Abstract
Recent studies show that many real-world software faults are due to slight modifications (mutations) to the program. Thus, analyzing transformations made by a developer and associating them with well-known mutation operators can help pinpoint and repair the root cause of failures. This paper proposes a mutation operator inference technique: given the original program and one of its subsequent forms, it infers which mutation operators would transform the original and produce such a version. Moreover, we implemented this technique as a tool called Morpheus, which analyzes faulty Java programs. We have also validated both the technique and tool by analyzing a repository with 1753 modifications for 20 different programs, successfully inferring mutation operators 78% of times. Furthermore, we also show that several program versions result from not just a single mutation operator but multiple ones. In the end, we resort to real-world case studies to demonstrate the advantages of this approach regarding program repair.

Abstract
To identify the most frequently developed topics in the area of Big Data and Digital Marketing, a quantitative analysis was developed in December 2020. This analysis was focused on 750 publications and later on 67 publications on Big Data and Digital Marketing from the Scopus database. A bibliometric analysis was developed using the VOSviewer software and the technique of term co-occurrence and author co-authorship. Clusters were found for each of the analyzed situations. © 2021 AISTI.

Abstract
Keywords: family business; internationalization; SMEs; family SMEs; international expansion; family ownership

Abstract
The detection and extraction of individual pylons and power lines from high-density point cloud (PC) LiDAR data are a relevant tool for evaluating the power lines utility corridors. Moreover, the presence of high vegetation and hilly terrain is a research challenger in the available methods. The paper presents a novel method for the extraction of pylons and power lines. Two steps compose the proposed approach: a pylon detection step based on top view projection, denoted by DFSS - Detect Filled Square Shapes, and a pylon arms detection step with the DPA Detect Pylon Arm algorithm. The results show that the proposed method could accurately and automatically extract pylons and the associated power lines, even if the dataset has low quality with downsampling, to reduce the processing time. Field tests were performed with a ground static LiDAR and a point cloud affected by downsampling voxel grid and Gaussian noise to simulate the expected LiDAR data from a UAV.

Abstract

Abstract
AbstractDigital technologies have gained ground among companies, researchers and policy makers in recent years due to their growing relevance to current and future supply chains. Technologies such as robotics, artificial intelligence, autonomous transport systems, data science, and additive manufacturing are gradually becoming part of people’s and companies’ daily lives and are changing the manufacturing, process industry and logistics sectors. Although recent attempts have been made to understand the implications of these technologies on supply chain management, the relevance of the different technologies in future scenarios is still unknown. Using a technology scouting approach, the most important enabling technologies for supply chains until 2030 are identified and selected and their implications on future supply chains are evaluated using an assessment methodology with different evaluation criteria.