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23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Shen, Chaochao Yang, Wenhua Pan, Minxue Zhou, Yu Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350319583

In the realm of collaborative software development, version control systems (VCS) like Git play an indispensable role, enabling concurrent development and facilitating seamless integration of disparate code contributions. Despite these benefits, merge conflicts resulting from simultaneous changes to identical code lines often pose significant challenges to the integration process. Addressing this challenge, our paper introduces a novel two-stage approach, termed as CHATMERGE, for resolving Git merge conflicts. CHATMERGE pioneers a unique strategy that employs machine learning to initially predict resolution strategies, and subsequently leverages a large language model, ChatGPT, to create resolutions for conflicts that necessitate complex resolution strategies. A series of comprehensive experiments validate CHATMERGE's efficacy, demonstrating its impressive alignment with historical manual resolutions and its superior performance relative to existing, publicly accessible tools. The paper further explores the influence of various classification algorithms and the prompt construction process for ChatGPT, providing further insights into the merge conflict resolution process. Moreover, to foster continued advancements in this area, CHATMERGE, along with its associated training and testing datasets, is made publicly available, offering a valuable resource for both developers and researchers. This work, therefore, provides both an innovative solution to merge conflict resolution and a strong foundation for future explorations in this domain. © 2023 IEEE.

关键词： Mergers and acquisitions

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DisVAE: Disentangled Variational Autoencoder for High-Quality Facial Expression Features 17

DisVAE: Disentangled Variational Autoencoder for High-Qualit...

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Wang, Tianhao Zhang, Mingyue Shang, Lin Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350345445

Facial expression feature extraction suffers from high inter-subject variations caused by identity-related personal attributes. The extracted expression features are consistently entangled with other identity-related features, which has an influence on related facial expression tasks such as recognition and editing. To achieve high-quality expression features, a Disentangled Variational Autoencoder (DisVAE) is proposed to disentangle expression and identity features. The identity features are removed from the facial features via facial image reconstruction firstly, and then the remaining features represent expression components. Extensive experiments on three public datasets have shown that the proposed DisVAE can effectively disentangle expression and identity features, and extract expression features without the interfere of identity attributes. The high-quality expression features improve the performance of facial expression recognition and can be well applied to facial expression editing. © 2023 IEEE.

关键词： Image reconstruction

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AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network 39

AutoAC: Towards Automated Attribute Completion for Heterogen...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Zhu, Guanghui Zhu, Zhennan Wang, Wenjie Xu, Zhuoer Yuan, Chunfeng Huang, Yihua Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350322279

Many real-world data can be modeled as heterogeneous graphs that contain multiple types of nodes and edges. Meanwhile, due to excellent performance, heterogeneous graph neural networks (GNNs) have received more and more attention. However, the existing work mainly focuses on the design of novel GNN models, while ignoring another important issue that also has a large impact on the model performance, namely the missing attributes of some node types. The handcrafted attribute completion requires huge expert experience and domain knowledge. Also, considering the differences in semantic characteristics between nodes, the attribute completion should be fine-grained, i.e., the attribute completion operation should be node-specific. Moreover, to improve the performance of the downstream graph learning task, attribute completion and the training of the heterogeneous GNN should be jointly optimized rather than viewed as two separate processes. To address the above challenges, we propose a differentiable attribute completion framework called AutoAC for automated completion operation search in heterogeneous GNNs. We first propose an expressive completion operation search space, including topology-dependent and topology-independent completion operations. Then, we propose a continuous relaxation schema and further propose a differentiable completion algorithm where the completion operation search is formulated as a bi-level joint optimization problem. To improve the search efficiency, we leverage two optimization techniques: discrete constraints and auxiliary unsupervised graph node clustering. Extensive experimental results on real-world datasets reveal that AutoAC outperforms the SOTA handcrafted heterogeneous GNNs and the existing attribute completion method. © 2023 IEEE.

关键词： Graph neural networks

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Distinguishing Sensitive and Insensitive Options for the Winograd Schema Challenge 28th

Distinguishing Sensitive and Insensitive Options for the ...

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

作者： Li, Dong Wang, Pancheng He, Liangliang Pang, Kunyuan Li, Shasha Tang, Jintao Wang, Ting College of Computer Science and Technology National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9783031306747

The Winograd Schema Challenge (WSC) is a popular benchmark for commonsense reasoning. Each WSC instance has a component that corresponds to the mention of the correct answer option of the two options in the context. We observe that the answers of many instances are insensitive to the options. In this paper, based on this observation, we propose an approach based on fine-tuning the pre-trained language model for WSC by distinguishing sensitive and insensitive options. First, we split WSC instances into option-sensitive and insensitive categories, and use option expanding and option masking strategies to weaken the options so that the model does not pay attention to options when they are insensitive during fine-tuning. Second, we treat the two categories as intermediate-task of each other, and use transfer learning to improve the performance. We fine-tune BERT-Large and T5-XXL with our approach on WINOGRANDE, a new dataset of WSC, and the experiment shows our method outperforms baselines by a large margin, achieving state-of-the-art, which indicates the effectiveness of our instance-distinguishing strategy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua Science and technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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FastVGG network and its application in automatic identification of traffic signs

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International Journal of Information and Communication technology 2022年第2期20卷 192-203页

作者： Gui, Kaige Gao, Shangbing Li, Xinyuan Lu, Zhonghe College of Computer and Software Engineering Huaiyin Institute of Technology Huaian223001 China Jiangsu Mobile Internet of Things Technology Engineering Laboratory Huaian223001 China

