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arXiv 2025年

作者： Li, Yang Xia, Chunhe Li, Chang Li, Xiaojian Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing100191 China Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China Guangxi Collaborative Innovation Center Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China College of Computer Science and Information Technology Guangxi Normal University Guilin541004 China School of Cyber Science and Technology Beihang University Beijing100191 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

Federated learning based on homomorphic encryption has received widespread attention due to its high security and enhanced protection of user data privacy. However, the characteristics of encrypted computation lead to three challenging problems: "computation-efficiency", "attack-tracing" and "contribution-assessment". The first refers to the efficiency of encrypted computation during model aggregation, the second refers to tracing malicious attacks in an encrypted state, and the third refers to the fairness of contribution assessment for local models after encryption. This paper proposes a federated learning storage security model with homomorphic encryption (FLSSM) to protect federated learning model privacy and address the three issues mentioned above. First, we utilize different nodes to aggregate local models in parallel, thereby improving encrypted models’ aggregation efficiency. Second, we introduce trusted supervise nodes to examine local models when the global model is attacked, enabling the tracing of malicious attacks under homomorphic encryption. Finally, we fairly reward local training nodes with encrypted local models based on trusted training time. Experiments on multiple real-world datasets show that our model significantly outperforms baseline models in terms of both efficiency and security metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Multi-label Recommendation of Web Services with the Combination of Deep Neural Networks 15th

Multi-label Recommendation of Web Services with the Combinat...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Gan, Yanglan Xiang, Yang Zou, Guobing Miao, Huaikou Zhang, Bofeng School of Computer Science and Technology Donghua University Shanghai201620 China School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030301453

With the increasing number of web services on the Internet, how to effectively classify and recommend service labels has become a research issue. It plays an important role in web service organization and management. However, the deficiency of current approaches is that they either recommend only a single label for a web service or a set of independent labels without order ranking that is still difficult for service providers to publish their web services. In this paper, together with label embedding techniques, we propose a novel approach for service multi-label recommendation using deep neural networks. Unlike traditional approaches, the predicted service labels of our approach not only satisfy the demands of service multi-label recommendation, but also provide the importance with an ordered label ranking. The experiments are conducted to validate the effectiveness on a large-scale dataset from ProgrammableWeb, involving 13,869 real-world Web services. The experimental results demonstrate that our approach for multi-label recommendation of web services outperforms the competing approaches in terms of multiple evaluation metrics. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Deep neural networks

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Comparison of query languages for semantic database models 2018

Comparison of query languages for semantic database models

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2018 International Conference on Computing and Data Engineering, ICCDE 2018

作者： Thiam, Ablaye Xu, Dong Miao, Huaikou Niu, Zhihua School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

ISBN: (纸本)9781450363938

A basic model of data illustrates the logical structure of a database including the relations and the constraints which determine how the data is stored and its accessibility. Individual database models are based upon the rules and concepts of a broader data model adopted by designer. Most data models can be represented with basic tables and diagrams. This paper will address the data model semantic and their interaction with Query languages. It will also discuss the general methods for implementing data models and data concepts, as they are applied to a wide range of semantic data models. © 2018 Association for Computing Machinery.

关键词： Semantics

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A Dynamic Planning Framework for QoS-Based Mobile Service Composition Under Cloud-Edge Hybrid Environments 15th

A Dynamic Planning Framework for QoS-Based Mobile Service Co...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Gao, Honghao Huang, Wanqiu Zou, Qiming Yang, Xiaoxian School of Computer Engineering and Science Shanghai University Shanghai China Computing Center Shanghai University Shanghai China Shanghai Shang Da Hai Run Information System Co. Ltd. Shanghai China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030301453

In cloud-edge hybrid environments, when QoS constraints of the SOA-based mobile service composition change, a dynamic reconfiguration needs to be performed. Different from the traditional cloud service, the cloud-edge hybrid environment has the characteristics of limited resource storage, limited energy at the edge and uncertain users who move frequently. Dynamic reconfiguration in this mode is challenging. QoS is an important indicator of service evaluation. Most studies focus on only the static QoS attributes of the service. However, the QoS of a service is not statically constant;it changes dynamically over time. Therefore, to avoid the immediate failure of the service and ensure the stability of the mobile service composition after dynamic reconfiguration, an LSTM neural network is applied to predict the future QoS value for candidate service. This value is used as a service evaluation indicator during dynamic reconfiguration. Then, attributes such as energy consumption, traffic and moving track are considered. A cost-reward mechanism is constructed to calculate the cost and reward of the service when it is invoked. The reasonable restriction conditions are added for controlling dynamic reconfiguration. Finally, the dynamic reconfiguration problem-solving process and framework for mobile service composition based on QoS in a cloud-edge hybrid environment is introduced, guiding the mobile service composition dynamic reconfiguration task in cloud-edge hybrid environments. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Quality of service

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A Test Data Design Method for Data Quality Evaluation

A Test Data Design Method for Data Quality Evaluation

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The 2018 2nd International Conference on Advances in Energy, Environment and Chemical Science (AEECS 2018)

作者： Zhenyu LIU Qiang CHEN Lizhi CAI Shanghai Key Laboratory of Computer Software Testing and Evaluating

The rapid development of data analyze needs high data quality requirements. The data quality also determine the software quality. This paper proposes a test data deisgn method for evaluating data quality. The paper analyzes the data quality requirement model of typical software applications, considering the typical data quilty problem or issus. According to different evaluation characteristics, the paper proposes a test data method for quality evalaution related to data requirement. The paper also gives detailed for data design to fulfill the evlauation requirements. The method is used to test some data set and found some data issues in actual application databases.

