Abstract
User modelling has become a central subject for anybody interested in understanding how users interact with technology. Personalization is a key issue in an era when there is so much information and so many people interacting in so many ways. Modern users desire a customized experience that adapts itself to their requirements and understands what they need even before they notice it. In order to morph any system into an adapting one, every relevant interaction with its users has to be maintained. Then, a mathematical structure capable of discovering patterns amongst that information is necessary, being able to classify users according to the roles they play. With a correct user categorization, the system knows when, how and what to do to adapt its content, via a mixed-initiative approach. In this paper, an artificial neural network is selected as classifier and users are divided in three roles, from beginners to experts. ADSyS, the target system of this proposal, adapts its content based on who is operating it, providing a higher usability. This guide on how to adapt a system to its users is built as part of ADSyS, but is intended to be generalized as a foundation to other systems.

Abstract
Optimal operation of power systems with high integration of renewable power sources has become difficult as a consequence of the random nature of some sources like wind energy and photovoltaic energy. Nowadays this problem is solved using the Monte Carlo Simulation (MCS) approach, which allows the consideration of important statistical characteristics of wind and solar power production, such as the correlation between consecutive observations, the diurnal profile of the forecasted power production, and the forecasting error. In this paper, a new model of the unit scheduling of power systems with significant renewable power generation based on the scenario generation/reduction method combined with the priority list (PL) method is proposed that finds the probability distribution function (PDF) of a determined generator be committed or not. This approach allows the recognition of the role of each generation unit on the day-ahead unit commitment (UC) problem with a probabilistic point of view, which is important for acquiring a cost-effective and reliable solution. The capabilities and performance of the proposed approach are illustrated through the analysis of a study case, where the spinning reserve requirements are probabilistically verified with success.

Abstract
The recent advances made in human-computer interaction have allowed us to manipulate digital contents exploiting recognitionbased technologies. However, no work has been reported that evaluates how these interfaces influence the performance of different user groups. With the appearance of multiple sensors and controllers for hand gesture recognition, it becomes important to understand if these groups have similar performance levels concerning gestural interaction, and if some sensors could induce better results than others when dealing with users of different age brackets. In this respect, it could also be important to realize if the device's sensor accuracy in terms of hand / full body recognition influences interaction performance. We compare two gesturesensing devices (Microsoft Kinect and Leap Motion) using Fitts' law to evaluate target acquisition performances, with relation to users' age differences. In this article, we present the results of an experiment implemented to compare the groups' performance using each of the devices and also realize which one could yield better results. 60 subjects took part in this study and they were asked to select 50 targets on the screen as quickly and accurately as possible using one of the devices. Overall, there was a statistically significant difference in terms of performance between the groups in the selection task. On the other hand, users' performance showed to be rather consistent when comparing both devices side by side in each group of users, which may imply that the device itself does not influence performance but actually the type of group does.

Abstract
This paper presents a novel prototype device for domestic load energy consumption monitoring. Zigbee-based wireless connectivity is included as a basic feature of the prototype. The proposed device allows individual tracking of major energy consumption loads. Real time energy data is acquired and transmitted through a RF link to a wireless terminal unit, which works as a data logger and as a human-machine interface. Both voltage and current measurements are implemented using Hall Effect principle based transducers, while C code is developed on two 16-bit RISC MCU. The experimental setup is described and tests are conducted in order to assess its performance.

Abstract
Group behaviour characterisation is a topic not so well studied in the video surveillance community due to its difficulty and large variety of topics involved, but mainly because the lack of valid semantic concepts that relate collective activity to social context. In this work, our proposal is three-fold: a new definition of semantic concepts for social group analysis considering environment context, a novel video surveillance dataset that conveys a sociological perspective, and a descriptor that emphasises social interactions cues within a group. Promising results were revealed in order to deal with such complex problem.

Abstract
Globalization forced manufactures to enhance their production planning strategies. The use of new approaches or the reformulation of existing ones is therefore vital for remaining competitive in this global market scenario. In this context also the reinforcement of approaches for some traditional scheduling problems and performance criteria, such as for solving the unrelated parallel-machines makespan minimization problem (Rm||Cmax) continues to be of upmost importance, as for this well-known NP-hard problem exact method remain impracticable. In this paper some heuristics are used for this problem optimization, such as MOMCT (Modified Ordered Minimum Completion Time), which will determine the sequence in which tasks will be allocated, before it uses the MCT (Minimum Completion Time) to distribute them between the machines. In this paper we propose NS-MOMCT (Neighborhood Search - Modified Ordered Minimum Completion Time), which uses the MOMCT solutions as start points for repeated local searches.