Abstract
The development of efficient methods for searching and browsing large assets of video content has been considered by the academia and content owners for long. Different approaches that range from manual structured annotations, to unstructured metadata collected from several sources, as well as multimedia processing for automatic description of the content, can be identified. The growth on the number of hours of video content put online in video sharing platforms has however shown that video retrieval is still quite inefficient as rich contextual data that describes the content is most of the times still not available. Additionally, metadata is usually not linked to timed moments of content, making direct access to the most relevant moments not possible. In this paper, an approach for making web videos available in the YouTube platform more accessible is presented. The solution is based on a collaborative process presented as a game that enables collecting metadata from the crowd while implementing mechanisms that remove erroneous information usually encountered in this type of information. Metadata, exported to YouTube in the form of captions and descriptions, contributes to enhance video retrieval, guaranteeing a better user experience and exposure of the content.

Abstract
Audiovisual didactic content has been in recent years disseminated, in the Physics domain, mainly through YouTube platform. Many aspects of video production activities can increase students' selfesteem, increase their satisfaction with the learning experience, promote a positive attitude towards the subject, provide weaker students with a broad individual tutoring, encouraging students to discuss with each other, exchange their opinions, and compare the results of lab activities. On the other hand, video can support research activities, offering the researcher access to a rich data aggregation to investigate the learning processes. The main objective of this study is to understand the use of online tools in the context of teaching, for this we make a correlation between studies using audiovisual resources in the study of Physics, and our testbed with an online video annotation tool. Results show that students expressed a gain from oral lectures and access to new sources of learning.

Abstract
Background: Wearable sensing and information and communication technologies are key enablers driving the transformation of health care delivery toward a new model of connected health (CH) care. The advances in wearable technologies in the last decade are evidenced in a plethora of original articles, patent documentation, and focused systematic reviews. Although technological innovations continuously respond to emerging challenges and technology availability further supports the evolution of CH solutions, the widespread adoption of wearables remains hindered. Objective: This study aimed to scope the scientific literature in the field of pervasive wearable health monitoring in the time interval from January 2010 to February 2019 with respect to four important pillars: technology, safety and security, prescriptive insight, and user-related concerns. The purpose of this study was multifold: identification of (1) trends and milestones that have driven research in wearable technology in the last decade, (2) concerns and barriers from technology and user perspective, and (3) trends in the research literature addressing these issues. Methods: This study followed the scoping review methodology to identify and process the available literature. As the scope surpasses the possibilities of manual search, we relied on the natural language processing tool kit to ensure an efficient and exhaustive search of the literature corpus in three large digital libraries: Institute of Electrical and Electronics Engineers, PubMed, and Springer. The search was based on the keywords and properties to be found in articles using the search engines of the digital libraries. Results: The annual number of publications in all segments of research on wearable technology shows an increasing trend from 2010 to February 2019. The technology-related topics dominated in the number of contributions, followed by research on information delivery, safety, and security, whereas user-related concerns were the topic least addressed. The literature corpus evidences milestones in sensor technology (miniaturization and placement), communication architectures and fifth generation (5G) cellular network technology, data analytics, and evolution of cloud and edge computing architectures. The research lag in battery technology makes energy efficiency a relevant consideration in the design of both sensors and network architectures with computational offloading. The most addressed user-related concerns were (technology) acceptance and privacy, whereas research gaps indicate that more efforts should be invested into formalizing clear use cases with timely and valuable feedback and prescriptive recommendations. Conclusions: This study confirms that applications of wearable technology in the CH domain are becoming mature and established as a scientific domain The current research should bring progress to sustainable delivery of valuable recommendations, enforcement of privacy by design, energy-efficient pervasive sensing, seamless monitoring, and low-latency 5G communications. To complement technology achievements, future work involving all stakeholders providing research evidence on improved care pathways and cost-effectiveness of the CH model is needed.

Abstract
An alternative approach to compute the signal to noise ratio of analogue to digital converters based on the computation of the cross-correlation coefficient of the captured response is proposed here. It is shown, after simulation and experimental results, that this approach allows obtaining good accuracy results with the added advantages of not requiring coherent sampling and high purity sine wave stimuli.

Abstract

In the last decade the advances in wearable technology have driven and transformed performance monitoring in fitness and wellness applications, surveillance in extreme (working) conditions, and management of chronic diseases. These innovations have opened a whole new perspective on health and social care, challenged by vast expenditures in ageing societies.

Abstract
Absenteeism is a major problem faced particularly by companies with a large number of employees. Therefore, the existence of absenteeism prediction tools is essential for such companies depending on intensive human-resources. This paper focuses on using machine learning technologies for predicting the absences of employees from work. More precisely, a few prediction models were tuned and tested with 241 features extracted from a population of 13.805 employees. This target population was sampled from the help desk work force of a major Brazilian phone company. The features were extracted from the profile of the help desk agents and then filtered by processes of correlation and feature selection. The selected features were then used to compare absenteeism prediction given by different classification algorithm (cf. Random Forest, Multilayer Perceptron, Support Vector Machine, Naive Bayes, XGBoost and Long Short Term Memory). The parameterization of these ML models was also studied to reach the classifier best suited for the prediction problem. Such parameterizations were tuned through the use of evolutionary algorithms, from which considerable precision was reached, the best being 72% (XGBoost) and 71% (Random Forest). © 2019, Springer Nature Switzerland AG.