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Segmentation of lesions in skin image based on salient object detection with deeply supervised learning 4

Segmentation of lesions in skin image based on salient objec...

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4th IEEE International Conference on computer and Communications, ICCC 2018

作者： Ji, Weijia Cai, Lizhi Chen, Wei Chen, Mingang Chai, Gang Laboratory of Computer Software Testing Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Department of Plastic and Reconstructive Surgery Shanghai Ninth People's Hospital Shanghai JiaoTong University School of Medicine Shanghai China

ISBN: (纸本)9781538683392

In this paper, a binary segmentation method HCDS based on salient object detection is proposed to solve the segmentation of lesions in skin images. The original U-Net model encounters some problems in its application to skin image segmentation, one of which is the non-pathological region information remained in the segmentation results. Our HCDS method improves its basic structure by adding a hybrid convolution module to the direct connection between the down-sampling and up-sampling. It deepens the abstract understanding of the input features in shallow layers and further eliminate the information interference of the non-pathological regions. Besides, the method employs a deeply supervised structure in each stage of up-sampling to learn from the output feature and ground truth. Finally, the HCDS integrates the multi-path outputs to obtain the best result. The experimental results show that the proposed method HCDS can effectively segment the lesion regions from the skin images. Our evaluation metrics about the HCDS are obviously superior to the original U-Net's. © 2018 IEEE.

关键词： Image segmentation

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A novel hybrid collaborative filtering approach to recommendation using reviews:the product attributes perspective 30

A novel hybrid collaborative filtering approach to recommend...

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30th International Conference on software Engineering and Knowledge Engineering, SEKE 2018

作者： Cao, Min Zhou, Sijing Gao, Honghao Li, Youhuizi School of Computer Engineering and Science Shanghai University Shanghai200444 China Computing Center Shanghai University Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China College of Computer Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)1891706446

The product recommendation research has been focusing on modelling users' reviews to construct the relation of users and products. Thus, the recommended performance can be improved by obtaining virtual ratings from corresponding reviews. However, these perspectives on reviews do not take into account the product field characteristic, which may impact the recommendation performance. To this point, this paper proposes a hybrid collaborative filtering approach to compute the correlation value considering product attributes. First, Product Attribute Weight and Product Attribute Score are introduced to formalize the product attributes for user and product respectively in a quantitative way. After that, the recommended ranking formula for the new model is presented. Finally, we carry out experimental analysis to show our method can effectively improve the performance of recommendation under a sparseness dataset. © 2018 Universitat zu Koln. All rights reserved.

关键词： Collaborative filtering

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Improved Collaborative Filtering Algorithm Based on Multifactor Fusion and User Features Clustering

Improved Collaborative Filtering Algorithm Based on Multifac...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Luyan Ni Jiulei Jiang Xiaofeng Wang College of Computer Science and Engineering North Minzu University Yinchuan China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9781538675199

Nowadays, collaborative filtering has become a core technology of personalized recommender systems, but the data sparsity and scalability problems seriously affected the accuracy of similarity computation. To alleviate these problems, a novel effective similarity calculation algorithm combined with user features clustering was proposed in this paper. The newly users similarities calculation model integrated the improved user rating similarity and items category preference similarity. Then, considering the K-means clustering algorithm, we clustered the users based on the user attributes through Euclidean distance so as to the nearest neighbors could be obtained in proper clusters. Experiments on MovieLens dataset are implemented. The experiments show that, compared with other improved algorithms, the method can effectively improve the prediction accuracy and quality of recommendation system.

关键词： Big Data

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Based on the analysis of mobile terminal application software performance test 18

Based on the analysis of mobile terminal application softwar...

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18th IEEE/ACIS International Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017

作者： Chunye, Du Wei, Song Jianhua, Wu Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Ruanzhong Information Technology Co. Ltd. Shanghai China

ISBN: (纸本)9781509055043

With the rapid development of mobile terminal, mobile applications are gradually penetrated into all aspects of people's life and work. Mobile games, mobile streaming media, location services, mobile Internet news, instant messaging, mobile music and other rich and colorful information era are changing the social life. In view of this, we propose the application software performance test based on the mobile terminal, and analyze the performance test technology and method of the mobile application. Experimental results show that the performance test of the application system can predict the pressure in real environment, the system will be applied in the problems exposed, through the analysis of the data of the test, it will provide help for performance optimization of application system. © 2017 IEEE.

关键词： Application programs

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A Novel Personalized Recommendation Algorithm Based on Trust Relevancy Degree

A Novel Personalized Recommendation Algorithm Based on Trust...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Weimin Li Heng Zhu Xiaokang Zhou Shohei Shimizu Mingjun Xin Qun Jin School of Computer Engineering and Technology Shanghai University Shanghai China Faculty of Data Science Shiga University Hikone Japan Software Evaluating and Testing Shanghai Key Laboratory of Computer Shanghai China Faculty of Human Sciences Waseda University Japan

ISBN: (纸本)9781538675199

The rapid development of the Internet and ecommerce has brought a lot of convenience to people's life. Personalized recommendation technology provides users with services that they may be interested according to users' information such as personal characteristics and historical behaviors. The research of personalized recommendation has been a hot point of data mining and social networks. In this paper, we focus on resolving the problem of data sparsity based on users' rating data and social network information, introduce a set of new measures for social trust and propose a novel personalized recommendation algorithm based on matrix factorization combining trust relevancy. Our experiments were performed on the Dianping datasets. The results show that our algorithm outperforms traditional approaches in terms of accuracy and stability.

关键词： Social network services Collaboration Filtering Prediction algorithms Training Data science Internet

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