Mustafa Ersel Kamaşak

The personalities of the group members working on a software development project affect the success of the work result, depending on the suitability of the team members for their roles. Therefore, it is important to determine the roles of team members in software projects according to their personality differences. This study proposes an innovative and creative expert system for determining the appropriate roles of software engineers in software projects and talent management in the software industry. The most obvious aspect of the proposed system is that it consists of questions that do not evoke the feeling of personality analysis for those who take the test. The questions are designed to be relevant to the tasks and natural behaviors of the software engineers in their projects. Software engineers often do not believe in psychometric tests and try to manipulate them. Furthermore, the number of questions in these tests is quite large, so they can get bored. The proposed test was compared with the results of the Jung-Based Software Talent Management (JBSTM) and the Keirsey Temperament Sorter test. These two tests were applied to 20 software industry employees in a single session in a random order. A comparison questionnaire was completed after the tests. A moderate correlation between JBSTM and the Keirsey test appeared in E / I, T / F and J / P. The JBTSM and Keirsey test results were also compared in terms of test completion times. Using the results of the comparison questionnaire, the JBTSM and Keirsey test were also compared in terms of easiness, suitability, comfort and usefulness, and it was found that the JBTSM received higher scores compared to the Keirsey test in all four respects.

In fluorescence microscopy imaging, noise is a very usual phenomenon. To some extent, it can be suppressed by increasing the amount of the photon exposure; however, it is not preferable since this may not be tolerated by the subjected specimen. Thus, a sophisticated computational method is needed to denoise each acquired micrograph, so that they become more adequate for further feature extraction and image analysis. However, apart from the difficulties of the denoising problem itself, one main challenge is that the absence of the ground-truth images makes the data-driven techniques less applicable. In order to tackle this challenge, we suggest to tailor a dataset by handpicking images from unrelated source datasets. Our tailoring strategy involves exploring some low-level view-based features of the candidate images, and their similarities to those of the fluorescence microscopy images. We pretrain and fine-tune the wellknown feed-forward denoising convolutional neural networks (DnCNNs) on our tailored dataset and a very limited amount of fluorescence images, respectively to ensure both the diversity and the content-awareness. The quantitative and visual experimentation show that our approach is able to curate a dataset, which is significantly superior to the arbitrarily chosen source images, and well-approximates to the fluorescence images. Moreover, the combination of the tailored dataset with a few fluorescence data through the use of fine-tuning offers a good balance between the generalization capability and the content-awareness, on the majority of considered scenarios.

Bu çalışmada açık kaynak kodlu teknolojiler ile geliştirilen yakın gerçek zamanlı iş zekâsı uygulaması açıklanmakta ve elde edilen sonuçlar irdelenmektedir. Uçtan uca kullanılacak bir raporlama aracı her sektörden şirketin ihtiyacı olup açık kaynak raporlama araçları lisans maliyetleri konusunda şirketlerin yükünü oldukça hafiflettiği için tercih edilmiştir. Yapılan çalışmalarla alt yapı olarak büyük veri üzerindeki çalışmaları, tahminleme modelleri, karar destek algoritmaları ve öğrenen modellerin geliştirilebileceği bir altyapı geliştirilerek, geniş bir veri tabanı portföyünü, yakın gerçek zamanlı raporlamayı, mobil cihazlarla uyumluluğu destekleyen iş zekâsı uygulaması yaklaşık 1000 kullanıcının kullanımına açılmıştır.

