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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xu, Tianyu Chen, Xianzhang Wu, Changze Wang, Jiapin Zheng, Rongwei Liu, Duo Tan, Yujuan Ren, Ao Li, Jian College of Computer Science Chongqing University Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350319934

data Distribution Service (DDS) is a widely-used middleware for data transmission in distributed real-time applications, such as autonomous vehicles and robotics. However, the communication of existing DDS middlewares generally relies on the network stacks of the operating system, which brings long latency to the real-time applications. In this paper, we propose a DPDK-based DDS, dubbed 3DS, to improve the performance of communication of DDS middlewares. First, 3DS reduces the latency of communication between distributed real-time applications by avoiding the latency of kernel. Second, 3DS reduces the overhead of dynamic memory allocation and deallocation by a reserved memory pool. Finally, 3DS adopts a thread-exclusive CPU model to reduce the resource contention between threads. We implement and evaluate 3DS on real systems running Linux. The experimental results show that 3DS reduces 44% of the communication latency over FastDDS, which is the state-of-the-art DDS middleware used by ROS2. Moreover, the throughput of data transfer in 3DS can be 1.51 times that of FastDDS. © 2022 IEEE.

关键词： data transfer

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A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making

A Complex Belief Jensen-Shannon Divergence in Complex Eviden...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Gao, Linlu Yin, Mingrui Xiao, Fuyuan Cao, Zehong The School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Mawson Lakes Campus University of South Australia Stem Adelaide Australia

ISBN: (纸本)9798350316308

Effective target detection and recognition are essential in remote sensing image field, and complex evidence theory (CET) is widely used for this purpose. However, measuring conflict between complex basic belief assignments (CBBAs) in CET is challenging. This study proposes a complex belief Jensen-Shannon divergence based on the complex Pignistic transformation to measure conflict, accounting for quantum interference effects in CBBAs. We analyze the properties of the CBJS divergence, including boundedness, nondegeneracy, and symmetry. Numerical examples are presented to show the effectiveness of the conflict measure method and the potential of improving the robustness of intelligent interpretation system. Moreover, an algorithm for decision-making is presented and applied in pattern recognition illustrating its superiority. © 2023 IEEE.

关键词： Decision making

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Weak Signal Detection based on Beta Divergence 24

Weak Signal Detection based on Beta Divergence

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Liu, Jiahao Liu, Haoxuan Pu, Wenqiang Zhou, Rui You, Ming-Yi Shi, Qingjiang Science and Technology on Communication Information Security Control Laboratory Jiaxing China The Chinese University of HongKong Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China

ISBN: (纸本)9781665496261

Among different electromagnetic spectrum monitoring tasks, a fundamental one is detecting the presence of weak communication signal under non-cooperative setting. Various detection methods have been studied in the literature. These detection methods usually construct a proper hypothesis testing metric to distinguish the signal-plus-noise and noise-only signals. Information entropy based metric has recently been studied and shown its superiority over traditional energy based metrics. We find that it can be explained as a realization of the general Kullback-Leibler divergence. Inspired by that, in this paper, we propose to detect the signal using a statistic divergence called β-divergence. Different choices of β will lead to several well known divergences, e.g., Itakura-Saito divergence and Euclidean distance. We study its detection performance for non-cooperative signal sources. Both simulation and real experiments demonstrate that β-divergence can improve detection performance for various modulation types of communication signals. © 2023 IEEE.

关键词： Cooperative communication

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OVERSAMPLING BY A CONSTRAINT-BASED CAUSAL NETWORK IN MEDICAL IMBALANCED data CLASSIFICATION

OVERSAMPLING BY A CONSTRAINT-BASED CAUSAL NETWORK IN MEDICAL...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Luo, Hao Liao, Jun Yan, Xuewen Liu, Li School of Big Data and Software Engineering Chongqing University Chongqing400044 China

