Abstract

Abstract

Abstract
Many aspects of our lives are associated with places and the activities we perform on a daily basis. Most of them are recurrent and demand displacement of the individual between regular places like going to work, school or other important personal locations. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics, especially because humans are frequently looking for uniformity to support their decisions and make their actions easier or even automatic. In this work, we propose a method for discovering common pathways across users' habits from human mobility data. By using a density-based clustering algorithm, we identify the most preferable locations the users visit, we apply a Gaussian mixture model over these places to automatically separate among all traces, the trajectories that follow patterns in order to discover the representations of individual's habits. By using the longest common sub-sequence algorithm, we search for the trajectories that are more similar over the set of users' habits trips by considering the distance that pairs of users or habits share on the same path. The proposed method is evaluated over two real-world GPS datasets and the results show that the approach is able to detect the most important places in a user's life, detect the routine activities and identify common routes between users that have similar habits paving the way for research techniques in carpooling, recommendation and prediction systems.

Abstract
One of the most intriguing facets of the climate system is that it exhibits variability across all temporal and spatial scales; pronounced examples are temperature and precipitation. The structure of this variability, however, is not arbitrary. Over certain spatial and temporal ranges, it can be described by scaling relationships in the form of power laws in probability density distributions and autocorrelation functions. These scaling relationships can be quantified by scaling exponents which measure how the variability changes across scales and how the intensity changes with frequency of occurrence. Scaling determines the relative magnitudes and persistence of natural climate fluctuations. Here, we review various scaling mechanisms and their relevance for the climate system. We show observational evidence of scaling and discuss the application of scaling properties and methods in trend detection, climate sensitivity analyses, and climate prediction.

Abstract
High-maturity software development processes and development environments with automated data collection can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes, and devise improvement actions. However, conducting the analysis manually is challenging because of the potentially large amount of data to analyze, the effort and expertise required, and the lack of benchmarks for comparison. In this article, we present ProcessPAIR, a novel method with tool support designed to help developers analyze their performance data with higher quality and less effort. Based on performance models structured manually by process experts and calibrated automatically from the performance data of many process users, it automatically identifies and ranks performance problems and potential root causes of individual subjects, so that subsequent manual analysis for the identification of deeper causes and improvement actions can be appropriately focused. We also show how ProcessPAIR was successfully instantiated and used in software engineering education and training, helping students analyze their performance data with higher satisfaction (by 25%), better quality of analysis outcomes (by 7%), and lower effort (by 4%), as compared to a traditional approach (with reduced tool support).

Abstract
Performance and power efficiency in edge and embedded systems can benefit from specialized hardware. To avoid the effort of manual hardware design, we explore the generation of accelerator circuits from binary instruction traces for several Instruction Set Architectures.