2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)最新文献

SENSEI: An Intelligent Advisory System for the eSport Community and Casual Players SENSEI:面向电子竞技社区和休闲玩家的智能咨询系统

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00010

Andrzej Janusz, D. Ślęzak, Sebastian Stawicki, K. Stencel

引用次数: 7

Topical Cluster Discovery in Semistructured Healthcare Data 半结构化医疗保健数据中的局部聚类发现

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00014

G. Costa, R. Ortale

引用次数: 0

Extending the VERIS Framework to an Incident Handling Ontology 将VERIS框架扩展为事件处理本体

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-55

Guilherme Baesso Moreira, Vanusa Menditi Calegario, J. C. Duarte, A. F. P. D. Santos

{"title":"Extending the VERIS Framework to an Incident Handling Ontology","authors":"Guilherme Baesso Moreira, Vanusa Menditi Calegario, J. C. Duarte, A. F. P. D. Santos","doi":"10.1109/WI.2018.00-55","DOIUrl":"https://doi.org/10.1109/WI.2018.00-55","url":null,"abstract":"Statistics show that while large amounts of money are being invested in cybersecurity, the number of incidents continues to grow, with cyber attacks motivated by political and financial issues, many times funded by States as part of cyberwarfare. Although the general perception is that the occurrence of incidents is almost inevitable, the literature demonstrates that cybersecurity initiatives are often focused on prevention of incidents rather than its response, with many organizations often poorly prepared and ignoring key incident handling processes. Some initiatives were proposed in order to fill this gap, one of them being the VERIS framework, a \"vocabulary for event recording and incident sharing.\" VERIS goal is to provide a basis for incident documentation, at the same time allowing the sharing of anonymized data to a community database, hence providing metrics for use within organizations or among external parties. As VERIS is a framework focused on information gathering and sharing, this work proposes the extension of the model from its original JSON representation to an OWL ontology, one of the main tools of the Semantic Web initiative, used for knowledge representation and strongly tied to the idea of information sharing. This work focus on the advantages of using such representation for incident handling.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127080264","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

Finding Semantic Relationships in Folksonomies 在大众分类法中寻找语义关系

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-92

Iman Saleh, Neamat El-Tazi

{"title":"Finding Semantic Relationships in Folksonomies","authors":"Iman Saleh, Neamat El-Tazi","doi":"10.1109/WI.2018.00-92","DOIUrl":"https://doi.org/10.1109/WI.2018.00-92","url":null,"abstract":"In this paper we study the problem of finding semantic relationships between folksonomy tags. We investigate different methods used to embed tags in the vector space and find similarities between them using word embedding vectors. We also present two new methods for embedding tags in the vector space utilizing labeled Latent Dirichlet Allocation (LDA) and Wikipedia category links. Related tags are grouped into communities using an overlapping community detection technique. In order to evaluate tag embedding methods, we use three different evaluation metrics, two of them do not require a ground truth dataset and the third is based on a manually created dataset of ground truth communities. Our results show that representing folksonomy tags using bag of words and embedding this representation in the vector space yields the best results compared to embedding co-occurring tags only or embedding tags along with textual content of tagged documents. We also compare between using word embedding, Latent Semantic Indexing (LSI), and LDA to find similarities between bag of words representations of tags. We show that word embedding outperforms LSI in one representation, while LDA is hard to beat.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123659153","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

Resting EEG Features and Their Application in Depressive Disorders 静息脑电图特征及其在抑郁症中的应用

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-74

Liqing Liu, Haiyan Zhou, Minghui Zhang, Jiajin Huang, Lei Feng, Ning Zhong

{"title":"Resting EEG Features and Their Application in Depressive Disorders","authors":"Liqing Liu, Haiyan Zhou, Minghui Zhang, Jiajin Huang, Lei Feng, Ning Zhong","doi":"10.1109/WI.2018.00-74","DOIUrl":"https://doi.org/10.1109/WI.2018.00-74","url":null,"abstract":"This research is aimed to analysis the resting EEG features in depression and the application in clinic. Sixteen patients with depression and sixteen healthy controls were involved in this study. Both features from the alpha and beta frequency bands were selected to analysis in this study. First the features' sensitivity to the group-difference and the correlation to the clinical HAMD scale score were analyzed, and then the classification method was used to further test the role of the resting EEG features in depression. The results showed that the difference between depression and healthy controls in the absolute power of beta band in the left prefrontal lobe was significant. And the alpha left-right asymmetry in the prefrontal cortex had a correlation with HAMD scale score. In addition, the classification based on the features showed that there was a relative higher accuracy rate to identify the depressions than to identify the healthy controls. Specifically, the classification based on alpha asymmetry was higher than that based on beta asymmetry, and the absolute power in beta band was higher than that in alpha band. Alpha asymmetry is a traditional sensitive resting EEG features for depression, this study provide new evidence to support the view. The findings here further suggest that absolute power in beta band would be important biomarker in depression.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116045810","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