Müşterilere yapılan anlık kur teklifleri, bankacılık sektöründeki en kritik konular arasında yer almaktadır. Verilen tekliflerin uygun seviyede olması hem banka hem de müşteri açısından oldukça önemlidir. Bu çalışmada, müşteriye verilen kur tekliflerinin tahmini için yaklaşık 8 aylık veri kullanılmış ve tahmin modelleri tasarlanmıştır. Toplamda 18 farklı kur üzerinden çalışma yürütülmüştür. Çalışmada bağımlı değişkenler müşteri segmenti, anlık kur değeri, gün bilgisi, saat bilgisi ve volatilite değeri olarak belirlenmiştir. Bağımsız değişken ise kur marjıdır. Eğitimler günlük verilerle ve Rastgele Ağaçlar (RF), Gradyan Arttırma Makinesi (GBM), Yapay Sinir Ağları (ANN), Derin Sinir Ağları (DNN), Evrişimli Sinir Ağları (CNN) ve Elman Sinir Ağı algoritmaları kullanılarak gerçekleştirilmiştir. Algoritmaların hiper-parametrelerini bulmak için rastgele arama algoritması kullanılmış ve model eğitimlerinin sonuçları karşılaştırılarak en düşük hata değerine sahip modeller tahmin aşamasında kullanılmak üzere seçilmiştir. Başarım ölçümü için Ortalama Kare Hata (MSE) ve Ortalama Mutlak Hata (MAE) hata fonksiyonları kullanılmıştır. Üç farklı model üzerinden gerçekleştirilen eğitimlere göre yapay sinir ağları ve evrişimli sinir ağları algoritmalarının diğer algoritmalara göre daha iyi sonuçlar verdiği gözlemlenmiştir. 18 kur için tahmin süresi yaklaşık 3s'dir.

The radiation balance of our planet affect climate system that showing signs of breaking down due to the rising temperatures, melting of ice and water flows to the oceans from glaciers. In the last decade, GNSS Meteorology and Reflectometry methods are increasingly used for global climate change studies that provides important parameters such as water vapor in the troposphere and ice/sea level measured based on reflected signals. The main purpose of the study is retrieving meteorological and physical parameters of the Earth's surface in the Antarctica to contribute monitoring climate change. For this purpose, dual antenna and single antenna GNSS stations were specially designed within the scope of TUBITAK research project 118Y322 to produce output by combining an ultrasonic sensor to detect real-time ice/sea level. These two GNSS stations including meteorological station were installed on Horseshoe Island during 4th National Antarctic Science Expedition of Turkey (TAE-4). It is believed that these stations will contribute to monitor global climate change by providing important information about troposphere and physical characteristics of Earth surface. In this study, the processes and objectives from the design works of the stations to their installation in Antarctica are explained.  

Direction-guided structure tensor total variation (DSTV) is a recently proposed regularization term that aims at increasing the sensitivity of the structure tensor total variation (STV) to the changes towards a predetermined direction. Despite of the plausible results obtained on the uni-directional images, the DSTV model is not applicable to the multi-directional images of real-world. In this study, we build a two-stage framework that brings adaptivity to DSTV. We design an alternative to STV, which encodes the first-order information within a local neighborhood under the guidance of spatially varying directional descriptors (i.e., orientation and the dose of anisotropy). In order to estimate those descriptors, we propose an efficient preprocessor that captures the local geometry based on the structure tensor. Through the extensive experiments, we demonstrate how beneficial the involvement of the directional information in STV is, by comparing the proposed method with the state-of-the-art analysis-based denoising models, both in terms of restoration quality and computational efficiency.

Purpose: The purpose of this paper is to propose a novel emotion recognition algorithm from multimodal physiological signals for emotion aware healthcare systems. In this work, physiological signals are collected from a respiratory belt (RB), photoplethysmography (PPG), and fingertip temperature (FTT) sensors. These signals are used as their collection becomes easy with the advance in ergonomic wearable technologies. Methods: Arousal and valence levels are recognized from the fused physiological signals using the relationship between physiological signals and emotions. This recognition is performed using various machine learning methods such as random forest, support vector machine and logistic regression. The performance of these methods is studied. Results: Using decision level fusion, the accuracy improved from 69.86 to 73.08% for arousal, and from 69.53 to 72.18% for valence. Results indicate that using multiple sources of physiological signals and their fusion increases the accuracy rate of emotion recognition. Conclusion: This study demonstrated a framework for emotion recognition using multimodal physiological signals from respiratory belt, photo plethysmography and fingertip temperature. It is shown that decision level fusion from multiple classifiers (one per signal source) improved the accuracy rate of emotion recognition both for arousal and valence dimensions.