage of MAs over other wireless technologies such as precoding: the ability to optimize polarization matching. This new understanding enables PAMA to extend the applicability of MAs beyond radio-frequency, multipath-rich scenarios to higher-frequency bands, such as mmWave, even with a single line-of-sight (LOS) path. Our findings demonstrate that incorporating polarization considerations into MAs significantly enhances efficiency, link reliability, and data throughput, paving the way for more robust and efficient future wireless networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Polarization

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Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network

Explainable Artificial Intelligence to Detect Image Spam Usi...

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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Zhang, Zhibo Damiani, Ernesto Hamadi, Hussam Al Yeun, Chan Yeob Taher, Fatma Khalifa University C2PS Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Zayed University Department of Computing and Applied Technology Dubai United Arab Emirates

ISBN: (纸本)9781665461221

Image spam threat detection has continually been a popular area of research with the internet's phenomenal expansion. This research presents an explainable framework for detecting spam images using Convolutional N eural Network (CNN) algorithms and Explainable Artificial Intelligence (XAI) algorithms. In this work, we use CNN model to classify image spam respectively whereas the post-hoc XAI methods including Local Interpretable Model Agnostic Explanation (LIME) and Shapley Additive Explanations (SHAP) were deployed to provide explanations for the decisions that the black-box CNN models made about spam image detection. We train and then evaluate the performance of the proposed approach on a 6636 image dataset including spam images and normal images collected from three different publicly available email corpora. The experimental results show that the proposed framework achieved satisfactory detection results in terms of different performance metrics whereas the model-independent XAI algorithms could provide explanations for the decisions of different models which could be utilized for comparison for the future study. © 2022 IEEE.

关键词： Lime

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Hybrid Adam_POA: Hybrid Adam_Pufferfish Optimization Algorithm Based Load Balancing in Cloud Computing

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SN computer science 2025年第2期6卷 1-18页

作者： Hegde, Sandeep Kumar Hegde, Rajalaxmi Kumar, C. Naveen Meenakshi, R. Raman, Ramakrishnan Jayaseelan, G.M. Department of Computer Science and Engineering NMAM Institute of Technology Affiliated to Nitte (Deemed to Be University) Karnataka Nitte 574110 India Department of CSE Aditya College of Engineering and Technology(ACET) No 2/9 Kamakshipura Hesaraghatta Hobli Yelahanka Karnataka Bengaluru 560089 India Department of CSE Chennai Institute of Technology Tamil Nadu Chennai 600069 India Symbiosis International (Deemed University) Maharashtra Pune 412115 India Department of Marketing and International Business M. S. Ramaiah Institute of Management Bengaluru 560054 India

Cloud Computing (CC) is the model of delivering services for users across internet. The CC is useful to address various issues like scheduling, security and Load balancing (LB). Amongst these problems, LB is the most demanding issue. LB is performed at VM or PM level. When the collection of tasks enter into VM, it uses VM resources and these resources become exhausted, that indicates that there are no resources available for handling the other task requests. Here, an efficient model named Adam_Pufferfish Optimization Algorithm (Adam_POA) is developed for LB in CC. Firstly, the tasks are assigned to the VM in the Data Center (DC) in round robin manner. Based upon VM parameters, VMs are classified as overloaded and underloaded VMs employing Deep Fuzzy Clustering (DFC). After that, divide the tasks in overloaded VM based upon priority. Following to this, the tasks are assigned in overloaded to underloaded VMs for balancing the load in the cloud using hybrid Adam_POA. It is recognized that Adam_POA obtained resource availability with 0.880, capacity with 0.915, load of 0.529 and 0.857 of reliability. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adam optimizer Cloud computing (CC) Deep fuzzy clustering (DFC) Load balancing (LB) Pufferfish optimization algorithm (POA)

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Activity Hours: Assessing Liveability During Heatwaves

SSRN

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SSRN 2024年

作者： Capol, Livia Keshav, Srinivasan Nagy, Zoltan Department of Computer Science ETH Zurich Zurich8092 Switzerland Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom Department of Civil Architectural and Environmental Engineering The University of Texas at Austin AustinTX78712 United States

