Abstract
The academic performance and social competence of a child in school is positively associated with the involvement of their parents. However, the researches about educational learning models often ignore the parents' part. Internet opens a new paradigm: education and communication approach is more complex than ever. Here, we would like to present an Internet based system to support students' homework. We believe that one of the major advantages of our system is time saving, particularly from the teacher's point of view. Also, this system gathers statistical data concerning different groups of students selected by the teacher. From these data on, the teacher can easily see where the students are having problems and decide what to do next. From the student (or parent) point of view, the prompt feedback about the exercises correctness, added to the training with different exercises sets about the same subject, besides the utilization of video, color, sound, etc., that positively reinforce child's senses, are elected as the main advantages. © 2011 IEEE.

Abstract

Abstract
Knowledge is central to the modern economy and society. Indeed, the knowledge society has transformed the concept of knowledge and is more and more aware of the need to overcome the lack of knowledge when has to make options or address its problems and dilemmas. One's knowledge is less based on exact facts and more on hypotheses, perceptions or indications. Even when we use new computational artefacts and novel methodologies for problem solving, like the use of Group Decision Support Systems (GDSSs), the question of incomplete information is in most of the situations marginalized. On the other hand, common sense tells us that when a decision is made it is impossible to have a perception of all the information involved and the nature of its intrinsic quality. Therefore, something has to be made in terms of the information available and the process of its evaluation. It is under this framework that a Multi-valued Extended Logic Programming language will be used for knowledge representation and reasoning, leading to a model that embodies the Quality-of-Information (QoI) and its quantification, along the several stages of the decision-making process. In this way, it is possible to provide a measure of the value of the QoI that supports the decision itself. This model will be here presented in the context of a GDSS for VirtualECare, a system aimed at sustaining online healthcare services.

Abstract
Ambient Assisted Living environments projects arise as technological responses of the scientific community to problems associated with the population-ageing phenomenon. In theory, these environments should allow de-localization of healthcare services delivery and management to the home, thus containing the economic and social costs associated with old age. The VirtualECare project is one of those environments, enhanced with proactive techniques for a better user experience, focused on elderly chronic patients, through the ability of constant learning and adaption based in user interaction and its contexts. This learning and, consequently, adaption needs, however, unequivocally user identification, especially in multi-user environments. Traditional identification techniques and methodologies are not suitable for these scenario since, usually, require user interaction and wireless identification technique (e.g. RFID, Bluetooth) are very exposed to personification. In order to obtain the expected results we needed a more advanced technology. One possible, appropriate and already fairly developed technique is Facial Recognition. In this paper we present the VirtualECare project approach to Facial Recognition authentication techniques its advantages, disadvantages and possible implementations paths.

Abstract

Abstract
Data-mining over software can reveal similar patterns on software code. This can give important insights for the design of hardware cores, especially considering the benefits of the merge of software kernels and their implementation as a single hardware core. However, software codes have characteristics that make inadequate the direct use of typical data mining tools, mainly related to their large number of samples and the imprecise definition of code features for mining. Those characteristics affect negatively the performance of the most known data mining methods. To solve this problem, we propose in this paper the use of three techniques: the Normalized Compression Distance, the Neighbor Joining, and the Fast Newman algorithm. We combine these three techniques and propose a new approach for data mining of code repositories (DAMICORE). DAMICORE works with different types of code representations. Experiments reveal DAMICORE can indicate important software similarities at source code level. Specifically, merging soft-ware kernels identified by DAMICORE results in FPGA cores with size smaller than the overall hardware size needed when implementing a core for each kernel. © 2011 IEEE.