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IEEE Transactions on Mobile Computing 2025年第6期24卷 4733-4746页

作者： Yang, Wei Lin, Chi Sun, Yu Dai, Haipeng Ren, Jiankang Wang, Lei Wu, Guowei Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology DUT-RU International School of Information Science and Engineering Dalian China Nanjing University State Key Laboratory for Novel Software Technology Jiangsu Nanjing210023 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as they can overcome the energy bottleneck problem of wireless sensor networks. However, existing 2D charging methods suffer from significant errors in 3D scenarios, which leads to a huge gap between theoretical results and practical applications, hindering the widespread adoption of WRSNs. In this paper, we address the chargIng utility maximizatioN problem In 3D environmenT (INIT) and provide a general solution suitable for any type of transceiver antenna. Specifically, we first establish an accurate 3D charging model to quantify the received power of sensors in 3D environments. Secondly, we design an angle-distance discretization scheme to determine appropriate charging spots for the Mobile Charger (MC). Then, we transform the mobile charging problem into a submodular function maximization problem and propose an approximation algorithm with guaranteed performance to solve it. Finally, our method has been extensively evaluated through experiments and simulations and has demonstrated considerable advantages over other comparison algorithms in real-world 3D environments. On average, it has achieved an impressive 34.8% improvement in charging utility and a remarkable 56.1% reduction in the number of dead sensors. © 2002-2012 IEEE.

关键词： Charging stations

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Through-Wall Mobile Charging: Theory, Methodology, and Implementation

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4971-4986页

作者： Sun, Yu Lin, Chi Yang, Wei Dai, Haipeng Ren, Jiankang Wang, Lei Wu, Guowei Dalian University of Technology School of Software Technology Dalian116024 China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116621 China Nanjing University State Key Laboratory for Novel Software Technology Jiangsu Nanjing210023 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

Wireless Power Transfer (WPT) has revolutionized the field of Wireless Rechargeable Sensor Networks (WRSNs), enabling sustainable operation of sensor nodes. Traditional mobile charging methods often require sensors to be within line-of-sight or physically accessed by the mobile charger, which may potentially lead to user safety or privacy concerns. Addressing this concern, this work is the first to introduce and validate the feasibility of Through-Wall charging. We formulate the Wireless charging thrOugh Walls (WOW) problem to simultaneously enhance user safety and maximize charging utility. Our approach leverages fundamental principles of electromagnetics to construct an accurate charging model for Magnetic Resonance Coupling-based WPT systems. Additionally, we thoroughly analyze the impact of wall obstruction and provide a generalized framework for through-wall charging. By employing discretization techniques and approximation algorithms, we derive a near-optimal solution to the WOW problem. Extensive simulations and test-bed experiments demonstrate that our proposed approach reduces the reliance on physical access to devices, simplifies deployment in complex environments, and thereby optimizes the travel paths of mobile chargers and enhances the overall performance and lifetime of WRSNs. Compared to conventional methods, our method benefits from more reasonable scheduling order and path construction, achieving an average energy efficiency improvement of 27.8%. © 2002-2012 IEEE.

关键词： Wireless charging

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Gloss-driven Conditional Diffusion Models for Sign Language Production

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ACM Transactions on Multimedia Computing, Communications and Applications 2025年第4期21卷 1-17页

作者： Tang, Shengeng Xue, Feng Wu, Jingjing Wang, Shuo Hong, Richang School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China School of Data Science School of Information Science and Technology University of Science and Technology of China Hefei China

Sign Language Production (SLP) aims to convert text or audio sentences into sign language videos corresponding to their semantics, which is challenging due to the diversity and complexity of sign languages, and cross-modal semantic mapping issues. In this work, we propose a Gloss-driven Conditional Diffusion Model (GCDM) for SLP. The core of the GCDM is a diffusion model architecture, in which the sign gloss sequence is encoded by a Transformer-based encoder and input into the diffusion model as a semantic prior condition. In the process of sign pose generation, the textual semantic priors carried in the encoded gloss features are integrated into the embedded Gaussian noise via cross-attention. Subsequently, the model converts the fused features into sign language pose sequences through T-round denoising steps. During the training process, the model uses the ground-truth labels of sign poses as the starting point, generates Gaussian noise through T rounds of noise, and then performs T rounds of denoising to approximate the real sign language gestures. The entire process is constrained by the MAE loss function to ensure that the generated sign language gestures are as close as possible to the real labels. In the inference phase, the model directly randomly samples a set of Gaussian noise, generates multiple sign language gesture sequence hypotheses under the guidance of the gloss sequence, and outputs a high-confidence sign language gesture video by averaging multiple hypotheses. Experimental results on the Phoenix2014T dataset show that the proposed GCDM method achieves competitiveness in both quantitative performance and qualitative visualization. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Forming

