Abstract

Abstract
Considering the many significant challenges faced by higher education in contemporary society, gamification and game-based approaches to Education have been gaining protagonism in research as well as in practice. Application of games can encourage-or require-students to apply deeper levels of knowledge and skills, focusing their knowledge acquisition in more than simple memorization and repetition in tests, allowing them to use their new-found knowledge, skills and abilities in problem solving-even if simulated and fairly accessible ones. Unlike traditional assessments, which typically require students to recall or demonstrate basic levels of skills, games and simulations can present students with more authentic environments to demonstrate strategic and critical thinking, which is highly compatible with the "competency model". Through games, learning can also be made more of a social and collaborative activity, which are important 21st century skills. Hence, a model was developed for applying gamification in a course of Human Resources Management of a Masters in Engineering. This model was based on a state of the art research of gamification in higher education, as well as some guidelines and main features of a gamification framework. This paper presents the game system, platform and strategies implemented by the teaching team, comparing the original project with the one actually implemented. Teacher and student reflections on this experience are presented, and guidelines for future practice are brought out, including the biggest blunders and the best features of the game-based approach used in this experience, their causes and consequences. We believe this work can contribute to further game-based approaches in higher education, stimulating reflection and insight for other researchers and practitioners.

Abstract
This work presents a method for the automatic segmentation of metacarpus and phalange bones in ultrasound images of the second metacarpophalangeal joint (MCPJ) using Active Contours. The MCPJ is known to be the one of the first structures to be affected by rheumatic diseases like rheumatoid arthritis. The early detection and follow-up of this disease is important to prevent irreversible damage of the joints, which occurs continuously and faster if no treatment is used. To our knowledge, there is no automatic system to quantify the extension of the lesions resulting from rheumatic activity. The objective of this work is to identify the metacarpus and the phalange bones using local active contours. To our knowledge, there is no well established method for this problem and this technique has not been used yet in these structures. Results proved that the automatic segmentation is possible with an error of 3 pixels for a confidence of 80%.

Abstract
A label-free fiber optic biosensor based on a long period grating (LPG) and a basic optical interrogation scheme using off the shelf components is used for the detection of in-situ DNA hybridization. A new methodology is proposed for the determination of the spectral position of the LPG mode resonance. The experimental limit of detection obtained for the DNA was 62 +/- 2 nM and the limit of quantification was 209 +/- 7 nM. The sample specificity was experimentally demonstrated using DNA targets with different base mismatches relatively to the probe and was found that the system has a single base mismatch selectivity.

Abstract
We present Symbiosis: a concurrency debugging technique based on novel differential schedule projections (DSPs). A DSP shows the small set of memory operations and dataflows responsible for a failure, as well as a reordering of those elements that avoids the failure. To build a DSP, Symbiosis first generates a full, failing, multithreaded schedule via thread path profiling and symbolic constraint solving. Symbiosis selectively reorders events in the failing schedule to produce a nonfailing, alternate schedule. A DSP reports the ordering and dataflow differences between the failing and nonfailing schedules. Our evaluation on buggy real-world software and benchmarks shows that, in practical time, Symbiosis generates DSPs that both isolate the small fraction of event orders and dataflows responsible for the failure and report which event reorderings prevent failing. In our experiments, DSPs contain 90% fewer events and 96% fewer dataflows than the full failure-inducing schedules. We also conducted a user study that shows that, by allowing developers to focus on only a few events, DSPs reduce the amount of time required to understand the bug's root cause and find a valid fix.

Abstract
Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version.