INVESTIGATION OF HIGH SCHOOL STUDENTS' COMPUTER ATTITUDES IN TERMS OF CERTAIN VARIABLES

The aim of this study is to research the role played by various factors (such as gender, the parents' level of education, level of income, the presence or not of a computer inside of the household, the type of school and class attended) in the attitudes towards computer of the students of different types of highschools. In order to do this, 340 pupils studying at the general high school, professional high school, scientific high school and Anatolia high school of the city of Hakkari were, during the course of the 2010-2011 school year, submitted to a Computer Attitude Scale test along with a form destined to gather personal information. The data thus collected was then analysed using the SPSS 15.0 program. Descriptive analysis, t-test and one-way variance analyses were also performed on the data. The results of the study demonstrate that, statistically speaking, the attitudes of students towards computer vary significantly according to the type of high school they attend. However, factors such as gender, the parents' level of education, the level of income, the presence of a computer in the household and the class attended were shown to not cause significant variation

Developing An Auxiliary Tool For Treatment Of Specific Phobias Via Virtual Reality Technology Applications: An Effectiveness Study

Objective: Purpose of the study is to develop an auxiliary tool that can be used by experts working in the clinical settings for psychological support processes to adults diagnosed with simple phobia. In that realm, one another related aim is also to evaluate the effectiveness of the developed tool via behavioral and physiological measurements. Method: Eleven participants diagnosed with specific phobia (7 females (X) over bar age=38.57, SD=8.89; 4 males (X) over bar age=41.75, SD=13.07) by mental health specialist were exposed to virtual reality scenarios in a systematic desensitization manner. There were 6 person with cynophobia, 2 with arachnophobia, 1 with acrophobia and 2 with claustrophobia. Each of four phobia scenario sessions consisted of diffferent number of stages to be completed. None of the participants were received any drug medication for phobia before and also during the study. In addition to physiological meaurements like galvanic skin response (GSR) and heart rate (HR), subjective units of distress scale (SUDS) measurements were also taken before and after exposure to each stage as dependent variables. To compare different phobia scenarios, minimum, maximum and peak-to-peak amplitude values of the first and last exposure to the most feared stimuli for physiological records and first and last exposure SUDS values again for the most feared stimuli were analyzed with Wilcoxon Signed Rank Test. Results: The most fear/anxiety procuding stimuli comparisons for the first (pre-test) and the last (post-test) exposures in SUDS, GSR and HR measurements indicated that there was significant decrements in post-test measurements with respect to the ones for pre-tests. Discussion: Analyses of behavioral and physiological measurements obtained from the participants showed that the learned-fear-responses have a tendency to extinct after being exposed to the relevant stimuli virtually. Thus, virtual reality applications can be effectively usable in the treatment of specific phobias.Wo

Isparta İli Orthoptera Faunası Üzerine Ön Bir Değerlendirme

Isparta ili ve çevresindeki Orthoptera faunası ile ilgili biyolojik çeşitliliği ortaya koymak amacı ile 2000-2005 yılları arasında arazi çalışmaları yürütülmüştür. Çalışmada; Tettigoniidae, Gryllidae, Gryllotalpidae ve Acrididae familyalarından toplam 23 tür saptanmıştır. Toplanan türler toplandıkları yer ve tarih bilgileriyle birlikte sunulmuştur. Calliptamus italicus (L., 1758) ve Calliptamus barbarus cephalotes (Fischer de Waldheim, 1846) en sık rastlanılan türler olarak kaydedilmiştir. Anahtar Kelimeler

Convolutional neural network - Support vector machine based approach for classification of cyanobacteria and chlorophyta microalgae groups

WOS:000807246000005Microalgae are single-celled organisms that have been extensively utilized in biotechnology, pharmacology and foodstuff in recent years. The description and classification of many existing microalgae groups are carried out with classical methods in a long time and with a remarkably qualified labor force. Deep learning methods have achieved success in many fields are applied to the classification of microalga groups. In this study, Cyanobacteria and Chlorophyta microalga groups images are captured by using an inverted microscope. Data augmentation process has been carried out to increase the classification success in Convolutional Neural Network (CNN) models. The collected images are classified by employing two different methods. For the first method, classification is performed with seven different CNN models. In the second method, the Support Vector Machine (SVM) is used to increase the classification success of the AlexNet model with the lowest accuracy. For this, deep features which are extracted from the AlexNet model are classified with SVM. Four different kernel functions are used in the SVM classification process. The highest accuracy is found to be 99.66% among the different CNN models. AlexNet, which has the lowest accuracy with 98%, has reached 99.66% accuracy as a result of its application with SVM

On building the largest and cross-linguistic Turkish dependency corpus

In this paper, we aim to introduce the dependency annotation process of the largest and the only cross-linguistic Turkish dependency treebank which was translated from the original Penn Treebank corpus. Within the scope of this project, 16.400 sentences have been morphologically and semantically annotated, and the dependency relations were manually carried out by a team of linguists. It is hoped that this project will serve as a base for a successful dependency parser and a system which can automatically perform the bi-directional conversion between constituency and dependency trees.Publisher's Versio

Creating a syntactically felicitous constituency treebank for Turkish

In this study, Bakay et. al [1] and Yildiz et. al.'s [2] work on Turkish constituency treebanks were developed further. Compared to the previous work, the most prominent feature of this study is the fact that every annotation and refinement process is held manually. In addition, constituency treebank created as a result of this study abides by the syntactic rules and typologic features of Turkish while the trees created by previous studies convey only the translated and simply inverted trees that completely ignore the syntactic properties of Turkish. The methodology followed in this study resulted in a significantly more accurate representation of Turkish language and simpler, relatively flatter trees. The straightforward style of trees in this study reduces the complexity and offers a better training dataset for learning algorithms.Publisher's Versio