2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)最新文献_第3页

Connecting the dots to infer followers' topical interest on Twitter 把这些点联系起来，推断Twitter上关注者的话题兴趣

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804498

Aastha Nigam, Salvador Aguiñaga, N. Chawla

引用次数: 4

Learning text to model: A Bayesian network based L-system modeling strategy 学习文本建模:基于贝叶斯网络的l系统建模策略

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804496

Cheng Chen, G. Ji, Bin Zhao

引用次数: 2

Research on interpretation of nominal compound 名称化合物的解释研究

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804478

Weiguang Qu, Rubing Dai, Taizhong Wu, Min Gu, Yanhui Gu, Junsheng Zhou

引用次数: 0

An effective system for managing biological data 管理生物数据的有效系统

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804492

Qian Li, Zhenglu Yang, W. Cao, K. Shimizu

引用次数: 1

Using media-based emotion to predict commodity price 基于媒体的情感预测商品价格

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804491

Jiancheng Shen, Feng Dong, Wu He

引用次数: 0

Towards effective web page classification 迈向有效的网页分类

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804494

Min Gu, Feng Zhu, Qing Guo, Yanhui Gu, Junsheng Zhou, Weiguang Qu

引用次数: 4

A comparison study of semi-supervised SVM algorithms for small business credit prediction 半监督支持向量机算法在小企业信用预测中的比较研究

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804484

Jie Zhang, Lin Li, Ge Zhu, Xiangfu Meng, Qing Xie

{"title":"A comparison study of semi-supervised SVM algorithms for small business credit prediction","authors":"Jie Zhang, Lin Li, Ge Zhu, Xiangfu Meng, Qing Xie","doi":"10.1109/BESC.2016.7804484","DOIUrl":"https://doi.org/10.1109/BESC.2016.7804484","url":null,"abstract":"The small companies become increasingly important in bank's lending business. But the challenge is how bank's credit assessment is made in a small amount of time and money. Compare with the big companies, the small companies often need a small amount of cash flow. They may not provide the complete certificates or documents, so that the bank has to collect information of the companies and evaluate their credit rating especially by experts. For the bank, it is worthless to spend time and money to investigate a small company, especially just to lend several hundred thousand dollars. In the real life, credits of most the companies are good, while only small of them cannot repay for some reasons. The few number of small companies' credit data is valuable while considerable unknowing credit data of small companies is within reach. Therefore, the binary classification of the good credit and the bad credit is asymmetry. we choose supervised learning algorithm (Regularized Least Squares Classification and SVM) and semi-supervised learning algorithm (Transductive SVM and Deterministic Annealing Semi-supervised SVM) to predict the credits of small companies. In this paper, we conduct a series of experiments on credit datasets with different proportion classification and the results show that the Deterministic Annealing Semi-supervised SVM (DAS3VM) performance better when the data set is rare and asymmetry.","PeriodicalId":225942,"journal":{"name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128988157","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

Parallelizing label propagation for overlapping community detection 用于重叠社区检测的并行标签传播

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804476

N. Chen, Yun Liu, Junjun Cheng, Qing Liu

引用次数: 0

An efficient and privacy-preserving ranked fuzzy keywords search over encrypted cloud data 一种高效且保护隐私的模糊关键词搜索算法

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804500

S. Ding, Yidong Li, Jianhui Zhang, Liang Chen, Zhen Wang, Qunqun Xu

{"title":"An efficient and privacy-preserving ranked fuzzy keywords search over encrypted cloud data","authors":"S. Ding, Yidong Li, Jianhui Zhang, Liang Chen, Zhen Wang, Qunqun Xu","doi":"10.1109/BESC.2016.7804500","DOIUrl":"https://doi.org/10.1109/BESC.2016.7804500","url":null,"abstract":"As cloud computing becomes widespread, more and more users prefer to outsource their local sensitive data into the cloud. In order to protect data privacy, these sensitive data usually has to be encrypted before outsourcing, which makes effective data utilization a very difficult task. Although traditional searchable encryption techniques allow users to securely search over encrypted cloud data, they only support exact single keyword search, i.e. they do not allow any minor spelling errors or format inconsistencies. Besides, these traditional schemes support only Boolean search, without capturing any relevance of data files and rarely sort the search result. Recently, fuzzy keyword search over encrypted data techniques are introdeced to resolve the problem of spelling errors and format inconsistencis. However, they may incur large index size, search result inaccuracy and high search complexity, which greatly reduce the system usability and efficiency. In this paper, we propose the solution for privacy preserving ranked fuzzy keyword search over encrypted cloud data with small index. We use k-grams and Jaccard coefficient to constrcuct fuzzy keyword set and produce fuzzy results, and efficient relevance criteria (e.g., TF × IDF) to capture the relevance between data files and search requests. Extensive experiment result shows the efficiency of proposed scheme.","PeriodicalId":225942,"journal":{"name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128689845","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: 7

Using mobile phone data to explore spatial-temporal evolution of home-based daily mobility patterns in Shanghai 基于手机数据的上海市居家日常出行模式时空演化研究

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804481

Zhicheng Liu, Jinbin Yu, Weiting Xiong, Jian Lu, Junyan Yang, Qiao Wang

{"title":"Using mobile phone data to explore spatial-temporal evolution of home-based daily mobility patterns in Shanghai","authors":"Zhicheng Liu, Jinbin Yu, Weiting Xiong, Jian Lu, Junyan Yang, Qiao Wang","doi":"10.1109/BESC.2016.7804481","DOIUrl":"https://doi.org/10.1109/BESC.2016.7804481","url":null,"abstract":"This paper aims at investigating home-based daily mobility patterns in Shanghai. The dataset consists of Data over Signaling (DoS) from 107,100 anonymous mobile phone subscribers in Shanghai over 9 days in different seasons, which contains spatial-temporal information of subscribers. Daily mobility pattern is characterized as motif of the individual's daily trajectory in this paper. Homes of subscribers are recognized with a priori knowledge. Motifs are extracted from each individuals' daily trajectories. In this way, we have revealed the spatial-temporal evolution of home-based daily mobility patterns in Shanghai. We find that the spatial distribution of home-based daily mobility patterns inside the enclosed area of Middle Ring Road in weekdays diffuses to the enclosed area of Outer Ring Road in weekends. However, the spatial distribution of home-based daily mobility patterns in the areas outside Outer Ring Road seems to be invariant to weekdays and weekends and is more active than that inside the enclosed area of Outer Ring Road. These active areas include affordable housing communities, such as New Gucun Big Homeland and Xinkai Homeland. The phenomenon presented in this paper may correlate with socioeconomic factors in different regions of Shanghai and worth further investigation.","PeriodicalId":225942,"journal":{"name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125410205","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: 8