Abstract
Machine Learning and Data Mining research strongly depend on the quality and quantity of the real world datasets for the evaluation stages of the developing methods. In the context of the emerging Online Multi-Target Regression and Multi-Label Classification methodologies, datasets present new characteristics that require specific testing and represent new challenges. The first difficulty found in evaluation is the reduced amount of examples caused by data damage, privacy preservation or high cost of acquirement. Secondly, few data events of interest such as data changes are difficult to find in the datasets of specific domains, since these events naturally scarce. For those reasons, this work suggests a method of producing synthetic datasets with desired properties(number of examples, data changes events, ... ) for the evaluation of Multi-Target Regression and Multi-Label Classification methods. These datasets are produced using First Principle Models which give more realistic and representative properties such as real world meaning ( physical, financial, ... ) for the outputs and inputs variables. This type of dataset generation can be used to produce infinite streams and to evaluate incremental methods such as online anomaly and change detection. This paper illustrates the use of synthetic data generation through two showcases of data changes evaluation.

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This paper describes the application of the Opt-aiNet algorithm to the reconfiguration problem of distribution systems considering variable demand levels. The Opt-aiNet algorithm is an optimization technique inspired in the immunologic bio system and it aims at reproducing the main properties and functions of this system. The reconfiguration problem of distribution networks with variable demands is a complex problem that aims at identifying the most adequate radial topology of the network that complies with all technical constraints in every demand level while minimizing the cost of power losses along an extended operation period. This work includes results of the application of the Opt-aiNet algorithm to distribution systems with 33, 84, 136 and 417 buses. These results demonstrate the robustness and efficiency of the proposed approach.

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This paper presents Enki, a web-based IDE that integrates several pedagogical tools designed to engage students in learning programming languages. Enki achieves this goal (1) by sequencing educational resources, either expository or evaluative, (2) by using gamification services to entice students to solve activities, (3) by promoting social interaction and (4) by helping students with activities, providing feedback on submitted solutions. The paper describes Enki, its concept and architecture, details its design and implementation, and covers also its validation.

Abstract
The economic crisis which started in 2008 has demonstrated more clearly the importance of conjuncture aspects for organizational results evidencing associated opportunities and vulnerabilities. In such a context, if on one hand innovation is affected by investment reduction by most enterprises, on the other it has shown to be one of the “antidotes” against the crisis and appears as fertile soil for investments, in the organizational as well as in the territorial context. Enterprises and countries which maintained or even raised efforts toward innovation have demonstrated a higher resilience in times of difficulty. This study aims at identifying opportunities and threats to the innovation performance of firms by means of the study of the crisis effects upon external facilitators and barriers. Besides the literature review, case studies developed in Portuguese and Brazilian enterprises are used for the analysis of the practical effects of the identified aspects in two distinct realities in what concerns the economic crisis. The results show that although Portugal has suffered the crisis effects, in a general way, more than Brazil, when we look to innovation performance, Portugal has shown better results than Brazil during and after the crisis. This study contributes to enterprise and academic purposes since it classifies factors which influence innovation positive or negatively in times of crisis.

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PRONUTRISENIOR is a holistic approach that considers older adults as part of their environment, in order to better assist health professionals, caregivers and other professionals to monitor their nutritional status and thus reduce malnutrition in this population group. The education and empowerment of professionals were preceded by an assessment of the community and its environment. Such information was subsequently incorporated into the educational and informational materials to support training and empowerment programs. This paper presents the project rationale, describes the methods applied to attain the objectives defined within the scope of the older adults living in its environment, and presents general data on the studied population and sample.