2021 IEEE International Conference on Web Services (ICWS)最新文献_第6页

Towards an adaptive curation services composition based on machine learning 迈向基于机器学习的自适应策展服务组合

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00022

Firas Zouari, C. Ghedira, N. Kabachi, Khouloud Boukadi

{"title":"Towards an adaptive curation services composition based on machine learning","authors":"Firas Zouari, C. Ghedira, N. Kabachi, Khouloud Boukadi","doi":"10.1109/ICWS53863.2021.00022","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00022","url":null,"abstract":"Data curation deals with managing the data by applying different tasks such as extraction, enrichment, cleaning to fit the purpose of use. Indeed, nowadays, there is an increasing need to implement such tasks in the big data era to maintain data management. Big data is involved in decision processes to perform analysis, visualization, prediction, etc. Thus, there is a dependency between the generated outcomes and the input data of such a process. Therefore, decision process features (e.g., decision context, user constraints, and requirements) need to be taken into account during the data management process, including the data curation phase. Although the proposed curation approaches in the literature are diverse, most of them are static and do not consider the decision process features. Moreover, most of the proposals are dedicated to curating a specific data source format (e.g., structured/unstructured data source). To overcome these limitations, we propose a new approach ACUSEC (Adaptive CUration SErvice Composition) that ensures adaptive curation services composition by considering different features: the source type, the user constraints and preferences, and the decision context. To do so, we rely on AI and machine learning mechanisms such as reinforcement learning. Following the approach's definition, we conducted experiments that show encouraging results in overall execution time and adaptation to the above features.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"77 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131071962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

LETO: An Efficient Load Balanced Strategy for Task Offloading in IoT-Fog Systems LETO:物联网雾系统中任务卸载的高效负载均衡策略

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00065

Chittaranjan Swain, M. N. Sahoo, Anurag Satpathy

引用次数: 7

2021 IEEE World Congress on Services: Message from the Chairs of the IEEE Symposium on Advances in Software Services Engineering (ISASSE) 2021年IEEE世界服务大会:来自IEEE软件服务工程进展研讨会(ISASSE)主席的信息

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/icws53863.2021.00011

引用次数: 0

Service Recommendation for Composition Creation based on Collaborative Attention Convolutional Network 基于协同注意卷积网络的作文创作服务推荐

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00059

Ruyu Yan, Yushun Fan, Jia Zhang, Junqi Zhang, Haozhe Lin

{"title":"Service Recommendation for Composition Creation based on Collaborative Attention Convolutional Network","authors":"Ruyu Yan, Yushun Fan, Jia Zhang, Junqi Zhang, Haozhe Lin","doi":"10.1109/ICWS53863.2021.00059","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00059","url":null,"abstract":"Service recommendation for composition creation is a widely applied technique, which expedites mashup development by reusing existing services. The core of service recommendations is to simultaneously understand user needs as well as the functions of available services. However, the descriptions provided by users and service providers may not always be accurate or up to date, which poses significant challenges to composition creating. To tackle this problem, in this paper we propose a deep learning-based service recommendation framework named coACN, short for Collaborative Attention Convolutional Network, which can effectively learn the bilateral information toward service recommendation. On the one hand, a domain-level attention module is constructed to refine user needs embeddings by drawing messages from related service domains. On the other hand, a graph convolutional network is established to excavate the service-composition graph and fuse structured information into service embeddings. For a service node in the graph, the information of its compositions as its first-order neighbor nodes is used to supplement the latest functions and features of the service; and the information of the services as its second-order neighbor nodes may bring collaborative relationships into the service. Extensive experiments on the real-world ProgrammableWeb dataset show the significant improvement of our proposed coACN framework over state-of-the-art methods.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126517608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Efficient Grammatical Error Correction with Hierarchical Error Detections and Correction 基于层次错误检测和纠错的高效语法纠错

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00073

Fayu Pan, Bin Cao

引用次数: 1

Efficient penetration of API sequences to test stateful RESTful services 有效地渗透API序列以测试有状态RESTful服务

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00101

Koji Yamamoto

引用次数: 1

Incremental Update of Knowledge Graph Embedding by Rotating on Hyperplanes 基于超平面旋转的知识图嵌入增量更新

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00072

Yuyang Wei, Wei Chen, Zhixu Li, Lei Zhao

{"title":"Incremental Update of Knowledge Graph Embedding by Rotating on Hyperplanes","authors":"Yuyang Wei, Wei Chen, Zhixu Li, Lei Zhao","doi":"10.1109/ICWS53863.2021.00072","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00072","url":null,"abstract":"Knowledge graph embedding (KGE) plays an important role in downstream tasks, such as question answering, recommendation system, and entity recognition. Most existing KGE methods focus on modeling static knowledge graphs. However, many knowledge graphs are incremental in reality. Existing KGE methods are time-consuming to update the embedding space incrementally, and have difficulty in keeping the timeliness of the knowledge graph embedding. To address this problem, we propose a novel knowledge graph embedding method by rotating on hyperplane (RotatH), which supports updating the embedding space incrementally and ensures the timeliness and accuracy of knowledge graph embedding. Specifically, our proposed method first employs relation-specific hyperplanes to update the incremental entities into the trained vector space efficiently. Meanwhile, by combining hyperplane and rotation, our method can deal with complex relations, such as many-to-many and symmetry relations, and has high performance in both incremental and static environments. Moreover, our method introduces a mean-based method to constraint the density of incremental entities. We conduct extensive link prediction experiments on two real-world incremental datasets and two benchmark datasets. The experimental results show that our model incrementally updates embedding space efficiently and outperforms static models on benchmarks.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133338159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

PRADA-TF: Privacy-Diversity-Aware Online Team Formation PRADA-TF:意识到隐私多样性的在线团队组建

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00069

Y. Mahajan, Jin-Hee Cho

{"title":"PRADA-TF: Privacy-Diversity-Aware Online Team Formation","authors":"Y. Mahajan, Jin-Hee Cho","doi":"10.1109/ICWS53863.2021.00069","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00069","url":null,"abstract":"In this work, we propose a PRivAcy-Diversity-Aware Team Formation framework, namely PRADA-TF, that can be deployed based on the trust relationships between users in online social networks (OSNs). Our proposed PRADA-TF is mainly designed to reflect team members' domain expertise and privacy preserving preferences when a task requires a wide range of diverse domain expertise for its successful completion. The proposed PRADA-TF aims to form a team for maximizing its productivity based on members' characteristics in their diversity, privacy preserving, and information sharing. We leveraged a game theory called Mechanism Design in order for a mechanism designer as a team leader to select team members that can maximize the team's social welfare, which is the sum of all team members' utilities considering team productivity, members' privacy preserving, and potential privacy loss caused by information sharing. To screen a set of candidate teams in the OSN, we built an expert social network based on real coauthorship datasets (i.e., Netscience) with 1,590 scientists. We used the semi-synthetic datasets to construct a trust network based on a belief model called Subjective Logic and identified trustworthy users as candidate team members. Via our extensive simulation experiments, we compared the seven different TF schemes, including our proposed and existing TF algorithms, and analyzed the key factors that can significantly impact the expected and actual social welfare, expected and actual potential privacy loss, and team diversity of a selected team.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134353269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Message from the QSAS Symposium Chairs QSAS研讨会主席致词

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/icws53863.2021.00012

引用次数: 0

A Visualization Interface for Exploring Similar Brands on a Fashion E-Commerce Platform 在时尚电商平台上探索相似品牌的可视化界面

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00086

Natsuki Hashimoto, Marie Katsurai, Ryosuke Goto

引用次数: 0