检索结果-内蒙古大学图书馆

23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Li, Changlong Zhu, Zongwei Wang, Chao Liu, Fangming Xu, Fei Sha, Edwin H.-M. Zhou, Xuehai School of Computer Science and Technology East China Normal University China Jianghuai Advance Technology Center Hefei230026 China MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application China School of Software Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Huazhong University of Science and Technology China Peng Cheng Laboratory China

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate association-rule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art. © 2025 FAST. All Rights Reserved.

关键词： Portable equipment

来源：评论

学校读者我要写书评

暂无评论

Temporal-Aware Multi-behavior Contrastive Recommendation 28th

Temporal-Aware Multi-behavior Contrastive Recommendation

引用

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

作者： Xuan, Hongrui Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing China National Engineering Laboratory for Integrated Aero-Space-GroundOcean Big Data Application Technology Nanjing China

ISBN: (纸本)9783031306716

Modeling various types of users’ interactions and jointly considering individual preferences from multiple perspectives, multi-behavior recommendation has attracted increasing attention recently. However, most existing multi-behavior recommendations only focus on the behavioral interaction itself, attempting to extract user preferences merely by modeling behaviors, while ignoring the properties of the interaction (e.g., the temporal information). Meanwhile, the introduction of multi-behavior information is bound to generate additional noise signals, which further affects the accuracy of recommendation. Thus, to tackle the above challenges, two aspects need to be explored: (1) How to fuse interaction attributes to construct unified cross-behavior dependencies while distinguishing subtle differences between individuals’ behaviors. (2) How to mitigate the effects of noise to a certain extent while preserving multi-behavioral information. In this work, we propose a Temporal-Aware Multi-Behavior Contrastive Learning (TMCL) framework to explore the patterns of multiple behaviors of individuals through temporal information, jointly capture the correlation of users’ preference evolution, and alleviate the impact of multi-behavior noise. Extensive experiments and ablation tests on the three datasets indicate our TMCL outperforms various state-of-the-art methods and verify the effectiveness of our model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： User profile

来源：评论

学校读者我要写书评

暂无评论

Refined multi-channel inception network for automobile insurance fraud identification based on attention mechanism and dense connection structure

Refined multi-channel inception network for automobile insur...

引用

2023 International Conference on Computer application and Information Security, ICCAIS 2023

作者： Wang, Deguang Zhang, Ting Li, Fengqi Tong, Ning Xu, Fengqiang School of Software Dalian Jiaotong University Liaoning Dalian116028 China School of Computer Communication and Engineering Dalian Jiaotong University Liaoning Dalian116028 China The Dalian Key Laboratory of Blockchain Technology and Application Liaoning Dalian116028 China

ISBN: (纸本)9781510675032

Automobile insurance fraud causes huge economic losses to insurance companies, detection technology has become an urgent research topic. However, feature selection manually is prone to subjective judgment, which results in a decline in recognition accuracy. To settle this problem, a refined multi-channel Inception network for automobile insurance fraud identification based on an attention mechanism and dense connection architecture is proposed. Firstly, we redesign the backbone structure of inception network to strengthen feature extraction ability. Secondly, attention mechanism is fused to inception module, enabling network to pay attention to important features. Furthermore, a dense connection structure is introduced to Inception network, which strengthens the transmission of features. Finally, this paper carries out mass experiments on a real-world "***" dataset. Experimental results reveal that our proposed approach achieves impressive performance on automobile insurance fraud identification tasks and can reach 98.12% accuracy, 98.19% precision and 98.12% recall and 98.12% f1_score, respectively. © 2024 SPIE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Leveraging Interactive Paths for Sequential Recommendation 28th

Leveraging Interactive Paths for Sequential Recommendation

引用

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

作者： Li, Aoran Zang, Yalei Wang, Yani Li, Bohan 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 Beijing China National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi’an China

