Nowadays, several productivity platforms provide effective capabilities to edit collaboratively the content of a document. In educational settings, e-Learning approaches have taken advantage of this functionality to encourage students to join others to complete projects that include the writing of text documents. Although collaborative writing may foster interaction among students, the existing analytical metrics on these platforms are limited and can slow down the process of review by instructors in trying to determine the level of contribution of each student in the document. In this paper, we describe an analytic framework to measure and visualize the contribution in collaborative writing.

This paper proposes a decentralized method for controlling a swarm of autonomous agents represented as Unmanned Aerial Vehicles (UAVs) to accomplish a set of tasks cooperatively. The task commissioning is carried out by a Fuzzy Logic-based task allocation strategy, and the movement behavior is based on the internal state of the agent as well as the external data from its neighbors through local communications. The solution was modified from a centralized strategy developed in a previous work in order to be enhanced, so the proposed distributed strategy was tested considering the same experiments, configurations and conditions applied to the centralized approach in a simulated environment. To test the strategy, the planned activity to be performed was planting seeds, in a field composed by a grid of points that represents the places to be sown. Completion times and collision avoidance attempts were measured to test the effectiveness, as well as to perform a comparison between the distributed and centralized methods. Important improvements were found considering the size of agents in a swarm.

The analysis of system reliability often requires the use of subjective-judgments, uncertain data, and approximate system models. By allowing imprecision and approximate analysis fuzzy logic provides an effective tool for characterizing system reliability in these circumstances; it does not force precision where it is not possible. Here we apply the main concepts of fuzzy logic, fuzzy arithmetic and linguistic variables to the analysis of system structures, fault trees, event trees, the reliability of degradable systems, and the assessment of system criticality based on the severity of a failure and its probability of occurrence.>