Thesis defense

thesis defense

Thesis defense - Galip Oral Okan

Graduate School of Informatics /Cyber Security

In partial fulfillment of the requirements for the degree of Master of Science Galip Oral Okan will defend his thesis.

Title: LYNXTUN

Date: 03th Sptember 2018

Time: 14:30 PM

Place: A-108

Thesis Abstract : Lynxtun is a VPN solution that allows the creation of a secure tunnel between two hosts over an insecure network. The Lynxtun Protocol transmits fully encrypted datagrams with a fixed size and at a fixed interval using UDP/IP. Our custom authenticated encryption scheme uses the AES-256 block cipher and modified version of GCM mode in order to decrypt and authenticate datagrams efficiently. It ensures traffic flow confidentiality by maintaining a constant bitrate that does not depend on underlying communication. In this sense, it provides unobservable communication. This constitutes a difficult engineering problem. The protocol design allows implementations to fulfill this requirement. We analyze factors that influence realtime behavior and propose solutions to mitigate this. We developed a full implementation for the GNU/Linux operating system in the C programming language. Our implementation succeeds in performing dispatch operations at the correct time, with a tolerance on the order of microseconds, as we have verified empirically.

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Thesis defense - Faruk Büyüktekin

Graduate School of Informatics /Cognitive Sciences

In partial fulfillment of the requirements for the degree of Master of Science Faruk Büyüktekin will defend his thesis.

Title: MULTI-MODAL REFERENCE RESOLUTION IN SITUATED DIALOGUE THROUGH LINGUISTIC AND EXTRA-LINGUISTIC INFORMATION

Date: 31st August 2018

Time: 10:00 AM

Place: A-108

Thesis Abstract : This thesis investigates the effect of extra-linguistic information incorporated into linguistic information on multimodal reference resolution in Turkish. Identifying reference relations such as anaphora and coreference within texts is quite critical in the fields of natural language processing and human computer interaction since such applications require interpreting the text. Previous research on reference resolution has employed mostly hand-crafted rule-based approaches. However, due to the success of machine learning algorithms, there has been a shift towards corpus-based approaches. Therefore, investigations of reference resolution within real world situations have received a great deal of attention. In line with these developments, this study focuses on a Turkish corpus which is based on the dialogues of two participants collaboratively solving a Tangram puzzle. The corpus includes eye gaze and mouse operations of the participants along with their utterances. This study offers a model that identifies the referent (the object) of a referring expression in the real world by taking eye gaze and operations on puzzle pieces into account. The model is built making use of machine learning algorithms. The model also examines which type of extra linguistic information is more successful in determining the referents of pronouns, non-pronouns and both within a machine learning based-approach.

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Thesis defense - Çağatay Taşcı

Graduate School of Informatics /Cognitive Sciences

In partial fulfillment of the requirements for the degree of Master of Science Çağatay Taşcı will defend his thesis.

Title: AN INVESTIGATION OF BRAIN-TO-BRAIN CONNECTIVITY PATTERNS DURING A COOPERATIVE FLUID INTELLIGENCE TASK

Date: 31st August 2018

Time: 11:30 AM

Place: A-108

Thesis Abstract : Several studies have shown that executing the same task simultaneously can create synchronization among participants’ brain hemodynamics (Funane et al. 2011; Osaka et al. 2015). In this thesis we aim to examine multiple participants’ brain hemodynamics while they engage with a cooperative Raven’s matrices task which requires them to combine and coordinate the information they individually posses to correctly solve the given puzzle. We will use NIRS (Near-infrared spectroscopy) hyperscanning to observe the brain hemodynamics of two participants simultaneously while they are engaged with a fluid intelligence task organized in a jigsaw format. Machine learning techniques will be employed to extract patterns from raw neuroimaging data in terms of brain-to-brain connectivity patterns in the prefrontal cortex to predict the level of success. We expect to observe increased coherence in the partners’ prefrontal cortices during successful trials. Through this study we aim to contribute to the literature with new findings on brain-to-brain correlates of social interaction.

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Thesis defense - Alaz Aydın

Graduate School of Informatics /Cognitive Sciences

In partial fulfillment of the requirements for the degree of Master of Science Alaz Aydın will defend his thesis.

