Abstract
In many applications, false positives (type I error) and false negatives (type II) have different impact. In medicine, it is not considered as bad to falsely diagnosticate someone healthy as sick (false positive) as it is to diagnosticate someone sick as healthy (false negative). But we are also willing to accept some rate of false negatives errors in order to make the classification task possible at all. Where the line is drawn is subjective and prone to controversy. Usually, this compromise is given by a cost matrix where an exchange rate between errors is defined. For many reasons, however, it might not be natural to think of this trade-off in terms of relative costs. We explore novel learning paradigms where this trade-off can be given in the form of the amount of false negatives we are willing to tolerate. The classifier then tries to minimize false positives while keeping false negatives within the acceptable bound. Here we consider classifiers based on kernel density estimation, gradient descent modifications and applying a threshold to classifying and ranking scores.

Abstract
The aim of this paper is to avoid overloading a private customer distribution transformer (DT) in a Portuguese insular area through the means of solar PV microgeneration. Firstly, the consequence of the penetration of electric vehicles (EV) on dielectric oil deterioration of a DT in an industrial unit is estimated. The workplace has local PV generation, allowing the EVs to charge while their owners are working at three different working shifts during a day. Secondly, the model is tested and the resulting scenarios are analyzed. This paper shows that the solar PV microgeneration decreases the overloading of the DT due to a lower daily load profile. It also contributes to the reduction of the loss-of-life (LoL) of the DT.

Abstract
Accessibility is a very under addressed topic in software engineering, a constraining factor for its mainstream implementation. In this paper, this problem is described as well as some of its consequences. Some key concepts of digital accessibility are presented, beyond those of Web accessibility. The major contribution of this paper is the presentation of a draft to a proposal for a framework development model. In this framework, accessibility is present throughout the software lifecycle, in an explicit and justified way, based on the four main phases present in most framework development models.

Abstract

Abstract
Human performance, in all its different dimensions, is a very complex and interesting topic. In this paper we focus on performance in the workplace which, asides from complex is often controversial. While organizations and generally competitive working conditions push workers into increasing performance demands, this does not necessarily correlates positively to productivity. Moreover, existing performance monitoring approaches (electronic or not) are often dreaded by workers since they either threat their privacy or are based on productivity measures, with specific side effects. We present a new approach for the problem of performance monitoring that is not based on productivity measures but on the workers' movements while sitting and on the performance of their interaction with the machine. We show that these features correlate with mental fatigue and provide a distributed architecture for the non -intrusive and transparent collection of this data. The easiness in deploying this architecture, its non -intrusive nature, the potential advantages for better human resources management and the fact that it is not based on productivity measures will, in our belief, increase the willingness of both organizations and workers to implement this kind of performance management initiatives.

Abstract
Several prominent public health incidents [29] that occurred at the beginning of this century due to adverse drug events (ADEs) have raised international awareness of governments and industries about pharmacovigilance (PhV) [6, 7], the science and activities to monitor and prevent adverse events caused by pharmaceutical products after they are introduced to the market. A major data source for PhV is large-scale longitudinal observational databases (LODs) [6] such as electronic health records (EHRs) and medical insurance claim databases. Inspired by the Multiple Self-Controlled Case Series (MSCCS) model [27], arguably the leading method for ADE discovery from LODs, we propose baseline regularization, a regularized generalized linear model that leverages the diverse health profiles available in LODs across different individuals at different times. We apply the proposed method as well as MSCCS to the Marshfield Clinic EHR. Experimental results suggest that incorporatingthe heterogeneity among different patients and different times help to improve the performance in identifying benchmark ADEs from the Observational Medical Outcomes Partnership ground truth [26]. © 2017 Copyright held by the owner/author(s).