2020 International Conference on Service Science (ICSS)最新文献_第3页

2020 International Conference on Service Science ICSS 2020 2020年国际服务科学会议ICSS 2020

2020 International Conference on Service Science (ICSS) Pub Date : 2020-08-01 DOI: 10.1109/icss50103.2020.00002

引用次数: 1

Urban Region Function Mining Service Based on Social Media Text Analysis 基于社交媒体文本分析的城市区域功能挖掘服务

2020 International Conference on Service Science (ICSS) Pub Date : 2020-08-01 DOI: 10.1109/ICSS50103.2020.00034

Yanchun Sun, Hang Yin, Jiu Wen, Zhiyu Sun

{"title":"Urban Region Function Mining Service Based on Social Media Text Analysis","authors":"Yanchun Sun, Hang Yin, Jiu Wen, Zhiyu Sun","doi":"10.1109/ICSS50103.2020.00034","DOIUrl":"https://doi.org/10.1109/ICSS50103.2020.00034","url":null,"abstract":"Urban region functions are the types of potential activities in an urban region, such as residence, commerce, transportation, entertainment, etc. A service which mines urban region functions is of great value for various applications, including urban planning and transportation management, etc. Many researches have been carried out to dig out different regions' functions, but few researches are based on social media text analysis. Considering that the semantic information embedded in social media texts is very useful to infer an urban region's main functions, we design a service which extracts human activities using Sina Weibo (the largest microblog system in Chinese, similar to Twitter) with location information and further describes a region's main functions with a function vector based on the human activities. Firstly, we predefine a variety of human activities to get the related activities corresponding to each Weibo post using an urban function classification model. Secondly, urban regions' function vectors are generated, with which we can easily do some high-level work such as similar place recommendation. At last, with the function vectors generated, we develop a web application for urban region function querying. We also conduct a case study among the urban regions in Beijing, and the experiment results demonstrate the feasibility of our method.","PeriodicalId":292795,"journal":{"name":"2020 International Conference on Service Science (ICSS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128918735","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

Scholarly Paper Recommendation via Related Path Analysis in Knowledge Graph 基于知识图谱相关路径分析的学术论文推荐

2020 International Conference on Service Science (ICSS) Pub Date : 2020-08-01 DOI: 10.1109/ICSS50103.2020.00014

Xiao Wang, Hanchuan Xu, Wenjie Tan, Zhongjie Wang, Xiaofei Xu

引用次数: 2

Traditional Chinese Medicine knowledge Service based on Semi-Supervised BERT-BiLSTM-CRF Model 基于半监督BERT-BiLSTM-CRF模型的中医知识服务

2020 International Conference on Service Science (ICSS) Pub Date : 2020-08-01 DOI: 10.1109/ICSS50103.2020.00018

Mingzhu Zhang, Zhongguo Yang, Chen Liu, Lei Fang

{"title":"Traditional Chinese Medicine knowledge Service based on Semi-Supervised BERT-BiLSTM-CRF Model","authors":"Mingzhu Zhang, Zhongguo Yang, Chen Liu, Lei Fang","doi":"10.1109/ICSS50103.2020.00018","DOIUrl":"https://doi.org/10.1109/ICSS50103.2020.00018","url":null,"abstract":"Most of Traditional Chinese Medicine (TCM) data and ancient records exist in the form of books. The unstructured medical information is the foundation for building TCM knowledge service. The existing methods are not accurate enough to solve TCM named entity recognition and require a lot of manual labeling data. This paper proposes a semi-supervised embedded Semi-BERT-BiLSTM-CRF model. Based on the book “Diagnosis of Traditional Chinese Medicine in Traditional Chinese Medicine”, we select the physical features from the cleaned-up text information according to the characteristics of Chinese medicine classics, and then use a small amount of labeled data to train the BERT-BiLSTM-CRF model. The obtained model is used to predict unlabeled data and obtain pseudo-label data. The pseudo-label and labeled data are used as a training set for model training. Experiments show that TCM entity recognition accuracy of this method reaches 81.24%, which effectively improves the TCM entity recognition accuracy and reduces the manual labeling work. The results of this research can be applied to scenarios such as auxiliary diagnosis of TCM and expert system after subsequent improvement and transformation.","PeriodicalId":292795,"journal":{"name":"2020 International Conference on Service Science (ICSS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132583519","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: 5

