Browsing by Author "Dissanayaka, R"

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Embargo
The advanced remote PC management suite
(IEEE, 2011-08-16) Wijekoon, J; Wijesundara, M; Dassanayaka, T; Samarathunga, D; Dissanayaka, R; Perera, D
Developing a system that helps system administrators to perform their administration task more effectively and efficiently is of great importance to reduce downtime, cost and man power requirement. The Advanced Remote PC Management Suite facilitates centralized management of PC infrastructure employing the Intel Active Management Technology (AMT). This technology enables the system administrators to monitor and manage computers via a dedicated channel regardless of whether the computer is powered on. This is known as Out-of-Band (OOB) management. Currently AMT is available in Desktops and Laptops with The 2nd generation Intel Core vPro processors. Using features of AMT, the The Advanced Remote PC Management Suite provides a real-time and intelligent asset management facility in addition to monitoring and administration capabilities. The system also features automated operating system deployment and centralized disk cloning mechanisms. It is also possible to isolate any computer in the network using the system, during incidents such as virus infections. Therefore, this system is able to drastically reduce the number of desk-side-visits by system administrators to setup and troubleshoot PCs in large enterprise networks.
Embargo
AI-based Behavioural Analyser for Interviews/Viva
(IEEE, 2022-01-03) Dissanayake, D. Y; Amalya, V; Dissanayaka, R; Lakshan, L; Samarasinghe, P; Nadeeshani, M; Samarasinghe, P
Globalization and technology have made virtual interviews to be the choice of recruitment. Even though online interviews/viva have eliminated time, budgetary, and geographical barriers, the lack of comprehension regarding the interviewee’s behavioural aspects is yet to overcome. Therefore, a machine-based approach is proposed in this research for detecting and assessing changes in interviewees’ behaviour and personality traits based on nonverbal cues. Additionally, a group analysis of other applicants, as well as a comparison of the interview environment with the non-interview environment is also being obtained. To achieve this, we focus on the candidate’s emotion, eye movement, smile, and head movements. The system was carried out using deep learning and machine learning models which achieved accuracies over 85% for all smile, eye gaze, emotion, and head pose analysis. Furthermore, several machine learning models were developed based on the analysed behavioural outcomes of the interviewee to identify big five personality traits with Random Forest model yielding highest accuracy rate of over 75%. Our findings indicate that nonverbal behavioural cues can be utilized to determine personality traits.