ISBN: (纸本)9783031306716

Sequential recommendation systems dynamically predict the users’ next behaviors from chronological historical records to provide more accurate recommendations. However, in many real-world scenarios, recommenders find it difficult to make recommendations for cold-start users who have limited interactions with items. To address this issue, existing methods utilize auxiliary information to capture user preferences, which have achieved performance improvement to some extent. But with the gradual awareness of personal privacy, external information becomes difficult to obtain. Therefore, we develop a novel model called Path-based sequential Recommender for cold-start users (PathRec). PathRec exploits the rich sequential dependencies within the interactive paths to give reasonable recommendations for cold-start users without additional external information. Specifically, (i) PathRec constructs the global graph to learn the representations of the items. (ii) Then, PathRec elaborately selects the sampled paths to derive the underlying dependencies of the cold-start sequence. (iii) Finally, PathRec provides rich and complementary information to user-item interactions by strongly coupling the paths to give suitable recommendations. Extensive experiments on three datasets demonstrate the effectiveness of PathRec. In particular, our model can adapt to the cold-start users and has potential interpretability for recommendations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

An efficient group key agreement protocol with administrator 3

An efficient group key agreement protocol with administrator

引用

3rd International Conference on Internet of Things and Smart City, IoTSC 2023

作者： Fang, Qihao Zhang, Lei Zhang, Mingrui Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Science and Technology on Communication Security Laboratory Chengdu610041 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education Shanghai200062 China

ISBN: (纸本)9781510666375

Group key agreement technology is one of the most popular solution to establish a reliable secure channel between communicating entities of a group. Group key agreement protocol with administrator (GKAA) can handle a large number of group operations in sequence to resolve disorder problem and it is widely used in practical applications. However, in most of existing protocol, the administrator has to compute materials for each participant in the group which brings huge calculate overhead. In this paper, we propose an efficient group key agreement protocol with Administrator. Our GKAA protocol is able to perform group operation in a high efficacy way with the help of a key tree. It is also flexible and scalable since it suits for any network communication environment like IoT, video meeting, social applications, etc. Our protocol satisfies the properties including forward secrecy, PCS and key control resistance. © 2023 SPIE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Practical privacy-preserving mixing protocol for Bitcoin 26

Practical privacy-preserving mixing protocol for Bitcoin

引用

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

作者： Chang, Qianqian Xu, Lin Zhang, Lei Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Science and Technology on Communication Security Laboratory Chengdu610041 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education Shanghai200062 China

ISBN: (纸本)9798350331684

The privacy of Cryptocurrencies are of great concern in various fields. Researches has shown that pseudonyms, which are used in Bitcoin, only provide weak privacy. The privacy of users may be put at risk under deanonymization attacks. The exisiting schemes typically require a trusted-third party to achieve anonymity, however this usually faces a single-point fault. In addition, existing schemes suffer from high communication complexity and impracticality. This paper proposes a practical privacy-preserving mixing protocol for Bitcoin to achieve unlink-ability of input and output address of transactions. Compared to existing schemes, our protocol improves practicality. The communication complexity of our protocol is linearly related to the number of peers. Moreover, our protocol is scalable as it works not only for Bitcoin, but also for other cryptocurrencies. © 2023 IEEE.

关键词： Computational complexity

来源：评论

学校读者我要写书评

暂无评论

Attribute Enhanced Graph Attention Networks for Session-based Recommendation 11

Attribute Enhanced Graph Attention Networks for Session-base...

引用

11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Xu, Yidan Wang, Ning Wu, Jiaqi Xu, Shuai Li, Bohan Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Natl. Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9798350345346

Session-based recommendation (SBR) aims to predict the next item that the anonymous users will interact with based on their short behavior sequence. However, most of the current approaches towards SBR only focus on modeling users' interest preferences based on a series of items, ignoring extra item attributes, i.e., the brand attribute. This paper proposes a novel approach, called the Attribute Enhanced Graph Attention Networks for Session-based Recommendation (AGSR), using the brand attribute to better model user preferences. Specifically, (i) AGSR constructs the session graph of the current session to capture the user's session-level item embedding;(ii) AGSR constructs the brand global graph to capture the brand-level item embedding and brand embedding;(iii) Based on the learned node embeddings, AGSR apply attention layers to extract users' interest and brand preferences. Finally, AGSR makes the recommendation based on item features and user interest and brand preferences, so as to provide better recommendations. Extensive experiments on three real-world datasets demonstrate the effectiveness of our approach. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Ratchet: Retrieval Augmented Transformer for Program Repair

