Abstract

Abstract
In this work, we present an optimal mapper for OFDM with index modulation (OFDM-IM). By optimal we mean the mapper achieves the lowest possible asymptotic computational complexity (CC) when the spectral efficiency (SE) gain over OFDM maximizes. We propose the spectro-computational (SC) analysis to capture the trade-off between CC and SE and to demonstrate that an -subcarrier OFDM-IM mapper must run in exact time complexity. We show that an OFDM-IM mapper running faster than such complexity cannot reach the maximal SE whereas one running slower nullifies the mapping throughput for arbitrarily large . We demonstrate our theoretical findings by implementing an open-source library that supports all DSP steps to map/demap an-subcarrier complex frequency-domain OFDM-IM symbol. Our implementation supports different index selector algorithms and is the first to enable the SE maximization while preserving the same time and space asymptotic complexities of the classic OFDM mapper.

Abstract
Computer programming plays a relevant role in the digital age as a key competency for project leverage and a driver of innovation for today's modern societies. Despite its importance, this domain is also well known for their higher learning failure rates. In this context, the study of how computer programming is taught is fundamental to clarify the teaching-learning process and to ensure the sharing of the best practices. This paper presents a survey on computer programming teaching in the first-year courses of Portuguese Universities, more precisely, what is taught and how it is taught. The study focuses essentially on the following facets: The class characterization, the methodologies used and the languages/technologies taught. Based on these criteria, a survey was done which gathers information of 59 courses included in a wide range of Universities spread across Portugal. The results were collected and analyzed. Based on this analysis a set of conclusions were taken revealing some interesting results on the teaching methods and languages used which can be useful to support a discussion on this subject and, consequently, to find new paths to shape the future of programming teaching. 2012 ACM Subject Classification Social and professional topics ! Computer science education.

Abstract
In clinical practice and in particular in the diagnostic process, the assessment of cardiac and respiratory functions is supported by electrocardiogram and auscultation. These exams are non-invasive, quick and inexpensive to perform and easy to interpret. For these reasons, this type of assessment is a constant in the daily life of a clinician and the information obtained is central to the decision-making process. Therefore, it is essential that during their training, students of health-related subjects acquire skills in the acquisition and evaluation of the referred physiological signals. Simulation, considering the technological possibilities of today, is an excellent preparation tool since it exposes trainees to near real contexts but without the associated risks. Hence, the simulation of physiological signals plays an important role in the education of healthcare professionals, bioengineering professionals and also in the development and calibration of medical devices. This paper describes a project to develop synchronized electrocardiogram (ECG), phonocardiogram (PCG) and breathing sounds simulators that aims to improve an existing phantom simulator. The developed system allows, in an integrated way, to generate normal and pathological signals, being contemplated several distinct pathologies. For engineering education, it is also possible to simulate the introduction of signal disturbances or hardware malfunctions.

Abstract
This file is a public data set about literature problems and contributions on learning in environments where students move.

Abstract
As the amount of generated information grows, reading and summarizing texts of large collections turns into a challenging task. Many documents do not come with descriptive terms, thus requiring humans to generate keywords on-the-fly. The need to automate this kind of task demands the development of keyword extraction systems with the ability to automatically identify keywords within the text. One approach is to resort to machine-learning algorithms. These, however, depend on large annotated text corpora, which are not always available. An alternative solution is to consider an unsupervised approach. In this article, we describe YAKE!, a light-weight unsupervised automatic keyword extraction method which rests on statistical text features extracted from single documents to select the most relevant keywords of a text. Our system does not need to be trained on a particular set of documents, nor does it depend on dictionaries, external corpora, text size, language, or domain. To demonstrate the merits and significance of YAKE!, we compare it against ten state-of-the-art unsupervised approaches and one supervised method. Experimental results carried out on top of twenty datasets show that YAKE! significantly outperforms other unsupervised methods on texts of different sizes, languages, and domains.