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IEEE International Conference on software Engineering and Service Sciences (ICSESS)

作者： Shiqi Zhang Xiaoqiang Liu Bo Xu Lizhi Cai Yun Hu School of Computer Science and Technology Donghua University & Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Software Center Shanghai China School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Software Center Shanghai China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Fast-growing cloud computing technology makes cloud application scenarios more diversified and difficult to choose. A knowledge recommending system is an effective way to reach automatic recommendation services. In this paper, we constructed a cloud scenario knowledge graph (CSKG) for cloud service market to assist cloud knowledge management and provided a web service interface for recommended applications. First a cloud scenario domain ontology was defined based on product knowledge of cloud suppliers and experience of domain experts. Then the BiLSTM-CRF model and Rule-based method were combined to automatically extract structured knowledge from multi-source and heterogeneous texts, where we proposed a tagging schema combined with entity extraction and relation classifier. Finally, a cloud scenario knowledge graph was constructed and visualized, several application examples based on the CSKG service were suggested.

关键词： Knowledge engineering Cloud computing Visualization Web services Tagging Ontologies software engineering

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Dietary Sequence Recommendations Based on TransFM

Dietary Sequence Recommendations Based on TransFM

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International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Jie Wu Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai P. R. China

ISBN: (数字)9781728182780

ISBN: (纸本)9781728182797

The rapid development of the Internet has changed people's lifestyles, and more and more people choose to eat online. The online catering platform for users inevitably needs to provide personalized recommendations for users, followed by fewer articles on dietary recommendations. This paper proposes a combination of GBDT and TransFM for dietary sequence recommendation. The hybrid model uses GBDT to process high-dimensional features of food data and capture high-order feature interactions, and then uses TransFM to combine the content features of food and mine the short-term interests of users. Perform sequence recommendations. After experimental comparison, the model is superior to the general sequence recommendation model, and has achieved good results on multiple performance indicators.

关键词： Zirconium Smart grids Hafnium Ground penetrating radar Geophysical measurement techniques Discrete wavelet transforms Conferences

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An Influence Maximization Algorithm Based on Real-Time and De-superimposed Diffusibility 15th

An Influence Maximization Algorithm Based on Real-Time and D...

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

作者： Ren, Yue Zhang, Xinyuan Xia, Liting Lin, Yongze Zhao, Yue Li, Weimin School of Computer Engineering and Technology Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030301453

Influence maximization is to find a small number of seed nodes in the network that maximize their influence on the network. Existing algorithms select a seed node with the greatest influence. This will inevitably have an influence on mutual coverage, which will have a more or less negative impact on the final results and reduce the performance of the algorithm. In this paper, Node Diffusibility is proposed, and it is updated in real time and eliminated the deviation caused by its overlay. On the basis of traditional calculation of node influence, more attention was paid to the influence of a node’s neighboring nodes rather than to the characteristics of the nodes themselves. The proposed algorithm was evaluated by experiments conducted on selected real data sets. Compared with the classical ranking-based algorithms, MaxDegree and PageRank, the proposed algorithm achieved better results in terms of efficiency and time complexity. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： computers

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Research Review on Web Service Composition testing 8th

Research Review on Web Service Composition Testing

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8th International Workshop on Structured Object-Oriented Formal Language and Method, SOFL+MSVL 2018

作者： Du, Zhoujie Miao, Huaikou School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9783030136505

Web services composition is designed to achieve a more powerful and large-grained services with organic synthesis of different Web services. In order to guarantee the quality of the Web services composition, comprehensive and adequate testing of the Web services composition is required. However, the dynamic and distributed characteristics of Web services combination make its testing technology and method have big difference with the traditional software testing and bring a large of challenges. In this paper, we summarize and analyze the definition, architecture, testing methods and testing techniques of Web service composition. In addition, we also analyze and prospect the progress of Web services combination testing. © 2019, Springer Nature Switzerland AG.

关键词： Web services

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Combining Product Line Approach and Formal Specification for Product Family Modeling 5

Combining Product Line Approach and Formal Specification for...

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5th IEEE International Conference on computer and Communications, ICCC 2019

作者： Wang, Xi Shanghai University School of Computer Engineering and Science Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201114 China

ISBN: (纸本)9781728147437

The lack of systematic approach for product family modeling leads to difficulties in developing a series of high-quality software products simultaneously. This paper tackles this challenge by combining product line approach and formal specification technique. After constructing the feature model of the target family, our approach will start the formalization of the bottom-level features. For each feature, a formal model will be built according to its inner structure and function details. Any problem occurred during this process will cause the evolvement of the feature model under the guidance of a set of pre-defined evolution rules. The final product family model will be established when no problem can be found in both feature model and feature specification. This interleaving of feature model evolvement and feature formalization facilitates consistent and complete product family modeling from both abstract and concrete levels. A case study is given to illustrate the validity of the approach. © 2019 IEEE.

关键词： Formal specification

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A differential evolution-based approach for effort-Aware just-in-Time software defect prediction 1

A differential evolution-based approach for effort-Aware jus...