Classification of IoT Malware based on Convolutional Neural Network 基于卷积神经网络的物联网恶意软件分类

2020 International Conference on Service Science (ICSS) Pub Date : 2020-08-01 DOI: 10.1109/ICSS50103.2020.00016

Qian-Guang Lin, Ni Li, Q. Qi, Jia-Bin Hu

引用次数: 0

A k-core Analysis to Large-Scale Web API Collaboration Networks 大规模Web API协作网络的k核分析

2020 International Conference on Service Science (ICSS) Pub Date : 2020-08-01 DOI: 10.1109/ICSS50103.2020.00022

Mingdong Tang, Wenquan Lei, Sixian Lian

{"title":"A k-core Analysis to Large-Scale Web API Collaboration Networks","authors":"Mingdong Tang, Wenquan Lei, Sixian Lian","doi":"10.1109/ICSS50103.2020.00022","DOIUrl":"https://doi.org/10.1109/ICSS50103.2020.00022","url":null,"abstract":"The Web has become a huge programmable platform in the Web 2.0 era. More and more companies are opening their data and services to the public through Web APIs. With the increasing number and variety of Web APIs, new Web applications and value-added services can be rapidly developed by combining different APIs. The ever-growing collaborations between APIs thus raise a kind of a large-scale networks, namely Web API collaboration networks. However, the structure and evolution process of Web API collaboration networks are still unclear to people so far. This paper provides a deep analysis to the internal structure of a real-world Web API collaboration network using k-core decomposition. We firstly construct the Web API collaboration network by using the data crawled from the largest Web API registry, Programmable Web.com, and then employ the k-core decomposition method to obtain different subgraphs of the network with different centrality or coreness (i.e., k-cores). We give an experimental analysis to the structures of the Web API collaboration network and its k-cores by using some classic statistical tools, such as degree distribution and clustering coefficient. The analysis results not only can identify the most central APIs in the Web API collaboration network, but also provides a basis for the visualization and understanding of Web API collaboration networks.","PeriodicalId":292795,"journal":{"name":"2020 International Conference on Service Science (ICSS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133861931","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

Fulfilling Functional Demands of BPM in Long-tailed Change Environments 长尾变化环境下BPM功能需求的实现

2020 International Conference on Service Science (ICSS) Pub Date : 2020-08-01 DOI: 10.1109/ICSS50103.2020.00028

Xi Chen, Hongmei Cao, Ye Lin, Liang Zhang

{"title":"Fulfilling Functional Demands of BPM in Long-tailed Change Environments","authors":"Xi Chen, Hongmei Cao, Ye Lin, Liang Zhang","doi":"10.1109/ICSS50103.2020.00028","DOIUrl":"https://doi.org/10.1109/ICSS50103.2020.00028","url":null,"abstract":"Enterprises rely on their process-aware information system (PAIS) to conduct business. With the increasingly fierce competition, the operation environment of enterprises is constantly changing due to uncertain factors or emerging opportunities. How to react to the changes in time and make a positive response is fundamental for enterprises to maintain their core competitiveness in the new economic era. Among these changes, a kind of long-tailed change has been omitted by traditional business process management (BPM) because of its wide variety and low frequency. Just as the long-tailed effect reveals, the impact of long-tailed change on PAIS change management will be no less than the impact of high-frequency changes. On the move to digital economy, enterprises can hardly win if they cannot catch the first opportunity and deal the long-tailed changes properly. On the other hand, business process models in PAIS are core assets of an enterprise. The requirement of keeping routine processes running smoothly and reusing these assets smartly in unexpected situations runs through the whole business. To solve this problem, this paper proposes a novel maintenance approach for business process models to cope with long-tailed changes. By decorating existing process models with business-oriented annotations, the PAIS can equip with the mechanism to support original processes while react to long-tailed situations agilely with a reinforced process engine. Several experiments revels the effectiveness and generality of proposed approach for BPM in the digital economy ear.","PeriodicalId":292795,"journal":{"name":"2020 International Conference on Service Science (ICSS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134377826","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

An Improved Weighted-Removal Sentence Embedding Based Approach for Service Recommendation 基于加权去除句子嵌入的改进服务推荐方法

2020 International Conference on Service Science (ICSS) Pub Date : 2020-08-01 DOI: 10.1109/ICSS50103.2020.00015

Jingxuan Li, Hanchuan Xu, Xiao Wang, Lanshun Nie, Xiaofei Xu

引用次数: 1