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A Neural Network Slab Quality Prediction Analysis Based on a Hybrid Intelligent Optimisation Algorithm

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第3期52卷 673-683页

作者： Li, Yiran Zhang, Chunna School of Applied Technology University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China

To address the problem of inaccurate prediction of slab quality in continuous casting, an algorithm based on particle swarm optimisation and differential evolution is proposed. The algorithm combines BP neural network prediction method. Firstly, the factors affecting the quality of the slab are analysed and a sufficient number of sample sets are extracted for training;Secondly, the particle swarm optimisation algorithm is optimised, and then the BP neural network is optimised and the chaos mechanism is introduced to increase the continuity of motion of the particles. The population is partitioned hierarchically, and different types of particles are processed separately to improve the global search capability of the algorithm. The differential evolution algorithm is also integrated to optimise cross-selection and increase particle diversity. Finally, a prediction model is built to predict and analyse the sample data. In the experiment, three algorithms are tested for different benchmark functions, namely the comparison of convergence, stability and searchability. The optimisation time comparison is completed, the target curve fit test is performed and the slab quality test results are checked. The experimental results show that the PSO-EIDE algorithm has strong optimisation ability, fast convergence speed and obvious improvement in stability and accuracy. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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An Automated Approach to Discovering software Refactorings by Comparing Successive Versions

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IEEE Transactions on software Engineering 2025年第05期51卷 1358-1380页

作者： Liu, Bo Liu, Hui Niu, Nan Zhang, Yuxia Li, Guangjie Jiang, He Jiang, Yanjie University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States National Innovation Institute of Defense Technology Beijing China School of Software Dalian University of Technology Dalian China Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University School of Computer Science Beijing China

software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evolution, and thus facilitate software maintenance and evolution. Besides that, the identified refactorings are also valuable for data-driven approaches in software refactoring. To this end, researchers have proposed a few approaches to identifying software refactorings automatically. However, the performance (especially precision) of such approaches deserves substantial improvement. To this end, in this paper, we propose a novel refactoring detection approach, called REEXTRACTOR+. At the heart of REEXTRACTOR+ is a reference-based entity matching algorithm that matches coarse-grained code entities (e.g., classes and methods) between two successive versions, and a context-aware statement matching algorithm that matches statements within a pair of matched methods. We evaluated REEXTRACTOR+ on a benchmark consisting of 400 commits from 20 real-world projects. The evaluation results suggested that REEXTRACTOR+ significantly outperformed the state of the art in refactoring detection, reducing the number of false positives by 59.6% and improving recall by 19.2%. We also evaluated the performance of the proposed matching algorithms that serve as the cornerstone of refactoring detection. The evaluation results suggested that the proposed algorithms excel in matching code entities, substantially reducing the number of mistakes (false positives plus false negatives) by 67% compared to the state-of-the-art approaches. © 2025 IEEE.

关键词： software quality

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Conditional Semantic Textual Similarity via Conditional Contrastive Learning 31

Conditional Semantic Textual Similarity via Conditional Cont...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Xinyue Qin, Zeyang Wang, Zeyu Liang, Wenxin Zong, Linlin Xu, Bo School of Software Dalian University of Technology China School of Computer Science and Technology Dalian University of Technology China

ISBN: (纸本)9798891761964

Conditional semantic textual similarity (C-STS) assesses the similarity between pairs of sentence representations under different conditions. The current method encounters the overestimation issue of positive and negative samples. Specifically, the similarity within positive samples is excessively high, while that within negative samples is excessively low. In this paper, we focus on the C-STS task and develop a conditional contrastive learning framework that constructs positive and negative samples from two perspectives, achieving the following primary objectives: (1) adaptive selection of the optimization direction for positive and negative samples to solve the over-estimation problem, (2) fully balance of the effects of hard and false negative samples. We validate the proposed method with five models based on bi-encoder and tri-encoder architectures, the results show that our proposed method achieves state-ofthe-art performance. The code is available at https://***/qinzeyang0919/CCL. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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HyperHatePrompt: A Hypergraph-based Prompting Fusion Model for Multimodal Hate Detection 31

HyperHatePrompt: A Hypergraph-based Prompting Fusion Model f...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xu, Bo Yu, Erchen Zhou, Jiahui Lin, Hongfei Zong, Linlin School of Computer Science and Technology Dalian University of Technology China School of Software Dalian University of Technology China

ISBN: (纸本)9798891761964

Multimodal hate detection aims to identify hate content across multiple modalities for promoting a harmonious online environment. Despite promising progress, three critical challenges, the absence of implicit hateful cues, the cross-modal-induced hate, and the diversity of hate target groups, inherent in the multimodal hate detection task, have been overlooked. To address these challenges, we propose a hypergraph-based prompting fusion model. Our model first uses tailored prompts to infer implicit hateful cues. It then introduces hyperedges to capture cross-modal-induced hate and applies a diversity-oriented hyperedge expansion strategy to account for different hate target groups. Finally, hypergraph convolution fuses diverse hateful cues, enhancing the exploration of cross-modal hate and targeting specific groups. Experimental results on two benchmark datasets show that our model achieves state-of-the-art performance in multimodal hate detection. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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LOFTune: A Low-Overhead and Flexible Approach for Spark SQL Configuration Tuning

