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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 586-597页

作者： Bang Wang Zhenglin Wang Wei Xiang Yijun Mo School of Software Engineering and Hubei Key Laboratory of Smart Internet Technology Huazhong University of Science and Technology (HUST) Wuhan China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Implicit discourse relation recognition (IDRR) aims at recognizing the discourse relation between two text segments without an explicit connective. Recently, prompt learning has been applied to the IDRR task with great performance improvements over various neural network-based approaches. However, the discrete nature of the state-art-of-art prompting approach requires manual design of templates and answers, a big hurdle for its practical applications. In this paper, we propose a continuous version of prompt learning together with connective knowledge distillation, called AdaptPrompt, to reduce manual design efforts via continuous prompting while further improving performance via knowledge transfer. In particular, we design and train a few virtual tokens to form continuous templates and automatically select the most suitable one by gradient search in the embedding space. We also design an answer-relation mapping rule to generate a few virtual answers as the answer space. Furthermore, we notice the importance of annotated connectives in the training dataset and design a teacher-student architecture for knowledge transfer. Experiments on the up-to-date PDTB Corpus V3.0 validate our design objectives in terms of the better relation recognition performance over the state-of-the-art competitors.

关键词： Training Encoding Adaptation models Text recognition Knowledge engineering Feature extraction Bidirectional control Annotations Vocabulary Vectors

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Task Offloading and Resource Allocation in NOMA-Enabled Vehicular Edge Computing Networks

Task Offloading and Resource Allocation in NOMA-Enabled Vehi...

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IEEE Conference on Wireless Communications and Networking

作者： Xinyu Dong Liping Qian Qian Wang Yuan Wu Institute of Cyberspace Security Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The increasing adoption of mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) in vehicular networks is to reduce the execution delay of computation-intensive tasks and improve the spectrum efficiency. In this paper, we introduce a NOMA-assisted vehicular edge computing network, where vehicular users (VUs) form NOMA groups to share the radio resource with cellular users (CUs) for offloading their computing tasks to the MEC server in a highway scenario. Under the VU's execution delay constraints, we jointly optimize the computation resource allocation of the MEC server, the data transmission time, and the offloading decision to minimize the long-term energy consumption of the system. However, the long-term stochastic optimization problem is intricate due to the VU's mobility and the time-varying of the wireless channel. We thus propose a Lyapunov optimization based algorithm to transform the original problem into a single time slot optimization problem. Specifically, we decouple this problem into the computation resource allocation sub-problem solved at the MEC server and the offloading decision sub-problem solved at each VU. The optimal computation resource allocation is obtained by solving the knapsack problem, while the cross-entropy based algorithm is used to determine the optimal offloading decision for VUs. After that, our numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithms.

关键词： Road transportation Wireless communication NOMA Energy consumption Transforms Delays Resource management Servers Copper Optimization

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Universal Fan-In/Fan-Out Solution for Few-Mode Multi-Core Fibers Using Multi-Mode Fibers

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Journal of Lightwave technology 2025年

作者： Hua, Xuchen Du, Haoze Liu, Zhuyixiao Zhang, Fengming Yang, Chen Zhao, Zhiyong Tang, Ming Yangtze Optical Fibre and Cable Joint Stock Limited Company State Key Laboratory of Optical Fiber and Cable Manufacture Technology Hubei Wuhan China National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Hubei Wuhan430074 China Optics Valley Laboratory Hubei Wuhan430074 China

With the development of AI, the capacity demand for interconnection between computing component is increasing. As a solution has been vigorously developed in the past half of century, optical interconnection has been put forward with higher demands. Few-mode multi-core fiber (FM-MCF), a key technology within spatial division multiplexing (SDM), has consistently pushed the boundaries of single optical fiber communication capacity records. However, the non-standardized fiber design of FM-MCF poses a significant challenge in terms of multiplexing and demultiplexing. The difficulty lies in the preservation of high-order modes and mode field adaptation. To address these challenges, we propose a universal fiber-core multiplexing/demultiplexing solution for FM-MCF, leveraging multi-mode optical fiber (MMF) as a versatile bridging medium. The simulation and experimental results demonstrate excellent insertion loss and high-order mode retention capabilities. This solution offers compatibility with a wide range of FM-MCF designs, making it highly adaptable for various research applications. Furthermore, by using commercially available MMF, the solution reduces both cost and complexity, presenting a practical and scalable approach to advancing FM-MCF technology as a next-generation transmission medium for AI-driven computing systems. © 1983-2012 IEEE.

