Abstract
Indoor Air Quality (IAQ) describes the air quality of a room, and it refers to the health and comfort of the occupants. Typically, people spend around 90% of their time in indoor environments where the concentration of air pollutants and, occasionally, more than 100 times higher than outdoor levels. According to the World Health Organization (WHO), indoor air pollution is responsible for the death of 3.8 million people annually. It has been indicated that IAQ in residential areas or buildings is significantly affected by three primary factors, they are outdoor air quality, human activity in buildings, and building and construction materials. In this context, this work consists of a real-time IAQ system to monitor thermal comfort and gas concentration. The system has a data acquisition stage, captured by the WSN with a set of sensors that measures the data and send it to be stored on the InfluxDB database and displayed on Grafana. A Linear Regression (LR) algorithm was used to predict the behavior of the measured parameters, scoring up to 99.7% of precision. Thereafter, prediction data is stored on InfluxDB in a new database and displayed on Grafana. In this way, it is possible to monitor the actual measurement data and prediction data in real-time. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Deep learning has recently gained popularity in the field of video anomaly detection, with the development of various methods for identifying abnormal events in visual data. The growing need for automated systems to monitor video streams for anomalies, such as security breaches and violent behaviours in public areas, requires the development of robust and reliable methods. As a result, there is a need to provide tools to objectively evaluate and compare the real-world performance of different deep learning methods to identify the most effective approach for video anomaly detection. Current state-of-the-art metrics favour weakly-supervised strategies stating these as the best-performing approaches for the task. However, the area under the ROC curve, used to justify this statement, has been shown to be an unreliable metric for highly unbalanced data distributions, as is the case with anomaly detection datasets. This paper provides a new perspective and insights on the performance of video anomaly detection methods. It reports the results of a benchmark study with state-of-the-art methods using a novel proposed framework for evaluating and comparing the different models. The results of this benchmark demonstrate that using the currently employed set of reference metrics led to the misconception that weakly-supervised methods consistently outperform semi-supervised ones. © 2023 The Authors

Abstract
For the average programmer, adjunctions are (if at all known) more respected than loved. At best, they are regarded as an algebraic device of theoretical interest only, not useful in common practice. This paper is aimed at showing the opposite: that adjunctions underlie most of the work we do as programmers, in particular those using the functional paradigm. However, functions alone are not sufficient to express the whole spectrum of programming, with its dichotomy between specifications—what is (often vaguely) required—and implementations—how what is required is (hopefully well) implemented. For this, one needs to extend functions to relations. Inspired by the pioneering work of Ralf Hinze on “adjoint (un)folds”, the core of the so-called (relational) Algebra of Programming is shown in this paper to arise from adjunctions. Moreover, the paper also shows how to calculate recursive programs from specifications expressed by Galois connections—a special kind of adjunction. Because Galois connections are easier to understand than adjunctions in general, the paper adopts a tutorial style, starting from the former and leading to the latter (a path usually not followed in the literature). The main aim is to reconcile the functional programming community with a concept that is central to software design as a whole, but rarely accepted as such. © 2023, Springer Nature Switzerland AG.

Abstract
This paper presents PyAnaDroid, an open-source, fully-customizable execution pipeline designed to benchmark the performance of Android native projects and applications, with a special emphasis on benchmarking energy performance. PyAnaDroid is currently being used for developing large-scale mobile software empirical studies and for supporting an advanced academic course on program testing and analysis. The presented artifact is an expandable and reusable pipeline to automatically build, test and analyze Android applications. This tool was made openly available in order to become a reference tool to transparently conduct, share and validate empirical studies regarding Android applications. This document presents the architecture of PyAnaDroid, several use cases, and the results of a preliminary analysis that illustrates its potential. Video demo: https://youtu.be/7AV3nrh4Qc8

Abstract
The systematic review reported by this article aimed to analyze and synthesize state-of-the-art studies focused on the cyber resilience of healthcare information systems, namely in terms of (i) their purposes and (ii) methods being used to validate the proposed solutions. An electronic search was conducted, and 14 studies were included in this systematic review after the selection process. The research related to cyber resilience in the context of healthcare information systems is relatively recent and it is increasing, and according to the findings five different purposes were identified: (i) strategies to improve the cyber resilience of healthcare information systems; (ii) methods to design cyber resilient healthcare information systems; (iii) risk assessment; (iv) cyber resilience frameworks; and (v) development of cyber resilient healthcare information systems. Moreover, the solutions reported by the included articles present a low maturity level, which means that further research is required to increase the cyber resilience of healthcare information systems that constitute critical assets of the healthcare provision. © 2024 The Author(s).

Abstract
Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot's operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.