Ratchet: Retrieval Augmented Transformer for Program Repair

引用

International Symposium on software Reliability engineering (ISSRE)

作者： Jian Wang Shangqing Liu Xiaofei Xie Jingkai Siow Kui Liu Yi Li Singapore Management University Singapore Nanyang Technological University Singapore Software Engineering Application Technology Laboratory Huawei China

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

Automated Program Repair (APR) presents the promising momentum of releasing developers from the burden of manual debugging tasks by automatically fixing bugs in various ways. Recent advances in deep learning inspire many works in employing deep learning techniques to fixing buggy programs. However, several challenges remain unaddressed: (1) state-of-the-art fault localization techniques often require additional artifacts, such as bug-triggering test cases or bug reports. These artifacts are not always available in the early development phases; (2) Sequence-to-Sequence model-based APR often requires additional contexts with high quality to generate patches. Yet, it is challenging to identify high-quality contexts that are not common in *** this paper, with the redundancy assumption in program repair, we propose a dual deep learning-based APR tool, RATCHET, for localizing (RATCHET-FL) and repairing (Ratchet-PG) buggy programs. Ratchet-FL localizes buggy statements based on the feature learned by a simple BiLSTM model from the code, without any bug-triggering test cases or bug reports. Ratchet-PG relies on our proposed retrieval augmented transformer to learn the historical patches and generate patches for fixing bugs. We evaluate the effectiveness of Ratchet with in-the-lab DrRepair dataset and in-the-wild dataset Ratchet-DS (curated in this work). Our experimental results show that Ratchet outperforms state-of-the-art deep learning approaches on fault localization with 39.8-96.4% accuracy and patch generation with 18.4-46.4% repair accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Survey on Modern Code Review: Progresses, Challenges and Opportunities

arXiv

引用

arXiv 2024年

作者： Yang, Zezhou Gao, Cuiyun Guo, Zhaoqiang Li, Zhenhao Liu, Kui Xia, Xin Zhou, Yuming Harbin Institute of Technology Shenzhen China Software Engineering Application Technology Lab Huawei Hangzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Over the past decade, modern code review (MCR) has been deemed as a crucial practice of software quality assurance, which is applied to improve software quality and transfer development knowledge within a software team. Despite its importance, MCR is often a complicated and time-consuming activity for practitioners. In recent years, many studies that are dedicated to the comprehension and the improvement of MCR have been explored so that the MCR activity can be carried out more conveniently and efficiently. To provide researchers and practitioners a clear understanding of the current research status on MCR, this paper conducts a systematic literature review of the past years. Given the collected 231 surveyed papers, this paper makes the following five contributions: First, we analyze the research trends of related MCR studies. Second, we provide a taxonomy for the current MCR, encompassing both Improvement Techniques and Understanding Studies. Third, we present the concrete research progress of each novel MCR methodology and prototype tool. Fourth, we exploit the main empirical insights from empirical study and user study that are helpful to improve MCR. Finally, we sum up unsolved challenges and outline several possible research opportunities in the future. Copyright © 2024, The Authors. All rights reserved.

关键词： Computer software selection and evaluation

来源：评论

学校读者我要写书评

暂无评论

Ppt4j: Patch Presence Test for Java Binaries 24

Ppt4j: Patch Presence Test for Java Binaries

引用

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

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Huawei Software Engineering Application Technology Lab Hangzhou China The Hong Kong Polytechnic University Hong Kong School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9798400702174

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2024 ACM.

关键词： Open source software

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：