66
Archived Theses
0
DOIs Assigned
0%
DOI Rate
Archived Theses
Denetçilerin bilgisayar destekli denetim araç ve tekniklerini kabulünü etkileyen faktörlerin belirlenmesi: Ampirik bir araştırma
Increasing use of Information Technologies in organizations both private and public, audit activities has become more complicated for audit bodies. At this stage, Computer Assisted Audit Tools and Techniques (CAATTs) provide many advantages to auditors to carry out their tasks in an effective and efficient manner in such an environment and expansion of CAATTs usage plays an important role for auditors and organizations. In order to increase usage of CAATTs, it is critical to know what factors are significantly affecting the adoption decision. In this respect, the main objective of this study is to reveal the factors affecting the Acceptance or Adoption of CAATTs by auditors. For this purpose, this study empirically explores the variables impacting use of CAATTs by Turkish auditors. As a result, a CAATTs adoption model is created in this study. In the scope of this study, firstly, studies related with the adoption of CAATTs were reviewed from 2000 to end of February 2019. This review gives information about past research on the field. At the end of the literature review, most significant factors affecting the CAATTs adoption are identified. Then, a technology adoption model and related hypotheses are proposed in the light of information derived from literature review. To test the hypotheses a quantitative method (questionnaire) is followed. Data is collected from auditors from Turkey. The model is tested using Structural Equation Modelling with Partial Least Squares (SEM-PLS). Inter-factor relationships are also introduced to the model after outcomes are obtained. At the end, the model's final version is developed and the most significant factors affecting the adoption of CAATTs by auditors are exposed.
Yüz yüze iletişime bakış merkezli çok modlu yaklaşım
Face-to-face conversation implies that interaction should be characterized as an inherently multimodal phenomenon involving both verbal and nonverbal signals. Gaze is a nonverbal cue that plays a key role in achieving social goals during the course of conversation. The purpose of this study is twofold: (i) to examine gaze behavior (i.e., aversion and gaze on face) and relations between gaze and speech in face to face interaction, (ii) to construct computational models to predict gaze behavior using high-level speech features. We employed a job interview setting, where pairs (a professional interviewer and an interviewee) conducted mock job interviews. Twenty-eight pairs of native speakers took part in the experiment. Two eye-tracking glasses recorded the scene video, the audio and the eye gaze position of the participants. To achieve the first purpose, we developed an open-source framework, named MAGiC (A Multimodal Framework for Analyzing Gaze in Communication), for the analyses of multimodal data including video recording data for face tracking, gaze data from the eye trackers, and the audio data for speech segmentation. We annotated speech with two methods: (i) ISO 24617-2 Standard for Dialogue Act Annotation and, (ii) using tags employed by the previous studies that examined gaze behavior in a social context. We then trained simplified versions of two CNN architectures (VGGNet and ResNet) by using both speech annotation methods.
Hepatosellüler karsinomda hedefe yönelik moleküler ajan tedavilerinin ağa dayalı keşfi
Hepatocellular carcinoma (HCC) is one of the most-deadly cancers and the most common type of primary liver cancer. Multikinase inhibitor Sorafenib is one of FDA approved targeted agents in HCC treatment. PI3K/AKT/mTOR pathway is altered in about 51% of HCC; hence, understanding how Sorafenib and PI3K/AKT/mTOR pathway inhibitors act at signaling level is crucial for targeted therapies and to reveal the off-target effects. In this work, we use gene expression profiles (GEPs) of HCC cells (Huh7 and Mahlavu) which were treated with seven different agents and their combination. Our aim is to reveal the important targets and modulators in agent treatments by inferring the dysregulation of Interactome. In other words, we search for the mechanism of action of the agents in a network context beyond the list of genes. For this purpose, we use the DeMAND (Detecting Mechanism of Action based on Network Dysregulation) algorithm developed by Califano Lab. DeMAND compares GEPs and assesses the change in the individual interactions from weighted interactome obtained from STRING database. As a result, we reconstructed 18 agent-specific networks from each GEPs. Each gene and interaction within these networks have a value signifies how strongly these genes are affected from the chemical network perturbation. Then, we found enriched pathways in each network. We initially compared the networks of single agents and their combination; i.e. PI3Ki-α, Sorafenib and their combined treatment. Then, we compared all networks simultaneously. The simultaneous comparison of the reconstructed networks at gene and pathway levels shows that several pathways and proteins are commonly affected across agent treatments (e.g., Wnt, HIF-1, Notch pathways and MCM proteins, mTOR). On the other hand, some pathways are only affected in a specific agent treatment (e.g., SNARE interactions).
