2021 IEEE International Conference on Web Services (ICWS)最新文献_第4页

Incentive-driven Edge Cooperation for Service Provision 服务提供激励驱动的边缘合作

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00080

Yishan Chen, Shuiguang Deng, Jianwei Yin

引用次数: 0

IEEE International Conference on Web Services (ICWS 2021): Message from the Chairs IEEE网络服务国际会议(ICWS 2021):主席的消息

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/icws53863.2021.00007

引用次数: 0

TWLR: A Novel Truth Inference Approach based on Worker Representations for Crowdsourcing in the Low Redundancy Situation TWLR:一种基于工作者表示的低冗余众包真值推理方法

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00023

Qianli Xing, Weiliang Zhao, Jian Yang, Jia Wu, Qi Wang

{"title":"TWLR: A Novel Truth Inference Approach based on Worker Representations for Crowdsourcing in the Low Redundancy Situation","authors":"Qianli Xing, Weiliang Zhao, Jian Yang, Jia Wu, Qi Wang","doi":"10.1109/ICWS53863.2021.00023","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00023","url":null,"abstract":"A redundancy-based strategy is widely employed by assigning each task to multiple workers and then inferring the correct answer (called truth) for each task in crowdsourcing. Most existing truth inference methods are designed for the situation with a fairly big number of answers for each task (referred to as high redundancy). However, the high redundancy unavoidably leads to a high cost. In this work, we propose a novel truth inference approach called TWLR based on worker representations for the situation with a small number of answers for each task (referred to as low redundancy). We develop a deep model to learn the representations of workers considering both answers and worker-task relations. For each task, we identify the worker with the highest quality, and select his/her answer as the predicted answer. To the best of our knowledge, this is the first work to perform truth inference by utilizing deep learning techniques to deal with the low redundancy situation in crowdsourcing. We have conducted a set of experiments against 7 real-world datasets to show the accuracy improvement of our truth inference approach by comparing with 11 baseline methods.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"2 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":"114979177","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

Organizing Committee ICWS 2021 组委会ICWS 2021

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/icws53863.2021.00008

引用次数: 0

WorP: A Novel Worker Performance Prediction Model for General Tasks on Crowdsourcing Platforms 面向众包平台通用任务的员工绩效预测模型

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00032

Qianli Xing, Weiliang Zhao, Jian Yang, Jia Wu, Qi Wang

{"title":"WorP: A Novel Worker Performance Prediction Model for General Tasks on Crowdsourcing Platforms","authors":"Qianli Xing, Weiliang Zhao, Jian Yang, Jia Wu, Qi Wang","doi":"10.1109/ICWS53863.2021.00032","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00032","url":null,"abstract":"Crowdsourcing platforms are widely used for requesters to find workers for general tasks. The answers to general tasks are usually open and not constrained by multiple choices. For the general tasks, the worker performance prediction models can facilitate the task assignment process in crowdsourcing. Worker performance prediction is affected by the three roles: the worker, the requester, and the task. The existing worker performance prediction models mainly consider the features of tasks and workers. However, these models rarely consider the features of requesters. And the existing worker performance prediction models for multiple-choice tasks are not suitable for general tasks as they are built based on the workers' accuracy on choices. In this work, we propose a worker performance prediction model by taking account of features of workers, tasks, and requesters to help requesters select workers for their general tasks on crowdsourcing platforms. We design a relationship learning module to learn the low dimension relationship representations of workers, tasks, and requesters. Furthermore, we design a performance learning model to predict workers' performance based on the features and relationship representations of workers, tasks, and requesters. A set of experiments against the realworld dataset from the Zhubajie platform has been conducted. Experimental results show that the proposed approach has better prediction results than the existing baseline methods.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"11 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":"134007688","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

Combining Label-wise Attention and Adversarial Training for Tag Prediction of Web Services 结合标签关注和对抗训练的Web服务标签预测

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00054

Qunbo Wang, Wenjun Wu, Yongchi Zhao, Yuzhang Zhuang, Yanni Wang

引用次数: 1

Heterogeneous Graph Attention Network-Enhanced Web Service Classification 异构图注意网络增强的Web服务分类

