Proceedings of the 9th International Symposium on Information and Communication Technology最新文献_第5页

Episode-Rule Mining with Minimal Occurrences via First Local Maximization in Confidence 基于置信度的第一次局部最大化最小化事件规则挖掘

Proceedings of the 9th International Symposium on Information and Communication Technology Pub Date : 2018-12-06 DOI: 10.1145/3287921.3287982

H. K. Dai

引用次数: 2

Automatic Embedding of Social Network Profile Links into Knowledge Graphs 自动嵌入社会网络配置文件链接到知识图谱

Proceedings of the 9th International Symposium on Information and Communication Technology Pub Date : 2018-12-06 DOI: 10.1145/3287921.3287926

Hussein Hazimeh, E. Mugellini, Simon Ruffieux, Omar Abou Khaled, P. Cudré-Mauroux

{"title":"Automatic Embedding of Social Network Profile Links into Knowledge Graphs","authors":"Hussein Hazimeh, E. Mugellini, Simon Ruffieux, Omar Abou Khaled, P. Cudré-Mauroux","doi":"10.1145/3287921.3287926","DOIUrl":"https://doi.org/10.1145/3287921.3287926","url":null,"abstract":"Recent Knowledge Graphs (KGs) like Wikidata and YAGO are often constructed by incorporating knowledge from semi-structured heterogeneous data resources such as Wikipedia. However, despite their large amount of knowledge, these graphs are still incomplete. In this paper, we posit that Online Social Networks (OSNs) can become prominent data resources comprising abundant knowledge about real-world entities. An entity on an OSN is represented by a profile; the link to this profile is called a social link. In this paper, we propose a KG refinement method for adding missing knowledge to a KG, i.e., social links. We target specific entity types, in the scientific community, such as researchers. Our approach uses both scholarly data resources and existing KG for building knowledge bases. Then, it matches this knowledge with OSNs to detect the corresponding social link(s) for a specific entity. It uses a novel matching algorithm, in combination with supervised and unsupervised learning methods. We empirically validate that our system is able to detect a large number of social links with high confidence.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130120540","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

Frozen Shoulder Rehabilitation: Exercise Simulation and Usability Study 肩周炎康复:运动模拟与可用性研究

Proceedings of the 9th International Symposium on Information and Communication Technology Pub Date : 2018-12-06 DOI: 10.1145/3287921.3287951

Nuntiya Chiensriwimol, P. Mongkolnam, Jonathan H. Chan

引用次数: 4

High Accuracy Forecasting with Limited Input Data: Using FFNNs to Predict Offshore Wind Power Generation 有限输入数据的高精度预测:使用ffnn预测海上风力发电

Proceedings of the 9th International Symposium on Information and Communication Technology Pub Date : 2018-12-06 DOI: 10.1145/3287921.3287936

Elaine Zaunseder, Larissa Müller, S. Blankenburg

引用次数: 5

CitationLDA++: an Extension of LDA for Discovering Topics in Document Network CitationLDA++: LDA在文献网络中主题发现的扩展

Proceedings of the 9th International Symposium on Information and Communication Technology Pub Date : 2018-12-06 DOI: 10.1145/3287921.3287930

T. Nguyen, P. Do

{"title":"CitationLDA++: an Extension of LDA for Discovering Topics in Document Network","authors":"T. Nguyen, P. Do","doi":"10.1145/3287921.3287930","DOIUrl":"https://doi.org/10.1145/3287921.3287930","url":null,"abstract":"Along with rapid development of electronic scientific publication repositories, automatic topics identification from papers has helped a lot for the researchers in their research. Latent Dirichlet Allocation (LDA) model is the most popular method which is used to discover hidden topics in texts basing on the co-occurrence of words in a corpus. LDA algorithm has achieved good results for large documents. However, article repositories usually only store title and abstract that are too short for LDA algorithm to work effectively. In this paper, we propose CitationLDA++ model that can improve the performance of the LDA algorithm in inferring topics of the papers basing on the title or/and abstract and citation information. The proposed model is based on the assumption that the topics of the cited papers also reflects the topics of the original paper. In this study, we divide the dataset into two sets. The first one is used to build prior knowledge source using LDA algorithm. The second is training dataset used in CitationLDA++. In the inference process with Gibbs sampling, CitationLDA++ algorithm use topics distribution of prior knowledge source and citation information to guide the process of assigning the topic to words in the text. The use of topics of cited papers helps to tackle the limit of word co-occurrence in case of linked short text. Experiments with the AMiner dataset including title or/and abstract of papers and citation information, CitationLDA++ algorithm gains better perplexity measurement than no additional knowledge. Experimental results suggest that the citation information can improve the performance of LDA algorithm to discover topics of papers in the case of full content of them are not available.","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125099134","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

