检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Momentum Posterior Regularization for Multi-hop Dense Retrieval 31

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

GridDroid--An Effective and Efficient Approach for Android Repackaging Detection Based on Runtime Graphical User Interface

引用

Journal of computer science & technology 2022年第1期37卷 147-181页

作者： Jun Ma Qing-Wei Sun Chang Xu Xian-Ping Tao State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China Huatai Securities Co. Ltd.Nanjing 210019China

Repackaging brings serious threats to Android *** birthmark techniques are typically applied to detect repackaged *** based on apps'runtime graphical user interfaces(GUI)are effective,especially for obfuscated or encrypted ***,existing studies are time-consuming and not suitable for handling apps in large *** this paper,we propose an effective yet efficient dynamic GUI birthmark for Android ***,we run an app with automatically generated GUI events and dump its layout after each *** divide each dumped layout into a grid,count in each grid cell the vertices of boundary rectangles corresponding to widgets within the layout,and generate a feature vector to encode the *** layouts are merged at runtime,and finally we obtain a graph as the birthmark of the *** a pair of apps to be compared,we build a weighted bipartite graph from their birthmarks and apply a modified version of the maximum-weight-bipartite-matching algorithm to determine whether they form a repackaging pair(RP)or *** implement the proposed technique in a prototype,GridDroid,and apply it to detect RPs in three datasets involving 527 *** reports only six false negatives and seven false positives,and it takes GridDroid merely 20 microseconds on average to compare a pair of birthmarks.

关键词： Android apps dynamic software birthmark graphical user interface(GUI) repackaging detection

来源：评论

学校读者我要写书评

暂无评论

Classifying Authentic and Fake Anger Based on High-Performance LSTM Networks Model, Fuzzy Logic Model and Fully Connected Neural Network 4

Classifying Authentic and Fake Anger Based on High-Performan...

引用

4th International Conference on Advances in Electrical Engineering and computer Applications, AEECA 2023

作者： Chen, Kangyu Peng, Zhiyuan The University of Hong Kong Department of Computer Science Hong Kong Nanjing University State Key Laboratory For Novel Software Technology Nanjing China

ISBN: (纸本)9798350308082

Understanding human's emotion is a vital task in the computer science and machine learning. People could easily distinguish the genuine and posed anger while it could be difficult for computers to judge the authenticity of anger. In this paper, apart from the fully connected Neural Networks built in the previous research, methods of LSTM (Long short-term memory) Networks and Fuzzy logic classification are also built for telling the posed angers from genuine ones using limited data. Then the paper compared the performance of these three popular classification methods. Carefully designed modifications have been made on these models to increase the performance. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Global video object segmentation with spatial constraint module

引用

Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

来源：评论

学校读者我要写书评

暂无评论

Alignment efficient image-sentence retrieval considering transferable cross-modal representation learning

引用

Frontiers of computer science 2024年第1期18卷 77-91页

作者： Yang YANG Jinyi GUO Guangyu LI Lanyu LI Wenjie LI Jian YANG School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Computing Hong Kong Polytechnic UniversityHong Kong 100872China State Key Lab.for Novel Software Technology Nanjing UniversityNanjing 210094China 14th Research Institute of China Electronics Technology Group Corporation Nanjing 210094China guangyu.li2017@***

Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities,thereby to search similar instances in one modality according to the query from another modality in *** basic assumption behind these methods is that parallel multi-modal data(i.e.,different modalities of the same example are aligned)can be obtained in *** other words,the image-sentence cross-modal retrieval task is a supervised task with the alignments as ***,in many real-world applications,it is difficult to realign a large amount of parallel data for new scenarios due to the substantial labor costs,leading the non-parallel multi-modal data and existing methods cannot be used *** the other hand,there actually exists auxiliary parallel multi-modal data with similar semantics,which can assist the non-parallel data to learn the consistent ***,in this paper,we aim at“Alignment Efficient Image-Sentence Retrieval”(AEIR),which recurs to the auxiliary parallel image-sentence data as the source domain data,and takes the non-parallel data as the target domain *** single-modal transfer learning,AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel ***,AEIR learns the image-sentence consistent representations in source domain with parallel data,while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial ***,we can effectively learn the consistent representations for target domain considering both the structure and semantic ***,extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.

