Musical conducting is the art of making real time manipulations on a musical piece as if the instruments are the musicians themselves. Orchestra responses affect the conductor’s actions while a musical piece is being played. Therefore there is a significant information flow on the stage. In this research, we focused on the causality relations between the conductor, orchestra and the musical scores. We collected data from an actual orchestral practice and processed the collected data for causality measurements using the Transfer Entropy method. We utilized the method also for unsupervised detection of conducting commands.

Date: 20.01.2023 / 10:00 Place: A-108

In Türkiye, Social Security Institution is the primary healthcare insurer. Turkish citizens are registered under General Medicare Insurance coverage. In 2003, Ministry of Health (MoH) has initiated the “Health Transformation Program”, and implemented the interoperable health data exchange standards. The MoH is focusing on collecting medical data in a coded, structured, and electronic format, generated at all healthcare providers. Contrarily, genetic test results are exchanged in narrative, unstructured form among governmental and private health care providers. In this dissertation, we lay out the bottlenecks and put forward a conceptual model for meaningful genomic data exchange for Turkish Electronic Health Records.

Date: 18.01.2023 / 15:00 Place: B-116

The fusion of information coming from different sensors (e.g., optics, audio, accelerometer) to recognize egocentric activities is still an active research area. Although the increase in sensor diversity brings out the need for adaptive fusion, there is a limited number of studies. In this work, we proposed two novel multi-modal decision fusion frameworks for egocentric activity recognition. The first framework combines hand-crafted features using Multi-Kernel Learning. The other framework utilizes deep features using a two-stage decision fusion mechanism. Additionally, a new egocentric activity dataset, named Egocentric Outdoor Activity Dataset (EOAD), was populated, containing 30 egocentric activities and 1392 video clips.

Date: 18.01.2023 / 14:00 Place: A108

A new method has been developed to study allosteric protein regulation, which is important for understanding how proteins function. The method represents proteins as networks and identifies allosteric pathways, or sites where molecules bind and regulate protein activity, in order to predict the presence and location of allosteric regions. This could potentially aid in the development of targeted therapies.

Date: 18.01.2023 / 10:00 Place: A212

The software industry is playing a significant role in development of economies all over the world. It is mainly made up of small and medium software enterprises (SMEs). These companies aim to benefit from Software Process Improvements (SPI) to increase product quality and productivity. As SPI require organizational change and adaptation to new work practices; organizations have to handle with several challenges emerged from this change. The objective of this study is to identify the factors that influence the success and the acceptance of SPI models used in SMEs. Moreover, it is aimed to analyze the attitude of SME employees towards the SPI models in Turkey.

Date: 25.01.2023 / 14:00 Place: B-116

The statistical analysis of the microbiome data obtained by the Next Generation Sequencing technology assumes that the data lies on the real space. Since 2017, it has been argued that microbiome data analyzes should be performed on simplex, that is a subspace of real space. Because, microbiome data is "compositional". In this thesis, the characteristics of microbiome data and the statistical difficulties of microbiome analysis are examined. Then, a new grouping method (Principal Microbial Groups) was introduced, using the compositional nature of the data, which is an alternative to the phylogenetic grouping of microbiome data.

Date: 18.11.2022 / 11:30 Place: A212

In deep learning, small input shifts or translations can cause dramatic changes in the output. This is because of the fact that commonly used down-sampling techniques such as max pooling, strided convolution, and average pooling ignore the sampling theorem. We have demonstrated that the cycle-spinning (CS) signal processing technique can be used before down-sampling in deep learning to increase accuracy without introducing any extra learnable parameters. The proposed method can be applied to different algorithms such as GAN, classification, and object detection.

Date: 29.11.2022 / 13:00 Place: METU Informatics Institute

Developing a tool having the capability of predicting future AAA growth rate is important in terms of surgical planning and patient management. In this study, 106 CT scan images from 25 Korean AAA patients were retrospectively obtained. 21 geometrical measurements derived from scans, their growth rates, and their pairwise correlations were analyzed, and the predictability of the growth for high-risk aneurysms was attempted to enhance. Furthermore, the prediction model was built specifically on patient characteristics using the various geometrical measurements enhanced the prediction capability of a measurement at any time-point, along with an evaluation of the associated uncertainty.

Date: 02.03.2020 / 12:40 Place: Conference Hall-1

In this thesis, we offer a context-aware security model extending the role-based access control model in order to prevent relay attacks in NFC enabled mobile devices with both theoretical and practical approach. Within this study, we identify possible vulnerabilities and requirements then design the model. Parallel to conceptual design, we also developed a complete test-bed to deploy the model on it. Finally, we verified the model theoretically and practically.

Date: 27.01.2020 / 16:00 Place: Conference Hall-1

Vector Dynamic Time Warping (VDTW), a novel multi-year classification approach based on warping of angular distances between phenological vectors was developed. 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.

Date: 15.01.2020 / 14:00 Place: Conference Hall-1