Tsinghua Science and Technology最新文献_第4页

FSRPCL: Privacy-Preserve Federated Social Relationship Prediction with Contrastive Learning 基于对比学习的隐私保护联合社会关系预测

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-03-03 DOI: 10.26599/TST.2024.9010077

Hanwen Liu;Nianzhe Li;Huaizhen Kou;Shunmei Meng;Qianmu Li

{"title":"FSRPCL: Privacy-Preserve Federated Social Relationship Prediction with Contrastive Learning","authors":"Hanwen Liu;Nianzhe Li;Huaizhen Kou;Shunmei Meng;Qianmu Li","doi":"10.26599/TST.2024.9010077","DOIUrl":"https://doi.org/10.26599/TST.2024.9010077","url":null,"abstract":"Cross-Platform Social Relationship Prediction (CPSRP) aims to utilize users' data information on multiple platforms to enhance the performance of social relationship prediction, thereby promoting socio-economic development. Due to the highly sensitive nature of users' data in terms of privacy, CPSRP typically introduces various privacy-preserving mechanisms to safeguard users' confidential information. Although the introduction mechanism guarantees the security of the users' private information, it tends to degrade the performance of the social relationship prediction. Additionally, existing social relationship prediction schemes overlook the interdependencies among items invoked in a user behavior sequence. For this purpose, we propose a novel privacy-preserve Federated Social Relationship Prediction with Contrastive Learning framework called FSRPCL, which is a multi-task learning framework based on vertical federated learning. Specifically, the users' rating information is perturbed with a bounded differential privacy technology, and then the users' sequential representation information acquired through Transformer is applied for social relationship prediction and contrastive learning. Furthermore, each client uploads their respective weight information to the server, and the server aggregates the weight information and distributes it purposes to each client for updating. Numerous experiments on real-world datasets prove that FSRPCL delivers exceptional performance in social relationship prediction and privacy preservation, and effectively minimizes the impact of privacy-preserving technology on social relationship prediction accuracy.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 4","pages":"1762-1781"},"PeriodicalIF":6.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Causality-Based Contrastive Incremental Learning Framework for Domain Generalization 基于因果关系的领域泛化对比增量学习框架

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-03-03 DOI: 10.26599/TST.2024.9010072

Xin Wang;Qingjie Zhao;Lei Wang;Wangwang Liu

{"title":"Causality-Based Contrastive Incremental Learning Framework for Domain Generalization","authors":"Xin Wang;Qingjie Zhao;Lei Wang;Wangwang Liu","doi":"10.26599/TST.2024.9010072","DOIUrl":"https://doi.org/10.26599/TST.2024.9010072","url":null,"abstract":"Learning domain-invariant feature representations is critical to alleviate the distribution differences between training and testing domains. The existing mainstream domain generalization approaches primarily pursue to align the across-domain distributions to extract the transferable feature representations. However, these representations may be insufficient and unstable. Moreover, these networks may also undergo catastrophic forgetting because the previous learned knowledge is replaced by the new learned knowledge. To cope with these issues, we propose a novel causality-based contrastive incremental learning model for domain generalization, which mainly includes three components: (1) intra-domain causal factorization, (2) inter-domain Mahalanobis similarity metric, and (3) contrastive knowledge distillation. The model extracts intra and inter domain-invariant knowledge to improve model generalization. Specifically, we first introduce a causal factori-zation to extract intra-domain invariant knowledge. Then, we design a Mahalanobis similarity metric to extract common inter-domain invariant knowledge. Finally, we propose a contrastive knowledge distillation with exponential moving average to distill model parameters in a smooth way to preserve the previous learned knowledge and mitigate model forgetting. Extensive experiments on several domain generalization benchmarks prove that our model achieves the state-of-the-art results, which sufficiently show the effectiveness of our model.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 4","pages":"1636-1647"},"PeriodicalIF":6.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908663","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Privacy Policy Text Compliance Reasoning Framework with Large Language Models for Healthcare Services 医疗保健服务的隐私策略文本遵从性推理框架与大型语言模型

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-03-03 DOI: 10.26599/TST.2024.9010089

Jintao Chen;Fan Wang;Shengye Pang;Mingshuai Chen;Meng Xi;Tiancheng Zhao;Jianwei Yin