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1st ACM SIGSOFT International Workshop on Representation Learning for software Engineering and Program Languages, RL+SE and PL 2020, co-located with ESEC/FSE 2020

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang East China University of Science and Technology Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China East China University of Science and Technology Shanghai Engineering Research Center of Smart Energy Shanghai China East China University of Science and Technology Shanghai China

ISBN: (纸本)9781450381253

software defect prediction technology is an effective method to improve software quality. Effort-Aware just-in-Time software defect prediction (JIT-SDP) aims to identify more defective changes in limited effort. Although many methods have been proposed for JIT-SDP, the prediction performance of existing prediction models still needs to be improved. To improve the effort-Aware prediction performance, we propose a new method called DEJIT based on differential evolution algorithm. First, we propose a metric called density-percentile-Average (DPA), which is used as the optimization objective of models on the training set. Then, we use logistic regression to build models and use the differential evolution algorithm to determine coefficients of logistic regression. We conduct empirical research on six open source projects. Empirical results demonstrate that the proposed method significantly outperforms the state-of-The-Art 4 supervised models and 4 unsupervised models. © 2020 ACM.

关键词： Defects

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FedForgery: Generalized Face Forgery Detection with Residual Federated Learning

arXiv

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arXiv 2022年

作者： Liu, Decheng Dang, Zhan Peng, Chunlei Zheng, Yu Li, Shuang Wang, Nannan Gao, Xinbo The State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China The School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China The State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University Shaanxi Xi’an710071 China The Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China

With the continuous development of deep learning in the field of image generation models, a large number of vivid forged faces have been generated and spread on the Internet. These high-authenticity artifacts could grow into a threat to society security. Existing face forgery detection methods directly utilize the obtained public shared or centralized data for training but ignore the personal privacy and security issues when personal data couldn’t be centralizedly shared in real-world scenarios. Additionally, different distributions caused by diverse artifact types would further bring adverse influences on the forgery detection task. To solve the mentioned problems, the paper proposes a novel generalized residual Federated learning for face Forgery detection (FedForgery). The designed variational autoencoder aims to learn robust discriminative residual feature maps to detect forgery faces (with diverse or even unknown artifact types). Furthermore, the general federated learning strategy is introduced to construct distributed detection model trained collaboratively with multiple local decentralized devices, which could further boost the representation generalization. Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery. The designed novel generalized face forgery detection protocols and source code would be publicly available at https://***/GANG370/FedForgery. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Energy-Aware Resource Scheduling with Fault-Tolerance in Edge Computing 16th

Energy-Aware Resource Scheduling with Fault-Tolerance in Edg...

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16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019

作者： Xue, Yanfen Fan, Guisheng Yu, Huiqun Sun, Huaiying Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030307080

Edge computing extends computation and storage resources to the edge of the network, which largely improve the performance problem of cloud computing incurred by the bandwidth limitation. And it still needs to address the challenges of energy and reliability. In this paper, we propose an energy-aware fault-tolerant resource scheduling algorithm to improve system reliability while minimizing the energy consumption. We allocate resources by reliability and energy-aware resource scheduling method for tasks firstly. Then, CPU temperature prediction and time between failures (TBF) prediction are used to trigger proactive fault tolerance mechanism (VM migration). The experimental results show that the reliability is greatly improved and energy consumption generated by VM migration is not very large compared to other methods. © 2019, IFIP International Federation for Information Processing.

关键词： Energy utilization

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An Optimization Method Based on Be-ACO Algorithm in Service Composition Context

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Computational intelligence and neuroscience 2022年第1期2022卷 5231262页

作者： Zhoujie Du Huaikou Miao School of Computer Engineering and Science Shanghai University Shanghai 200444 China. Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai 201112 China.

With the increasing complexity of users' needs and increasing uncertainty of a single web service in big data environment, service composition becomes more and more difficult. In order to improve the solution accuracy and computing speed of the constrained optimization model, several improvements are raised on ant colony optimization (ACO) and its calculation strategy. We introduce beetle antenna search (BAS) strategy to avoid the danger of falling into local optimization, and a service composition method based on fusing beetle-ant colony optimization algorithm (Be-ACO) is proposed. The model first generates search subspace for ant colony through beetle antenna search strategy and optimization service set by traversing subspace based on ant colony algorithm. Continuously rely on beetle antenna search strategy to generate the next search subspace in global scope for ant colony to traverse and converge to the global optimal solution finally. The experimental results show that compared with the traditional optimization method, the proposed method improves combination optimization convergence performance and solution accuracy greatly.

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An empirical study on optimal solutions selection strategies for effort-aware just-in-time software defect prediction 31

An empirical study on optimal solutions selection strategies...

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31st International Conference on software Engineering and Knowledge Engineering, SEKE 2019

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China

ISBN: (纸本)1891706489

Just-in-time software defect prediction (JIT-SDP) is an active topic in the filed of software engineering, and many methods have been proposed to solve this problem. State-of-the-art method MULTI applies multi-objective optimization algorithm to the effort-aware JIT-SDP problem, and obtains good average performance. Although the average performance of the MULTI method is high, there are many optimal solutions with poor performance. If an optimal solution is randomly selected, a poor prediction model may be obtained. In order to further improve the performance of the MULTI method, we propose three optimal solutions selection strategies: benefit priority (BP), cost priority (CP), and a compromise between cost and benefit (CCB). In order to compare and validate the effectiveness of the strategies, we conduct a large-scale empirical study on data sets of six open source projects. The experimental results show that, compared with the average performance of MULTI, the optimal solutions selection strategy based on BP has a significant improvement in ACC and Popt indicators. Therefore, we recommend using the BP-based optimal solutions selection strategy to improve the performance of MULTI when using the MULTI method to solve the effort-aware JIT-SDP problem. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Multiobjective optimization

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