2021 IEEE World Congress on Services (SERVICES)最新文献_第4页

An End-to-end Attention Transfer Network for Cross-domain Service Recommendation 面向跨域服务推荐的端到端注意力转移网络

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00037

Ruyu Yan, Yushun Fan

{"title":"An End-to-end Attention Transfer Network for Cross-domain Service Recommendation","authors":"Ruyu Yan, Yushun Fan","doi":"10.1109/services51467.2021.00037","DOIUrl":"https://doi.org/10.1109/services51467.2021.00037","url":null,"abstract":"The number of available online services increases sharply with the development of the Internet. These services typically belong to varying service domains. To address the data-sparse issue, cross-domain recommendation techniques are proposed to transfer the information in relevant service domains to improve the recommendation effects. In this paper, we presented a novel end-to-end cross-domain service recommendation learning framework, named EATN, short for End-to-end Attention Transfer Network, which is different from most existing cross-domain step-by-step learning frameworks. To realize this end-to-end framework, we design a workflow to achieve user preferences cross-domain matching procedure. We capture fine-grained and multi-faceted user preferences by using multiple Multi-Layer Perceptron layers. To reasonably integrate multi-faceted transfer preferences, we design a service-level attention module, which learns weight based on the relevance to services. Finally, it can improve the recommendation effect of cold-start users in the target domain. Extensive experiments on the real-world Amazon dataset show the significant improvement of our proposed EATN framework.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":"72 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":"114495857","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

Scheduling Real Tim Security Aware Tasks in Fog Networks 在雾网络中调度实时安全感知任务

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00017

Ashutosh Kumar Singh, Nitin Auluck, Omer F. Rana, S. Nepal

引用次数: 1

Hybrid Cloud Resource Scheduling With Multi-dimensional Configuration Requirements 具有多维配置要求的混合云资源调度

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00049

Zhaokun Qiu, Long Chen, Xiaoping Li

引用次数: 1

Plenary Panel 1 - Cloud HPC: Exploring the Growing Synergy between Cloud and High Performance Computing 全体会议1 -云高性能计算:探索云计算与高性能计算之间日益增长的协同作用

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00058

引用次数: 0

Lightweight Privacy-preserving Medical Diagnosis in Edge Computing 边缘计算中的轻量级隐私保护医疗诊断

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00020

Zhuo Ma, Jianfeng Ma, Yinbin Miao, Ximeng Liu, K. Choo, Ruikang Yang, Xiangyu Wang

{"title":"Lightweight Privacy-preserving Medical Diagnosis in Edge Computing","authors":"Zhuo Ma, Jianfeng Ma, Yinbin Miao, Ximeng Liu, K. Choo, Ruikang Yang, Xiangyu Wang","doi":"10.1109/services51467.2021.00020","DOIUrl":"https://doi.org/10.1109/services51467.2021.00020","url":null,"abstract":"In the era of machine learning, mobile users are able to submit their symptoms to doctors at any time, anywhere for personal diagnosis. It is prevalent to exploit edge computing for real-time diagnosis services in order to reduce transmission latency. Although data-driven machine learning is powerful, it inevitably compromises privacy by relying on vast amounts of medical data to build a diagnostic model. Therefore, it is necessary to protect data privacy without accessing local data. However, the blossom has also been accompanied by various problems, i.e., the limitation of training data, vulnerabilities, and privacy concern. As a solution to these above challenges, in this paper, we design a lightweight privacy-preserving medical diagnosis mechanism on edge. Our method redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to reduce amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edges. Additionally, the proposed scheme is able to provide a secure diagnosis on edge while maintaining privacy to ensure an accurate and timely diagnosis. The proposed system with secure computation could securely construct the XGBoost model with lightweight overhead, and efficiently provide a medical diagnosis without privacy leakage. Our security analysis and experimental evaluation indicate the security, effectiveness, and efficiency of the proposed system.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":"12 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":"127654442","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

Towards Green Service Composition Approach in the Cloud 迈向云中的绿色服务组合方法

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00025

Shangguang Wang, Ao Zhou, Ruo Bao, Chou Wu

引用次数: 0

Plenary Panel 5 - Future Trends of Strategic Advances of Services Computing Using AI Technologies 全体讨论小组5 -使用人工智能技术的服务计算战略进步的未来趋势

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00062

引用次数: 1

Delegated Authorization Framework for EHR Services using Attribute Based Encryption 基于属性加密的EHR服务授权框架

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00028

Maithilee P. Joshi, K. Joshi, Timothy W. Finin

引用次数: 0

ASMaaS: Automatic Semantic Modeling as a Service 自动语义建模即服务

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00033

Zaiwen Feng, W. Mayer, M. Stumptner, G. Grossmann, Selasi Kwashie, Da Ning, K. He

{"title":"ASMaaS: Automatic Semantic Modeling as a Service","authors":"Zaiwen Feng, W. Mayer, M. Stumptner, G. Grossmann, Selasi Kwashie, Da Ning, K. He","doi":"10.1109/services51467.2021.00033","DOIUrl":"https://doi.org/10.1109/services51467.2021.00033","url":null,"abstract":"Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is an approach that is inflexible, incurs high costs, and leads to “silos” that prevent sharing data across different agencies or tasks. A standard approach to tackling this problem is to design a common ontology and to construct source descriptions which specify mappings between the sources and the ontology. Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Automatic semantic model has been gaining attention in data integration [5], federated data query [14] and knowledge graph construction [6]. This paper proposes an service-oriented architecture to create a correct semantic model, including annotating training data, training the machine learning model, and predict an accurate semantic model for new data source. Moreover, a holistic process for automatic semantic modeling is presented. By the usage of ASMaaS, historical semantic annotations for training machine learning model used in automatic semantic modeling can be shared, reducing costs of human resources from users. By specifying a well defined interface, users are able to have access to automatic semantic modeling process at any time, from anywhere. In addition, users must not be concerned with machine learning technologies and pipeline used in automatic semantic modeling, focusing mainly on the business itself.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":"161 5 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":"129070495","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