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Solving dynamic optimization problems using parent–child multi-swarm clustered memory (PCSCM) algorithm

Neural Computing and Applications 2024年第31期36卷 19549-19583页

作者： Mohammadpour, Majid Mostafavi, Seyedakbar Mirjalili, Seyedali Department of Computer Engineering Yazd University Yazd Iran Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic

The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-dimensional search spaces, the incapability to detect environmental changes, a continual trade-off between exploration and exploitation, and the potential loss of population diversity within the problem space. To address these challenges, we propose a novel hybrid PSO algorithm, denoted as Parent–Child Multi-Swarm Clustered Memory (PCSCM). PCSCM is explicitly designed to leverage an enhanced memory system, capable of mitigating the issue of outdated particle memory after convergence, and efficiently adapting to changing environmental conditions. This innovative memory system retains and retrieves promising solutions from the past when environmental alterations occur. Additionally, PCSCM introduces clustering mechanisms for particles within each swarm, aimed at augmenting diversity within the problem space. This clustering strategy substantially bolsters the algorithm’s performance in tracking evolving optimal solutions and positively contributes to its scalability. Crucially, the clustering approach is implemented not only for the main population but also for stored solutions in memory, which collectively strike a balance between exploration and exploitation. In the proposed method, particle swarms are divided into parent and child swarms, with parent swarms dedicated to preserving diversity;while, child swarms focus on identifying local solutions. These clustering and memory strategies are consistently applied within each sub-swarm to effectively address the challenges posed by high-dimensional search spaces. In addition to addressing challenges related to dynamic optimization, our proposed Parent–Child Multi-Swarm Clustered Memory (PCSCM) algorithm introduces an innovative mechanism for detecting environmental changes. This n

关键词： Chaos theory

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Evaluating Usability as a Key Human Factor in Agile SPM Tools Through Expert-Based Techniques 39

Evaluating Usability as a Key Human Factor in Agile SPM Tool...

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39th ACM/IEEE International Conference on Automated Software Engineering Workshops, ASEW 2024

作者： Alshabeeb, Esra'a Alshammare, Haifa Alakkas, Noor Alshayeb, Mohammad Faisal University Imam Abdulrahman Bin Information and Computer Science Department KFUPM Department of Computer Science Dhahran Saudi Arabia Digital Technical College for Girls Technical and Vocational Training Corporation Information and Computer Science Department KFUPM Department of Computer Science Dhahran Saudi Arabia Faisal University Imam Abdulrahman Bin Information and Computer Science Department KFUPM Department of Computer Science Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems Information and Computer Science Department KFUPM Department of Computer Science Dhahran Saudi Arabia

ISBN: (纸本)9798400712494

Despite the importance of adopting SPM tools for supporting agile practices and the significance of usability as an essential component of human factors engineering, their usability evaluation was neglected in the literature. This study aims to identify the common usability issues in SPM tools and propose suggestions for enhancing their usability. Two of the most widely used SPM tools were selected as a case study for this study, namely Jira and Pivotal Tracker. Three independent evaluators participated in the usability evaluation, using two human factors techniques: Heuristic Evaluation and Cognitive Walkthrough. The results revealed a total frequency of 123 issues in both tools. Both tools had a nearly equal frequency of issues in each evaluation method but different severity levels. Learnability issues were the issues that were raised the most in the evaluation for both tools. Most of the issues had minor to major severity, with the following percentages of total issues: 41.33% and 46.67%, respectively, while only 13.33% were catastrophic. Considering the proposed solutions by designers and developers for this type of tool can help enhance the design of SPM tools, leading to increased user satisfaction and tool adoption. © 2024 ACM.

关键词： Project management

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Learning Resilient Distributed Channel Access Policies in V2I Networks Under Intelligent Jamming

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IEEE Internet of Things Journal 2025年第13期12卷 24570-24585页

作者： Basit, Abdul Kaddoum, Georges Mourad, Azzam MontréalQC Canada Khalifa University KU6G Research Center Department of Computer Science United Arab Emirates Lebanese American University Artificial Intelligence and Cyber Systems Research Center Lebanon