ISBN: (纸本)9781665438643

A key challenge of oversampling in medical imbalanced data classification is that the generation of new minority samples often neglects rich causal dependencies among features, with each being responsible for disease diagnosis. This leads us to define a constraint-based approach that generates new samples by explicitly discovering and leveraging the inherent local causal variability of features under a global view. Our approach employs causal Markov property to construct a causal network that explicitly characterizes these unique causal configurations of a particular disease as a variable number of nodes and links. By perturbing those learned causal features from majority class, we synthesize new samples in the territory of minority space. An additional sample selection estimator is introduced to choose the most representative samples. Empirical evaluations on four medical datasets suggest our approach significantly outperforms the state-of-the-art methods. © 2021 IEEE

关键词： Classification (of information)

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Evaluating the Generalizability of LLMs in Automated Program Repair

arXiv

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arXiv 2025年

作者： Li, Fengjie Jiang, Jiajun Sun, Jiajun Zhang, Hongyu College of Intelligence and and Computing Tianjin University Tianjin China School of Big Data and Software Engineering Chongqing University Chongqing China

LLM-based automated program repair methods have attracted significant attention for their state-of-the-art performance. However, they were primarily evaluated on a few well-known datasets like Defects4J, raising questions about their effectiveness on new datasets. In this study, we evaluate 11 top-performing LLMs on DEFECTS4J-TRANS, a new dataset derived from transforming Defects4J while maintaining the original semantics. Results from experiments on both Defects4J and DEFECTS4J-TRANS show that all studied LLMs have limited generalizability in APR tasks, with the average number of correct and plausible patches decreasing by 49.48% and 42.90%, respectively, on DEFECTS4J-TRANS. Further investigation into incorporating additional repair-relevant information in repair prompts reveals that, although this information significantly enhances the LLMs’ capabilities (increasing the number of correct and plausible patches by up to 136.67% and 121.82%, respectively), performance still falls short of their original results. This indicates that prompt engineering alone is insufficient to substantially enhance LLMs’ repair capabilities. Based on our study, we also offer several recommendations for future research. © 2025, CC BY-NC-SA.

关键词： Repair

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Differentially Private Distance Query with Asymmetric Noise

arXiv

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arXiv 2025年

作者： Sheng, Weihong Chen, Jiajun Hu, Chunqiang Cai, Bin Han, Meng Yu, Jiguo School of Big Data & Software Engineering Chongqing University Chongqing400044 China Binjiang Institute Zhejiang University China Big Data Institute Qilu University of Technology Shandong Jinan250353 China

With the growth of online social services, social information graphs are becoming increasingly complex. Privacy issues related to analyzing or publishing on social graphs are also becoming increasingly serious. Since the shortest paths play an important role in graphs, privately publishing the shortest paths or distances has attracted the attention of researchers. Differential privacy (DP) is an excellent standard for preserving privacy. However, existing works to answer the distance query with the guarantee of DP were almost based on the weight private graph assumption, not on the paths themselves. In this paper, we consider edges as privacy and propose distance publishing mechanisms based on edge DP. To address the issue of utility damage caused by large global sensitivities, we revisit studies related to asymmetric neighborhoods in DP with the observation that the distance query is monotonic in asymmetric neighborhoods. We formally give the definition of asymmetric neighborhoods and propose Individual Asymmetric Differential Privacy with higher privacy guarantees in combination with smooth sensitivity. Then, we introduce two methods to efficiently compute the smooth sensitivity of distance queries in asymmetric neighborhoods. Finally, we validate our scheme using both real-world and synthetic datasets, which can reduce the error to 0.0862. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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SynC: A Dense Retrieval Method based on Syntactical Contrastive Learning

SynC: A Dense Retrieval Method based on Syntactical Contrast...

