Abstract
This paper presents an overview and main features of Enki, a web-based learning environment for computer science languages. Enki was designed to be a sort of entry level IDE, aggregating tools for navigating and viewing course materials, for solving exercises and receiving automated feedback, as well as promoting the learning process. Enki uses services from several other systems, namely for content sequencing and recommendation, exercise assessment, and gamification.

Abstract
Interactions between internet users are mediated by their devices and the common support infrastructure in data centres. Keeping track of causality amongst actions that take place in this distributed system is key to provide a seamless interaction where effects follow causes. Tracking causality in large scale interactions is difficult due to the cost of keeping large quantities of metadata; even more challenging when dealing with resource-limited devices. In this paper, we focus on keeping partial knowledge on causality and address deduction from that knowledge. We provide the first proof-theoretic causality modelling for distributed partial knowledge. We prove computability and consistency results. We also prove that the partial knowledge gives rise to a weaker model than classical causality. We provide rules for offline deduction about causality and refute some related folklore. We define two notions of forward and backward bisimilarity between devices, using which we prove two important results. Namely, no matter the order of addition/ removal, two devices deduce similarly about causality so long as: (1) the same causal information is fed to both. (2) they start bisimilar and erase the same causal information. Thanks to our establishment of forward and backward bisimilarity, respectively, proofs of the latter two results work by simple induction on length.

Abstract
This paper presents the proposal of a Low cost Mobile Robot prototype with On Board Laser Scanner, prototyped to compete at the Robot (R) Factory Mobile Robot competition. The robot is equipped with a hacked Neato XV-11 Laser Scanner, being a very low cost, alternative, when compared with the current available laser scanners. It is presented the description of its sensors and actuators, providing valuable information that can be used to develop better designs of controllers and localization systems. The robot is equipped with the 37Dx52L, which is a low cost 12v motor equipped with encoders and a 29:1 reduction gearbox, being a very popular actuator in the mobile robotics domain. The robot is also equipped with an USB camera applied to acquire image, that will be processed, in order to provide information concerning the part material status.

Abstract
This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient conditions) on the number of measurements required to drive the probability of misclassification to zero in the low-noise regime, both for random measurements and designed ones. Such bounds reveal two important operational regimes that are a function of the characteristics of the source: 1) when the number of classes is less than or equal to the dimension of the space spanned by signals in each class, reliable classification is possible in the low-noise regime by using a one-vs-all measurement design; 2) when the dimension of the spaces spanned by signals in each class is lower than the number of classes, reliable classification is guaranteed in the low-noise regime by using a simple random measurement design. Simulation results both with synthetic and real data show that our analysis is sharp, in the sense that it is able to gauge the number of measurements required to drive the misclassification probability to zero in the low-noise regime.

Abstract
In the latest years, the number of elderly people that has been living alone and need regular support has highly increased. Meal intake monitoring is a well-known strategy that enables premature detection of health problems. There are several attempts to develop automatic meal intake monitoring systems, but they are inadequate to monitor elderly people at home. In this context, we propose an automatic meal intake monitoring system that helps tracking people's eating behaviors, and is adequate for elderly remote monitoring at home due to its nonintrusive features. The system uses the MS Kinect sensor that provides the coordinates of the user's sitting skeleton during his meals. It analyzes the coordinates, detects eating gestures, and classifies them using Hidden Markov Models (HMMs) to estimate the user's eating behavior. A demonstrative prototype for detection and classification of gestures was implemented and tested. The detection module got satisfactory percentages of sensitivity, having a minimum of 72.7% and a maximum of 90%. The Classification module was tested with 3 proposed methods and the best method had a good average percentage of success (approximately 83%) in the classification of Soup and Main dish; regarding the left hand transporting Liquids, the results were less successful. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.