Biblio

CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) is a widely used challenge-measures to distinguish humans and computer automated programs apart. Several existing CAPTCHAs are reliable for normal users, whereas visually impaired users face a lot of problems with the CAPTCHA authentication process. CAPTCHAs such as Google reCAPTCHA alternatively provides audio CAPTCHA, but many users find it difficult to decipher due to noise, language barrier, and accent of the audio of the CAPTCHA. Existing CAPTCHA systems lack user satisfaction on smartphones thus limiting its use. Our proposed system potentially solves the problem faced by visually impaired users during the process of CAPTCHA authentication. Also, our system makes the authentication process generic across users as well as platforms.

Since a training system using VR can reproduce an actual training environment, training systems have been studied in commercial fields such as medical care and construction. This immersive experience in a virtual space can have a great effect on learning a second language. In this paper, we propose an immersive learning system that learns phrases used in the customer service industry in the customer service experience. We asked the subjects to experience the system, measured the effects of learning, and evaluated the system. Evaluating the learning effect of phrases used in customer service English on 8 students, all student achieved good learning results. Besides, to evaluate the usability of the system, the VR system was evaluated by performing SSQ to measure VR sickness shows this system doesn't cause virtual sickness, SUS to measure usability shows this system evaluation is higher than average system, and IPQ to measure presence in an immersive space shows this system gives average virtual reality experience.

In this paper, the design as well as complete implementation of a robot which can be autonomously controlled for surveillance. It can be seamlessly integrated into an existing security system already present. The robot's inherent ability allows it to map the interiors of an unexplored building and steer autonomously using its self-ruling and pilot feature. It uses a 2D LIDAR to map its environment in real-time and HD camera records suspicious activity. It also features an in-built display with touch based commands and voice recognition that enables people to interact with the robot during any situation.

eAssessment uses technology to support online evaluation of students' knowledge and skills. However, challenging problems must be addressed such as trustworthiness among students and teachers in blended and online settings. The TeSLA system proposes an innovative solution to guarantee correct authentication of students and to prove the authorship of their assessment tasks. Technologically, the system is based on the integration of five instruments: face recognition, voice recognition, keystroke dynamics, forensic analysis, and plagiarism. The paper aims to analyze and compare the results achieved after the second pilot performed in an online and a blended university revealing the realization of trust-driven solutions for eAssessment.