Software engineers are expected to possess a variety of technical, social and personal competencies to be well prepared for real world working environments. At the German University in Cairo (GUC), we were able to guide large groups of students into becoming self managing and self learning communities whose members work together as a team to build large scale software. The students were able to experience many of the challenges in software engineering and develop a variety of related skills in a short period of time. This report describes our detailed experience in reaching such results using agile development practices in a simulated enterprise environment.With an aim to enable educators realise the same success, this report serves as a guide for educators who wish to replicate the process. The resulting successes and the concerns from this unique experience are discussed along with future recommendations.

This paper presents a new approach for detecting pain in sequences of spontaneous facial expressions. The motivation for this work is to accompany mobile-based self-management of chronic pain as a virtual sensor for tracking patients' expressions in real-world settings. Operating under such constraints requires a resource efficient approach for processing non-posed facial expressions from unprocessed temporal data. In this work, the facial action units of pain are modeled as sets of distances among related facial landmarks. Using standardized measurements of pain versus no-pain that are specific to each user, changes in the extracted features in relation to pain are detected. The activated features in each frame are combined using an adapted form of the Prkachin and Solomon Pain Intensity scale (PSPI) to detect the presence of pain per frame. Painful features must be activated in N consequent frames (time window) to indicate the presence of pain in a session. The discussed method was tested on 171 video sessions for 19 subjects from the McMaster painful dataset for spontaneous facial expressions. The results show higher precision than coverage in detecting sequences of pain. Our algorithm achieves 94% precision (F-score=0.82) against human observed labels, 74% precision (F-score=0.62) against automatically generated pain intensities and 100% precision (F-score=0.67) against self-reported pain intensities.

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Handheld Augmented Reality (AR)