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arXiv 2025年

作者： Pei, Ziqian Wang, Jintao Zhang, Pingping Shi, Zheng Yang, Guanghua Ma, Shaodan State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau Macau999078 China School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

In this letter, we investigate a dynamic reconfigurable distributed antenna and reflection surface (RDARS)driven secure communication system, where the working mode of the RDARS can be flexibly configured. We aim to maximize the secrecy rate by jointly designing the active beamforming vectors, reflection coefficients, and the channel-aware mode selection matrix. To address the non-convex binary and cardinality constraints introduced by dynamic mode selection, we propose an efficient alternating optimization (AO) framework that employs penalty-based fractional programming (FP) and successive convex approximation (SCA) transformations. Simulation results demonstrate the potential of RDARS in enhancing the secrecy rate and show its superiority compared to existing reflection surface-based schemes. Copyright © 2025, The Authors. All rights reserved.

关键词： Secure communication

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MCTS-Judge: Test-Time Scaling in LLM-as-a-Judge for Code Correctness Evaluation

arXiv

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arXiv 2025年

作者： Wang, Yutong Ji, Pengliang Yang, Chaoqun Li, Kaixin Hu, Ming Li, Jiaoyang Sartoretti, Guillaume Department of Mechanical Engineering National University of Singapore Singapore Robotics Institute Carnegie Mellon University United States School of Computing National University of Singapore Singapore Department of Electrical and Computer Systems Monash University Australia

The LLM-as-a-Judge paradigm shows promise for evaluating generative content but lacks reliability in reasoning-intensive scenarios, such as programming. Inspired by recent advances in reasoning models and shifts in scaling laws, we pioneer bringing test-time computation into LLM-as-a-Judge, proposing MCTS-Judge, a resource-efficient, System-2 thinking framework for code correctness evaluation. MCTS-Judge leverages Monte Carlo Tree Search (MCTS) to decompose problems into simpler, multi-perspective evaluations. Through a node-selection strategy that combines self-assessment based on historical actions in the current trajectory and the Upper Confidence Bound for Trees based on prior rollouts, MCTS-Judge balances global optimization and refinement of the current trajectory. We further designed a high-precision, unit-test-level reward mechanism to encourage the Large Language Model (LLM) to perform line-by-line analysis. Extensive experiments on three benchmarks and five LLMs demonstrate the effectiveness of MCTS-Judge, which improves the base model’s accuracy from 41.0% to 80.0%, surpassing the o1-series models with 3× fewer tokens. Further evaluations validate the superiority of its reasoning trajectory in logic, analytics, thoroughness, and overall quality, while revealing the test-time scaling law of the LLM-as-a-Judge paradigm. Copyright © 2025, The Authors. All rights reserved.

关键词： Scaling laws

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Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer systems for EV Charging

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series Data

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IEEE Transactions on Services computing 2025年

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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DDPS: Dynamic Differential Pricing-Based Edge Offloading System With Energy Harvesting Devices

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IEEE Transactions on Network Science and engineering 2025年

作者： Xue, Hai Xia, Yun Xiong, Neal N. Zhang, Di Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Sul Ross State University Department of Computer Science and Mathematics AlpineTX79830 United States Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Zhengzhou University Henan International Joint Laboratory of Intelligent Health Information System Zhengzhou450001 China Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China

Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (Userver, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8% Userver on average with different ES computation capacities. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Mamba in Speech: Towards an Alternative to Self-Attention

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1933-1948页

作者： Xiangyu Zhang Qiquan Zhang Hexin Liu Tianyi Xiao Xinyuan Qian Beena Ahmed Eliathamby Ambikairajah Haizhou Li Julien Epps School of Electrical Engineering and Telecommunications The University of New South Wales (UNSW) Sydney NSW Australia College of Computing and Data Science Nanyang Technological University Singapore School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong (Shenzhen) Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Transformer and its derivatives have achieved success in diverse tasks across computer vision, natural language processing, and speech processing. To reduce the complexity of computations within the multi-head self-attention mechanism in Transformer, Selective State Space Models (i.e., Mamba) were proposed as an alternative. Mamba exhibited its effectiveness in natural language processing and computer vision tasks, but its superiority has rarely been investigated in speech signal processing. This paper explores solutions for applying Mamba to speech processing by discussing two typical speech processing tasks: speech recognition, which requires semantic and sequential information, and speech enhancement, which focuses primarily on sequential patterns. The experimental results confirm that bidirectional Mamba (BiMamba) consistently outperforms vanilla Mamba, highlighting the advantages of a bidirectional design for speech processing. Moreover, experiments demonstrate the effectiveness of BiMamba as an alternative to the self-attention module in the Transformer model and its derivates, particularly for the semantic-aware task. The crucial technologies for transferring Mamba to speech are then summarized in ablation studies and the discussion section, offering insights for extending this research to a broader scope of tasks.

