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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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“Half of the Node Records Are Forged?”: The Problemof Node Records Forgery in Ethereum Network

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computer Modeling in Engineering & sciences 2024年第2期138卷 1713-1729页

作者： Yang Liu Zhiyuan Lin Yuxi Zhang Lin Jiang Xuan Wang School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen518055China Peng Cheng Laboratory Shenzhen518055China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055China

Ethereum, currently the most widely utilized smart contracts platform, anchors the security of myriad smartcontracts upon its own robustness. Its foundational peer-to-peer network facilitates a dependable node connectionmechanism, whereas an efficient data-sharing protocol constitutes as the bedrock of Blockchain network *** this paper, we propose NodeHunter, an Ethereum network detector implemented through the application ofsimulation technology, which is capable of aggregating all node records within the network and the interconnectednessbetween them. Utilizing this connection information, NodeHunter can procure more comprehensive insightsfor network status analysis compared to preceding detection methodologies. Throughout a three-month period ofunbroken surveillance of the Ethereum network, we obtained an excess of two million node records along with overone hundred million node acquaintances. Analysis of the gathered data revealed that an alarming 49% or more ofthese node records were maliciously forged.

关键词： Blockchain ethereum peer-to-peer networks node discovery protocol malicious behavior

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Git Merge Conflict Resolution Leveraging Strategy Classification and LLM 23

Git Merge Conflict Resolution Leveraging Strategy Classifica...

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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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Momentum Posterior Regularization for Multi-hop Dense Retrieval 31

Momentum Posterior Regularization for Multi-hop Dense Retrie...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xia, Zehua Wu, Yuyang Xia, Yiyun Nguyen, Cam-Tu State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science Nanjing University China School of Artificial Intelligence Nanjing University China

ISBN: (纸本)9798891761964

Multi-hop question answering (QA) often requires sequential retrieval (multi-hop retrieval), where each hop retrieves missing knowledge based on information from previous hops. To facilitate more effective retrieval, we aim to distill knowledge from a posterior retrieval, which has access to posterior information like an answer, into a prior retrieval used during inference when such information is unavailable. Unfortunately, current methods for knowledge distillation in one-time retrieval are ineffective for multi-hop QA due to two issues: 1) Posterior information is often defined as the response (i.e. the answer), which may not clearly connect to the query without intermediate retrieval;and 2) The large knowledge gap between prior and posterior retrievals makes existing distillation methods unstable, even resulting in performance loss. As such, we propose MoPo (Momentum Posterior Regularization) with two key innovations: 1) Posterior information of one hop is defined as a query-focus summary from the golden knowledge of the previous and current hops;2) We develop an effective training strategy where the posterior retrieval is updated along with the prior retrieval via momentum moving average method, allowing smoother and effective distillation. Experiments on HotpotQA and StrategyQA demonstrate that MoPo outperforms existing baselines in both retrieval and downstream QA tasks. © 2025 Association for Computational Linguistics.

关键词： Question answering

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MPGTSRN: Scene Text Image Super-Resolution Guided by Multiple Visual-Semantic Prompts 27th

MPGTSRN: Scene Text Image Super-Resolution Guided by Multip...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Mingjun Zhuang, Zeming Xu, Shuo Su, Feng State Key Laboratory for Novel Software Technology Nanjing University 163 Xianlin Road Nanjing China

ISBN: (纸本)9783031781247

Scene text image super-resolution (STISR) aims at enhancing the visual clarity of a low-resolution text image for human perception or tasks like text recognition. In recent STISR work, various visual and semantic clues of the text play a key role in recovering the details of the text, but the utilization of different clues and their interactions is still insufficient, which often results in distorted or blurred appearances of the reconstructed text. To address this problem, we propose a multi-prompt guided text image super-resolution network (MPGTSRN). Specifically, we introduce multiple visual prompts for the text and combine them with semantic features to comprehensively capture the diverse characteristics of the text. We then propose a recurrent reconstruction network integrating multiple visual-semantic prompts to enhance the representation of the text and yield a high-resolution text image. We further propose a cross-representation attention mechanism that utilizes the complementarity of different prompts to guide the reconstruction network to adaptively focus on salient parts of the text and effectively improves the text details. The experimental results show the superiority of our proposed MPGTSRN in the STISR task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multiple prompts Scene text image Super-resolution Visual-semantic clue

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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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Practical Non-Intrusive GUI Exploration Testing with Visual-based Robotic Arms 24

Practical Non-Intrusive GUI Exploration Testing with Visual-...

