Abstract

Abstract
In this paper, the topological and dynamical properties of the heart sounds are assessed. The signal is preprocessed and projected into an embedding subspace, which is more suitable to detect the irregularities and the unstable trajectories registered during the cardiac murmurs than the original heart sound signal. We present a method for heart murmur classification divided into five major steps: a) signal is divided into heart beats; b) entropy gradient envelogram is computed from the pre-processed signal; c) the orbital trajectories are reconstructed using the embedding theory; d) n orbits in the embedding subspace are extracted ( per heart beat); e) the median of the n orbits is used as an input to K-Nearest Neighbors ( KNN) classifier. The experimental results achieved are in agreement with the current state of art for heart murmur classification.

Abstract
Existing gamification services have features that preclude their use by e-learning tools. Odin is a gamification service that mimics the API of state-of-theart services without these limitations. This paper presents Odin as a gamification service for learning activities, describes its role in an e-learning system architecture requiring gamification, and details its implementation. The validation of Odin involved the creation of a small e-learning game, integrated in a Learning Management System (LMS) using the Learning Tools Interoperability (LTI) specification. Odin was also integrated in an e-learning tool that provides formative assessment in online and hybrid courses in an adaptive and engaging way.

Abstract
Several schedulability analyses have been proposed for a variety of parallel task systems with real-time constraints. However, these analyses are mostly restricted to global scheduling policies. The problem with global scheduling is that it adds uncertainty to the lower-level timing analysis which on multicore systems are heavily context-dependent. As parallel tasks typically exhibit intense communication and concurrency among their sequential computational units, this problem is further exacerbated. This paper considers instead the schedulability of partitioned parallel tasks. More precisely, we present a response time analysis for sporadic DAG tasks atop multiprocessors under partitioned fixed-priority scheduling. We assume the partitioning to be given. We show that a partitioned DAG task can be modeled as a set of self-suspending tasks. We then propose an algorithm to traverse a DAG and characterize such worst-case scheduling scenario. With minor modifications, any state-of-the-art technique for sporadic self-suspending tasks can thus be used to derived the worstcase response time of a partitioned DAG task. Experiments show that the proposed approach significantly tightens the worst-case response time of partitioned parallel tasks comparatively to the state-of-the-art when the most accurate technique is chosen.

Abstract
This paper describes the assessment of performance of educational services recurring to benchmarking. We adopted two perspectives for the evaluation of secondary schools, aligned with the objectives of different stakeholders. In the society perspective schools are viewed as promoting students achievement ( ideally including not only academic results but also interpersonal capacities) given the students characteristics in terms of academic abilities and socio-economic backgrounds. In the educational authorities perspective schools are viewed as transforming a set of resources ( including students with given characteristics in terms of academic abilities and socioeconomic backgrounds and also school resources, such as teachers) into students achievement. The relative performance assessment was carried out using Data Envelopment Analysis models, followed by an exploratory analysis of contextual indicators that potentially affect schools' performance, in order to understand their impact on the educational process.

Abstract
To help laypeople surpass the common difficulties they face when searching for health information on the Web, we built Health Suggestions, an extension for Google Chrome to assist users obtaining high-quality search results in the health domain. This is achieved by providing users with suggestions of queries formulated in different terminologies and languages. Translations of health expressions might not be obvious and queries in languages with a strong presence on the Web facilitate the access to high-quality health contents. On the other hand, the use of lay terminology might contribute to increase users' comprehension and medico-scientific terminology to obtain more detailed and technical contents. Results show a good acceptance of the suggestions, confirm the utility of a multilingual and multi-terminology approach and show its usefulness to more successful searches.