Abstract
According to the World Health Organization, 85% of the world population suffers from back pain, which accounts for over 50% of physical incapacity, permanent or temporary, among individuals in working-age. In most situations, this is caused by an incorrect posture, which causes changes in the spine structure. This paper proposes an instrumented vest for postural reeducation to address this issue. The vest has a set of inertial measurement unit (IMU) sensors strategically placed to provide an accurate characterization of the spine profile. The sensor readings are classified by a central processing unit. In case of an incorrect posture, users are alerted by an audio signal and through vibration. The wearable system works in stand-alone mode, but can also communicate with external systems through an API. Two applications were developed to communicate with the device through this API, one intended to run on a desktop computer and the other one for Android devices. These applications monitor spine profiles in real time and notify the user of incorrect postures, among other functionalities. The device prototype and the applications have been tested by 10 individuals in two different settings, first without any kind of feedback and then with feedback enabled. The tests demonstrate the usability, accuracy and robustness of the system, proving its high level of reliability in classifying postures and effectiveness for postural reeducation. In the future, the system is expected to be used as a platform for a serious game, to promote posture reeducation in a real world scenario.

Abstract
Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., "philip seymour hoffman'') search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections.

Abstract
This paper describes a methodology for Diabetic Retinopathy detection from eye fundus images using a generalization of the Bag-of-Visual-Words (BoVW) method. We formulate the BoVW as two neural networks that can be trained jointly. Unlike the BoVW, our model is able to learn how to perform feature extraction, feature encoding and classification guided by the classification error. The model achieves 0.97 Area Under the Curve (AUC) on the DR2 dataset while the standard BoVW approach achieves 0.94 AUC. Also, it performs at the same level of the state-of-the-art on the Messidor dataset with 0.90 AUC.

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Abstract
The European Project Semester (EPS) is a one semester capstone project/internship framework offered by the EPS providers to engineering, product design and business undergraduates. While a student-centred project-based learning offer, EPS proposes a unique multidisciplinary and multicultural teamwork set up to promote soft, technical and scientific competencies. In the spring of 2016, the EPS at the Instituto Superior de Engenharia do Porto (ISEP) welcomed a team of engineering students who chose to develop a sustainable water desalinator, the working principle relying on solar energy and natural temperature differences to convert saline water into fresh water. This paper describes the team's journey, including the motivation, the solution design process, considering the technical & scientific state of the art as well as the potential impact in terms of ethics, sustainability and marketing, and the development and testing of the prototype. The results obtained validate the purpose of the developed system since a significant reduction of the salt water conductivity, to values of the same order of magnitude of tap water, were observed. Although improvements can be made, the desalinator prototype produced 70 ml/d of distilled water in late spring and 7 ml/d in midwinter atmospheric conditions.

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