Yucel Batu Salman

Execution Time Prediction for 3D Interactive CAPTCHA by Keystroke Level Model

CAPTCHA is a standard defense system which stands for Completely Automated Public Turing test to tell Computers and Human Apart. The basic idea of CAPCHA is providing questions that human users can easily recognize and answer but computer programs cannot. In this study, an interactive 3D CAPTCHA was introduced, and developed for better security. The design and evaluation phases are presented in detail. The proposed model passed the necessary security tests. The Keystroke Level Model (KLM) can be used to evaluate and predict task execution time for a specific system usage scenario. KLM is based on calculation on the sequences of the users’ tasks or actions. This study attempts to estimate execution times for 3D animated CAPTCHA by KLM. An interactive CAPTCHA method is developed by Flash Movie Technology which is used commonly in the web sites. The results show that performing the 3D CAPTCHA requires a serious amount of time. New interaction methods should be considered to improve the users’ performance.

Designing CAPTCHA Algorithm: Splitting and Rotating the Images against OCRs

Internet is being used for several activities by a great range of users. These activities include communication, e-commerce, education, and entertainment. Users are required to register regarding Website in order to enroll Web activities. However, registration can be done by automated hacking software. That software make false enrollments which occupy the resources of the Website by reducing the performance and efficiency of servers, even stop the entire Web service. It is crucial for the Websites to have a system which has the capability of differing human users and computer programs in reading images of text. Completely automated public Turing test to tell computers and human apart (CAPTCHA) is such a defense system against optical character recognition (OCR) software. OCR can be defined as a software which work for defeating CAPTCHA images and make countless number of registration on the Websites. This study focuses on a new method which is splitting CAPTCHA images into several parts with random rotation values, and drawing random lines on a grid background. Lines are in the same color with the CAPTCHA text and they provide a distortion of image with grid background. In this paper, the algorithm of our method is introduced in detail.

Factors affecting the adoption of mobile banking: Sample of Turkey

Rapid developments in communication technologies make fundamental changes in the behaviour of people while performing basic tasks. Mobile banking (m-banking) as an innovative product of mobile industry is becoming more common to reduce and manage time-intensive banking affairs. Understanding the primary determinants of m-banking adoption is significant for banks and users. This study investigates seven factors that affect implementation and adoption of m-banking by Turkish customers. Perceived usefulness, perceived ease of use, security and privacy, compatibility, social influence, facilitating conditions and perceived cost were measured to identify their predictor roles for the use of m-banking. 128 valid data were collected in order to test our conceptual model. Seven hypotheses were tested. It was found that perceived usefulness, compatibility, and social influence have positive impact on m-banking adoption in Turkey.

A user study: the effects of mobile phone prototypes and task complexities on usability

Mobile phones are increasingly becoming ubiquitous computational devices that are almost always available, individually adaptable, and nearly universally connectable (using both wide area and short range communication capabilities). Up to now, mobile phones have been used in purpose of the fact that we can send SMS messages among each other, save our important notes, save our schedules; connect to internet and so on. But how much can a mobile phone simplify our everyday interactions, before it itself becomes a usability burden? What are the capabilities and limitations of using mobile phones with different prototypes and complexities of the interaction items in itself. This paper presents a user study investigating the use of a prototypical, mobile phone based interaction system to send text messages and save schedules classified as simple and complex task settings. Our results show that mobile devices can greatly simplify our usage by using computer type prototypes with complex task settings. We have studied the effect of prototype of mobile phone and complexity of task for the usability test on subjective user evaluation. In this experiment, mobile phones with special software were given to test users (N=60) for a period of a week. Prototype design was done by using different kinds of platforms such as: paper, computer, and a fully operational appliance and also complexity of task was chosen as sending message and editing a saved schedule with the mobile phone, simple and complex task respectively. Later, a subjective user evaluation was measured in 5 points scale. Experiment results yielded that computer type prototype has significantly higher usability rate than paper type prototype and there is a significant interaction between prototype and complexity. Eventually, computer type prototype with complex task yielded highest rate while paper type prototype with simple task yielded lowest usability rates.

Gender Recognition using Hog with Maximized Inter-Class Difference

Several methods and features have been proposed for gender recognition problem. Histogram of oriented gradients (Hog) is a widely used feature in image processing. This study proposes a gender recognition method using full body features. Human body from side and front view were represented by Hog. Using all bins in the histogram requires longer time for training. In order to decrease the computation time, descriptor size should be decreased. Inter-class difference was obtained as a vector and sorted in a descending order. The bins with the largest value were selected among this vector. Random forest and Adaboost methods were used for the recognition. As a result of both tests, the classifier using first 100 bins with maximum difference gives the optimum performance in terms of accuracy rate and computation time. Although Adaboost performed faster, the accuracy of random forest is higher in full body gender recognition.