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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Xin, Yi Luo, Siqi Jin, Pengsheng Du, Yuntao Wang, Chongjun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783031306778

Mainstream approaches for unsupervised domain adaptation (UDA) learn domain-invariant representations to address the domain shift. Recently, self-training has been used in UDA, which exploits pseudo-labels for unlabeled target domains. However, the pseudo-labels can be unreliable due to distribution shifts between domains, severely impairing the model performance. To address this problem, we propose a novel self-training framework-Self-Training with Label-Feature-Consistency (ST-LFC), which selects reliable target pseudo-labels via label-level and feature-level voting consistency principle. The former means target pseudo-labels generated by a source-trained classifier and the latter means the nearest source-class to the target in feature space. In addition, ST-LFC reduces the negative effects of unreliable predictions through entropy minimization. Empirical results indicate that ST-LFC significantly improves over the state-of-the-arts on a variety of benchmark datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Entropy

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Grey-box Adversarial Attack on Communication in Multi-agent Reinforcement Learning 22

Grey-box Adversarial Attack on Communication in Multi-agent ...

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Ma, Xiao Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Although research on communication in multi-agent reinforcement learning (MARL) has achieved some progress, the vulnerability of the communication mechanism in MARL caused by adversarial communication messages generated by malicious agents has not been well investigated. Existing works about adversarial communication messages in MARL focus on the black-box scenario where the attacker cannot access any model information about the multi-agent system (MAS). But a more practical setting is the grey-box scenario where the attacker can access the model information about its controlled agent. To the best of our knowledge, there has not been any work investigating grey-box attacks on communication in MARL. In this paper, we propose the first grey-box attack method on communication in MARL, which is called victim-simulation based adversarial attack (VSA). At each timestep, the attacker simulates a victim attacked by other regular agents' communication messages and generates adversarial perturbations on its received communication messages. The aggregation of these perturbations is sent by the attacker to the regular agents through communication messages, which will induce non-optimal actions of the regular agents. Experimental results show that VSA can effectively degrade the performance of the MAS on Predator-Prey. The findings in this paper will make researchers aware of the grey-box attack in MARL. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

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Fix the Tests: Augmenting LLMs to Repair Test Cases with Static Collector and Neural Reranker 35

Fix the Tests: Augmenting LLMs to Repair Test Cases with Sta...

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35th IEEE International Symposium on software Reliability Engineering, ISSRE 2024

作者： Liu, Jun Yan, Jiwei Xie, Yuanyuan Yan, Jun Zhang, Jian State Key Laboratory of Computer Science Institute of Software China Chinese Academy of Sciences Technology Center of Software Engineering Institute of Software China China UCAS School of Intelligent Science and Technology Hangzhou Institute for Advanced Study China

ISBN: (纸本)9798350353884

During software evolution, it is advocated that test code should co-evolve with production code. In real development scenarios, test updating may lag behind production code changing, which may cause compilation failure or bring other troubles. Existing techniques based on pre-trained language models can be directly adopted to repair obsolete tests caused by such unsynchronized code changes, especially syntactic-related ones. However, the lack of task-oriented contextual information affects the repair accuracy on large-scale projects. Starting from an obsolete test, the key challenging task is precisely identifying and constructing Test-Repair-Oriented Contexts (TROCtxs) from the whole repository within a limited token *** this paper, we propose Synter (SYNtactic-breaking- changes-induced TEst Repair), a novel approach based on LLMs to automatically repair obsolete test cases via precise and concise TROCtxs construction. Inspired by developers' programming practices, we design three types of TROCtx: class context, usage context, and environment context. Given an obsolete test case to repair, Synter firstly collects the related code information for each type of TROCtx through static analysis techniques automatically. Then, it generates reranking queries to identify the most relevant TROCtxs, which will be taken as the repair-required key contexts and be input to the large language model for the final test *** evaluate the effectiveness of Synter, we construct a benchmark dataset that contains a set of obsolete tests caused by syntactic breaking changes. The experimental results show that Synter outperforms baseline approaches both on textual- and intent-matching metrics. With the augmentation of constructed TROCtxs, hallucinations are reduced by 57.1%. © 2024 IEEE.