关键词： Optical interconnects

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Comprehensive improvement of energy efficiency and indoor environmental quality for university library atrium—A multi-objective fast optimization framework

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建筑学研究前沿（英文版） 2025年第2期14卷 449-470页

作者： Shen Xu Yongzhong Chen Jianlin Liu Jian Kang JinFeng Gao Yuchen Qin Wenjun Tan Gaomei Li School of Architecture and Urban Planning Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Subtropical Building and Urban Science Guangzhou 510641China Hubei Engineering and Technology Research Center of Urbanization Wuhan 430074China School of Architecture and Urban Planning Huazhong University of Science and TechnologyWuhan 430074China Hubei Engineering and Technology Research Center of Urbanization Wuhan 430074China CCCC Comprehensive Planning and Design Institute Co. Ltd.Beijing 100024China College of Environmental Science and Engineering Donghua UniversityShanghai 201620China Bartlett Faculty of Built Environment University College London(UCL)London WC1E 6BTUnited Kingdom Tianjin Key Laboratory of Smart City Planning Tianjin Urban Planning and Design Institute Co.LtdTianjin 300190China School of Architecture and Urban Planning Huazhong University of Science and TechnologyWuhan 430074China Hubei Engineering and Technology Research Center of Urbanization Wuhan 430074China

Low-carbon,energy-saving,and health have become a common trend for the whole of ***,how to balance the relationship between energy-saving and healthy in-door environment is a key issue for sustainable building *** paper extracted the prototypical form of university library atrium based on 44 library cases in Wuhan.A methodol-ogy verified with measured data for evaluating building performance was constructed,and the synergistic influence of spatial morphology parameters on the building energy efficiency(BEE)and indoor environmental quality(IEQ)was ***,a multi-objective fast optimiza-tion framework coupled with machine learning algorithms was used to achieve the optimal design of university library *** results showed that the parameters that influence the building energy consumption,indoor thermal comfort,daylighting performance most were the height-to-width ratio,the skylight solar heat gain coefficient,and the sidewall window-to-wall ratio,*** machine learning models predicted performance 400 times faster than traditional performance *** compared with the worst-performance scheme,the maximum optimization rate of building energy consumption,indoor thermal comfort,daylighting performance was 29.46％,10.46％,and 65.56％,*** multi-objective fast optimization framework could provide guidance for policy makers and architects to performance-based design in the early design stages of university library atrium.

关键词： University library atrium Spatial morphology Building energy efficiency Indoor environmental quality Machine learning Multi-objective optimization

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Moving Target Defense Meets Artificial Intelligence-Driven Network: A Comprehensive Survey

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IEEE internet of Things Journal 2025年第10期12卷 13384-13397页

作者： Zhang, Tao Kong, Fanyu Deng, Dongshang Tang, Xiangyun Wu, Xuangou Xu, Changqiao Zhu, Liehuang Liu, Jiqiang Ai, Bo Han, Zhu Deng, Robert H. Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui243002 China Minzu University of China School of Information Engineering Beijing100081 China Chongqing University College of Computer Science Chongqing400044 China Anhui University of Technology School of Computer Science and Technology Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui243002 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Jiaotong University School of Electronic and Information Engineering Frontiers Science Center for Smart High-speed Railway System Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Singapore Management University Department of Information Systems Singapore Singapore

Based on emerging Artificial Intelligence (AI) tasks, cloud-edge-terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats such as malicious network reconnaissance, side-channel attacks, and Distributed Denial of Service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of Moving Target Defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this paper, we firstly provide a thorough analysis of the threats in the cloud-edge-terminal network. Then we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this paper is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network. © 2014 IEEE.

关键词： Cloud security

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science Macau China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City Macau China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer Information Science Macau China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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EVA-MVC: Equitable View-weight Allocation for Generic Multi-View Clustering 25

EVA-MVC: Equitable View-weight Allocation for Generic Multi-...

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Proceedings of the ACM on Web Conference 2025

作者： Yuan Fang Xiaofeng Feng Geping Yang Ruichu Cai Yiyang Yang Zhiguo Gong Zhifeng Hao School of Computer Science and Technology Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Macao China College of Engineering Shantou University Shantou China

ISBN: (纸本)9798400712746

Contemporary datasets sourced from the web often adopt a multi-view format, collecting data from diverse sources, domains, or modules. Existing methodologies employed to analyze such datasets frequently overlook or inaccurately allocate the view-weights, pivotal metrics reflecting each view's significance. This work introduces EVA-MVC, a simple yet effective algorithm designed for Equitable View-weight Allocation (EVA) seamlessly integrated with arbitrary Multi-view Clustering (MVC) methods. Within the EVA module, we establish theoretical connections between view supplementarity and Multi-view Subspace Learning (MSL), leading to the partition of views into View Communities (VCs) based on these foundational principles. These VCs exhibit internal supplementarity similarities, facilitating Equitable View-weights Allocation through VC-specific MSL. The proposed EVA process precedes and operates independently of traditional or SOTA MVC approaches, requiring no additional processing or specialized design, making it an ideal preprocessing step for MVC applications. Through comprehensive evaluations across diverse multi-view datasets, our findings reveal that our EVA significantly enhances the effectiveness of mainstream MVC frameworks, resulting in a notable performance improvement.