{"title":"A Privacy Policy Text Compliance Reasoning Framework with Large Language Models for Healthcare Services","authors":"Jintao Chen;Fan Wang;Shengye Pang;Mingshuai Chen;Meng Xi;Tiancheng Zhao;Jianwei Yin","doi":"10.26599/TST.2024.9010089","DOIUrl":"https://doi.org/10.26599/TST.2024.9010089","url":null,"abstract":"The advancement of artificial intelligence-generated content drives the diversification of healthcare services, resulting in increased private information collection by healthcare service providers. Therefore, compliance with privacy regulations has increasingly become a paramount concern for both regulatory authorities and consumers. Privacy policies are crucial for consumers to understand how their personal information is collected, stored, and processed. In this work, we propose a privacy policy text compliance reasoning framework called FACTOR, which harnesses the power of large language models (LLMs). Since the General Data Protection Regulation (GDPR) has broad applicability, this work selects Article 13 of the GDPR as regulation requirements. FACTOR segments the privacy policy text using a sliding window strategy and employs LLM-based text entailment to assess compliance for each segment. The framework then applies a rule-based ensemble approach to aggregate the entailment results for all regulation requirements from the GDPR. Our experiments on a synthetic corpus of 388 privacy policies demonstrate the effectiveness of FACTOR. Additionally, we analyze 100 randomly selected websites offering healthcare services, revealing that nine of them lack a privacy policy altogether, while 29 have privacy policy texts that fail to meet the regulation requirements.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 4","pages":"1831-1845"},"PeriodicalIF":6.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Lasserre SDP Rounding Approximation Algorithm for Max Directed 3-Section 最大有向3截面的Lasserre SDP舍入逼近算法

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-03-03 DOI: 10.26599/TST.2024.9010214

Guangfeng Li;Jian Sun;Donglei Du;Xiaoyan Zhang

引用次数: 0

CCDive: A Deep Dive into Code Clone Detection Using Local Sequence Alignment CCDive：深入研究使用本地序列比对的代码克隆检测

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-03-03 DOI: 10.26599/TST.2024.9010075

Yasir Glani;Luo Ping;Syed Asad Shah;Lin Ke

{"title":"CCDive: A Deep Dive into Code Clone Detection Using Local Sequence Alignment","authors":"Yasir Glani;Luo Ping;Syed Asad Shah;Lin Ke","doi":"10.26599/TST.2024.9010075","DOIUrl":"https://doi.org/10.26599/TST.2024.9010075","url":null,"abstract":"The rapid evolution of software development has accentuated the deficiencies of prevailing code clone detection techniques. As modern applications become more complex, traditional cloning tools often struggle to detect general and large-gap clones that undergo regular modification. Such challenges pose threats to software integrity, emphasizing the critical need for improved code cloning techniques. Observing the prevailing gap, we propose an innovative code clone dive (CCDive) code cloning technique, which is designed to detect an extensive range of clones, from direct clones to the often challenging large-gap clones, thoroughly covering different categories, such as very strongly Type-III, strongly Type-III, and moderate Type-III clones. In CCDive, the fusion of a level-by-level abstraction and an innovative similarity matching algorithm ensures the recognition of clones even when nearly half the original code in the chunk has been modified. Furthermore, by integrating the Smith-Waterman local sequence alignment, the capability of CCDive to spot exact code transformation locations can be enhanced. In a comprehensive evaluation, CCDive was compared with well-known code cloning techniques. The efficacy of CCDive was measured using precision, recall, F1-score, accuracy, and efficiency. CCDive consistently surpassed other techniques in the precision, recall, F1-score, and accuracy metrics for both file-based and function-based clone detection. The robust performance of CCDive emphasizes its effectiveness, reliability, accuracy, and efficiency, making it well-suited for practical applications in the real world.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 4","pages":"1435-1456"},"PeriodicalIF":6.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CRESP: Cost-Aware Recommendation-Oriented Edge Service Provision CRESP：以成本为导向的边缘服务提供

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-03-03 DOI: 10.26599/TST.2024.9010151

Li Huang;Bo Li;Lu Zhao

{"title":"CRESP: Cost-Aware Recommendation-Oriented Edge Service Provision","authors":"Li Huang;Bo Li;Lu Zhao","doi":"10.26599/TST.2024.9010151","DOIUrl":"https://doi.org/10.26599/TST.2024.9010151","url":null,"abstract":"In the 5G environment, the edge computing paradigm enables service providers to deploy their service instances on distributed edge servers to serve nearby end users with extremely low latency. This boosts the emergence of modern applications, like AR/VR, online gaming, and autonomous vehicles. Existing approaches find service provision strategies under the assumption that all the user requirements are known. However, this assumption may not be true in practice and thus the effectiveness of existing approaches could be undermined. Inspired by the great success of recommender systems in various fields, we can mine users' interests in new services based on their similarities in terms of current service usage. Then, new service instances can be provisioned accordingly to better fulfil users' requirements. We formulate the problem studied in this paper as a Cost-aware Recommendation-oriented Edge Service Provision (CRESP) problem. Then, we formally model the CRESP problem as a Constrained Optimization Problem (COP). Next, we propose CRESP-O to find optimal solutions to small-scale CRESP problems. Besides, to solve large-scale CRESP problems efficiently, we propose an approximation approach named CRESP-A, which has a theoretical performance guarantee. Finally, we experimentally evaluate the performance of both CRESP-O and CRESP-A against several state-of-the-art approaches on a public testbed.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 4","pages":"1865-1884"},"PeriodicalIF":6.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human Morality Difference when Programming and Actually Operating Autonomous Machines 编程和实际操作自主机器时的人类道德差异

