Abstract
The preliminary credibility assessment screening system (PCASS) is a US-based program, which is currently being implemented by intelligence units of the North Atlantic Treaty Organization (NATO) to make the initial screening of individuals suspected of infiltrating the Afghan National Defense and Security Forces (ANDSF). Sensors have been instrumental in the PCASS, leading to organizational change. The aim of this research is to describe how the ANDSF adapted to the implementation of PCASS, as well as implemented changes since the beginning of the program. To do so, we have conducted a qualitative, exploratory, and descriptive case study that allows one to understand, through the use of a series of data collection sources, a real-life phenomenon of which little is known. The results suggest that the sensors used in PCASS empower security forces with reliable technologies to identify and neutralize internal threats. It then becomes evident that the technological leadership that PCASS provides allows the developing of a relatively stable and consistent organizational change, fulfilling the objectives of the NATO and the ANDSF.

Abstract

Abstract
Mixed-type data is common in the real world. However, supervised learning algorithms such as support vector machines or neural networks can only process numerical features. One may choose to drop qualitative features, at the expense of possible loss of information. A better alternative is to encode them as new numerical features. Under the constraints of time, budget, and computational resources, we were motivated to search for a general-purpose encoder but found the existing benchmarks to be limited. We review these limitations and present an alternative. Our benchmark tests 16 encoding methods, on 15 regression datasets, using 7 distinct predictive models. The top general-purpose encoders were found to be Catboost, LeaveOneOut, and Target.

Abstract
Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.

Abstract
Deepfake in multimedia content is being increasingly used in a plethora of cybercrimes, namely those related to digital kidnap, and ransomware. Criminal investigation has been challenged in detecting manipulated multimedia material, by applying machine learning techniques to distinguish between fake and genuine photos and videos. This paper aims to present a Support Vector Machines (SVM) based method to detect tampered photos. The method was implemented in Python and integrated as a new module in the widely used digital forensics application Autopsy. The method processes a set of features resulting from the application of a Discrete Fourier Transform (DFT) in each photo. The experiments were made in a new and large dataset of classified photos containing both legitimate and manipulated photos, and composed of objects and faces. The results obtained were promising and reveal the appropriateness of using this method embedded in Autopsy, to help in criminal investigation activities and digital forensics.

Abstract
Experienciar a cidade enquanto espaço de aprendizagem implica entrelaçamento de seus vários tempos, espaços e de suas dimensões humanas e não humanas, digitais, biológicas, históricas, econômicas, etc. Entender a cidade como entidade viva, complexa e comunicativa se afasta da visão antropocêntrica de mundo e instaura outras formas de comunicação, habitação e aprendizagem. Portanto, este artigo apresenta o processo de desenvolvimento de uma prática pedagógica gamificada cujo objetivo é compreender como o coengendramento dos diversos entes constituintes da cidade produz o pensamento computacional na perspectiva da aprendizagem inventiva, segundo uma proposta de Educação OnLIFE. Tal prática integra o projeto de pesquisa A cidade como espac¸o de aprendizagem: pra´ticas pedago´gicas inovadoras para a promoc¸a~o da cidadania e do desenvolvimento social sustenta´vel, financiado pela Fundação Carlos Chagas e pelo Itaú Social, e desenvolvida pelo Grupo de Pesquisa Educação Digital — GPe-dU, UNISINOS/CNPq. O estudo, qualitativo, apropria-se do método cartográfico de pesquisa-intervenção para produção e análise dos dados. Como resultado, apresentam-se pistas que permitem compreender a emergência do pensamento computacional para explorar a cidade, bem como a necessidade de inovação de práticas pedagógicas a partir da problematização do mundo para uma Educação conectada com a vida.