关键词： image-sentence retrieval transfer learning semantic transfer structure transfer

来源：评论

学校读者我要写书评

暂无评论

Efficient Nearest Neighbor Emotion Classification with BERT-whitening

Efficient Nearest Neighbor Emotion Classification with BERT-...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Yin, Wenbiao Shang, Lin State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Retrieval-based methods have been proven effective in many NLP tasks. Previous methods use representations from the pre-trained model for similarity search directly. However, the sentence representations from the pre-trained model like BERT perform poorly in retrieving semantically similar sentences, resulting in poor performance of the retrieval-based methods. In this paper, we propose KNN-EC, a simple and efficient non-parametric emotion classification (EC) method using nearest neighbor retrieval. We use BERT-whitening to get better sentence semantics, ensuring that nearest neighbor retrieval works. Meanwhile, BERT-whitening can also reduce memory storage of datastore and accelerate retrieval speed, solving the efficiency problem of the previous methods. KNN-EC average improves the pre-trained model by 1.17 F1-macro on two emotion classification datasets. © 2022 Association for Computational Linguistics.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Evaluation of TCP Variants on Dynamic UAV Networks 26

Evaluation of TCP Variants on Dynamic UAV Networks

引用

26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Fei, Junkai Zhu, Xiaojun Zhang, Ruyan Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Nanjing University State Key Laboratory of Novel Software Technology Nanjing China

ISBN: (纸本)9798350331684

UAV networks are wireless networks composed of dynamic UAVs, characterized by a complex and constantly changing wireless environment. A recent TCP variant, BBR, has been verified to significantly improve packet loss resistance when compared to traditional TCP variants on wired networks. In this paper, we evaluate multiple TCP variants on dynamic UAV networks, taking into account wireless dynamics caused by factors such as mobility, malicious attacks, and traffic from other legitimate users. Our results demonstrate that BBR does not always perform better than other TCP variants in a wireless environment, as opposed to wired networks. Depending on the cause of wireless environment dynamics, other TCP variants may work more effectively. Our findings suggest that no single TCP variant consistently outperforms others in UAV networks. Therefore, when selecting a TCP variant, careful consideration of the different UAV missions is necessary. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Introduction to the Special Issue on Formal Methods and Models for System Design

引用

ACM Transactions on Embedded Computing Systems 2025年第3期24卷 1-3页

作者： Brandt, Jens Saha, Indranil Zhang, Lijun Hochschule Niederrhein Krefeld Germany Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

来源：评论

学校读者我要写书评

暂无评论

Generalized Image Embedding for Multi-Domain Image Retrieval 26

Generalized Image Embedding for Multi-Domain Image Retrieval

引用

26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xiao, Boao Wu, Siyuan He, Xin Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University of Posts and Telecommunications School of Computer Science China

ISBN: (纸本)9798350331684

Image embedding, being a fundamental task in computer vision, plays a crucial role in various downstream tasks such as image retrieval. Widely adopted in e-commerce and social media collaboration, image retrieval benefits greatly from representations learned by the embedding model. However, conventional embedding models are often trained on a single domain, leading to inadequate performance in the multi-domain scenario. To address this challenge, we introduce a generalized image embedding model designed for multi-domain image retrieval. The proposed method employs a contrastively learned Vision Transformer and a carefully crafted training scheme to enhance domain generalization capability. Our theoretical analysis and experimental results, conducted on a large-scale, real-world multi-domain image retrieval dataset, demonstrate the superiority of the proposed method over existing embedding models in terms of both accuracy and domain generalization capability. © 2023 IEEE.

关键词： Image retrieval

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：