Traffic sign recognition is an important part of driver assistance systems. Because the types of traffic signs are complex and diverse, they are difficult to identify. Traditional recognition methods require manual extraction of features, which is difficult and inaccurate. This paper proposes a FastVGG network based on VGG neural network to extract the features of the target image to realise the recognition of traffic signs under different angles and illumination. In the method of this paper, the connection layer parameters and the number of network layers are moderately reduced, the merging step is increased, and the recognition speed is improved. When the parameter value is less than zero, the leaky ReLU is used to replace the activation function to solve the problem of neuron death. The experimental results of the German traffic sign recognition dataset (GTSRD) show that the algorithm can achieve accurate classification while increasing the speed. © 2022 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Traffic signs

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Discriminative Word Alignment Over Multiple Word Segmentations

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Chinese Journal of Electronics 2025年第2期23卷 263-270页

作者： Ning Xi Xinyu Dai Shujian Huang Jiajun Chen Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Conventional bilingual word alignment is conducted on sentence pairs with single word segmentation for languages such as Chinese, viz. Single-segmentation-based word alignment (SSWA). However, SSWA may run the risk of losing optimal word segmentation granularities or causing data sparseness in word alignment. This paper proposes Multiple-segmentation-based word alignment (MSWA). In MSWA, diverse and complementary knowledge in multiple word segmentations can be employed to lower the above risks in word alignment. Given $k$ word segmentations of a Chinese sentence, a skeleton segmentation is firstly constructed. The alignment between the skele-ton segmentation and the parallel English sentence is log-linearly modeled, where various features defined over multiple word segmentations are incorporated. The Viterbi alignment, the alignment with the highest score, is mapped back to $k$ word alignments based on $k$ segmentations respectively. Experimentally, MSWA outperformed SSWA on all $k$ segmentations in both alignment quality and translation performance.

关键词： Translation Viterbi algorithm Social sciences Education Medical services Skeleton

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AISE v2.0: Combining Loop Transformations: (Competition Contribution) 31th

AISE v2.0: Combining Loop Transformations: (Competition Cont...

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31st International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2025, which was held as part of the International Joint Conferences on Theory and Practice of software, ETAPS 2025

作者： Lin, Yao Chen, Zhenbang Wang, Ji College of Computer Science and Technology National University of Defense Technology Changsha China State Key Laboratory of Complex and Critical Software Environment National University of Defense Technology Changsha China

ISBN: (纸本)9783031906596

AISE is a C program verifier that synergizes symbolic execution and abstract interpretation. This year, AISE v2.0 introduces a loop transformation scheme based on recurrence analysis to handle programs involving nonlinear arithmetic. By combining loop transformations, AISE v2.0 achieved a score of 1031 and won first place in the ReachSafety-Loops category, demonstrating the effectiveness of the methods employed in AISE v2.0. © The Author(s) 2025.

关键词： C (programming language)

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Run-Time Assured Reinforcement Learning for Safe Spacecraft Rendezvous with Obstacle Avoidance 9th

Run-Time Assured Reinforcement Learning for Safe Spacecraft...

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9th International Symposium on Dependable software Engineering: Theories, Tools and Applications, SETTA 2023

作者： Xiao, Yingmin Yang, Zhibin Zhou, Yong Huang, Zhiqiu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing211106 China

ISBN: (纸本)9789819986637

Autonomous spacecraft rendezvous poses significant challenges in increasingly complex space missions. Recently, Reinforcement Learning (RL) has proven effective in the domain of spacecraft rendezvous, owing to its high performance in complex continuous control tasks and low online storage and computation cost. However, the lack of safety guarantees during the learning process restricts the application of RL to safety-critical control systems within real-world environments. To mitigate this challenge, we introduce a safe reinforcement learning framework with optimization-based Run-time Assurance (RTA) for spacecraft rendezvous, where the safety-critical constraints are enforced by Control Barrier Functions (CBFs). First, we formulate a discrete-time CBF to implement dynamic obstacle avoidance within uncertain environments, concurrently accounting for soft constraints of spacecraft including velocity, time, and fuel. Furthermore, we investigate the effect of RTA on reinforcement learning training performance in terms of training efficiency, satisfaction of safety constraints, control efficiency, task efficiency, and duration of training. Additionally, we evaluate our method through a spacecraft docking experiment conducted within a two-dimensional relative motion reference frame during proximity operations. Simulation and expanded test demonstrate the effectiveness of the proposed method, while our comprehensive framework employs RL algorithms for acquiring high-performance controllers and utilizes CBF-based controllers to guarantee safety. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Obstacle avoidance Run-time assurance Safe reinforcement learning Safety-critical system Spacecraft rendezvous

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DBKEM-AACS: a distributed key escrow model in blockchain with anonymous authentication and committee selection

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Science China(Information Sciences) 2023年第3期66卷 289-290页

作者： Axin XIANG Hongfeng GAO Youliang TIAN Liang WAN College of Computer Science and Technology Guizhou University Institute of Cryptography and Data Security Guizhou University Network and Information Management Center Guizhou University State Key Laboratory of Public Big Data Guizhou University Institute of Computer Software and Theory Guizhou University

Dear editor, Key security is of great practical significance and demand to guarantee the security of digital assets in the blockchain system. At present, users prefer to escrow their assets on centralized institutions, but this phenomenon has gone against the unique characteristics of decentralization and anonymity in the blockchain. Among them, the suspense incidents of assets lock or lost are enough to prove that the security of escrowed keys on the exchanges is questionable.

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