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3528-3542页

作者： Li, Jiahui Ye, Junhao Mao, Yuren Gao, Yunjun Chen, Lu Zhejiang University School of Software Technology Ningbo315048 China Zhejiang University College of Computer Science Hangzhou310027 China

The query efficiency of Spark SQL is significantly impacted by its configurations. Therefore, configuration tuning has drawn great attention, and various automatic configuration tuning methods have been proposed. However, existing methods suffer from two issues: (1) high tuning overhead: they need to repeatedly execute the workloads several times to obtain the training samples, which is time-consuming;and (2) low throughput: they need to occupy resources like CPU cores and memory for a long time, causing other Spark SQL workloads to wait, thereby reducing the overall system throughput. These issues impede the use of automatic configuration tuning methods in practical systems which have limited tuning budget and many concurrent workloads. To address these issues, this paper proposes a Low-Overhead and Flexible approach for Spark SQL configuration Tuning, dubbed LOFTune. LOFTune reduces the tuning overhead via a sample-efficient optimization framework, which is proposed based on multi-task SQL representation learning and multi-armed bandit. Furthermore, LOFTune solves the low throughput issue with a recommendation-sampling-decoupled tuning framework. Extensive experiments validate the effectiveness of LOFTune. In the sampling-allowed case, LOFTune can save up to 90% of the workload runs comparing with the state-of-the-art methods. Besides, in the zero-sampling case, LOFTune can reduce up to 41.26% of latency. © 2025 IEEE.

关键词： Budget control

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Research on ant colony deep learning under combinatorial generation 3

Research on ant colony deep learning under combinatorial gen...

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3rd International Conference on Algorithms, Network, and Communication technology, ICANCT 2024

作者： Wei, Yong Computer and Software School Shenzhen Institute of Information Technology Shenzhen China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Combinatorial generation problems have a wide range of applications, and there have been over ten algorithms since the 1960s. As the combination size expands, it is difficult to find the optimal generating solution through enumeration in practice. If the path is defined as a step in two-dimensional coordinates that can only move in the positive direction of X or Y, each generation of combinatorial problem C(m+n, m) can be seen as a route generated by all paths starting from (0, 0) to (m, n) in two-dimensional coordinates. When there are too many generated solutions to be fully enumerated, ant colony algorithm can be used to simulate the foraging process of ant colonies, continuously exploring each route process, and assigning higher pheromones (weights) to paths with high fitness. Obtain weight information maps for each path through deep learning, enabling the algorithm to remember them. Finally, following the path with high weights can quickly obtain a high fitness route. Grouping is a more complex combinatorial problem, and the famous TTP problem is actually the grouping of course units. The article deduces the geometric model generated by the grouping problem, which is a circuit diagram formed by multiple rectangles connected end-to-end in two-dimensional coordinates. Complex combinatorial generation problems can be solved through ant colony deep learning on basic combinatorial generation geometric models, leaving behind memories. This method is simple, efficient, and feasible. © 2025 SPIE.

关键词： Ant colony optimization

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Finite-Time Sliding Mode Control of Vehicle Formation with Specified Performance

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Engineering Letters 2025年第3期33卷 620-629页

作者： Guo, Dongyang Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

The current urban intelligent transportation is in a rapid development stage, and coherence control of vehicle formations has important implications in urban intelligent transportation research. This article focuses on the problem of urban intelligent transportation planning, and proposes a new control method for vehicle formation regulation tracking performance control with bounded disturbances and model uncertainty, combining the multiple advantages of sliding mode control and finite time control and utilizing the universal approximation of neural network. This method can ensure that the vehicle formation can achieve the specified tracking performance and be stable in finite time instead of asymptotically stable. First, the third-order dynamics model and spacing strategy of the vehicle are given. The tracking performance of the vehicle formation is specified. Then, the control objective of the system is transformed by using the error transformation to obtain a new system. An improved sliding mode surface is designed for the transformed system. Furthermore, the universal approximation property of the neural network is utilised to overcome the parameter uncertainty in the system. The reconstruction error of neural network is handled by a robust term. In addition, the jittery vibration phenomenon of sliding mode control has been overcome. Finally, the finite-time stability of the system is analysed by constructing the Lyapunov function, simulations were performed to validate and compare with conventional control methods, simulation results show that the novel control method proposed in this paper is significantly faster than the conventional control method, validated the effectiveness of the method proposed in this paper. © 2025, International Association of Engineers. All rights reserved.

关键词： Lyapunov functions

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