Development of a Vietnamese Large Vocabulary Continuous Speech Recognition System under Noisy Conditions 嘈杂条件下越南语大词汇量连续语音识别系统的开发

Proceedings of the 9th International Symposium on Information and Communication Technology Pub Date : 2018-12-06 DOI: 10.1145/3287921.3287938

Quoc Bao Nguyen, Van Tuan Mai, Quang Trung Le, Ba Quyen Dam, Van Hai Do

引用次数: 6

A New Assessment of Cluster Tendency Ensemble approach for Data Clustering 一种新的聚类倾向集成方法用于数据聚类

Proceedings of the 9th International Symposium on Information and Communication Technology Pub Date : 2018-12-06 DOI: 10.1145/3287921.3287927

Van Nha Pham, L. Ngo, L. T. Pham, Pham Van Hai

引用次数: 0

A Binarization Method for Extracting High Entropy String in Gait Biometric Cryptosystem 步态生物识别密码系统中高熵字符串提取的二值化方法

Proceedings of the 9th International Symposium on Information and Communication Technology Pub Date : 2018-12-06 DOI: 10.1145/3287921.3287960

Lam Tran, Thao M. Dang, Deokjai Choi

{"title":"A Binarization Method for Extracting High Entropy String in Gait Biometric Cryptosystem","authors":"Lam Tran, Thao M. Dang, Deokjai Choi","doi":"10.1145/3287921.3287960","DOIUrl":"https://doi.org/10.1145/3287921.3287960","url":null,"abstract":"Inertial-sensors based gait has been considered as a promising approach for user authentication in mobile devices. However, securing enrolled template in such system remains a challenging task. Biometric Cryptosystems (BCS) provide elegant approaches for this matter. The primary task of adopting BCS is to extract from raw biometric data a discriminative, high entropy and stable binary string, which will be used as input of BCS. Unfortunately, the state-of-the-art researches does not notice the gait features' population distribution when extracting such string. Thus, the extracted binary string has low entropy, and degrades the overall system security. In this study, we address the aforementioned drawback to improve entropy of the extracted string, and also enhance the system security. Specifically, we design a binarization scheme, in which the distribution population of gait features are analyzed and utilized to allow the extracted binary string achieving maximal entropy. In addition, the binarization is also designed to provide strong variation toleration to produce highly stable binary string which enhances the system friendliness. We analyzed the proposed method using a gait dataset of 38 volunteers which were collected under nearly realistic conditions. The experiment results show that our proposed binarization method improves the extracted binary string's entropy 30%, and the system achieved competitive performance (i.e., 0.01% FAR, 9.5% FRR with 139-bit key).","PeriodicalId":448008,"journal":{"name":"Proceedings of the 9th International Symposium on Information and Communication Technology","volume":"50 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131369027","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

Large Scale Fashion Search System with Deep Learning and Quantization Indexing 基于深度学习和量化索引的大规模时尚搜索系统

Proceedings of the 9th International Symposium on Information and Communication Technology Pub Date : 2018-12-06 DOI: 10.1145/3287921.3287964

Thoi Hoang Dinh, Toan Pham Van, Ta Minh Thanh, Hau Nguyen Thanh, Anh Pham Hoang

引用次数: 2

Vietnamese Speaker Authentication Using Deep Models 使用深度模型的越南语说话人身份验证

Proceedings of the 9th International Symposium on Information and Communication Technology Pub Date : 2018-12-06 DOI: 10.1145/3287921.3287954

Son T. Nguyen, Viet Dac Lai, Quyen Dam-Ba, Anh Nguyen-Xuan, Cuong Pham

引用次数: 4