Title: CORTICAL PROCESSES UNDERLYING METACONTRAST MASKING ACROSS DIFFERENT CONTRAST POLARITIES

Date: 31st August 2018

Time: 14:00 PM

Place: A-108

Thesis Abstract :In this study a metacontrast masking experiment is conducted together with electroencephalography (EEG) recordings in order to investigate the neural mechanisms underlying visual masking phenomena. We employed a feature-based detection task under same and opposite target-mask contrast polarity conditions, together with varying onset asynchronies. Behavioral experiments resulted in unimodal type- A and B backward masking functions for opposite and same contrast polarity conditions, respectively. Relying on this difference as an indication of non-identical neural mechanisms we collected EEG data for an exploratory analysis.

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Thesis defense - Rauf Kaan Denizer

Graduate School of Informatics /Cyber Security

In partial fulfillment of the requirements for the degree of Master of Science Rauf Kaan Denizer will defend his thesis.

Title: AN INVESTIGATION OF GRAMMATICAL PATTERNS IN USER PASSWORDS: A PROBABILISTIC CONTEXT-FREE GRAMMAR APPROACH

Date: 03th Sptember 2018

Time: 13:00 PM

Place: A-108

Thesis Abstract : Websites and applications mostly implement authentication methods that employ an account name and a password. According to a recent survey, an e-mail address is associated with 130 different user accounts on average in the US. The high number of user accounts and passwords enforces users to create weak or easily guessable passwords with common patterns such as adding numbers at the end of a password. Developing more complex and costly protective techniques for a password motive attackers to develop methods that are able to guess passwords rather than brute forcing them. Recently studies mostly involve the analyses from users with English as their native language. On the other hand, users who speak different languages may exhibit different patterns while creating passwords. This thesis focuses on grammatical patterns in passwords created by users with Turkish as the native language. We present the findings as well as a framework for probabilistic context-free grammar and its evaluation.

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Thesis defense - Alperen Taciroğlu

Graduate School of Informatics / Bioinformatics

In partial fulfillment of the requirements for the degree of Master of Science Alperen Taciroğlu will defend his thesis.

Title: ODYSSEY: A TOOL FOR MICRORNA-MRNA EXPRESSION AND INTERACTION VISUALIZATION

Date: 03th September 2018

Time: 14:00 PM

Place: A-212

Thesis Abstract : MicroRNAs (miRNAs) are non-coding short RNA molecules that are found in all metazoa studied so far. When distinct metazoa genomes considered up to 200 genes encode for unique miRNAs that show variability between species. Regulatory functions of miRNAs have been studied for 20 years starting after their discovery. The research suggests that they are involved in a wide spectrum of biological activities including apoptosis, tumorigenesis, development, homeostasis and viral infections. miRNAs regulate these cellular processes at the posttranscriptional level by binding to the messenger RNAs (mRNAs), leading to an unstable derivative of the initial biological molecule. miRNA targets are under strict evolutionary pressure which further implicates the importance of underlying biological mechanisms. Although there are several Gene/mRNA-miRNA interaction visualization and analysis tools "Odyssey" was built for improved interactive visualization the interaction network of miRNAs with along with their target expressions for a user uploaded dataset. It is built using Shiny package of the R programming language leading to seamless online access and modularity. In the end, I aim to provide users a user-friendly web-application which consists of modules that allows: uploading of their own data; performing differential expression (DGEx) analysis; and visualization of the network of which "Odyssey" builds from either experimentally validated or predicted interactions for individual miRNAs queried by the user. Odyssey further enables the user to filter selected nodes of the networks using fold change cut-offs obtained in DGEx step or expand the network using Gene Ontology (GO) terms to act as a strong predictor of the phenotype of interest for the user-specified biological data. Furthermore, the application has been demonstrated using two different public miRNA-mRNA expression datasets.

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Thesis defense - Mani Tajaddini

Graduate School of Informatics /Cognitive Sciences

In partial fulfillment of the requirements for the degree of Master of Science Mani Tajaddini will defend his thesis.

Title: RECURRENCE QUANTIFICATION ANALYSIS ON GROUP EYE TRACKING DATA

Date: 28nd August 2018

Time: 10:00 AM

Place: A-108

Thesis Abstract : Eye movements can provide insight into the cognitive processes behind them. To study group cognition through investigating eye movements, we have developed a software tool (GETapp) to collect eye movement data from groups of participants performing a task on a set of computers in the scope of the Group Eye Tracking (GET) paradigm. Like many real-world systems, the data from group eye tracking experiments are non-linear. We have developed a software tool, a package for the R programming language, called the generalRQA package for analyzing these non-linear data. The methods used in this package are based on recurrences of time-series and implement a part of the theory of Recurrence Quantification Analysis (RQA). In this thesis we describe both the software tools and apply them to propose measures that differentiate between different group formation patterns in a group eye tracking search task.

