The advances in network and collaboration technologies enable the creation of powerful environments for Collaborative Programming. One such environment is COLLECE, a groupware system to support collaborative edition, compilation and execution of programs in a synchronous distributed fashion, which includes advanced tools for communication, coordination and workspace awareness. The article analyses firstly some usability and design issues, discussing strengths and weaknesses of the system as a basis for the development of groupware tools to support Collaborative Programming. Then, the focus is on a number of experimental activities carried out. COLLECE was used to conduct a set of experimental activities about work productivity and program quality when comparing the activity of pair and solo programmers, and to analyse potential associations between ways of working and collaborating, and specific characteristics of the programs produced.
Functional data analysis and artificial neural networks are the building blocks of the proposed methodology that distinguishes the movement patterns among c’s patients on different stages of the disease and classifies new patients to their appropriate stage of the disease. The movement patterns are obtained by the accelerometer device of android smartphones that the patients carry while moving freely. The proposed methodology is relevant in that it is flexible on the type of data to which it is applied. To exemplify that, it is analyzed a novel real three-dimensional functional dataset where each datum is observed in a different time domain. Not only is it observed on a difference frequency but also the domain of each datum has different length. The obtained classification success rate of 83% indicates the potential of the proposed methodology.
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