As climate change increases the frequency and severity of heatwaves, understanding their impact on the well-being, health, and permissible activities of building occupants becomes crucial. Existing work focuses primarily on the thermal comfort of an average individual, neglecting both the impact of extreme heat on liveability and variability in resilience of different population groups. To address these gaps, we introduce the Activity hours (Ah) metric, which quantifies the liveability of indoor environments during heatwaves while taking into account air temperature, humidity, occupant age, permissible activity levels, and duration of exposure. We also present the Heatalyzer tool that allows the computation of Ah for different geographies, building archetypes, and heatwave durations. Through a case study of residential housing in London, we compare Ah with established thermal comfort metrics, highlighting Ah's ability to quantify heatwave impacts on occupant liveability for different building types and demographic groups. Our results are made widely accessible through the Heatalyzer dashboard, an intuitive website that enables London residents to evaluate their exposure to heat-related risks. © 2024, The Authors. All rights reserved.

关键词： Climate change

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Efficient Large-Scale Expensive Optimization via Surrogate-assisted Sub-problem Selection

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IEEE Transactions on Evolutionary Computation 2025年

作者： Zhao, Kaili Wang, Xilu Sun, Chaoli Jin, Yaochu Hayat, Asad Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China Shanxi Engineering Vocational College Department of Mechanical Engineering Taiyuan030009 China University of Surrey School of Computer Science and Electronic Engineering SurreyGU2 7XH United Kingdom Taiyuan University of Science and Technology School of Computer Science and Technology Taiyuan030024 China Westlake University School of Engineering Hangzhou310030 China Taiyuan University of Science and Technology School of International Education Taiyuan030024 China

Traditional large-scale evolutionary algorithms are limited in their ability to solve certain real-world applications with high-dimensional, black-box, and computationally expensive objectives due to their need for numerous objective evaluations. Surrogate-assisted evolutionary algorithms (SAEAs) have shown effective for expensive black-box optimization by relying on inexpensive surrogate models. However, large-scale optimization remains challenging for SAEAs due to the exponentially growing search space and the presence of multiple local optima, resulting in difficulty in training a proper model due to the lack of samples. To address these challenges, we propose constructing an initial surrogate model on randomly selected dimensions and calculating a Gaussian distribution for each sampled dimension. The surrogate then provides predictions when perturbing each sampled dimension by sampling from the distribution, enabling the identification of the most important variables for constructing an active sub-problem to reduce the search space. A secondary surrogate model, built for the active sub-problem, guides the offspring generation and environmental selection for a modified particle swarm optimization algorithm to effectively explores the sub-space while escaping local optima in large-scale problems. Experimental results on CEC'2013 and CEC'2010 benchmark problems show that the proposed method outperforms state-of-the-art algorithms in addressing large-scale expensive optimization problems. The efficiency of the proposed method is further verified on CEC'2010 benchmark problems extended to 2000 dimensions. © 1997-2012 IEEE.

关键词： Particle swarm optimization (PSO)

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pFedDHPO: A Differentiable Approach for Personalized Hyperparameter Optimization in Federated Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Shen, Jinglong Cheng, Nan Xu, Wenchao Wang, Haozhao Quan, Wei Shen, Xuemin Xidian University School of Telecommunications Engineering Shaanxi Xi’an710126 China Hong Kong Polytechnic University Department of Computing China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Hyperparameter optimization (HPO) is crucial for federated learning (FL) performance. Given the inherent data heterogeneity across clients, recent research has focused on providing personalized hyperparameters for individual clients. However, such personalized approaches introduce exponential search complexity as the number of clients increases, significantly reducing the efficiency of existing HPO methods. To address this challenge, we propose pFedDHPO, a novel personalized HPO framework that efficiently optimizes hyperparameters in a differentiable manner. Specifically, pFedDHPO formulates personalized HPO as an optimization problem targeting joint distribution parameters within the clients’ search space and leverages gradient information from differentiable validation loss to substantially enhance the efficiency of the HPO process. Experimental results demonstrate that pFedDHPO achieves state-of-the-art performance compared to baseline methods, improving accuracy by up to 18.35% under extreme Non-IID data distributions. Additionally, the framework reduces communication overhead by 41.2% compared to conventional HPO methods, making it highly scalable for resource-constrained FL deployments. © 2015 IEEE.

