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Toprak, Mustafa
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03.04. Department of Computer Engineering
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4QUALITY EDUCATION
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4
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1
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2234/1002
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2
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7
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5
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1.75
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1.25
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4
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2
| Journal | Count |
|---|---|
| EMNLP 2017 - 2nd Workshop on Natural Language Processing Meets Journalism, NLPmJ 2017 - Proceedings of the Workshop -- EMNLP 2017 2nd Workshop on Natural Language Processing Meets Journal., NLPmJ 2017 -- 7 September 2017 -- Copenhagen -- 173847 | 1 |
| Journal of Integrative Bioinformatics | 1 |
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Now showing 1 - 4 of 4
Conference Object A News Chain Evaluation Methodology Along With a Lattice-Based Approach for News Chain Construction(Association for Computational Linguistics (ACL), 2017) Toprak, Mustafa; Toprak, Mustafa; Özkahraman,Ö.; Tekir, Selma; Tekir, Selma; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyChain construction is an important requirement for understanding news and establishing the context. A news chain can be defined as a coherent set of articles that explains an event or a story. There's a lack of well-established methods in this area. In this work, we propose a methodology to evaluate the "goodness" of a given news chain and implement a concept latticebased news chain construction method by Hossain et al. The methodology part is vital as it directly affects the growth of research in this area. Our proposed methodology consists of collected news chains from different studies and two "goodness" metrics, minedge and dispersion coefficient respectively. We assess the utility of the lattice-based news chain construction method by our proposed methodology. © EMNLP 2017.All right reserved.Master Thesis Intrusion Detection System Alert Correlation With Operating System Level Logs(Izmir Institute of Technology, 2009) Toprak, Mustafa; Aytaç, İsmail Sıtkı; Toprak, Mustafa; Aytaç, İsmail Sıtkı; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyInternet is a global public network. More and more people are getting connected to the Internet every day to take advantage of the Internetwork connectivity. It also brings in a lot of risk on the Internet because there are both harmless and harmful users on the Internet. While an organization makes its information system available to harmless Internet users, at the same time the information is available to the malicious users as well. Most organizations deploy firewalls to protect their private network from the public network. But, no network can be hundred percent secured. This is because; the connectivity requires some kind of access to be granted on the internal systems to Internet users. The firewall provides security by allowing only specific services through it. The firewall implements defined rules to each packet reaching to its network interface. The IDS complements the firewall security by detected if someone tries to break in through the firewall or manages to break in the firewall security and tried to have access on any system in the trusted site and alerted the system administrator in case there is a breach in security. However, at present, IDSs suffer from several limitations. To address these limitations and learn network security threats, it is necessary to perform alert correlation. Alert correlation focuses on discovering various relationships between individual alerts. Intrusion alert correlation techniques correlate alerts into meaningful groups or attack scenarios for ease to understand by human analysts. In order to be sure about the alert correlation working properly, this thesis proposed to use attack scenarios by correlating alerts on the basis of prerequisites and consequences of intrusions. The architecture of the experimental environment based on the prerequisites and consequences of different types of attacks, the proposed approach correlates alerts by matching the consequence of some previous alerts and the prerequisite of some later ones with OS-level logs. As a result, the accuracy of the proposed method and its advantage demonstrated to focus on building IDS alert correlation with OS-level logs in information security systems.Master Thesis A Lattice-Based Approach for News Chain Construction(Izmir Institute of Technology, 2015) Toprak, Mustafa; Allmer, Jens; Tekir, Selma; Toprak, Mustafa; Tekir, Selma; Allmer, Jens; 03.04. Department of Computer Engineering; 04.03. Department of Molecular Biology and Genetics; 03. Faculty of Engineering; 04. Faculty of Science; 01. Izmir Institute of TechnologyEach news article and column can be part of a manually created news story or chain by journalists and columnists. However, increasing amounts of data published by news companies each year makes manual analysis thus creation of news stories and chains almost impossible. When the amount of data is considered, it is obvious that automated systems’ support is vital to journalists, columnists and intelligence analysts. A news chain is a set of news articles that form a connected and coherent whole. In the traditional “connecting the dots” approach, news chains are constructed based on given two articles as start and end news of the chain. In this study, a method is proposed to create coherent news chains without the predetermination of start and end articles of the chain. Intuition of the method comes from the partial order relation among news articles. We try to show that lattice structure can represent relation or hierarchy among news articles that have a partial order in nature. Creating concept lattice is prepared out of the inverted index structure of news articles which is one of the main contributions of the study. In the experimental work, an artificial dataset is processed to show the steps of the method. After that, we also provide the evaluation using real dataset results.Article Citation - WoS: 7Citation - Scopus: 5A Machine Learning Approach for Microrna Precursor Prediction in Retro-Transcribing Virus Genomes(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2016) Saçar Demirci, Müşerref Duygu; Allmer, Jens; Toprak, Mustafa; Toprak, Mustafa; Allmer, Jens; 03.04. Department of Computer Engineering; 04.03. Department of Molecular Biology and Genetics; 03. Faculty of Engineering; 04. Faculty of Science; 01. Izmir Institute of TechnologyIdentification of microRNA (miRNA) precursors has seen increased efforts in recent years. The difficulty in experimental detection of pre-miRNAs increased the usage of computational approaches. Most of these approaches rely on machine learning especially classification. In order to achieve successful classification, many parameters need to be considered such as data quality, choice of classifier settings, and feature selection. For the latter one, we developed a distributed genetic algorithm on HTCondor to perform feature selection. Moreover, we employed two widely used classification algorithms libSVM and random forest with different settings to analyze the influence on the overall classification performance. In this study we analyzed 5 human retro virus genomes; Human endogenous retrovirus K113, Hepatitis B virus (strain ayw), Human T lymphotropic virus 1, Human T lymphotropic virus 2, Human immunodeficiency virus 2, and Human immunodeficiency virus 1. We then predicted pre-miRNAs by using the information from known virus and human pre-miRNAs. Our results indicate that these viruses produce novel unknown miRNA precursors which warrant further experimental validation.