Çoklu-yıl zaman serisi ürün haritalama
Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. However, most crop mapping studies are limited to same-year crop mapping in which the present year's labeled data is used to predict the same year's crop map. Classification accuracies of these methods degrade considerably in cross-year mapping. Cross-year crop mapping is more useful as it allows the prediction of the following years' crop maps using previously labeled data. We propose Vector Dynamic Time Warping (VDTW), a novel multi-year classification approach based on the warping of angular distances between phenological vectors. The results prove that the proposed VDTW method is robust to temporal and spectral variations compensating for different farming practices, climate and atmospheric effects, and measurement errors between years. We also describe a method for determining the most discriminative time window that allows high classification accuracies with limited data. We carried out tests of our approach with Landsat 8 time-series imagery from years 2013 to 2015 for classification of corn and cotton in the Harran Plain, and corn, cotton, and soybean in the Bismil Plain of Southeastern Turkey. In addition, VDTW was tested with corn and soybean in Kansas, the US for 2017 and 2018 with the Harmonized Landsat Sentinel data. The VDTW method improved the cross-year overall accuracies by 3% with fewer training samples compared to other state-of-the-art approaches including spectral angle mapper (SAM), dynamic time warping (DTW), time-weighted DTW (TWDTW), random forest (RF), support vector machines (SVM) and deep long short-term memory (LSTM).
Ses alanı dışdeğerlendirmesi ile 3 boyutlu algısal ses alanı oluşturma
Perceptual sound field reconstruction (PSR) is a spatial audio recording and repro-duction method based on the application of stereophonic panning laws in microphone array design. PSR allows rendering a perceptually veridical and stable auditory per-spective in the horizontal plane of the listener, and involves recording using near-coincident microphone arrays. This thesis extends the two dimensional PSR concept to three dimensions and allows reconstructing an arbitrary sound field based on measurements with a rigid spherical microphone array. This work offers a method for em-ulating near coincident microphone recordings by using rigid spherical microphonearrays via sound field extrapolation carried out in the spherical harmonic domain.An active intensity-based analysis of the rendered sound field shows that the proposedapproach can render direction of monochromatic plane waves accurately even with astraightforward extension of PSR directivity patterns designed for the 2D case. Forthe real recordings, listening tests are conducted using binaural audio recordings of the reconstructed sound field and compared with higher order ambisonics recordings.
Metin tabanlı captcha araçlarında görsel özelliklerin rolü: kullanılabilirlilik için fnirs çalışması
In order to mitigate dictionary attacks or similar undesirable automated attacks to information systems, developers mostly prefer using CAPTCHA challenges as Human Interactive Proofs (HIPs) to distinguish between human users and scripts. An appropriate use of CAPTCHA requires a setup balance between robustness and usability during the design of a challenge. The previous research reveals that most of the usability studies have used accuracy and response time as measurement criteria for quantitative analysis. The present study aims at applying optical neuroimaging techniques for the analysis of CAPTCHA design. In particular, fNIRS (Functional Near Infrared Spectroscopy) is a neuroimaging technique used for mental workload analysis by means of analyzing hemodynamic responses on brain. The present study reports an experimental investigation in which 25 participants solved a group of text-based CAPTCHA with various visual characteristics.
Tekil amino asit mutasyonlarının protein işlevleri üzerindeki etkisinin yapısal ve anotasyon odaklı yaklaşımla tahmini
Whole-genome and exome sequencing studies have indicated that genomic variations may cause deleterious effects on protein functionality via various mechanisms. Single nucleotide variations that alter the protein sequence, and thus, the structure and the function, namely non-synonymous SNPs (nsSNP), are associated with many genetic diseases in human. The current rate of manually annotating the reported nsSNPs cannot catch up with the rate of producing new sequencing data. To aid this process, automated computational approaches are being developed and applied on the unknown data. In this study, we propose a new methodology to collect and organize the information related to the effects of nsSNPs at the amino acid sequence level from various biological databases and to utilize this information in a supervised machine-learning based system to predict the function disrupting capacities of mutations with unknown consequences. For this, 157,138 annotated mutation data points (89,363 deleterious and 67,775 neutral) were collected from multiple resources such as UniProt, ClinVar and Protein Mutant Database. For each mutation data point, a feature vector was constructed using protein 3-D structure information and site-specific feature annotations in the UniProt database. The information about the spatial proximity of the reported mutations to these protein features were also incorporated to the feature vector. The system was trained with these feature vectors and their respective labels in a supervised fashion using random forest, where the ultimate aim was to construct a model that classifies unknown mutations either as deleterious or neutral. The prediction model was evaluated in detail to observe the contribution of different feature types to the prediction success. The finalized model displayed a satisfactory performance (AUROC:0.86, precision: 0.77, recall 0:90, accuracy: 0.78, F1-score: 0.83 and MCC: 0.54) on the independent test dataset. Besides, the performance of the proposed model was compared to the widely used variant effect predictors in the literature, over standard benchmark datasets. As future work, we plan to conduct a case study over interesting prediction examples and to validate our results via literature-based information. Finally, we plan to construct a ready-to-use command line based variant effect prediction tool and to share it with the research community over an open access data repository. We believe that this system will be complementary to the well-known methods in the literature and its incorporation to ensemble-based tools will increase the performance of the state-of-the-art in variant effect prediction.