关键词： Federated learning

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A novel eXplainable AI agent for social interaction training of people with Autism Spectrum Disorder (ASD)

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International Journal of Information Technology (Singapore) 2025年 1-13页

作者： Gupta, Prashant K. Mazumdar, Bireshwar Dass Mishra, Manmohan Chadha, Rubina Komaragiri, Rama S. School of Computer Science Engineering and Technology Bennett University Plot Nos 8-11 TechZone II Uttar Pradesh Greater Noida 201310 India Department of Computer Applications United Institute of Management A-31 UPSIDC Industrial Area Naini Chak Noor Uttar Pradesh Prayagraj 211010 India Psychology / Counselling The Neuroaid and Research Foundation (NRF) C-19 Pamposh Enclave Delhi New Delhi 110048 India School of Engineering Galgotias University Plot No. 2 Yamuna Expy opposite Buddha International Circuit Sector 17A Uttar Pradesh Greater Noida 203201 India

Autism Spectrum Disorder (ASD) is a neurological disorder that impacts a subject’s ability to be involved in a social interaction. Machine learning (ML) and deep learning (DL) models have been mainly used for ASD detection, which are however criticized for their ‘black-boxness’, advocating the use of eXplainable artificial intelligence (XAI) algorithms. As per the literature search so far, no social interaction model-based eXplainable Autism Spectrum Disorder (ASD) care agent exists for social interaction training of an ASD subject. Therefore, this work makes a beginning in this direction. A novel architecture of an eXplainable ASD care agent for social interaction training of ASD subjects, with an underlying intelligent AI algorithm is presented. Its working is elucidated through six representative social interaction scenarios and some practical considerations are also presented when the agent is put in practice with the ASD subject. The working of the proposed ASD care agent is also depicted through a suitable case study. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： AI Agent Artificial intelligence (AI) Autism spectrum disorder EXplainable AI Neurological disorders Social interaction training

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A Survey on Network Management for xANET: Evolution, Challenges, Future Directions

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IEEE Communications Surveys and Tutorials 2025年

作者： Li, Tong Yang, Chungang Wang, Yao Cai, Lin Anpalagan, Alagan Han, Zhu Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China National Key Laboratory of Multi-domain Data Collaborative Processing and Control Xi'an710068 China Hangzhou Institute of Technology of Xidian University Hangzhou311231 China University of Victoria Department of Electrical and Computer Engineering VictoriaBCV8P 5C2 Canada Ryerson University Department of Electrical and Computer Engineering TorontoONM5B 2K3 Canada University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446701 Korea Republic of

The x ad hoc network (xANET) termed as a family of ad hoc networks, including mobile ad hoc network (MANET), vehicular ad hoc network (VANET), flying ad hoc network (FANET) and satellite ad hoc network (SANET), has found a wide range of applications in providing ubiquitous wireless services. Despite its broad utility, the dynamic nature and lack of a centralized controller pose significant challenges to effective and flexible network management for xANET. Conventional network management protocols face challenges such as scalability, security vulnerabilities, configuration complexity, robustness, and performance resilience. Recent efforts have presented different approaches, focusing on policy-based network management (PBNM) and intent-driven network management (IDNM). However, there is no comprehensive survey to clarify their concepts and classifications. This paper presents a survey of the network management evolution for xANET, covering configuration-based, policy-based and the latest intent-driven approaches. We first introduce the characteristics and applications of xANET. Meanwhile, we investigate the network management concepts and challenges. Moreover, we survey the evolution of management protocols for xANET, including simple network management protocol (SNMP), PBNM, and IDNM. Then, we follow the detailed network management of xANET from configuration-based to policy-based and intent-driven approaches. Through comparative analysis, it is found that IDNM employs a more intelligent management protocol, demonstrating higher efficiency and flexibility in handling complex tasks and dynamic network management. This makes it better suited to addressing the challenges of xANET management. Finally, we summarize the remaining challenges and possible future research directions. © 2025 IEEE.

关键词： Vehicular ad hoc networks

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