Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)最新文献_第6页

Flexible semantic query expansion for process exploration 灵活的语义查询扩展，用于流程探索

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-03-02 DOI: 10.1109/ICOSC.2015.7050847

Sven Mielke, Martin Pelke, Sebastian Pospiech, R. Mertens

{"title":"Flexible semantic query expansion for process exploration","authors":"Sven Mielke, Martin Pelke, Sebastian Pospiech, R. Mertens","doi":"10.1109/ICOSC.2015.7050847","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050847","url":null,"abstract":"Process exploration tools help to identify business processes that are not executed according to their documentation or lack such documentation at all. These process steps are executed by human beings, therefore process traces can often be found in unstructured documents. In order to reconstruct a process execution, these documents have to be retrieved. Natural language properties such as hyponyms, hypernyms, homonyms and synonyms make searching for a specific element a hard task. Integrating word relations in the search index is the standard solution for tackling this problem. In our process exploration scenario, however, this approach comes to its limits as ontologies defining word relations may vary from process step to process step. The problem is that the approach is rather inflexible. In order to change the relation of the words, the index needs to be rebuilt. This in turn would require running an analysis of the whole document base. Query Expansion, on the other hand, works by adding related words to a query, making it very flexible. In a classic search scenario, it still comes with a number of disadvantages such as retrieving unrelated documents. In our scenario, these disadvantages do not apply, since information from previous steps in the explored process can be used to constrain the result set.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129664706","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: 6

Audio-based event detection in office live environments using optimized MFCC-SVM approach 使用优化的MFCC-SVM方法的基于音频的办公实时环境事件检测

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-03-02 DOI: 10.1109/ICOSC.2015.7050855

Selver Ezgi Küçükbay, M. Sert

{"title":"Audio-based event detection in office live environments using optimized MFCC-SVM approach","authors":"Selver Ezgi Küçükbay, M. Sert","doi":"10.1109/ICOSC.2015.7050855","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050855","url":null,"abstract":"Audio data contains several sounds and is an important source for multimedia applications. One of them is unstructured Environmental Sounds (also referred to as audio events) that have noise-like characteristics with flat spectrums. Therefore, in general, recognition methods applied for music and speech data are not appropriate for the Environmental Sounds. In this paper, we propose an MFCC-SVM based approach that exploits the effect of feature representation and learner optimization tasks for efficient recognition of audio events from audio signals. The proposed approach considers efficient representation of MFCC features using different window and hop sizes by changing the number of Mel coefficients in the analyses as well as optimizing the SVM parameters. Moreover, 16 different audio events from the IEEE Audio and Acoustic Signal Processing (AASP) Challenge Dataset, namely alert, clear throat, cough, door slam, drawer, keyboard, keys, knock, laughter, mouse, page turn, pen drop, phone, printer, speech, and switch that are collected from office live environments are utilized in the evaluations. Our empirical evaluations show that, when the results of the proposed methods are chosen for MFFC feature and SVM classifier, the tests conducted through using 5-fold cross validation gives the results of 62%, 58% and 55% for Precision, Recall and F-measure scores, respectively. Extensive experiments on audio-based event detection using the IEEE AASP Challenge dataset show the effectiveness of the proposed approach.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123383672","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: 29

Intelligent malware detection based on file relation graphs 基于文件关系图的恶意软件智能检测

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-03-02 DOI: 10.1109/ICOSC.2015.7050784

Lingwei Chen, Tao Li, Melih Abdulhayoglu, Yanfang Ye

{"title":"Intelligent malware detection based on file relation graphs","authors":"Lingwei Chen, Tao Li, Melih Abdulhayoglu, Yanfang Ye","doi":"10.1109/ICOSC.2015.7050784","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050784","url":null,"abstract":"Due to its damage to Internet security, malware and its detection has caught the attention of both anti-malware industry and researchers for decades. Many research efforts have been conducted on developing intelligent malware detection systems. In these systems, resting on the analysis of file contents extracted from the file samples, like Application Programming Interface (API) calls, instruction sequences, and binary strings, data mining methods such as Naive Bayes and Support Vector Machines have been used for malware detection. However, driven by the economic benefits, both diversity and sophistication of malware have significantly increased in recent years. Therefore, anti-malware industry calls for much more novel methods which are capable to protect the users against new threats, and more difficult to evade. In this paper, other than based on file contents extracted from the file samples, we study how file relation graphs can be used for malware detection and propose a novel Belief Propagation algorithm based on the constructed graphs to detect newly unknown malware. A comprehensive experimental study on a real and large data collection from Comodo Cloud Security Center is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our proposed method outperform other alternate data mining based detection techniques.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125711818","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: 31

Urban network parcellation using graph theoretic approach 基于图论方法的城市网络划分

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-02-26 DOI: 10.1109/ICOSC.2015.7050841

Goni Dubnov, S. Dubnov, Y. Kiyoki

引用次数: 0

Semantic modeling of analytic-based relationships with Direct Qualification 使用直接限定的基于分析的关系语义建模

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-02-15 DOI: 10.1109/ICOSC.2015.7050845

Norman Ahmed, Jason Bryant, G. Hasseler, M. Paulini

引用次数: 0

The cost of reasoning with RDF updates 使用RDF更新进行推理的成本

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-02-06 DOI: 10.1109/ICOSC.2015.7050829

Sana Al Azwari, John N. Wilson

引用次数: 2

Semantic search using a similarity graph 使用相似图的语义搜索

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-02-01 DOI: 10.1109/ICOSC.2015.7050785

L. Stanchev

{"title":"Semantic search using a similarity graph","authors":"L. Stanchev","doi":"10.1109/ICOSC.2015.7050785","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050785","url":null,"abstract":"Given a set of documents and an input query that is expressed in a natural language, the problem of document search is retrieving the most relevant documents. Unlike most existing systems that perform document search based on keywords matching, we propose a search method that considers the meaning of the words in the query and the document. As a result, our algorithm can return documents that have no words in common with the input query as long as the documents are relevant. For example, a document that contains the words “Ford”, “Chrysler” and “General Motors” multiple times is surely relevant for the query “car” even if the word “car” does not appear in the document. Our semantic search algorithm is based on a similarity graph that contains the degree of semantic similarity between terms, where a term can be a word or a phrase. We experimentally validate our algorithm on the Cranfield benchmark that contains 1400 documents and 225 natural language queries. The benchmark also contains the relevant documents for every query as determined by human judgment. We show that our semantic search algorithm produces a higher value for the mean average precision (MAP) score than a keywords matching algorithm. This shows that our approach can improve the quality of the result because the meaning of the words and phrases in the documents and the queries is taken into account.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129060251","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

Finding trendy products from pins 从别针中寻找时尚产品

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-02-01 DOI: 10.1109/ICOSC.2015.7050844

Dingding Wang, M. Ogihara

引用次数: 0

Evolving the User Graph: From unsupervised topic models to knowledge assisted networks 演进用户图:从无监督主题模型到知识辅助网络

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-02-01 DOI: 10.1109/ICOSC.2015.7050792

S. Sathish, A. Patankar, N. Neema, Swetha Jagadeesha, Nimesh Priyodit

引用次数: 7

SemTree ontology for enriching Arabic text with lexical semantic annotations SemTree本体，用于用词法语义注释丰富阿拉伯文本

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-02-01 DOI: 10.1109/ICOSC.2015.7050800

M. Al-Yahya, Mona Al-Shaman, Nehal Al-Otaiby, Wafa Al-Sultan, A. Al-Zahrani, Mesheal Al-Dalbahie

引用次数: 3