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

作者： Yu, Shengcheng Fang, Chunrong Du, Mingzhe Ling, Yuchen Chen, Zhenyu Su, Zhendong Nanjing University State Key Laboratory for Novel Software Technology China Eth Zurich Switzerland

ISBN: (纸本)9798400702174

Graphical User Interface (GUI) testing has been a significant topic in the software engineering community. Most existing GUI testing frameworks are intrusive and can only support some specific platforms, which are quite limited. With the development of dis-tinct scenarios, diverse embedded systems or customized operating systems on different devices do not support existing intrusive GUI testing frameworks. Some approaches adopt robotic arms to re-place the interface invoking of mobile apps under test and use computer vision technologies to identify GUI elements. However, some challenges remain unsolved with such approaches. First, existing approaches assume that GUI screens are fixed so that they cannot be adapted to diverse systems with different screen conditions. Second, existing approaches use XY-plane robotic arm system, which cannot flexibly simulate human testing operations. Third, existing approaches ignore the compatibility bugs of apps and only focus on the crash bugs. To sum up, a more practical approach is required for the non-intrusive scenario. In order to solve the remaining challenges, we propose a practi-cal non-intrusive GUI testing framework with visual-based robotic arms, namely ROBOTEST. ROBOTEST integrates a set of novel GUI screen and widget detection algorithm that is adaptive to detecting screens of different sizes and then to extracting GUI widgets from the detected screens. Then, a complete set of widely-used testing operations are applied with a 4-DOF robotic arm, which can more effectively and flexibly simulate human testing operations. During the app exploration, ROBOTEST integrates the specially designed Principle of Proximity-guided (PoP-guided) exploration strategy, which chooses close widgets of the previous operation targets to reduce the robotic arm movement overhead and improve exploration efficiency. Moreover, ROBOTEST can effectively detect some compat-ibility bugs beyond crash bugs with a GUI comparison on different devices of

关键词： software testing

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A fully connected polarization-entangled network via integrated spontaneous four-wave mixing engineering

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science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 72-79页

作者： Pingyu Zhu Yan Wang Yuxing Du Miaomiao Yu Kaikai Zhang Kun Wang Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China Hefei National Laboratory Hefei 230088China

Quantum communication is rapidly developing and is gradually being commercialized due to its technological *** dense communication links among multiple users in a scalable and efficient way is of great significance for realizing a large-scale quantum communication ***,we propose a novel scheme to construct a fully connected polarizationentangled network,utilizing the engineering of spontaneous four-wave mixings(SFWMs)and a path-polarization *** does not require active optical switches which limit the communication speed,or trusted nodes which lead to potential security *** required frequency channels in the network grow linearly with the number of *** experimentally demonstrate a six-user fully connected network with on-chip SFWM processes motivated by four *** user in the network receives a frequency channel,and all fifteen connections between the users are implemented *** work opens up a promising scheme to efficiently construct fully connected large-scale networks.

关键词： quantum network quantum entanglement nonlinear optics integrated optics

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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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Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

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Digital Communications and Networks 2024年第2期10卷 380-388页

作者： Renwan Bi Mingfeng Zhao Zuobin Ying Youliang Tian Jinbo Xiong Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China Faculty of Data Science City University of Macao999078MacaoChina State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in ***,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy *** solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement ***,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also ***,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy ***,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

关键词： Mobile edge crowdsensing Dynamic privacy measurement Personalized privacy threshold Privacy protection Reinforcement learning

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