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Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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Reliable Routing and Scheduling in Time Sensitive Networks based on Reinforcement Learning

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IEEE Transactions on Network Science and Engineering 2025年

作者： Cheng, Hao Yang, Lei Zhang, Qingfeng Zhu, Weiping South China University of Technology School of Software Engineering Guangzhou510006 China Wuhan University School of Computer Science Wuhan430072 China

Time Sensitive Network (TSN) provides strict low latency and bounded jitter requirements for applications such as industrial systems, autonomous driving, etc. One of the important problems in TSN is to achieve high reliability and low latency by effectively routing and scheduling time-sensitive data flows. Existing work applies heuristic or integer programming to address flow routing and scheduling, yet often fail to achieve optimal solutions quickly. In this paper, we propose a new Reinforcement Learning (RL) based approach for routing and scheduling of redundant data flows, aiming to achieve load balancing on the network links as well as meeting the reliability and delay constraints. Our approach first leverages a simple heuristic algorithm to decide the redundant path candidate set, and then incorporates Proximal Policy Optimization (PPO) method to choose the most suitable multi-routing flows from the candidates, which can be aware of the network status dynamically to reduce the load on the bottleneck link of the network. On this basis, we further retrain the RL model by fine-tuning to adapt to the online environment. The simulation results show that our proposed solution outperforms the benchmark algorithms in terms of the degree of network balance by 38.7% in offline network environments and in terms of average delay by 14.0% in online network environments. © 2025 IEEE. All rights reserved.

关键词： Delay tolerant networks

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EventLens: Enhancing Visual Commonsense Reasoning by Leveraging Event-Aware Pretraining and Cross-modal Linking

EventLens: Enhancing Visual Commonsense Reasoning by Leverag...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingjie Ma Zhihuan Yu Yichao Ma Guohui Li Zhong Yang School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Visual Commonsense Reasoning (VCR) is a cognitive task, challenging models to answer visual questions, and to explain the rationale behind their answers. While Large Language Models (LLMs) offer potential for this task, VCR’s complex scenes require specialized approaches to activate their commonsense reasoning abilities, as existing Multimodal LLMs struggle with VCR’s visual events and unique reference tags. To address these challenges, we propose EventLens, which enhances VCR through Event-Aware Pretraining and Cross-modal Linking in Supervised Fine-tuning. First, we introduce a new pretraining stage that emulates human cognitive processes to improve LLM comprehension of complex scenarios. Second, during supervised fine-tuning, we leverage reference tags to explicitly bridge RoI features with text, maintaining semantic integrity across modalities. Additionally, instruct prompts and task-specific adapters help integrate LLMs’ knowledge with new commonsense reasoning. Experimental results demonstrate competitive performance with state-of-the-art methods and ablation studies verify the effectiveness of proposed EventLens.

关键词： Bridges Visualization Cognitive processes Large language models Semantics Speech enhancement Signal processing Acoustics Commonsense reasoning Video recording

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Building Modeling Tools Based on Metamodeling and Product Line Technologies

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Chinese Journal of Electronics 2025年第2期23卷 219-226页

作者： Zhiyi Ma Xiao He Software Institute School of Electronics Engineering and Computer Science Peking University Beijing China Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

With the evolution of existing modeling languages and the emergence of more and more new modeling languages, it is necessary to rapidly build the corresponding software modeling tools with good quality. However, modeling tools for larger modeling languages are usually diversity in function and complexity in implementation technology. Taking building modeling tools as a domain, this paper presents an approach to building software modeling tools based on metamodeling and product line technologies. The paper provides the concept system of the approach and a feature model from diverse functions of modeling tools in order to specify the commonality and variability of the tools by deeply making the domain analysis, discusses the design and implementation of a general tool framework that provides the conveniences for reusing components and generating code for components, and specifies the mapping between the feature model and the components for modeling tools.

关键词： Analytical models Codes Buildings Metamodeling software Complexity theory Research and development

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Multiscale Graph Transformer for Brain Disorder Diagnosis

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Shehzad, Ahsan Zhang, Dongyu Yu, Shuo Abid, Shagufta Kumar, Dinesh Kant Xia, Feng School of Software Technology Dalian University of Technology Dalian116620 China School of Foreign Languages School of Software Technology Dalian University of Technology Dalian116024 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Engineering RMIT University MelbourneVIC3000 Australia School of Computing Technologies RMIT University MelbourneVIC3000 Australia

—Multiscale brain networks are crucial for diagnosing brain disorders by revealing the hierarchical organization of brain function and connectivity. However, previous methods that explored multi-atlas approaches to model these networks often failed to represent this organization across multiple spatial and temporal scales, leading to limited representations and potentially inaccurate diagnoses. To address this issue, we propose the Multiscale Brain Graph Transformer (BrainMGT), which captures the hierarchical organization of brain connectivity at various spatial and temporal scales to improve the diagnosis of brain disorders. BrainMGT constructs multiscale brain networks that model spatial hierarchies, i.e. microscale, mesoscale, and macroscale, while preserving their modular structure. It also incorporates multiple temporal scales, i.e. fast, intermediate, and slow, for estimating connectivity, rather than relying on a single temporal scale from blood-oxygen-level-dependent (BOLD) signals. Using self-attention and cross-attention mechanisms, BrainMGT extracts and integrates features both within and between these scales, generating fine-coarse feature representations that improve diagnostic precision. We validated BrainMGT on three real-world functional magnetic resonance imaging (fMRI) datasets, and the results show that BrainMGT outperformed existing methods in diagnosing neurological disorders. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Magnetic resonance imaging

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Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests 24

Automatic Adjustment of HPA Parameters and Attack Prevention...

