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Interactive Web API Recommendation for Mashup Development based on Light Neural Graph Collaborative Filtering

Interactive Web API Recommendation for Mashup Development ba...

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International Conference on computer Supported Cooperative Work in Design

作者： Jiayan Xiang Wanjun Chen Yang Wang Bowen Liang Zihao Liu Guosheng Kang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

With the development of Mashup technique, the number of Web APIs released on the Web continues to grow year by year. However, it is a challenging issue to find and select the desirable Web APIs among the large amount of Web APIs. Consequently, interactive Web API recommendation is used to alleviate the difficulty of service selection, when users or developers try to invoke Web APIs for solving their business requirements or software development requirements. Currently, there are several collaborative filtering based approaches proposed for Web API recommendation, while their recommendation performance is limited on both optimality and scalability. This paper proposes a light neural graph collaborative filtering based Web API recommendation approach, named LNGCF. Specifically, LNGCF learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted summation of the embeddings learned at all layers as the final embedding. Such simple, linear, and neat model is much easier to implement and train. A set of experiments are conducted on a real-world dataset. Experimental results demonstrate the substantial improvements on both optimality and scalability over the baselines.

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A General Complementary API Recommendation Framework based on Learning Model

A General Complementary API Recommendation Framework based o...

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IEEE International Conference on Web Services (ICWS)

作者： Qiqi Chen Guosheng Kang Yamei Nie Jianxun Liu Buqing Cao HNUST Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Xiangtan China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the advancement of service computing technology, the Internet has witnessed an exponential proliferation of Web APIs. However, the selection of suitable APIs from this vast pool for Mashup creation poses a great challenge for users. Various Web API recommendation methods have been proposed to address this issue, aiming to simplify the complex selection process. Despite these efforts, limited studies have been conducted on complementary function recommendation. In this context, a general complementary Web API recommendation framework based on a learning model, named CoWAR, is designed to recommend complementary Web APIs tailored for Mashup creation, based on the user’s selected Web APIs. Specifically, we propose a data labeling algorithm to generate the labeled dataset based on Mashup-API interactions derived from historical Mashups and Web APIs. Additionally, we employ BERT model to generate representation vectors of Web APIs based on the functionality description documents. Subsequently, we utilize SANFM (Self-Attentional Neural Factorization Machines) to train the complementary Web API recommendation model with the labeled sample dataset based on representation vectors of Web APIs. To the best of our knowledge, this is the first work addressing the complementary function recommendation problem with a learning model. By conducting a set of experiments over a real-world dataset, the effectiveness of the proposed approach is validated. The experimental results demonstrate that the learning model outperforms the traditional machine learning-based models and several deep learning-based models.

关键词： Training Measurement Mashups Service computing Vectors Data models labeling Context modeling

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BPMN++: Comprehensive Business Process Modeling for Industrial Internet Application

BPMN++: Comprehensive Business Process Modeling for Industri...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Hangyu Cheng Guosheng Kang Jianxun Liu Yiping Wen Buqing Cao Zhen Wang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

Business process management is the end-to-end business process modeling, analysis, and optimization to achieve business goals. Business Process Modeling Notation (BPMN) is usually used to represent business process models. However, BPMN cannot support the intuitive modeling for some typical tasks in complex business scenarios of the Industrial Internet context, increasing the communication cost between modelers and business personnel. The current solutions try to extend the attributes of BPMN elements with annotations, but a large number of annotations makes business process models complex and cumbersome. This paper proposes to extend new business process modeling elements for Industrial Internet application scenarios based on BPMN extension mechanism, generating an extended business process modeling language, namely BPMN++. And an extended process modeling tool is implemented, which is available online. A case study over a real-world Industrial Internet process validates the usefulness of this extension mechanism and the well understandability of business process models.