关键词： Syntactics

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DPPS: A novel dual privacy-preserving scheme for enhancing query privacy in continuous location-based services

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Frontiers of computer Science 2023年第5期17卷 197-205页

作者： Long LI Jianbo HUANG Liang CHANG Jian WENG Jia CHEN Jingjing LI College of Cyber Security Jinan UniversityGuangzhou 510632China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Department of Computer Applications Guilin University of Technology at NanningNanning 530000China

Since smartphones embedded with positioning systems and digital maps are widely used,location-based services(LBSs)are rapidly growing in popularity and providing unprecedented convenience in people’s daily lives;however,they also cause great concern about privacy *** particular,location queries can be used to infer users’sensitive private information,such as home addresses,places of work and appointment ***,many schemes providing query anonymity have been proposed,but they typically ignore the fact that an adversary can infer real locations from the correlations between consecutive locations in a continuous *** address this challenge,a novel dual privacy-preserving scheme(DPPS)is proposed that includes two privacy protection ***,to prevent privacy disclosure caused by correlations between locations,a correlation model is proposed based on a hidden Markov model(HMM)to simulate users’mobility and the adversary’s prediction ***,to provide query probability anonymity of each single location,an advanced k-anonymity algorithm is proposed to construct cloaking regions,in which realistic and indistinguishable dummy locations are *** validate the effectiveness and efficiency of DPPS,theoretical analysis and experimental verification are further performed on a real-life dataset published by Microsoft,i.e.,GeoLife dataset.

关键词： location-based services privacy-preserving hidden Markov model k-anonymity query probability

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Knowledge Graph Driven Inference Testing for Question Answering software 24

Knowledge Graph Driven Inference Testing for Question Answer...

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44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Wang, Jun Li, Yanhui Chen, Zhifei Chen, Lin Zhang, Xiaofang Zhou, Yuming Nanjing University State Key Laboratory for Novel Software Technology China School of Computer Science and Engineering Nanjing University of Science and Technology China School of Computer Science and Technology Soochow University China

ISBN: (纸本)9798400702174

In the wake of developments in the field of Natural Language Processing, Question Answering (QA) software has penetrated our daily lives. Due to the data-driven programming paradigm, QA software inevitably contains bugs, i.e., misbehaving in real-world applications. Current testing techniques for testing QA software include two folds, reference-based testing and metamorphic testing. This paper adopts a different angle to achieve testing for QA software: we notice that answers to questions would have inference relations, i.e., the answers to some questions could be logically inferred from the answers to other questions. If these answers on QA software do not satisfy the inference relations, an inference bug is detected. To generate the questions with the inference relations automatically, we propose a novel testing method Knowledge Graph driven Inference Testing (KGIT), which employs facts in the Knowledge Graph (KG) as the seeds to logically construct test cases containing questions and contexts with inference relations. To evaluate the effectiveness of KGIT, we conduct an extensive empirical study with more than 2.8 million test cases generated from the large-scale KG YAGO4 and three QA models based on the state-of-the-art QA model structure. The experimental results show that our method (a) could detect a considerable number of inference bugs in all three studied QA models and (b) is helpful in retraining QA models to improve their inference ability. © 2024 ACM.

关键词： Knowledge graph

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Large-Scale Traffic Flow Forecast with Lightweight LLM in Edge Intelligence

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IEEE Internet of Things Magazine 2025年第1期8卷 12-18页

作者： Rong, Yi Mao, Yingchi He, Xiaoming Chen, Mingkai College of Computer and Software Hohai University China College of Internet of Things Nanjing University of Posts and Telecommunications China Nanjing University of Posts and Telecommunications Laboratory of Broadband Wireless Communication and Sensor Network Technology China

