Graduate School of Informatics

Bilişsel Bilimler EABD Doktora Programına 2019-2020 Eğitim Öğretim Yılı Sonbahar dönemi için yapılan başvurular neticesinde,

26 Haziran 2018 Çarşamba günü 1.Grup mülakatları yapılacaktır.

Mülakata davet edilen aşağıda isimleri yer alan adayların, isimleri karşılığında belirtilen zamandan 15 dk. önce Enformatik Enstitüsü II-03 (S-03) no.lu sınıf önünde hazır bulunmaları beklenmektedir.

NOT: ODTU Akademik Takvimi’nde yapilan degisiklik nedeniyle sonbahar donemi basvurularinda iki gruplu degerlendirme uygulanacaktir. Bu uygulama hakkinda detayli bilgi icin: http://ii.metu.edu.tr/tr/duyuru/lisansustu-basvurularla-ilgili-onemli-duyuru”.

Çoklu Ortam Bilişimi Yüksek Lisans ve Doktora Programlarına 2019-2020 Eğitim Öğretim Yılı Sonbahar dönemi için yapılan başvurular neticesinde,

28 Haziran 2019 Cuma günü 1.grup mülakatları yapılacaktır.

Mülakata davet edilen aşağıda isimleri yer alan adayların, isimleri karşılığında belirtilen zamandan 15 dk. önce Enformatik Enstitüsü II-01 (S-01) no.lu sınıf önünde hazır bulunmaları beklenmektedir.

NOT: ODTU Akademik Takvimi’nde yapilan degisiklik nedeniyle sonbahar donemi basvurularinda iki gruplu degerlendirme uygulanacaktir. Bu uygulama hakkinda detayli bilgi icin: http://ii.metu.edu.tr/tr/duyuru/lisansustu-basvurularla-ilgili-onemli-duyuru”.

Title: MINING EYETRACKING DATA TO CHARACTERISE USERS AND THEIR PATTERNS OF USE 

PhD Candidate: Melih Öder

Program: Informations Systems Department 

Date: 26 June 10:00

Place: A-212

Abstract: Eye tracking studies typically collect an enormous amount of data that encodes a lot of information about the users’ behavior and characteristics on the web. However, there are not many studies that mine such data to learn and discover user characteristics and profiles. The main goal of this study is to mine eye tracking data by machine learning methods to create data models which characterise users and predict their characteristics, in particular, familiarity and gender. Detecting users’ characteristics can be used in creating adaptive user interfaces to improve user experience and interaction efficiency. In a typical eye tracking study, collected demographics data have participants’ educational backgrounds, gender, age, and familiarity degree to a web page (subjectively). In this thesis, a model focusing on the users’ familiarity degree and gender is first created based on an existing eye-tracking dataset, and then a new eye-tracking study is conducted to validate this model. The main contribution of this thesis is a machine learning approach that can be used to characterise users, in particular, familiarity and gender, based on eye-tracking data and also a tool that can be used to extract features and metrics from an eye-tracking dataset.

Title: SECURITY VISUALIZATION INFRASTRUCTURES, TECHNIQUES, AND METHODOLOGIES FOR IMPROVED ENTERPRISE SECURITY 

PhD Candidate: Fatma Ferda Özdemir

Program: Informations Systems Department 

Date: 17 June 15:30

Place: Conference Hall-01

Abstract: This thesis represents research focuses on providing designs to allow monitoring of the security status of enterprises at the organization level. The audience of this research is all enterprise level IT and security experts, and the other users who may be engaged in the security visualization designs, including the top level management. Numerous tools and programs are being used to analyze to overcome security vulnerabilities of the organizations. However, the outputs of these programs are rarely understood clearly. During the research, existing security visualization requirements and designs along with the corresponding technologies used for security visualization are examined. For the sake of being user-centric, a visualization requirements survey is held. The results of the literature review and the survey are converted to a substantial requirement set for a generic enterprise security visualization infrastructure. This infrastructure is implemented using industry best standards and the contemporary big data solutions. The resulting design is validated through the use of expert reviews. Later, one of the favorite security visualization subjects for the enterprises, namely web application security is depicted. A dashboard type holistic design to visualize black-box vulnerability test results is proposed along with forty plus metrics and measures. SIEM systems are also examined for their custom data visualization capabilities in parallel to this part of the study. Finally, security management related issues for the organizations was focused. In this part of this study , a decision support system for the optimization of security costs which relies on analytical methods and uses treemap type visualizations to visualize the threats, risks, corresponding precautions, and the costs is proposed. A real-world case study is used to demonstrate the benefits of this system.

Title: MOBILE USER DATA MINING FOR INFERRING INDIVIDUAL’S DIFFERENCES IN INFLUENCE STRATEGIES

PhD Candidate: Şeyma Çavdar

Program: Informations Systems Department 

Date: 28 August 13:00

Place: Conference Hall-01

Abstract: Owing to the widespread and ubiquitous nature of mobile technologies, a large amount of data about users including location, access and interaction behavior is currently available. These data have recently become important as it has the potential to reveal personal information and user characteristics such as personality traits. In the literature, data of mobile phone use (such as number of calls, messages) is generally collected with questionnaires or special applications and then analyzed. However, self-reported data is often difficult to collect as well as mobile call data is limited for inferring user preferences and characteristics. In addition, people increasingly use different types of mobile applications. In this thesis, personal data obtained by mobile applications will be used and analyzed with data mining techniques in order to infer personality characteristics of users (e.g. BIG5) and how they are affected by influence strategies (e.g. Cialdini’s six strategies). The results are expected to improve personalization of mobile applications and develop successful user profiles. The health domain will be selected in this thesis.

For further information please visit Graduate Awards.

Application deadline:10 May 2019