关键词： Industries Process modeling Annotations Internet Personnel Task analysis Optimization

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Towards Dynamic Evolutionary Analysis of ProgrammableWeb for API-Mashup Ecosystem

Towards Dynamic Evolutionary Analysis of ProgrammableWeb for...

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International Conference on computer Supported Cooperative Work in Design

作者： Yang Wang Jiayan Xiang Hangyu Cheng Wanjun Chen Yong Xiao Guosheng Kang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

With the wide adoption of Web APIs released on Internet, users tend to reuse them for business requirements or software development. Mashup is a useful technology for composing Web APIs into a new and value-added application. With the increasing number of Web APIs and Mashups, the API-Mashup ecosystem has emerged based on the invocation relationship between Mashups and Web APIs. In this paper, we take ProgrammableWeb, a typical API-Mashup ecosystem, as an example to investigate its dynamic evolutionary analysis. Although there have been some works on the API-Mashup ecosystem, they mainly focus on static analysis, i.e., the static characteristics of the API- Mashup ecosystem on a fixed time point. This paper conducts a comprehensive study on the dynamic evolutionary analysis of the API-Mashup ecosystem with a long time range from 2005 to 2021. First, we conduct a dynamic statistical analysis based on the API-Mashup ecosystem dataset. Next, we construct two cooperation networks, one between Web APIs, and the other between their categories. And the general characteristics of the two cooperation networks are presented. Finally, we investigate the derived cooperation networks from four perspectives: dynamic characteristics, degree distribution, betweenness centrality, and assortative mixing. Meanwhile, the corresponding insights are uncovered. Our work provides a foundation for visualization and understanding of the API-Mashup ecosystem from the timeline.

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Graph-based Multi-view Clustering for Web services

Graph-based Multi-view Clustering for Web services

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Wang Yang Yuan Zhenzhen Kang Guosheng Cao Buqing Liu JianXun Xiao Yong HNUST Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Computer Science and Engineering

The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process.

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Online Course Recommendation by Exploring User Interaction and Course Description

Online Course Recommendation by Exploring User Interaction a...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Weike Zhou Xiaokang Zhou Jing He Yamei Nie Qiqi Chen Guosheng Kang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the wide use of Massive Open Online Courses (MOOCs) and online education, the large number and variety of educational resources on the online course open platform make it difficult for users to choose, and the requirements for course recommendation algorithms and model performance are getting higher and higher. The existing course recommendation algorithms often ignore the interactive information between n learners and courses and the course descriptions, resulting in poor recommendation performance. To solve the problems above, our work proposes an online course recommendation approach via exploring user interactions and course description documents. Specifically, LightGCN model is used to represent the user-course interaction graph to derive the structural embeddings of users and courses. Moreover, BERT model is used to represent the course description documents to derive the description embeddings of courses. Based on the three embeddings, ANFM (Attention Neural Factorization Machine) model is applied to perform online course recommendation by modeling low-order and high-order feature interactions, and learning the weights of the feature interactions through the attention mechanism. Finally, solid experiments are conducted over a real-world public dataset. Experimental results show that the proposed approach outperforms the baseline methods under NDCG, Recall and Precision.

关键词： Electronic learning Attention mechanisms Federated learning Education Solids

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Research on personalized learning path management based on blockchain technology 16

Research on personalized learning path management based on b...

引用

16th IEEE International Conference on computer science and Education, ICCSE 2021

作者： Changhua, Zhao Chunqiao, Mi Huaihua University School of Computer Science and Engineering Hunan Huaihua418000 China Key Lab. of Wuling-Mt. Hlth. Big Data Intelligent Proc. and Applic. in Hunan Province Universities Hunan Huaihua418000 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua18000 China

ISBN: (纸本)9781665414685

Aiming at the problem that learners' personal information, courses and relevant certificates can not provide detailed information about learning depth and learning or teaching methods, this paper takes learners' learning information, learning record smart contract and Ethereum Blockchain related technology as the research object, aiming to use Ethereum Blockchain and smart contract to connect learners' learning records. On the one hand, it can use the block data stored in the Blockchain to easily verify the authenticity of the data and ensure the privacy and security of the data. On the other hand, it can provide personalized learning path for learners and be used to evaluate a person's educational achievement, employment suitability and intelligence evaluation. © 2021 IEEE.