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International Joint Conference on Neural Networks (IJCNN)

作者： Hongjin Tao Jun Zeng Yang Yu Ziwei Wang Xiaolin Hu School of Big Data and Software Chongqing University Chogqing China Innovation Center of Industrial Big-Data National Engineering Laboratory for Industrial Big-Data Application Technology Chogqing China

Recently, dense retrieval method has significantly outperformed sparse retrieval technology. It becomes a main-stream approach of relevant passage retrieving task. Dense retrieval tasks encode query and passage into dense representation space and apply contrastive learning to obtain more representative vectors, then retrieve the most similar passage by the inner product of the dense vectors. However, previous achievements rely on extremely large batch size, epoch and large-scaled pre-trained models, which results in tremendous computational resource consumption. Also, the expensive equipment prerequisite and low training efficiency constrain the development of dense retrieval community. Therefore, we present an alternative solution to improve training efficiency and quality by syntactical contrastive learning methods with specially designed masking strategy. To alleviate the computational consumption problem, this paper proposes query-based and passage-based masking strategies to obtain syntactical-isolated representations. Besides, instead of only considering query-to-passage similarity while conducting contrastive learning, we additionally consider query-to-query and passage-to-passage similarity when training the dual-encoder retriever. The experiments show that the proposed approach achieved competitive results in small batch size and epoch comparing to previous state-of-the-art dense retrieval methods, and also to strong baseline.

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CONTEXT DRIVEN NETWORK WITH BAYES FOR WEAKLY SUPERVISED TEMPORAL ACTION LOCALIZATION

CONTEXT DRIVEN NETWORK WITH BAYES FOR WEAKLY SUPERVISED TEMP...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Yu, Jiaruo Ge, Yongxin Li, Ziqiang Chen, Zhongming Qin, Xiaolei School of Big Data and Software Engineering Chongqing University Chongqing 400030 China

ISBN: (纸本)9781665438643

Weakly supervised temporal action localization (WTAL) aims to detect action instances from untrimmed videos. It may cause two problems, namely action incompleteness and background disturbance, due to only video-level class labels given. In this paper, we propose a context driven network with Bayes to alleviate the two problems, in which an attention module is used to predict coarse probability for each snippet, and then a Bayesian refinement module is designed to refine the coarse results by capturing the relationship between context snippets. Finally, the coarse and refined probabilities are combined as the inputs of the classifier for training. Quantitative and qualitative studies on two benchmark datasets, i.e., THUMOS'14 and ActivityNet 1.2, demonstrate that the proposed approach exceeds state-of-the-art methods. © 2021 IEEE

关键词： Computer vision

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Sparse Aggregation-Based Channel Estimation For Massive Mimo Systems With Decentralized Baseband Processing 48

Sparse Aggregation-Based Channel Estimation For Massive Mimo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xu, Yanqing Song, Enbin Shi, Qingjiang Chang, Tsung-Hui The Chinese University of Hong Kong School of Science & Engineering Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Sichuan University College of Mathematics Chengdu610064 China Tongji University School of Software Engineering Shanghai201804 China

ISBN: (纸本)9781728163277

To cope with the bottlenecks of the high computational complexity and excessive inter-connection communication in the conventional centralized baseband processing architecture, the decentralized baseband processing (DBP) architecture has been proposed, where the antennas are partitioned into multiple clusters, each connected to a local baseband unit (BBU). In this paper, we are interested in the distributed channel estimation (DCE) method under such DBP architecture, which is rarely studied in the literature. Our goal is to devise a DCE algorithm that can perform as well as the centralized scheme but with a small inter-connection communication cost. Specifically, based on the low-complexity diagonal minimum mean square error channel estimator, we propose an aggregate-then-estimate based DCE algorithm. In contrast to the existing DCE algorithm which requires iterative information exchanges among BBUs, our algorithm only requires one round-trip communication between the nodes. Experiment results are presented to demonstrate the efficacy of the proposed DCE algorithm. © 2023 IEEE.

关键词： channel estimation decentralized baseband processing Massive MIMO

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Evaluating the Generalizability of LLMs in Automated Program Repair

Evaluating the Generalizability of LLMs in Automated Program...

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IEEE/ACM International Conference on software engineering: New Ideas and Emerging Technologies Results Track (ICSE-NIER)

作者： Fengjie Li Jiajun Jiang Jiajun Sun Hongyu Zhang College of Intelligence and and Computing Tianjin University Tianjin China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331537111

ISBN: (纸本)9798331537128

关键词： Semantics Maintenance engineering Prompt engineering software engineering

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