Large-scale traffic flow forecasting affiliated with the time is valuable for the management in Intelligent Transportation Systems (ITS). Recently, Large Language Models (LLMs) have shown the prominence on this issue. Unfortunately, the existing LLMs cannot forecast the entire road network, including two problems, i.e., (1) training the large-scale data generated by a road network on the central cloud can impose computational pressure. (2) LLMs fail to capture the spatiotemporal correlations in the road network. To overcome the challenges, we propose a novel architecture named Light-weight Spatio-temporal Generative Large Language Model on Edges (LSGLLM-E). Specifically, we first decompose the entire large-scale road network into several subparts, and deploy the Rode-side Unit (RSU) as an edge on each subpart, thus avoiding the computational pressure on the central cloud. In addition, we design an LLM-based method named Light-weight Spatio-temporal Generative Large Language Model (LSGLLM) to extract the spatiotemporal correlations, which compensates for the lack of spatiotemporal features. Finally, the experimental results illustrate that the LSGLLM-E is superior to some advanced baselines in terms of the accuracy and efficiency of the prediction. © 2025 IEEE.

关键词： Roads Cloud computing Correlation Training Predictive models Time series analysis Forecasting Edge computing Large language models Feature extraction

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Property Constrained Video Summarization via Regret Minimization

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SN computer Science 2024年第2期5卷 254页

作者： Xu, Yuyao Zheng, Jiping Tao, Yanxin Zhu, Kaiqin College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 211106 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210016 China

Video summarization has become one of the most effective solutions for quickly understanding a large amount of video data. Video properties such as importance, diversity, representativeness, and storyness have been widely adopted for summarization based on kinds of features of video frames. To fully exploit these properties, in this paper, we propose a property constrained video summarization framework to output fixed-size summaries based on the concept of regret minimization which is popular in the database community for solving multi-criteria decision-making problems. Our framework first uses representativeness property and effective techniques to generate a candidate frame set. Then, the candidate set is transformed into a multi-dimensional point set where dimensions are composed of entities of the importance property and the diversity property. Moreover, our framework runs the regret minimization query on the point set to output a summary. To further improve the quality of the output summary, the property storyness is exploited to add frames satisfying the size constraint. Experimental results on the benchmark datasets demonstrate the effectiveness and superiority of our framework. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Multiple properties Regret minimization Video summarization

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of computer Science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Developers’ Views on Commercial Involvement in OSS - A Survey from Three Projects

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IEEE Transactions on software Engineering 2025年第6期51卷 1818-1837页

作者： Qin, Mian Zhang, Yuxia Zhou, Minghui Wang, Zhe Li, Haoyang Liu, Hui Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University School of Computer Science Beijing China Ministry of Education Key Laboratory of High Confidence Software Technologies Beijing China Beihang University School of Humanities and Social Sciences Beijing China OpenInfra Foundation Shanghai China

Given the well-established merits of open source software (OSS), many profit-oriented companies actively participate in OSS communities, making significant contributions. Existing studies have predominantly focused on several advanced and specific questions regarding this phenomenon, mainly from the companies’ perspective, such as companies’ domination and withdrawal. A more basic and comprehensive understanding is missing, i.e., how OSS developers perceive such corporate engagement. Individual developers, including both volunteers and developers assigned by companies, are directly impacted by and have personal experiences with the consequences of commercial participation in OSS projects. This paper aims to bridge this gap by amplifying the voices of individual developers and providing valuable insights that have the potential to enhance companies’ participation in OSS projects. We conducted a survey involving developers from three OSS projects, i.e., Rust, OpenStack, and the Linux kernel, focusing on their attitudes and expectations regarding corporate involvement. We received 84 meaningful responses and analyzed their open-ended responses through thematic analysis. The results suggest that regardless of whether developers were paid or voluntary contributors, a prevailing attitude emerged – 67.9% of developers expressed a positive view of companies’ participation in OSS. The key idea behind their positive attitudes is perceiving commercial participation as a win-win for both the OSS community and companies. The Rust community remains more neutral when compared with the other two communities. We also surveyed and analyzed developers’ expectations of companies’ better participation, which can shed light on how OSS ecosystems can sustainably evolve with the companies involved. © 1976-2012 IEEE.

关键词： Machine oriented languages

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