Matris factorizasyonu yöntemi ile biyolojik veri entegrasyonu ve ilişki tahmini
The available molecular sequence data has increased greatly in the last decades, thanks to the new technological developments in the field of life-sciences. In order for this data to be useful to the scientific community, it should be characterized. Traditionally, this characterization is done manually, where the experimentally produced molecular data is curated and stored in the biological databases. The huge volume of the currently available data summons the need for the automatic and systematic analysis. A crucial part of this systematic analysis is data integration with the identification of the relationships between the elements from different biological data types. In this study, we propose to integrate large-scale gene/protein annotation data by using non-negative matrix factorization (NMF), which is a frequently used method for recommender systems with successful real-world applications. NMF has also been employed for uniting multi-relational data in many different fields including bioinformatics and cheminformatics. Within the purposes of this study, we first collected protein annotations such as molecular functions, biological processes, sub-cellular localizations and disease relations from different resources such as UniProt-GOA and DisGeNET, and organized them as binary relation matrices. We then applied various NMF-based algorithms to this multi-dimensional relational biomolecular sequence annotation data (i.e. genes/proteins vs. functions, genes/proteins vs. diseases, diseases vs. functions) and evaluated the results of each model in terms of their capacity to learn the intrinsic structure in relational data, via cross-validation. The results indicated that NMF has the capacity to retrieve most of the known protein annotations without using any sequence or structure-based protein features (AUROC: 0.80 – 0.94, accuracy: 0.53 – 0.64, F1-score: 0.06 – 0.40, MCC: 0.13 – 0.38). Using NMF, the ultimate aim here is to predict the unknown binary relationships between these biological entities; and to represent these entities (i.e., proteins, functions and disease entries) as informative and non-redundant quantitative feature vectors (using the low-rank feature matrices generated by the factorization process), which can be used in diverse data mining and machine learning tasks in the future, such as the automated annotations of proteins or the construction of biological knowledge graphs.
Kelimeleri hecelerine bölmenin okuma güçlügü çeken çocukların okuma performansı üzerine etkileri: Okuma becerileri geri kalmış çocuklar için yardımcı bir teknik
There is a growing body of evidence supporting the idea that dyslexia exhibits itself differently in different languages. Based on how reading is taught in Turkish Language, the symptoms exhibited by students with reading difficulties may be different than the ones exhibited by English speaking students. Even though there is no support for the role of reading syllables as a reading unit in the literature, it is clear that the students in Turkey are taught how to read syllables and are actively using the strategy of reading a word syllable by syllable in their early reading development. Decoding words syllable by syllable, naturally, requires segmentation of the words into their subunits, namely syllables. During the slow and struggling serial decoding of syllables, which is also based on serial decoding of phonemes to corresponding graphemes, the problem is transformed into bringing the correct number of phonemes together since the syllable length is not constant. It entails an increase in the number of the mistakes while reading a word in relation to its complexity in terms of the number and the variability of the syllables it includes. Deciding how many phonemes are supposed to be brought together and isolating them from the other graphemes in the word until the decoding of the syllable finishes is of critical importance during this process. The current study tested and found significant effects of segmenting the words into its syllables on behalf of the learner. The results suggest that aiding the segmentation process significantly improved the pronunciation of the syllables and words and decreased the number of mistakes during reading. It is hoped the results will help a better understanding of reading difficulties in Turkish, which in turn might help the development of more effective intervention techniques to the problem at hand.
Oryantiring sporunda rota özelliklerinin karar vermeye etkisi üzerine bir durum çalışması
Orienteering is a sport where athletes need to find located targets at certain points on a predetermined terrain or in the city with the help of a map. In this sport where performance is measured with time, it is important to combine their physical endurance with mental processes and their ability to adapt to the environment and optimize them correctly. One of the outcomes that we can best observe these choices is the routes chosen and each route has its own environmental characteristics. Therefore, athletes need to analyze these characteristics well and as a result, they need to choose the most suitable route for themselves. In this thesis, the components affecting route selection are investigated. For this purpose, athletes' data was obtained through GPS containing watches from an orienteering race held by Turkey Orienteering Federation. The collected data were examined by quantitative and qualitative methods, and a general understanding of athletes' behavior was obtained and the distinction of modelable and subject-dependent factors in decision making of athletes was made. From the modelable components, a model that computes the shortest distance based on the distance and terrain surface has been created and then its compatibility with the behaviors of athletes was examined. Additionally, the results were supported by various statistical analyzes. According to the results of the study, environmental variables play a major role in the decision-making of athletes and model performance is more accurate in short-distance routes than long-distance routes in greater need of reducing the cognitive load.