Abstract
Social network analysis (SNA) is a core pursuit of analyzing social networks today. In addition to the usual statistical techniques of data analysis, these networks are investigated using SNA measures. It helps in understanding the dependencies between social entities in the data, characterizing their behaviors and their effect on the network as a whole and over time. Therefore, this article attempts to provide a succinct overview of SNA in diverse topological networks (static, temporal, and evolving networks) and perspective (ego-networks). As one of the primary applicability of SNA is in networked data mining, we provide a brief overview of network mining models as well; by this, we present the readers with a concise guided tour from analysis to mining of networks. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Commercial, Legal, and Ethical Issues > Social Considerations

Abstract
Information Extraction (IE) and fusion are complex fields and have been useful in several domains to deal with heterogeneous data sources. Criminal police are challenged in forensics activities with the extraction, processing and interpretation of numerous documents from different types and with distinct formats (templates), such as narrative criminal reports, police databases and the result of OSINT activities, just to mention a few. Such challenges suggest, among others, to cope with and manually connect some hard to interpret meanings, such as license plates, addresses, names, slang and figures of speech. This paper aims to deal with forensic IE and fusion, thus a system was proposed to automatically extract, transform, clean, load and connect police reports that arrived from different sources. The same system aims to help police investigators to identify and correlate interesting extracted entities. © 2020, Springer Nature Switzerland AG.

Abstract
Nowadays, the world is getting increasingly competitive and the quality and the amount of the work presented are one of the decisive factors when choosing an employee. It is no longer necessary to only perform but, to achieve a product with quality, on time, at the lowest possible cost and with the minimum resources. For this reason, the employee must have a high score of attention when performing a task, and the factors that influence attention negatively must be reduced. This is true in many different domains, from the workplace to the classroom. In this paper, we present a nonintrusive smart environment for monitoring people's attention when working in teams. The presented system provides real time information about each individual and information about the team. It can be very useful for team managers to identify potentially distracting events or individuals because when the attention of an individual is not at its best when performing the proposed task, her/his performance will be negatively affected, with consequences for the individual and for the organization.

Abstract

Abstract
This paper presents a new modulation and control strategies for the high-frequency link matrix converter (HFLMC). The proposed method aims to achieve controllable power factor in the grid interface as well as voltage and current regulation for a battery energy storage device. The matrix converter (MC) is a key element of the system, since it performs a direct ac to ac conversion between the grid and the power transformer, dispensing the traditional dc-link capacitors. Therefore, the circuit volume and weight are reduced and a longer service life is expected when compared with the existing technical solutions. A prototype was built to validate the mathematical analysis and the simulation results. Experimental tests developed in this paper show the capability of controling the grid currents in the synchronous reference frame in order to provide grid services. Simultaneously, the battery current is well regulated with small ripple, which makes this converter ideal for battery charging of electric vehicles and energy storage applications.

Abstract
Configuring program parallelism and selecting optimal compiler options according to the underlying platform architecture is a difficult task. Tipically, this task is either assigned to the programmer or done by a standard one-fits-all policy generated by the compiler or runtime system. A runtime selection of the best configuration requires the insertion of a lot of glue code for profiling and runtime selection. This represents a programming wall for application developers. This paper presents a structured approach, called SOCRATES, based on an aspect-oriented language (LARA) and a runtime autotuner (mARGOt) to mitigate this problem. LARA has been used to hide the glue code insertion, thus separating the pure functional application description from extra-functional requirements. mARGOT has been used for the automatic selection of the best configuration according to the runtime evolution of the application. 1 © 2018 EDAA.