引用

9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Chan, Huah Yong Zhou, Hanlin Ni, Jingfei Wu, Mengchun Deng, Qing School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Xiamen Institute of Software Technology Xiamen China Department of Manzhouli Customs Manzhouli China College of Information Science and Engineering Jimei University Xiamen China

ISBN: (纸本)9798400717161

In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predicted, dynamically adjusting the maximum pod parameter in the HPA to manage attack traffic. This approach enables the adjustment of HPA parameters using machine learning scripts in targeted attack scenarios while effectively managing attack traffic. All access from attacking IPs is redirected to honeypot pods, achieving a lower incidence of 5XX status codes through HPA pod adjustments under high load conditions. This method also ensures effective isolation of attack traffic, preventing excessive HPA expansion due to attacks. Additionally, experiments conducted under various conditions demonstrate the importance of setting appropriate thresholds for HPA adjustments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Random forests

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HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous Driving

arXiv

引用

arXiv 2025年

作者： Lin, R.D. Weng, Pengcheng Wang, Yinqiao Ding, Han Han, Jinsong Wang, Fei School of Software Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China College of Computer Science and Technology Zhejiang University China

LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., only two adjacent frames, often overlooking the rich long-term temporal properties inherent in autonomous driving scenarios. In driving experience, we observe that nearby objects, such as roads and vehicles, remain stable while driving, whereas distant objects exhibit greater variability in category and shape. This natural phenomenon is also captured by LiDAR, which reflects lower temporal sensitivity for nearby objects and higher sensitivity for distant ones. To leverage these characteristics, we propose HiLoTs, which learns high-temporal sensitivity and low-temporal sensitivity representations from continuous LiDAR frames. These representations are further enhanced and fused using a cross-attention mechanism. Additionally, we employ a teacher-student framework to align the representations learned by the labeled and unlabeled branches, effectively utilizing the large amounts of unlabeled data. Experimental results on the SemanticKITTI and nuScenes datasets demonstrate that our proposed HiLoTs outperforms state-of-the-art semi-supervised methods, and achieves performance close to LiDAR+Camera multimodal approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

引用

computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Path-aware Few-shot Knowledge Graph Completion

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Yu, Shuo Wang, Yingbo Wan, Zhitao Shen, Yanming Zhang, Qiang Xia, Feng Dalian University of Technology School of Computer Science and Technology Dalian116024 China Dalian University of Technology School of Software Dalian116620 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Few-shot Knowledge Graph Completion (FKGC) has emerged as a significant area of interest for addressing the long-tail problem in knowledge graphs. Traditional approaches often focus on the sparse few-shot neighborhood to derive semantic representation, overlooking other critical information forms such as relation paths. In this paper, we introduce an innovative method, called PARE, which fully leverages relation paths to enhance the few-shot representation by simultaneously incorporating both neighborhood and relation path information. Inspired by the principles of information transmission, PARE directly models relation paths between entities and parameterizes the information interference within different relation paths. Through parameter learning, PARE effectively captures information propagation along relation paths while mitigating the influence of relation dependency. To preserve neighborhood information, we employ a two-step neighborhood aggregator to resolve few-shot neighbors' ambiguity and develop a reconstruction module. By integrating the representations of relation paths and contextual neighborhoods, we achieve a comprehensive fewshot representation for two given entities. We utilize a matching processor for knowledge triplet evaluation. Extensive experiments demonstrate that our PARE model outperforms state-of-the-art baselines on widely-used benchmark datasets. © 2020 IEEE.

关键词： Knowledge graph

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Tracets4J: A Traceable Unit Test Generation Dataset

Tracets4J: A Traceable Unit Test Generation Dataset

引用

IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Xuancheng Jin Zhuang Liu Junwei Zhang Xing Hu Xin Xia School of Software Technology Zhejiang University Ningbo China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Automated test case generation enhances the efficiency and quality of software testing. Learning-based test case generation methods require an understanding of the relationships between test cases and focal methods. Accurate traceability links between test cases and focal methods provide a clear relational model, facilitating more effective training of test case generation models. However, existing techniques frequently struggle to provide precise traceability links. We conduct an empirical study on the Methods2Test dataset, which includes a wide range of test cases and corresponding focal methods, to identify the limitations of current techniques. The causes of inaccurate traceability links are divided into two categories: incorrect extraction and missed extraction. The incorrect extraction category includes method overloading errors, name matching failures, and constructor ignorance. The missed extraction category involves constructor ignorance, similar but non-identical names, and difficulties in locating test classes involving subclasses. Based on the insights from this study, we propose Coach(Combine trACe Heuristics), an automated approach for establishing test-to-code traceability links. Coach establishes file-level and class-level links as a foundation, integrates multiple heuristics, and defines their scopes to build method-level links, improving both applicability and precision. We evaluate Coach against the M2T method (used in Methods2Test), the NC and LCBA baselines. Experimental results show that Coach outperforms other methods in terms of precision, coverage, and efficiency. In addition, we apply Coach to 19,518 real-world Java projects, creating a novel large-scale dataset called Tracets4J (TRACE TeSt for Java). Models fine-tuned on Tracets4J outperform those trained on the Methods2Test dataset, as shown by BLEU-4 and CodeBLEU. This demonstrates the superiority of Tracets4J and the effectiveness of Coach in improving test case generation.

关键词： software testing Training Java computer languages Accuracy Machine learning software Test pattern generators Reliability software development management

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