While the Internet of Vehicles (IoV) can revolutionize transportation systems through intelligent connectivity, a critical challenge in realizing this potential lies in ensuring efficient channel allocation in the IoV ecosystem, particularly considering dynamic channel conditions and adversarial jamming exacerbated by the emergence of Artificial Intelligence (AI)-based jamming. To address these challenges, in this study, we use Distributed Edge Intelligence (DEI) to propose a distributed channel access mechanism for the Vehicle-to-Infrastructure (V2I) mode of IoV networks. Specifically, using an actor-critic-based Multi-agent Reinforcement Learning (MARL) framework with a common critic, we model the distributed channel access problem in V2I communications under varying channel conditions and an intelligent jamming device (iJD) interference as a Decentralized Partially Observable Stochastic Game (Dec-POSG). Furthermore, by addressing challenges, such as partial observations, non-stationarity, and credit assignment, our proposed approach fosters collaboration among intelligent vehicles (iVs) without direct communication. In addition, our unique counterfactual reasoning-aided action evaluation mechanism and a novel utility function design enable the iVs to learn mixed collaborative-competitive channel access policies, thereby enhancing channel utilization, mitigating the impact of the, and improving the network's Sum Cross-Layer Achievable Rate (SCLAR). © 2025 IEEE.

关键词： Stochastic systems

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Skeletonmae: Spatial-Temporal Masked Autoencoders for Self-Supervised Skeleton Action Recognition

Skeletonmae: Spatial-Temporal Masked Autoencoders for Self-S...

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2023 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Wu, Wenhan Hua, Yilei Zheng, Ce Wu, Shiqian Chen, Chen Lu, Aidong University of North Carolina at Charlotte Department of Computer Science United States School of Information Science and Engineering Wuhan University of Science and Technology China Center for Research in Computer Vision University of Central Florida United States

ISBN: (纸本)9798350313154

Self-supervised skeleton-based action recognition has attracted more attention in recent years. By utilizing the unlabeled data, more generalizable features can be learned to alleviate the overfitting problem and reduce the demand for massive labeled training data. Inspired by the MAE [1], we propose a spatial-temporal masked autoencoder framework for self-supervised 3D skeleton-based action recognition (SkeletonMAE). Following MAE's masking and reconstruction pipeline, we utilize a skeleton-based encoder-decoder transformer architecture to reconstruct the masked skeleton sequences. A novel masking strategy, named Spatial-Temporal Masking, is introduced in terms of both joint-level and frame-level for the skeleton sequence. This pre-training strategy makes the encoder output generalizable skeleton features with spatial and temporal dependencies. Given the unmasked skeleton sequence, the encoder is fine-tuned for the action recognition task. Extensive experiments show that our SkeletonMAE achieves remarkable performance and outperforms the state-of-the-art methods on both NTU RGB+D 60 and NTU RGB+D 120 datasets. © 2023 IEEE.

关键词： Learning systems

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An Energy-Aware Resource Management Strategy Based on Spark and YARN in Heterogeneous Environments

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期8卷 635-644页

作者： Shabestari, Fatemeh Jafari Navimipour, Nima Islamic Azad University Sofian Branch Department of Computer Engineering Sofian Iran Kadir Has University Department of Computer Engineering Istanbul34083 Turkey National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

Apache Spark is a popular framework for processing big data. Running Spark on Hadoop YARN allows it to schedule Spark workloads alongside other data-processing frameworks on Hadoop. When an application is deployed in a YARN cluster, its resources are given without considering energy efficiency. Furthermore, there is no way to enforce any user-specified deadline constraints. To address these issues, we propose a new deadline-aware resource management system and a scheduling algorithm to minimize the total energy consumption in Spark on YARN for heterogeneous clusters. First, a deadline-aware energy-efficient model for the considered problem is proposed. Then, using a locality-aware method, executors are assigned to applications. This algorithm sorts the nodes based on the performance per watt (PPW) metric, the number of application data blocks on nodes, and the rack locality. It also offers three ways to choose executors from different machines: greedy, random, and Pareto-based. Finally, the proposed heuristic task scheduler schedules tasks on executors to minimize total energy and tardiness. We evaluated the performance of the suggested algorithm regarding energy efficiency and satisfying the Service Level Agreement (SLA). The results showed that the method outperforms the popular algorithms regarding energy consumption and meeting deadlines. © 2017 IEEE.