关键词： Smart contract

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A study on the differences of attitude learning and learning behavior sequences for university computer MOOC+ SPOC Course 15

A study on the differences of attitude learning and learning...

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15th International Conference on computer science and Education, ICCSE 2020

作者： Changhua, Zhao Chunqiao, Mi Huaihua University School of Computer Science and Engineering Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua Hunan418000 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9781728172675

The outbreak of COVID-19 make University computer MOOC+SPOC course face opportunities and challenges. There are some problems in MOOC+SPOC course teaching, such as learners' curiosity decreasing, lack of accomplishment and etc. In order to deal with these problems, this study explores the correlation and differences in the learning behavior sequences of learners with different attitudes to high happiness, medium happiness, and low happiness. Through the learners' comments on University computer MOOC+SPOC course, the learner's attitude information is tapped. By analyzing learning behavior sequences, this study finds out the degree of learning behavior change of learners with different attitudes. The result of this study can improve the existing University computer MOOC+SPOC instructional design and promote the learning mode to move towards intelligent personalized adaptive learning. © 2020 IEEE.

关键词： Curricula

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Towards a taxonomy framework of evolution for SOA solution: From a practical point of view

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Combined Workshops on Web Information Systems engineering, WISE 2011 and WISE 2012

作者： Feng, Zaiwen Hung, Patrick C. K. He, Keqing Ma, Yutao Farwick, Matthias Li, Bing Peng, Rong State Key Lab. of Software Engineering Wuhan University China School of Computer Wuhan University China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Techonology China Faculty of Business and IT University of Ontario Institute of Technology Canada Institute of Computer Science University of Innsbruck Austria

ISBN: (纸本)9783642383328

Presently, the research on evolution of SOA solution is becoming more and more important in industry. It is necessary to extract a taxonomy framework for evolution of SOA solution because by using it, evolution of SOA-based systems can be analyzed and compared comprehensively and objectively. In this paper, a taxonomy framework for evolution of SOA solution is proposed that is illustrated from four perspectives: (a) motivations for evolutionary changes (why), (b) locations where evolutionary changes happen (where), (c) times when evolutionary changes happen (when), and (d) support mechanisms in the process of evolutionary changes (how). Furthermore, the taxonomy framework is applied on analyzing a SOA-based configurable system for urban logistics delivery management, as an application of taxonomy framework. © 2013 Springer-Verlag.

关键词： Taxonomies

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A process distance metric based on alignment of process structure trees

A process distance metric based on alignment of process stru...

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SenDe, IDP, IEKB, MBC, International Workshops, APWeb 2012

作者： Fu, Xiaodong Yue, Kun Zou, Ping Wang, Feng Ji, Kaifan Yunnan Provincial Key Lab. of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming 650500 China School of Information Science and Engineering Yunnan University Kunming 650091 China Faculty of Management and Economics Kunming University of Science and Technology Kunming 650093 China

ISBN: (纸本)9783642294259

For various applications in today's service-oriented enterprise computing systems, such as process-oriented service discovering or clustering, it is necessary to measure the distance between two process models. In this paper, we propose a quantitative measure to calculate the distance or similarity between different block-structured processes. We first transform each process into a process structure tree, and then calculate the process distance based on the alignment of two process structure trees. The proposed distance metric satisfies four distance measure properties, i.e., non-negativity, identity of indiscernible, symmetry and triangle inequality. These properties make the distance metric can be used as a quantitative tool in effective process model management activities. We illustrate the methodology with examples, by which its features are shown. Moreover, experiment study shows that the proposed method is feasible. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Trees (mathematics)

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