This paper describes a study that examines the effect of cohesion-based feedback on a team members' behaviors in a global software development project. Chat messages and forum posts were collected from a software development project involving students living in the US and Mexico. Half of the teams in the project received feedback in the form of a graphical representation that displayed the group's cohesion level, while the other teams received no feedback. The nature of the group interactions as well as the linguistic content of such interactions was then analyzed and compared. Results from this analysis show statistically significant differences between the feedback and non-feedback conditions. More specifically, cohesion-based feedback had a positive relation to a team's total message count, response rate, and individual cohesion score. In addition, the analysis of linguistic categories showed that the most salient categories observed were related to words about time and work. Although the feedback system did not appear to affect individual performance, the findings suggest that the cohesion measure defined in the study is positively correlated to the task cohesion construct and is also related to individual and team performance.

Entropy is a key concept in the characterization of uncertainty for any given signal, and its extensions such as Spectral Entropy and Permutation Entropy. They have been used to measure the complexity of time series. However, these measures are subject to the discretization employed to study the states of the system, and identifying the relationship between complexity measures and the expected performance of the four selected forecasting methods that participate in the M4 Competition. This relationship allows the decision, in advance, of which algorithm is adequate. Therefore, in this paper, we found the relationships between entropy-based complexity framework and the forecasting error of four selected methods (Smyl, Theta, ARIMA, and ETS). Moreover, we present a framework extension based on the Emergence, Self-Organization, and Complexity paradigm. The experimentation with both synthetic and M4 Competition time series show that the feature space induced by complexities, visually constrains the forecasting method performance to specific regions; where the logarithm of its metric error is poorer, the Complexity based on the emergence and self-organization is maximal.

High-Performance Computing systems rely on the software’s capability to be highly parallelized in individual computing tasks. However, even with a high parallelization level, poor scheduling can lead to long runtimes; this scheduling is in itself an NP-hard problem. Therefore, it is our interest to use a heuristic approach, particularly Cellular Processing Algorithms (CPA), which is a novel metaheuristic framework for optimization. This framework has its foundation in exploring the search space by multiple Processing Cells that communicate to exploit the search and in the individual stagnation detection mechanism in the Processing Cells. In this paper, we proposed using a Greedy Randomized Adaptive Search Procedure (GRASP) to look for promising task execution orders; later, a CPA formed with Iterated Local Search (ILS) Processing Cells is used for the optimization. We assess our approach with a high-performance ILS state-of-the-art approach. Experimental results show that the CPA outperforms the previous ILS in real applications and synthetic instances.