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00035

Mi Peng, Buqing Cao, Junjie Chen, Guosheng Kang, Jianxun Liu, Yiping Wen

{"title":"Heterogeneous Graph Attention Network-Enhanced Web Service Classification","authors":"Mi Peng, Buqing Cao, Junjie Chen, Guosheng Kang, Jianxun Liu, Yiping Wen","doi":"10.1109/ICWS53863.2021.00035","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00035","url":null,"abstract":"Service classification helps to improve the efficiency of service discovery. Previous methods mainly focus on homogeneous graph-based service classification. However, due to the heterogeneity of service data in the real world, these methods cannot deal with many types of nodes and edges in service relationship network well, and lack the usage of rich semantic information. The emergence of heterogeneous graph attention network can effectively solve the problems, because it can more completely and naturally extracts the relationships and nodes from the service relationship network, and well distinguishes the importance of neighbor nodes and meta paths. Therefore, this paper proposes a heterogeneous graph attention network-enhanced Web service classification method. In this method, firstly, a heterogeneous information service network is constructed by using composite service information, atomic service information and their attribute information. Then, the meta path is defined according to different semantic information, and the similarity matrix of service is constructed by using the commuting matrix and the similarity measurement technology based on meta path. Finally, a two-layer attention model is designed to calculate the node-level attention and meta path-level attention of the service, so as to obtain the node-level representations and meta path-level representations of the services, and generate more representative embedding features of services for achieving more accurate service classification. Finally, the experimental results on real datasets of ProgrammableWeb show that our method is better than GAT, GCN, Metapath2Vec, Node2Vec, BiLSTM and LDA in terms of precision, recall and macro F1, and improves the accuracy of Web service classification.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"21 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":"123559572","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

WSGCN4SLP: Weighted Signed Graph Convolutional Network for Service Link Prediction WSGCN4SLP:用于业务链路预测的加权签名图卷积网络

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00029

Yong Xiao, Guosheng Kang, Jianxun Liu, Buqing Cao, Linghang Ding

{"title":"WSGCN4SLP: Weighted Signed Graph Convolutional Network for Service Link Prediction","authors":"Yong Xiao, Guosheng Kang, Jianxun Liu, Buqing Cao, Linghang Ding","doi":"10.1109/ICWS53863.2021.00029","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00029","url":null,"abstract":"Learning network representations of Web services plays a critical role in the service ecosystem and facilitates many downstream tasks, e.g., service composition, service recommendation, service clustering, and service classification, etc. However, the performance of most of the existing approaches is limited by the sparse and non-interaction relationships between services. Considering these shortcomings, by proposing a balance theory based weighted signed graph convolutional network, we explore a dedicated signed service link prediction method to expand accurate links in service relation networks. Concretely, we first define the positive and negative links based on historical prior knowledge concerning services, and then construct a signed service relation network. Furthermore, on the basis of quantifying the influence of different neighbor nodes, we employ balance theory to correctly aggregate and propagate the information across layers through a weighted signed graph convolutional network. Finally, we splice all service embeddings in pairs, and a multi-layer perceptron classifier is used to predict the links between services. Comparative experiments with six baselines demonstrate that our method significantly outperforms the state-of-the-art link prediction models.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"253 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":"124173353","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: 3

SiaSL: A Siamese Neural Network for Service Level Prediction SiaSL:服务水平预测的连体神经网络

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00042

Chenyu Hou, Bin Cao

{"title":"SiaSL: A Siamese Neural Network for Service Level Prediction","authors":"Chenyu Hou, Bin Cao","doi":"10.1109/ICWS53863.2021.00042","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00042","url":null,"abstract":"Service level is an important metric to measure the reasonability of service systems. However, traditional mathematical service level prediction models have strict restrictions and suffer from accuracy degradation in complex real scenarios. In this paper, we propose to use a Siamese neural network to solve the service level prediction problem. This task is challenging due to two reasons: insufficient information and prior knowledge constraint. To tackle these issues, we develop a method entitled SiaSL based on the Siamese neural network. Our model consists of three key modules: 1) a time embedding module to embed the time period into low-dimensional vectors to model the time-independent characteristics of the service level; 2) a feature extractor to extract deep feature from raw scheduling information; 3) an output module to predict the final service level. Besides, to make SiaSL learn the prior knowledge, we propose a data augmentation method and an iterative training mechanism. Extensive experiments on a real-world dataset validate the effectiveness and efficiency of our method. On the premise of satisfying prior knowledge, our model achieves state-of-the-art performance on this problem.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"9 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":"129140806","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

QoS-based Trust Evaluation for Data Services as a Black Box 基于qos的黑箱数据服务信任评估

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00067

Senda Romdhani, Genoveva Vargas-Solar, N. Bennani, C. Ghedira

引用次数: 3