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DeepSwarm:towards swarm deep learning with bi-directional optimization of data acquisition and processing

Frontiers of computer science 2025年第3期19卷 125-127页

作者： Sicong LIU Bin GUO Ziqi WANG Lehao WANG Zimu ZHOU Xiaochen LI Zhiwen YU School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Data Science City University of Hong KongHong Kong 999077China College Of Computer Science And Technology Harbin Engineering UniversityHarbin 150001China

1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.

关键词： drone swarm classification efficient local data processing data processing deep learning dl device deep learning bi-directional optimization iot devices swarm deep learning

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Multi-Task Visual Semantic Embedding Network for Image-Text Retrieval

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Journal of computer science & technology 2024年第4期39卷 811-826页

作者： Xue-Yang Qin Li-Shuang Li Jing-Yao Tang Fei Hao Mei-Ling Ge Guang-Yao Pang School of Computer Science and Technology Dalian University of TechnologyDalian 116024China School of Computer Science Shaanxi Normal UniversityXi’an 710119China School of Computer Engineering Weifang UniversityWeifang 261061China Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou UniversityWuzhou 543002 China

Image-text retrieval aims to capture the semantic correspondence between images and texts,which serves as a foundation and crucial component in multi-modal recommendations,search systems,and online *** mainstream methods primarily focus on modeling the association of image-text pairs while neglecting the advantageous impact of multi-task learning on image-text *** this end,a multi-task visual semantic embedding network(MVSEN)is proposed for image-text ***,we design two auxiliary tasks,including text-text matching and multi-label classification,for semantic constraints to improve the generalization and robustness of visual semantic embedding from a training ***,we present an intra-and inter-modality interaction scheme to learn discriminative visual and textual feature representations by facilitating information flow within and between ***,we utilize multi-layer graph convolutional networks in a cascading manner to infer the correlation of image-text *** results show that MVSEN outperforms state-of-the-art methods on two publicly available datasets,Flickr30K and MSCOCO,with rSum improvements of 8.2%and 3.0%,respectively.

关键词： image-text retrieval cross-modal retrieval multi-task learning graph convolutional network

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Local Fine-grained Visual Tracking

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IEEE Transactions on Multimedia 2025年 27卷 3426-3436页

作者： Wu, Jingjing Sun, Yifan Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Baidu China

This paper introduces a novel local fine-grained visual tracking task, aiming to precisely locate arbitrary local parts of objects. This task is motivated by our observation that in many realistic scenarios, the user demands to track a local part instead of a holistic object. However, the absence of an evaluation dataset and the distinctive characteristics of local fine-grained targets present extra challenges in conducting this research. To tackle these issues, firstly, this paper constructs a local fine-grained tracking (LFT) dataset to evaluate the tracking performance for local fine-grained targets. Secondly, this paper designs a cutting-edge solution to handle the challenges posed by properties of local objects, including ambiguity and high-proportion backgrounds. It consists of a hierarchical adaptive mask mechanism and foreground-background differentiated learning. The former adaptively searches for and masks ambiguity, which drives the network to concentrate on the local target instead of the holistic objects. The latter is constructed to distinguish foreground and background in an unsupervised manner, which is beneficial to mitigate the impacts of high-proportion backgrounds. Extensive analytic experiments are performed to verify the effectiveness of each submodule in the proposed fine-grained tracker. Our datasets and code will be publicly available soon: https://***/hfutwujingjing/local-fine-grained-tracking. © 1999-2012 IEEE.

关键词： Coarse-grained modeling

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AN ITERATIVE TWO-GRID METHOD FOR STRONGLY NONLINEAR ELLIPTIC BOUNDARY VALUE PROBLEMS

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Journal of Computational Mathematics 2025年第3期43卷 673-689页

作者： Jiajun Zhan Lei Yang Xiaoqing Xing Liuqiang Zhong School of Computer Science and Engineering Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SAR 999078China School of Mathematical Sciences South China Normal UniversityGuangzhou 510631China

We design and analyze an iterative two-grid algorithm for the finite element discretizations of strongly nonlinear elliptic boundary value problems in this *** propose an iterative two-grid algorithm,in which a nonlinear problem is first solved on the coarse space,and then a symmetric positive definite problem is solved on the fine *** main contribution in this paper is to establish a first convergence analysis,which requires dealing with four coupled error estimates,for the iterative two-grid *** also present some numerical experiments to confirm the efficiency of the proposed algorithm.

关键词： Iterative two-grid method Convergence Strongly nonlinear elliptic problems

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

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computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

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A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

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Digital Communications and Networks 2024年第2期10卷 481-497页

作者： Sheng Hao Huyin Zhang Fei Yang Chenghao Li Jing Wang School of Computer Science Central China Normal UniversityWuhan430079PR China Hubei Key Laboratory of intelligent Robot(Wuhan Institute of Technology) Wuhan430079PR China School of Computer Science Wuhan UniversityWuhan430079China School of Information Science and Engineering Shandong Normal UniversityJinan250000China School of Computer Science Hubei University of TechnologyWuhan430079China

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware *** research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)*** designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network *** solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 ***,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,*** on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC *** the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into ***,we prove the proposed analytical model has the ***,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

关键词： Hybrid power-line/Visible light communication (HPVC)networks MAC protocol CSMA/CA IEEE 802.15.7 IEEE 1901 Performance analysis

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Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1513-1528页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Ma, Xingjun Qi, Peng Ding, Zhijun Jin, Jiong Tongji University School of Computer Science and Technology Shanghai201804 China Ant Group Shanghai200122 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Fudan University School of Computer Science Shanghai200438 China Tongji University Department of Control Science and Engineering Shanghai201804 China Swinburne University of Technology School of Science Computing and Engineering Technologies Melbourne3122 Australia

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this article, we propose a hypergraph learning framework named learning from heterogeneity (LFH) that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterward, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with twelve state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experimental results demonstrate a significant performance gain (an average of 12.9% in node classification and 12.8% in link prediction) compared with recent state-of-the-art methods. © 2020 IEEE.

关键词： Contrastive Learning

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Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios

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Journal of Electronic science and technology 2025年第2期23卷 1-18页

作者： Zarina Kutpanova Mustafa Kadhim Xu Zheng Nurkhat Zhakiyev Department of Computer Engineering Astana IT UniversityAstana010000Kazakhstan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Department of Computational and Data Science Astana IT UniversityAstana010000Kazakhstan

Unmanned aerial vehicles(UAVs)are widely used in situations with uncertain and risky areas lacking network *** natural disasters,timely delivery of first aid supplies is *** UAVs face risks such as crashing into birds or unexpected *** systems with parachutes risk dispersing payloads away from target *** objective here is to use multiple UAVs to distribute payloads cooperatively to assigned *** civil defense department must balance coverage,accurate landing,and flight safety while considering battery power and *** Q-network(DQN)models are commonly used in multi-UAV path planning to effectively represent the surroundings and action *** strategies focused on advanced DQNs for UAV path planning in different configurations,but rarely addressed non-cooperative scenarios and disaster *** paper introduces a new DQN framework to tackle challenges in disaster *** considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return.A new DQN model is developed,which incorporates the battery life,safe flying distance between UAVs,and remaining delivery points to encode surrounding hazards into the state space and ***,a unique reward system is created to improve UAV action sequences for better delivery coverage and safe *** experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance.

关键词： Deep Q-network First aid delivery Multi-UAV path planning Reinforcement learning Unmanned aerial vehicle(UAV)

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