Abstract
Learning Management Systems (LMS) provide minimal support for educational use of virtual worlds. Integration efforts assume the educators are inside the virtual world, providing hooks to services in the external LMS, to setup and manage virtual world activities. We present the inverse approach, enabling educators to setup and manage virtual world activities using the traditional LMS Web interface as an integral part of the overall educational activities of a course. In our approach, the LMS enables the teacher/trainer to setup, control, track, and store virtual world activities and its elements. It is the result of a joint effort by academic and corporate teams, implemented in the Formare LMS for OpenSimulator and Second Life Grid virtual world platforms. We explain how the Multis architecture can be used for integration, with concrete cases, an approach that can be implemented in other LMS and virtual world platforms, to overcome the limitations of existing systems for organizational management of e-learning activities.

Abstract
This work describes SituMan (Situation Manager), a mobile system that makes use of the sensors commonly included in most mobile platforms and a fuzzy inference engine to attempt to infer user context and environment. Such "situation" information, has been used to enhance the behaviour of MoodBuster, another mobile application used in the scope of the mental health domain to collect Ecological Momentary Assessments (EMA). EMA has been used in psychotherapy to minimize the effects of recall bias in the assessment of patient mood, as well as in the recollection of other experiences and behaviours. SituMan can enhance the user experience in the scope of EMA by prompting users in the desired situation, instead of at random or fixed-times, thus reducing obtrusiveness. It can also provide new insight to mental health professionals by summarizing the situations experienced by the patient, further allowing correlation of situation information with patient mood within the same time frame.

Abstract
The Web is frequently used as a way to access health information. In the health domain, the terminology can be very specific, frequently assuming a medico-scientific character. This can be a barrier to users who may be unable to understand the retrieved documents. Therefore, it would be useful to automatically assess how well a certain document will be understood by a certain user. In the present work, we analyse whether it is possible to predict the comprehension of documents using document features together with user features, and how well this can be achieved. We use an existing dataset, composed by health documents on the Web and their assessment in terms of comprehension by users, to build two multivariate prediction models for comprehension. Our best model showed very good results, with 96.51% accuracy. Our findings suggest features that can be considered by search engines to estimate comprehension. We found that user characteristics related to web and health search habits, such as the success of the users with Web search and the frequency of the users' health search, are some of the most influential user variables. The promising results obtained with this dataset with manual comprehension assessment will lead us to explore the automatic assessment of document and user characteristics. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
This paper presents the development framework for an energy management platform that is being developed within the AnyPLACE project. In order to ensure that end-users become active participants in services like demand response, a combined approach is necessary in terms of monitoring, automation, and user interfacing. The success in engaging the end-user, as the centerpiece of the energy management challenge, is vital in taking advantage of a more efficient use of energy, as it is shown in this paper. The proposed framework can be run in a single board computer.

Abstract
It is known that code readability has an impact on software quality. This paper introduces a preliminary list of 33 good practices for code readability that could be taught in object oriented programming courses of informatics programs. In this set are included practices with both positive and negative impact, the latter mainly from bad smells. A survey was made with OOP teachers in order to assess the significance of teaching a set of good practices. The results showed that teachers consider it important to teach the defined set. Such result was statistically significant. Still, the same level of significance was not awarded to all practices. Some additional results are presented.

Abstract
Nowadays, many companies have migrated their applications and data to the cloud. Among other benefits of this technology, the ability to answer quickly business requirements has been one of the main motivations. Thereby, in cloud environments, resources should be acquired and released automatically and quickly at runtime. This way, to ensure QoS, the major cloud providers emphasize ensuring of availability, CPU instance and cost measure in their SLAs (Service Level Agreements). However, the QoS performance are not completely handled or inappropriately treated in SLAs. Although from the user's point of view, it is considered one of the main QoS parameters. Therefore, the aim of this work consists in development of a solution to efficient query processing on large databases available in the cloud environments. It integrates adaptive re-optimization at query runtime and their costs are based on the SRT (Service Response Time) QoS performance parameter of SLA. Finally, the solution was evaluated in Amazon EC2 cloud infrastructure and the TPC-DS like benchmark was used for generating a database.