Abstract
Computational systems operating in open, dynamic and decentralized environments are required to share data with previously unknown computational systems. Due to this ill specification and emergent operation the systems are required to share the data's respective schemas and semantics so that the systems can correctly manipulate, understand and reason upon the shared data. The schemas and semantics are typically provided by ontologies using specific semantics provided by the ontology language. Because computational systems adopt different ontologies to describe their domain of discourse, a consistent and compatible communication relies on the ability to reconcile (in run-time) the vocabulary used in their ontologies. Since each computational system might have its own perspective about what are the best correspondences between the adopted ontologies, conflicts can arise. To address such conflicts, computational systems may engage in any kind of negotiation process that is able to lead them to a common and acceptable agreement. This paper proposes an argumentation-based approach where the computational entities describe their own arguments according to a commonly agreed argumentation meta-model. In order to support autonomy and conceptual differences, the community argumentation model can be individually extended yet maintaining computational effectiveness. Based on the formal specification, a software development framework is proposed.

Abstract
With the growing popularity of micro-task crowdsourcing platforms, a renewed interest in the resolution of complex tasks that require the cooperation of human and machine participants has emerged. This interest has led to workflow approaches that present new challenges at different dimensions of the human-machine computation process, namely in micro-task specification and human-computer interaction due to the unstructured nature of micro-tasks in terms of domain representation. In this sense, a semi-automatic generation environment for human-computer micro-task workflows from domain ontologies is proposed. The structure and semantics of the domain ontology provides a common ground for understanding and enhances human-computer cooperation.

Abstract
Nowadays, there are many technologies that support location systems involving intrusive and nonintrusive equipment and also varying in terms of precision, range, and cost. However, the developers some time neglect the noise introduced by these systems, which prevents these systems from reaching their full potential. Focused on this problem, in this research work a comparison study between three different filters was performed in order to reduce the noise introduced by a location system based on RFID UWB technology with an associated error of approximately 18 cm. To achieve this goal, a set of experiments was devised and executed using a miniature train moving at constant velocity in a scenario with two distinct shapes-linear and oval. Also, this train was equipped with a varying number of active tags. The obtained results proved that the Kalman Filter achieved better results when compared to the other two filters. Also, this filter increases the performance of the location system by 15% and 12% for the linear and oval paths respectively, when using one tag. For a multiple tags and oval shape similar results were obtained (11-13% of improvement).

Abstract
The work described in this paper is part of the development of a framework to support the joint execution of cooperative missions by a group of robotic vehicles, in a simulated, augmented or real environment. Such framework brings forward the need for formal languages in which to specify the group of vehicles that compose a team; the scenario in which they will operate; and the definition of the mission to be performed (surveillance, pollution detection, search and rescue operations, and so on). This paper presents the Mission Description Language (MDL), a XML dialect that describes the specifics of a mission to be performed by a team of autonomous vehicles. The MDL definition decomposes a mission into a number of possibly dependent phases and uses high-level concepts that facilitate the specification of a mission by both experts and non-experts. The dialect was defined using XML Schema, with all the required information being integrated in the definition. An interface was developed and incorporated into the framework, as to allow for the creation and edition of XML files following the defined schema. Once the information is correctly specified, it can be used in the framework, thus facilitating the process of mission specification and deployment. A survey answered by both practitioners and researchers shows that the degree of satisfaction with MDL is elevated (the overall evaluation of MDL achieved a 3.97 score (out of 5), with 82.05% of the answers being equal to or above 4); also, the usability of the interface was evaluated, having achieved a score of 82.9 in the SUS scale. These results imply that MDL is flexible enough to represent several types of missions, through a user-friendly interface.

Abstract
One of the research topics on multi-agent systems focuses on the development of mechanisms such as plans to empower a team of agents to cooperate in order to perform complex tasks. In many cases, the definition of these plans are based on a specific and rather complex grammar and stored in structured text files. In the context of the 2D simulated Robotic Soccer domain, a set-play language was proposed to coordinate the execution of teammates' behaviors to improve a team's overall performance. The process of manually writing set-play definitions is hazardous and can benefit from the use of a graphical tool to reach new users and allow typical users to become more productive. This work presents such a tool for which several experiments were run to measure its usability with forty two users by having them perform a set of tasks for which their execution time, number errors and satisfaction were recorded. The tool reduced the previous average time required to completely define a set-play by 90% and enabled even non-expert users to use it Moreover, users were on average satisfied with SPlanner having ranked it with a score of 77 (out of 100) using a System Usability Scale questionnaire.

Abstract
The prediction of overall survival in patients has an important role, especially in diseases with a high mortality rate. Encompassed in this reality, patients with oncological diseases, particularly the more frequent ones like woman breast cancer, can take advantage of a very good customization, which in some cases may even lead to a disease-free life. In order to achieve this customization, in this work a comparison between three algorithms (evolutionary, hierarchical and k-medoids) is proposed. After constructing a database with more than 800 breast cancer patients from a single oncology center with 15 clinical variables (heterogeneous data) and having 25% of the data missing, which illustrates a real clinical scenario, the algorithms were used to group similar patients into clusters. Using Tukey's HSD (Honestly Significant Difference) test, from both comparison between k-medoids and the other two approaches (evolutionary and hierarchical clustering) a statistical difference were detected (p- value < 0.0000001) as well as for the other comparison (evolutionary versus hierarchical clustering) - p-value = 0.0061354 - for a significance level of 95%. The future work will consist primarily in dealing with the missing data, in order to achieve better results in future prediction. © 2014, Springer International Publishing Switzerland.