Abstract
Confidentiality protects users' data from digital eavesdroppers when traveling through the Internet. Confidentiality is complex and costly, especially on applications that involve communication and data exchange between multiple users. Cryptography has been the most used medium to achieve confidentiality, being the greatest challenge the sharing of a secret key to a group of people in a safe and effective way. This paper presents a chat application that implements an innovative way of sending messages with end-to-end encryption, in real-time, with a dynamic key store, and without the existence of data persistence. The application stands out from the others by the fact that it innovates the way the keys are shared with multiple users.

Abstract
The Inven!RA architecture is an approach for online tracking of progression towards learning objectives, from analytics of distributed learning activities, provided by multiple third parties. However, there are few examples on how to implement such third-party learning activities, known as Activity Provider modules. We followed the Inven!RA architecture interfacing specification to create and implement two sample learning activities: a technical documentation analysis activity and an Arduino microelectronics programming activity. Integration tests with an Inven!RA architecture prototype confirmed the adequacy of this implementation. Thus, these samples provide clarification on how to design and develop Inven!RA Activity Provider modules.;A arquitetura Inven!RA é uma abordagem para o acompanhamento online da evolução face a objetivos de aprendizagem, através de dados analíticos de atividades de aprendizagem distribuídas, proporcionadas por um leque variado de entidades externas. Como são escassos os exemplos de implementação destas atividades de aprendizagem externas, designadas por módulos de Prestadores de Atividades,seguimos a especificação de interfaces da arquitetura para criar e implementar dois exemplos de atividades de aprendizagem: uma atividade de análise de documentação técnica e uma de programação de microeletrónica com Arduino. Testes de integração com um protótipo da Inven!RA confirmaram a adequação destas implementações. Consequentemente, proporcionam clarificação quanto à forma de conceber e desenvolver módulos de Prestadores de Atividades para a arquitetura Inven!RA.

Abstract
There have been several research works proposing new Explainable AI (XAI) methods designed to generate model explanations having specific properties, or desiderata, such as fidelity, robustness, or human-interpretability. However, explanations are seldom evaluated based on their true practical impact on decision-making tasks. Without that assessment, explanations might be chosen that, in fact, hurt the overall performance of the combined system of ML model + end-users. This study aims to bridge this gap by proposing XAI Test, an application-grounded evaluation methodology tailored to isolate the impact of providing the end-user with different levels of information. We conducted an experiment following XAI Test to evaluate three popular XAI methods - LIME, SHAP, and TreeInterpreter - on a real-world fraud detection task, with real data, a deployed ML model, and fraud analysts. During the experiment, we gradually increased the information provided to the fraud analysts in three stages: Data Only, i.e., just transaction data without access to model score nor explanations, Data + ML Model Score, and Data + ML Model Score + Explanations. Using strong statistical analysis, we show that, in general, these popular explainers have a worse impact than desired. Some of the conclusion highlights include: i) showing Data Only results in the highest decision accuracy and the slowest decision time among all variants tested, ii) all the explainers improve accuracy over the Data + ML Model Score variant but still result in lower accuracy when compared with Data Only; iii) LIME was the least preferred by users, probably due to its substantially lower variability of explanations from case to case.

Abstract
AbstractDriven by the current digital transformation, European companies rely on accurate forecasting of future trends and prediction of most useful technologies in order to maintain their competitive edge. For this purpose, the mapping of enabling technologies to future scenarios becomes a valuable tool for practitioners and researchers alike, especially when considering the disruptive events that surround SCs design, implementation and management. This research sets forth to fill this gap by presenting a technology mapping of enabling technologies based on technology portfolio approach, expert elicitation and literature. The final outcome is the mapping of the enabling technologies to the characteristics of the future European SC scenarios.

Abstract
Due to its pervasive nature, the Internet of Things (IoT) is demanding for Low Power Wide Area Networks (LPWAN) since wirelessly connected devices need battery-efficient and long-range communications. Due to its low-cost and high availability (regional/city level scale), this type of network has been widely used in several IoT applications, such as Smart Metering, Smart Grids, Smart Buildings, Intelligent Transportation Systems (ITS), SCADA Systems. By using LPWAN technologies, the IoT devices are less dependent on common and existing infrastructure, can operate using small, inexpensive, and long-lasting batteries (up to 10 years), and can be easily deployed within wide areas, typically above 2 km in urban zones. The starting point of this work was an overview of the security vulnerabilities that exist in LPWANs, followed by a literature review with the main goal of substantiating an attack vector analysis specifically designed for the IoT ecosystem. This methodological approach resulted in three main contributions: (i) a systematic review regarding cybersecurity in LPWANs with a focus on vulnerabilities, threats, and typical defense strategies; (ii) a state-of-the-art review on the most prominent results that have been found in the systematic review, with focus on the last three years; (iii) a security analysis on the recent attack vectors regarding IoT applications using LPWANs. Results have shown that LPWANs communication technologies contain security vulnerabilities that can lead to irreversible harm in critical and non-critical IoT application domains. Also, the conception and implementation of up-to-date defenses are relevant to protect systems, networks, and data.

Abstract
Understanding the intellectual landscape of scientific communities and their collaborations has become an indispensable part of research per se. In this regard, measuring similarities among scientific documents can help researchers to identify groups with similar interests as a basis for strengthening collaboration and university-industry linkages. To this end, we intend to evaluate the performance of hybrid crowd-computing methods in measuring the similarity between document pairs by comparing the results achieved by crowds and artificial intelligence (AI) algorithms. That said, in this paper we designed two types of experiments to illustrate some issues in calculating how similar an automatic solution is to a given ground truth. In the first type of experiments, we created a crowdsourcing campaign consisting of four human intelligence tasks (HITs) in which the participants had to indicate whether or not a set of papers belonged to the same author. The second type involves a set of natural language processing (NLP) processes in which we used the TF-IDF measure and the Bidirectional Encoder Representation from Transformers (BERT) model. The results of the two types of experiments carried out in this study provide preliminary insight into detecting major contributions from human-AI cooperation at similarity calculation in order to achieve better decision support. We believe that in this case decision makers can be better informed about potential collaborators based on content-based insights enhanced by hybrid human-AI mechanisms.