Abstract
Internet-of-Things systems are assemblies of highly-distributed and heterogeneous parts that, in orchestration, work to provide valuable services to end-users in many scenarios. These systems depend on the correct operation of sensors, actuators, and third-party services, and the failure of a single one can hinder the proper functioning of the whole system, making error detection and recovery of paramount importance, but often overlooked. By drawing inspiration from other research areas, such as cloud, embedded, and mission-critical systems, we present a set of patterns for self-healing IoT systems. We discuss how their implementation can improve system reliability by providing error detection, error recovery, and health mechanisms maintenance. © 2020 ACM.

Abstract
Text categorization is a supervised learning task which aims to assign labels to documents based on the predicted outcome suggested by a classifier trained on a set of labelled documents. The association of text classification to facilitate labelling reports/complaints in the economic and health related fields can have a tremendous impact in the speed at which these are processed, and therefore, lowering the required time to act upon these complaints and reports. In this work, we aim to classify complaints into the main 4 economic activities given by the Portuguese Economic and Food Safety Authority. We evaluate the classification performance of 9 algorithms (Complement Naïve Bayes, Bernoulli Naïve Bayes, Multinomial Naïve Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, AdaBoost and Logistic Regression) at different layers of text preprocessing. Results reveal high levels of accuracy, roughly around 85%. It was also observed that the linear classifiers (support vector machine and logistic regression) allowed us to obtain higher f1-measure values than the other classifiers in addition to the high accuracy values revealed. It was possible to conclude that the use of these algorithms is more adequate for the data selected, and that applying text classification methods can facilitate and help the complaints and reports processing which, in turn, leads to a swifter action by authorities in charge. Thus, relying on text classification of reports and complaints can have a positive influence in either economic crime prevention or in public health, in this case, by means of food-related inspections. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
High-level Synthesis (HLS) is of paramount importance to leverage the use of FPGA-based accelerators by software developers. To achieve efficient FPGA implementations, code restructuring and source code annotating with HLS directives are necessary. However, this is still a manual process conducted by experienced developers. This paper proposes a step on a framework to automatically optimize C code via directives, using a source-to-source compiler on a stage before HLS. This optimization is primarily applied by strategies that select, configure, and insert directives on the code input to the Vivado HLS tool to synthesize more latency-efficient FPGA hardware. Those strategies rely on very simple but effective heuristics, which use a small set of properties extracted from the control/dataflow graphs generated from the input source code. We evaluate the framework using a variety of source codes. The experiments show that it can achieve efficient results while maintaining a low resource usage in most cases. Our experiments also compare the framework results to code optimized manually with directives, and they show that for most benchmarks used, it achieves similar results.

Abstract
Mobile Augmented Reality (MAR) systems have emerged and greatly evolved in the last two decades. They have application in many domains, most notably in the field of Cultural Heritage (CH) and tourism, where people tend to rely on smartphones when visiting a new city to obtain additional information on the city landmarks. Expectations are that they obtain precise and tailored information to the visitor’s needs. Therefore, researchers started to investigate innovative approaches for presenting and suggesting digital content related to cultural and historical places. This article presents a novel MAR application, NearHeritage, which uses emergent technologies to assist visitors in finding and exploring Cultural Heritage. The research focuses on combining the use of context-awareness with Augmented Reality (AR). By sensing the context surrounding the user, the NearHeritage app discloses not only the list of nearby points-of-interest (POI) but also detailed information about the POIs in the form of AR content adapted to the user context. The solution presented uses built-in sensors of Android devices and takes advantage of various APIs (Foursquare API, Google Maps API and IntelContextSensing SDK) to retrieve information about the landmarks and the visitor context. Results from initial experimentation indicate that the concept of a context-aware MAR application can improve the user experience in discovering and learning more about Cultural Heritage, creating an interactive, enjoyable and unforgettable adventure. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Guaranteeing that safety-critical Cyber-Physical Systems (CPS) do not fail upon deployment is becoming an even more complicated task with the increased use of complex software solutions. To aid in this matter, formal methods (rigorous mathematical and logical techniques) can be used to obtain proofs about the correctness of CPS. In such a context, Runtime Verification has emerged as a promising solution that combines the formal specification of properties to be validated and monitors that perform these validations during runtime. Although helpful, runtime verification solutions introduce an inevitable overhead in the system, which can disrupt its correct functioning if not safely employed. We propose the creation of a Domain Specific Language (DSL) that, given a generic CPS, 1) verifies if its real-time scheduling is guaranteed, even in the presence of coupled monitors, and 2) implements several verification conditions for the correct-by-construction generation of monitoring architectures. To achieve it, we plan to perform statical verifications, derived from the available literature on schedulability analysis, and powered by a set of semi-automatic formal verification tools.

Abstract
O sistema de distribuição de energia elétrica é a parcela do sistema de potência mais vulnerável aos eventos de interrupção, originados por fatores naturais externos ou intrínsecos aos equipamentos elétricos. Na mitigação dos impactos desses eventos, simuladores digitais em tempo real são utilizados na obtenção dos transitórios do sistema elétrico, todavia, essa técnica demanda grande esforço computacional. Nesse contexto, propõe-se uma técnica híbrida para simulação do transitório em sistemas de distribuição, combinando a alta taxa de amostragem dos modelos no domínio do tempo para monitoramento de tensão e corrente com a velocidade de processamento dos algoritmos que operam os modelos fasoriais em regime quase-estacionário, ou permanente. A metodologia proposta também permite considerar diferentes tecnologias de geradores distribuídos acoplados na rede. Os resultados dos testes realizados indicam a consistência da metodologia proposta, representando o comportamento do transitório no sistema de distribuição de energia elétrica. Todas as simulações realizadas são comparadas com valores amostrais obtidos usando um software comercial especializado.