关键词： Transformers Speech enhancement Speech recognition Convolution Computational modeling Natural language processing Training Random access memory Kernel Semantics

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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural computing and Applications, (2025), 10.1007/s00521-025-10991-3)

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Neural computing and Applications 2025年 1-2页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘Department of electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘Department of electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of Information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘Department of computer Science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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Lag Synchronization and Lag H∞ Synchronization for Multiweighted Coupled Reaction-Diffusion Neural Networks Suffering Topology Attacks

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IEEE Transactions on Control of Network systems 2025年

作者： Wang, Jin-Liang Zhao, Xin-Yu Ren, Shun-Yan Huang, Tingwen Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Tiangong University School of Electronics & Information Engineering Tianjin300387 China University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper mainly discusses two kinds of coupled reaction-diffusion neural networks (CRNN) under topology attacks, that is, the cases with multistate couplings and with multiple spatial-diffusion couplings. On one hand, by adopting state feedback control scheme, the problem of the lag synchronization for the multistate coupled CRNN is addressed. Moreover, the influences of external disturbances for the lag synchronization of the CRNN with multistate couplings are also taken into consideration. On the other hand, several lag and lag H∞ synchronization conditions for CRNN with multiple spatial-diffusion couplings are derived under the help of the Kronecker product and the state feedback control method. Finally, two numerical examples are provided to verify the correctness of the derived criteria. © 2014 IEEE.

关键词： State feedback

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Model authentication hashing: Identifying pirated neural network models

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Expert systems with Applications 2025年 283卷

作者： Xiong, Cheng Feng, Guorui Sun, Yunlong Qian, Zhenxing Yao, Heng Qin, Chuan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Communication and Information Engineering Shanghai University Shanghai200444 China Department of Tourism Fudan university Shanghai200433 China Key Laboratory of Culture & Tourism Intelligent Computing of China Ministry of Culture and Tourism Shanghai200433 China School of Computer Science Fudan University Shanghai200433 China

Nowadays, a great number of neural network models have been trained and applied in many fields. At the same time, the emergence of vast network models has already highlighted the security problems of intellectual property protection. High-value pre-trained models of original inventors may be pirated and used by illegal users without authorization. In order to solve this problem, we first give the definition of model differentiation, and then propose an effective robust hashing method based on model parameters for piracy identification in this paper. Through our model hash generation network, the parameters of original model are transformed into a one-way hash sequence with the fixed length, and the robustness and discrimination of the model hashes can be utilized to authenticate model parameters. Experimental results show that the accuracy rates of similar model detection and different model discrimination under different cases are almost all greater than 96.5%, and the comprehensive performance of proposed method is also the best one compared with other 3 SOTA methods. These results mean that the proposed method can be applied for identifying illegal model piracy effectively. © 2025 Elsevier Ltd

关键词： Terrorism

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Multiagent Deep Reinforcement Learning for Transactive Energy Management of MMGs Incorporating Battery Swapping Stations

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IEEE Transactions on Industrial Informatics 2025年

作者： Cai, Ting Zhang, You Wu, Yuxin Yan, Haoyuan Zhao, Tianyang Hubei University of Technology School of Computer Science Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan430068 China Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Jinan University Energy and Electricity Research Center International Energy College Zhuhai519070 China Xi'an Jiaotong University School of Electrical Engineering Xi'an710049 China

Optimal energy management between microgrids (MGs) and battery swapping stations (BSSs) offers significant economic benefits. However, existing works face challenges in formulating optimal interaction strategies between MGs and BSSs, due to the temporal-spatial uncertainty of distributed renewables and emerging loads, as well as incomplete information. This article addresses the energy transaction problem between multi-MGs and multi-BSSs using a hybrid multiagent deep reinforcement learning approach to minimize operation costs. Specifically, a hierarchical transactive energy management community is introduced to facilitate energy exchange between MMGs and BSSs, considering different stakeholder interests. The problem is modeled as a partially observable Markov game. The proposed hybrid algorithm, combining multiagent proximal policy optimization (MAPPO) and double deep Q-network (DDQN), handles the continuous scheduling of MGs and the discrete operations of BSSs. Numerical results show that, averaged over the baselines, the proposed MAPPO-DDQN reduces 13.71% of MGs' operation costs and increases 14.62% of BSSs' profit. © 2005-2012 IEEE.

关键词： Deep reinforcement learning

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