关键词： Energy utilization

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Temporal Difference-Aware Graph Convolutional Reinforcement Learning for Multi-Intersection Traffic Signal Control

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 327-337页

作者： Lin, Wei-Yu Song, Yun-Zhu Ruan, Bo-Kai Shuai, Hong-Han Shen, Chih-Ya Wang, Li-Chun Li, Yung-Hui National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu30010 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Hon Hai Research Institute Ai Research Center New Taipei City11492 Taiwan

Traffic light control plays a crucial role in intelligent transportation systems. This paper introduces Temporal Difference-Aware Graph Convolutional Reinforcement Learning (TeDA-GCRL), a decentralized RL-based method for efficient multi-intersection traffic signal control. Specifically, we put forward a new graph architecture using each lane as a node for considering intersection relations. Additionally, we propose two new rewards by considering temporal information, namely Temporal-Aware Pressure on Incoming Lanes (TAPIL) and Temporal-Aware Action Consistency (TAAC), which enhance learning efficiency and time-interval sensitivity. Experimental results on five datasets show the superiority of TeDA-GCRL over state-of-the-art methods by at least 9.5% in average travel time. © 2000-2011 IEEE.

关键词： Reinforcement learning

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Zero Trust Networks: Evolution and Application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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MSRFormer: Hybrid Scale Self-Attention and Local Fast Convolution Transformer for Facial Expression Recognition

MSRFormer: Hybrid Scale Self-Attention and Local Fast Convol...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shen, Zi-Qiang Tang, Yu-Yi Yan, Jun-Feng Li, Yang Zhao, Guo-Ying Hunan University of Chinese Medicine China Hunan Digital Chinese Medicine Engineering Technology Research Center China Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu Finland

ISBN: (纸本)9798350368741

With the emergence of the Transformer architecture, the accuracy of deep learning within the domain of facial emotion recognition has seen further enhancement. However, Transformer comes with increased training complexity and time due to the large parameter count. Additionally, the global receptive field in Transformer's attention leads to unnecessary computations for features with limited spatial extent in image sentiment analysis. In this paper we presents a MSRFormer model, which combines Hybrid-scale self-attention and local fast convolution to address existing issues. The Hybrid-scale self-attention enables precise focus on salient regions of the image, where key features are located. A Local Fast Convolution module was integrated into the MLP head of the Transformer model, enhancing training speed and reducing parameters while maintaining feature learning. we conducted experiments on FERPlus, RAF-DB, and KDEF datasets and confirming its strong performance and broad applicability in facial expression recognition. © 2025 IEEE.

关键词： facial expression recognition hybrid scale self attention local fast convolution transformer

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Intelligent Crowd Density Classification Using Improved Metaheuristics with Transfer Learning Model on Smart Cities

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SN computer science 2024年第8期5卷 1-12页

作者： Ahmad, Sultan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Computer Science and Engineering University Center for Research and Development (UCRD) Chandigarh University Punjab Gharuan Mohali 140413 India

As a part of smart city schemes, many governments are developed intelligent methods to improve the quality of living by offering them intelligent services to enhance sustainability, livability, and workability. These intelligent methods gathered massive data counts, provide more opportunities for governments for analysing these data, and create data-driven decisions. Video surveillance in smart cities offers efficacy in city activities, safe society, and enhanced municipal services. Crowd density analysis is a most commonly used applications of object recognition, but crowd density classifying approaches face difficulties including non-uniform density, inter-scene deviation, intra-scene deviation, and occlusion. Therefore, this study develops an Intelligent Crowd Density Classification using Improved Metaheuristics with Transfer Learning (ICDC-IMTL) model on smart cities. In the presented ICDC-IMTL technique, the major aim is to recognize various kinds of crowd densities efficiently. To achieve this, the ICDC-IMTL technique makes use of the Guided filtering (GF) approach to preprocess the input images. The ICDC-IMTL algorithm exploits a neural architectural search network (NASNet) model and the hyperparameter tuning process is done by the improved artificial flora algorithm (IAFA) for feature extraction purposes. At last, the multilayer extreme learning machine (MLELM) model is applied for the crowd classification into distinct types. The stimulation validation of the ICDC-IMTL approach takes place on a crowd dataset comprising four distinct classes. The series of experiments highlighted the betterment of ICDC-IMTL algorithm over other existing DL technique. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crowd density analysis Intelligent systems Smart cities Transfer learning Video surveillance

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