Automated Identification of Type-Specific Dependencies between Requirements 需求之间特定类型依赖关系的自动识别

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-10

Muesluem Atas, Ralph Samer, A. Felfernig

引用次数: 12

Blockchain for Trustworthy Coordination: A First Study with LINDA and Ethereum 可信协调的区块链:与LINDA和以太坊的首次研究

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.000-9

Giovanni Ciatto, S. Mariani, Andrea Omicini

引用次数: 8

Boosting Reinforcement Learning in Competitive Influence Maximization with Transfer Learning 用迁移学习促进竞争影响最大化中的强化学习

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-62

Khurshed Ali, Chih-Yu Wang, Yi-Shin Chen

{"title":"Boosting Reinforcement Learning in Competitive Influence Maximization with Transfer Learning","authors":"Khurshed Ali, Chih-Yu Wang, Yi-Shin Chen","doi":"10.1109/WI.2018.00-62","DOIUrl":"https://doi.org/10.1109/WI.2018.00-62","url":null,"abstract":"Companies aim to promote their products under competitions and try to gain more profit than other companies. This problem is formulated as a Competitive Influence Maximization (CIM). Recently, a reinforcement learning has been used to solve the CIM problem, that is, to find an optimal strategy against competitor in order to maximize the commutative reward under the competition from other agents. However, reinforcement learning agents require huge training time to find an optimal strategy whenever the settings of the agents or the networks change. To tackle this issue, we propose a transfer learning method in reinforcement learning to reduce the training time and utilize the knowledge gained on source network to target network. Our method relies on two ideas, the first one is the state representation of the source and target networks in order to efficiently utilize the knowledge gained on source network to target network. The second idea is to transfer the final Q-solution of source network while learning on the target network. We validate our transfer learning method in similar or different settings of source and target networks while competing against the competitor's known strategies. Experimental results show that our proposed transfer learning method achieves similar or better performance as a baseline model while significantly reducing training time in all settings.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130353109","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: 17

Air Pollutant Severity Prediction Using Bi-Directional LSTM Network 基于双向LSTM网络的大气污染物严重程度预测

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-19

Ishan Verma, Rahul Ahuja, Hardik Meisheri, Lipika Dey

引用次数: 29

SNS Retrieval Based on User Profile Estimation Using Transfer Learning from Web Search 基于Web搜索迁移学习的用户轮廓估计的SNS检索

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2018-12-01 DOI: 10.1109/WI.2018.00-79

Daisuke Kataoka, Keishi Tajima

{"title":"SNS Retrieval Based on User Profile Estimation Using Transfer Learning from Web Search","authors":"Daisuke Kataoka, Keishi Tajima","doi":"10.1109/WI.2018.00-79","DOIUrl":"https://doi.org/10.1109/WI.2018.00-79","url":null,"abstract":"In this paper, we propose a method of retrieving posts on social networking services (SNSs) by specifying a pair of queries: a topic query and an entity query. A topic query specifies the topic of the posts to retrieve (e.g., \"iPhone\") and an entity query specifies the type of users who posted them (e.g., \"students\"). In the existing search systems for SNS posts, we can specify topics of posts by keywords, but we cannot specify types of users. Even if we include keywords specifying types of users in a query, such keywords are not usually included in tweets or user profile data. In our method, we estimate types of users by learning vocabulary whose appearance is correlated with specific types of users. We learn it from the datasets obtained through Web search. We retrieve Web documents through the search with a keyword specifying the type of users (e.g., \"student\"), and we also retrieve Web documents by using a keyword specifying its opposite (e.g., \"adult\"). We regard the documents retrieved by these queries as positive and negative examples of documents describing the target type, and we train a model for recognizing users of the given type. We recognize users of the target type by inputting their posts and their profile data into the model. We use Web documents instead of SNS posts for training the model because the Web has more documents describing types of people.","PeriodicalId":405966,"journal":{"name":"2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"277 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121296253","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