关键词： consistency

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LGNet: Linear Graph Representation for Efficient Cold-Start Recommendations

LGNet: Linear Graph Representation for Efficient Cold-Start ...

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

作者： Zhenglong Li Ruiqi Luo Bangchao Wang Lin Li Xian Zhong School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Convolutional Networks (GCNs) demonstrate significant potential in recommendation systems but face difficulties with the cold-start problem, especially in integrating new nodes during inference. The typical solution leverages meta-learning for few-shot learning, though it often fails to fully capture collaborative filtering between nodes. In this paper, we revisit the node embedding propagation algorithm in GCNs, emphasizing the importance of collaborative filtering and elucidating the relation between high-order and low-order embeddings. Given the substantial interaction data required for training recommendation models, maintaining a simple model structure remains crucial. To address these challenges, we propose the Linear Graph Network (LGNet), which theoretically compresses multi-layer GCNs into a single layer, enabling the embedding of new nodes during inference. Experimental results on benchmark datasets for link prediction and user cold-start tasks demonstrate that LGNet outperforms existing methods. The code will be available at https://***/kunbeibei/LGNet.

关键词： Training Metalearning Collaborative filtering Signal processing algorithms Predictive models Prediction algorithms Data models Inference algorithms Speech processing Recommender systems

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The impact of feature selection and feature reduction techniques for code smell detection: A comprehensive empirical study

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Automated Software Engineering 2025年第2期32卷 1-49页

作者： Zhang, Zexian Zhu, Lin Yin, Shuang Hu, Wenhua Gao, Shan Chen, Haoxuan Li, Fuyang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology Wuhan China School of Computer Wuhan Qingchuan University Wuhan China Hokkaido University Sapporo Japan

Code smell detection using machine/deep learning methods aims to classify code instances as smelly or non-smelly based on extracted features. Accurate detection relies on optimizing feature sets by focusing on relevant features while discarding those that are redundant or irrelevant. However, prior studies on feature selection and reduction techniques for code smell detection have yielded inconsistent results, possibly due to limited exploration of available techniques. To address this gap, we comprehensively analyze 33 feature selection and 6 feature reduction techniques across seven classification models and six code smell datasets. And we apply the Scott-Knott effect size difference test for comparing performance and McNemar’s test for assessing prediction diversity. The results show that (1) Not all feature selection and reduction techniques significantly improve detection performance. (2) Feature extraction techniques generally perform worse than feature selection techniques. (3) Probabilistic significance is recommended as a “generic” feature selection technique due to its higher consistency in identifying smelly instances. (4) High-frequency features selected by the top feature selection techniques vary by dataset, highlighting their specific relevance for identifying the corresponding code smells. Based on these findings, we provide implications for further code smell detection research.

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An active power control of DFIG-based wind turbine generators for frequency regulation with expected dynamic performance

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Applied Energy 2025年 391卷

作者： Zhao, Teyang Liu, Hui Wang, Ni Su, Jinshuo Luo, Zhiqiang Ma, Siyu School of Electrical Engineering Guangxi University Nanning530004 China Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning530004 China The State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of Macau 519000 China

The escalating wind power penetration presents a substantial challenge in ensuring the system frequency stability. Although wind turbine generators (WTGs) can offer rapid frequency support by the presetting power trajectory response (PPTR), it will result in a secondary frequency dip (SFD) while facing the demand of restoring rotor speed after frequency regulation. Moreover, the frequency overshoot will occur due to the fixed power increment provided by PPTR under minor disturbances. In this paper, an active power control strategy of DFIG-based WTGs is proposed for eliminating frequency overshoot and mitigating SFD while providing power support for the system frequency. A rotor-speed-based power increment and power attenuation trajectory are designed to enhance the primary frequency nadir while ensuring the operation stability of WTGs. After that, SFD will be mitigated by the proposed parabolic-formed compensation power. Moreover, a power increment control mode is proposed to eliminate the frequency overshoot by a membership-function-based global controller, which can smoothly switch the combined inertial response and the improved PPTR to provide a flexible power increment. Finally, the validity of the proposed strategy in improving frequency nadir, eliminating frequency overshoot, and mitigating SFD are verified by simulations under different wind power penetration levels, disturbances, and wind conditions. © 2025

关键词： Frequency stability

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