Abstract
In this work a numerical model related to an optical inclinometer is presented. This model is based on a fused fiber taper monitored in the transmitted power. Comparisons are made between the numerical and experimental results and it is demonstrated good agreement with them. Thus, the model is proven to be suitable to simulate variation of parameters in order to obtain better performance of the sensor response. The numerical results demonstrate that is possible to enhance the inclinometer sensitivity by varying the legnth and waist of the taper. It is obtained a sensitivity of about 0,7 dB/degree using a taper length and waist of 1200 mu m and 30 mu m, respectively, at an angular range of 35 to 45 degrees.

Abstract
Given a retinal image, can we automatically determine whether it is of high quality (suitable for medical diagnosis)? Can we also explain our decision, pinpointing the region or regions that led to our decision? Images from human retinas are vital for the diagnosis of multiple health issues, like hypertension, diabetes, and Alzheimer's; low quality images may force the patient to come back again for a second scanning, wasting time and possibly delaying treatment. However, existing retinal image quality assessment methods are either black boxes without explanations of the results or depend heavily on feature engineering or on complex and error-prone anatomical structures' segmentation. Therefore, we propose EyeQual, that solves exactly this problem. EyeQual is novel, fast for inference, accurate and explainable, pinpointing low-quality regions on the image. We evaluated EyeQual on two real datasets where it achieved 100% accuracy taking just 36 milliseconds for each image.

Abstract
Clustering is a powerful and versatile tool for knowledge discovery, able to provide a valuable information for data analysis in various domains. To perform this task based on streaming data is quite challenging: outdated knowledge needs to be disposed while the current knowledge is obtained from fresh data; since data are continuously flowing, strict efficiency constraints have to be met. This paper presents WCDS, an approach to this problem based on the WiSARD artificial neural network model. This model already had useful characteristics as inherent incremental learning capability and patent functioning speed. These were combined with novel features as an adaptive countermeasure to cluster imbalance, a mechanism to discard expired data, and offline clustering based on a pairwise similarity measure for WiSARD discriminators. In an insightful experimental evaluation, the proposed system had an excellent performance according to multiple quality standards. This supports its applicability for the analysis of data streams.

Abstract
Jasmin is a framework for developing high-speed and high-assurance cryptographic software. The framework is structured around the Jasmin programming language and its compiler. The language is designed for enhancing portability of programs and for simplifying verification tasks. The compiler is designed to achieve predictability and efficiency of the output code (currently limited to x64 platforms), and is formally verified in the Coq proof assistant. Using the SUPER COP framework, we evaluate the Jasmin compiler on representative cryptographic routines and conclude that the code generated by the compiler is as efficient as fast, hand-crafted, implementations. Moreover, the framework includes highly automated tools for proving memory safety and constant-time security (for protecting against cache-based timing attacks). We also demonstrate the effectiveness of the verification tools on a large set of cryptographic routines.

Abstract
Bad posture while working or playing videogames can affect our life quality and impose negative economic consequences over time. There's raising concern in companies regarding worker's wellness, many adopting preventive measures. Specialized training in posture is important to prevent occupational activities risks and to foster health promotion. In this paper, we present a study of different classifiers to detect good and bad body postures in workplaces. A set classifiers, namely artificial neural networks, support vector machine, decision trees, discriminant analysis, logistic regression, treebagger and naïve Bayes, were tested in three-dimensional acquisitions of 100 people for automatic determination of the type of body posture. The best classifier was the treebagger with a rating of True Positive and True Negative of 93.3% and 96.2%, respectively.

Abstract
Due to growing concerns with sustainability issues and the emergence of the Circular Economy (CE) paradigm, combined with recent technological changes and consequent increase in competitiveness, there is a pressing need to redefine the Product Lifecycle Management (PLM) approach. PLM needs to incorporate aspects that would enable the shift to this paradigm, such as enhanced collection and evaluation of information coming from production processes, distribution, retail, consumers, and collaboration in an extended enterprise context, by implementing enabling technologies such as the Internet of Things (IoT) and Big Data. This paper proposes a vision, based on the state of the art, for a CE enabled PLM, having the Portuguese footwear industry scenario as a reference. © IFIP International Federation for Information Processing 2017.