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Thesis defense - Efecan Yılmaz

Graduate School of Informatics /Cognitive Sciences

In partial fulfillment of the requirements for the degree of Master of Science Efecan Yılmaz will defend his thesis.

Title: DEICTIC GAZE IN VIRTUAL ENVIRONMENTS

Date: 28nd August 2018

Time: 11:30 AM

Place: A-108

Thesis Abstract : The research in human-robot interaction (HRI) involve topics, such as interlocutor collaboration in joint action, deixis in HRI, or the properties of shared environments. Moreover, referring expressions are particularly studied in joint action from both expression generation and resolution perspectives. Selective visual attention in gaze interaction and saliency patterns are also active topics in HRI. The present thesis investigated in a virtual reality (VR) environment an HRI and joint action situation with the assistance of eye tracking in a head-mounted display device in order to explore the augmentation of non-verbal communication in HRI. For this purpose, we employed a multimodal approach in communication with both non-verbal deictic expressions and verbal references in a multi-robot agent, single human experiment setting. We also utilized two distinct robot agent designs to explore an interaction effect in robot agents’ design as we evaluated participants’ deixis resolution time and accuracy, as well as their gaze interaction patterns.

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Thesis defense - Alper Sarıkaya

Graduate School of Informatics /Information Systems

In partial fulfillment of the requirements for the degree of Master of Science Alper Sarıkaya will defend his thesis.

Title: ANOMALY-BASED CYBER INTRUSION DETECTION SYSTEM WITH ENSEMBLE CLASSIFIER

Date: 06th September 2018

Time: 14:00 PM

Place: A-212

Thesis Abstract : Nowadays, cyber attacks are beginning to occur at an increasing rate. Along with this, diversity, size and density of the cyber attacks are increasing accordingly. When we analyze log in the security devices, we find that the massive amount of attacks sign was created. Besides, It is also difficult for human to evaluate the log accurately. Therefore, the identification of key data, which can be used to distinguish attack from this very large data set, is important for both rapid detection of attack and rapid response of security devices. In this study, we will focus on selection of appropriate data from the log via machine learning and determine the distinctive attributes specific to the attack in the selection of these data. Thus, we will research on the classification of the data to be used in the attack detection via machine learning.

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Thesis defense - Betül Aygün

Title: APPLICATION OF BINARY PSO TECHNIQUE FOR THE SOLUTION OF MATHEMATICAL MODELLING OF PUBLIC CLOUD RESOURCES ALLOCATION SYSTEM FOR VIDEO ON DEMAND (VOD) APPLICATIONS

PhD Candidate: Betül Aygün

Program: Informations Systems Department 

Date: 03 September 14:00

Place: Conference Hall-01

Abstract: Video streaming services whether on demand or live has become one of the most popular services used recently. However, investments made for these type of applications cause a very serious financial problem just because video type of multimedia data needs more real time storage and high data transfer than other type of multimedia data. Furthermore, for the video streaming applications, significant amount of system resource in computing is required. To tackle this problem, cloud computing emerges as a preferred technology. Cloud services organizations are becoming more and more sophisticated as they enable the organizations to offer services without investing in hardware or software. A huge number of cloud service providers offer different pricing methods for various applications in various regions. For this reason, it is of great importance that incoming service requests are assigned to appropriate cloud services with minimum cost and maximum user satisfaction (QoS). Because of issues like multiple cloud providers, different quality of service requirements, different service level agreements (SLA) and uncertainties in demand, price and availability, optimization of resource allocation has some challenges. The objective of this study is to optimize the cost and performance of video on demand services using cloud CDNs, storage and transcoders based on QoS requirements of users. In this paper, Mixed Integer Quadratic Programming (MIQP) and different variants of Particle Swarm Optimization (PSO) algorithm are used to schedule video requests to cloud resources to achieve minimum cost of cloud services and maximum of user satisfaction. Due to the nature of the problem, it is not possible to use the classic PSO, but the new algorithms which combine Binary PSO with heuristics algorithms are proposed. These algorithms are compared with LP algorithms which gives best result. The results show that proposed algorithms yield better results than the benchmarking algorithms.

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