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-03-03 DOI: 10.26599/TST.2024.9010062

Wenfeng Yi;Wenhan Wu;Maoyin Chen;Xiaoping Zheng

引用次数: 0

Security Challenges in Internet of Vehicles (IoV) for ITS: A Survey 面向ITS的车联网（IoV）安全挑战研究

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-03-03 DOI: 10.26599/TST.2024.9010083

Edris Khezri;Hiwa Hassanzadeh;Rebaz Othman Yahya;Mahdi Mir

{"title":"Security Challenges in Internet of Vehicles (IoV) for ITS: A Survey","authors":"Edris Khezri;Hiwa Hassanzadeh;Rebaz Othman Yahya;Mahdi Mir","doi":"10.26599/TST.2024.9010083","DOIUrl":"https://doi.org/10.26599/TST.2024.9010083","url":null,"abstract":"Due to their diverse applications, including safety, welfare, and improving traffic efficiency, inter-vehicle ad-hoc networks have been extensively studied. Globally, road congestion, accidents, fuel consumption, and environmental pollution caused by the large number of vehicles have become serious problems that have caused a lot of human and financial losses. Intelligent transportation systems (ITS) have introduced VANETs in order to overcome these problems. In vehicular ad hoc networks (VANETs), vehicles equipped with wireless interfaces can communicate with other vehicles and with fixed roadside equipment via mobile ad hoc networks (MANETs). Messages are transmitted over open wireless channels in VANETs. Malicious nodes target these networks to protect them from various attacks, such as interference, eavesdropping, spoofing, denial of service, Sybil, black holes, worm holes, gray holes, etc. Security of VANETs is therefore one of the most significant issues. Security issues, attacks, attackers, and secure routing protocols in VANETs are discussed in this article, as well as available solutions to solve security issues.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 4","pages":"1700-1723"},"PeriodicalIF":6.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908658","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trust-Aware Hybrid Collaborative Recommendation with Locality-Sensitive Hashing 基于位置敏感哈希的信任感知混合协同推荐

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-03-03 DOI: 10.26599/TST.2023.9010096

Dejuan Li;James A. Esquivel

引用次数: 0

Generating Medical Report via Joint Probability Graph Reasoning 联合概率图推理生成医疗报告

IF 6.6 1区计算机科学

Tsinghua Science and Technology Pub Date : 2025-03-03 DOI: 10.26599/TST.2024.9010058

Junsan Zhang;Ming Cheng;Xiangyang Li;Xiuxuan Shen;Yuxue Liu;Yao Wan

{"title":"Generating Medical Report via Joint Probability Graph Reasoning","authors":"Junsan Zhang;Ming Cheng;Xiangyang Li;Xiuxuan Shen;Yuxue Liu;Yao Wan","doi":"10.26599/TST.2024.9010058","DOIUrl":"https://doi.org/10.26599/TST.2024.9010058","url":null,"abstract":"In medical X-ray images, multiple abnormalities may occur frequently. However, existing report generation methods cannot efficiently extract all abnormal features, resulting in incomplete disease diagnoses when generating diagnostic reports. In real medical scenarios, there are co-occurrence relations among multiple diseases. If such co-occurrence relations are mined and integrated into the feature extraction process, the issue of missing abnormal features may be addressed. Inspired by this observation, we propose a novel method to improve the extraction of abnormal features in images through joint probability graph reasoning. Specifically, to reveal the co-occurrence relations among multiple diseases, we conduct statistical analyses on the dataset, and extract disease relationships into a probability map. Subsequently, we devise a graph reasoning network for conducting correlation-based reasoning over the features of medical images, which can facilitate the acquisition of more abnormal features. Furthermore, we introduce a gating mechanism focused on cross-modal features fusion into the current text generation model. This optimization substantially improves the model's capabilities to learn and fuse information from two distinct modalities-medical images and texts. Experimental results on the IU-X-Ray and MIMIC-CXR datasets demonstrate that our approach outperforms previous state-of-the-art methods, exhibiting the ability to generate higher quality medical image reports.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 4","pages":"1685-1699"},"PeriodicalIF":6.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0