Indoor environment dataset based on RSSI collected with bluetooth devices.

This paper describes a data collection experiment focused on researching indoor positioning systems using Bluetooth Low Energy (BLE) devices. The study was conducted in a real-world scenario with 150 test points and collected signals from 11 mobile devices. The dataset contains RSSI values from the mobile devices in relation to 15 fixed anchor nodes in the experimentation scenario. The dataset includes data on device identification, labels and coordinates of test points, and the room where the data was collected. The data is organized as CSV files and offers valuable information for researchers developing and assessing location models. By sharing this dataset, we aim to support the creation of robust and precise indoor localization models.

Performance counter dataset for behavioural biometric purpose.

In the pursuit of advancing research in continuous user authentication, we introduce COUNT-OS-I and COUNT-OS-II, two distinct performance counter datasets from Windows operating systems, crafted to bolster research in continuous user authentication. Encompassing data from 63 computers and users, the datasets offer rich, real-world insights for developing and evaluating authentication models. COUNT-OS-I spans 26 users in an IT department, capturing 159 attributes across diverse hardware and software environments over 26 h on average per user. COUNT-OS-II, on the other hand, encompasses 37 users with identical system configurations, recording 218 attributes per sample over a 48-hour period. Both datasets utilize pseudonymization to safeguard user identities while maintaining data integrity and statistical accuracy. The well-balanced nature of the data, confirmed by comprehensive statistical analysis, positions these datasets as reliable benchmarks for the continuous user authentication domain. Through their release, we aim to empower the development of robust, real-world applicable authentication models, contributing to enhanced system security and user trust.

Android malware detection with MH-100K: An innovative dataset for advanced research.

High-quality datasets are crucial for building realistic and high-performance supervised malware detection models. Currently, one of the major challenges of machine learning-based solutions is the scarcity of datasets that are both representative and of high quality. To foster future research and provide updated and public data for comprehensive evaluation and comparison of existing classifiers, we introduce the MH-100K dataset [1], an extensive collection of Android malware information comprising 101,975 samples. It encompasses a main CSV file with valuable metadata, including the SHA256 hash (APK's signature), file name, package name, Android's official compilation API, 166 permissions, 24,417 API calls, and 250 intents. Moreover, the MH-100K dataset features an extensive collection of files containing useful metadata of the VirusTotal1 analysis. This repository of information can serve future research by enabling the analysis of antivirus scan result patterns to discern the prevalence and behaviour of various malware families. Such analysis can help to extend existing malware taxonomies, the identification of novel variants, and the exploration of malware evolution over time.

Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals.

The use of machine learning (ML) techniques in affective computing applications focuses on improving the user experience in emotion recognition. The collection of input data (e.g., physiological signals), together with expert annotations are part of the established standard supervised learning methodology used to train human emotion recognition models. However, these models generally require large amounts of labeled data, which is expensive and impractical in the healthcare context, in which data annotation requires even more expert knowledge. To address this problem, this paper explores the use of the self-supervised learning (SSL) paradigm in the development of emotion recognition methods. This approach makes it possible to learn representations directly from unlabeled signals and subsequently use them to classify affective states. This paper presents the key concepts of emotions and how SSL methods can be applied to recognize affective states. We experimentally analyze and compare self-supervised and fully supervised training of a convolutional neural network designed to recognize emotions. The experimental results using three emotion datasets demonstrate that self-supervised representations can learn widely useful features that improve data efficiency, are widely transferable, are competitive when compared to their fully supervised counterparts, and do not require the data to be labeled for learning.

How Validation Methodology Influences Human Activity Recognition Mobile Systems.

In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestimate the prediction accuracy of an algorithm because of the inappropriate choice of validation methodology. We show how the SHAP (Shapley additive explanations) framework, used in literature to explain the predictions of any machine learning model, presents itself as a tool that can provide graphical insights into how human activity recognition models achieve their results. Now it is possible to analyze which features are important to a HAR system in each validation methodology in a simplified way. We not only demonstrate that the validation procedure k-folds cross-validation (k-CV), used in most works to evaluate the expected error in a HAR system, can overestimate by about 13% the prediction accuracy in three public datasets but also choose a different feature set when compared with the universal model. Combining explainable methods with machine learning algorithms has the potential to help new researchers look inside the decisions of the machine learning algorithms, avoiding most times the overestimation of prediction accuracy, understanding relations between features, and finding bias before deploying the system in real-world scenarios.

Indoor Positioning System Using Dynamic Model Estimation.

Indoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using fixed model parameters, treating the whole scenario as having a uniform signal propagation. This might work for most small scale experiments, but not for larger scenarios. In this paper, we propose PoDME (Positioning using Dynamic Model Estimation), a model-based IPS that uses dynamic parameters that are estimated based on the location the signal was sent. More specifically, we use the set of anchor nodes that received the signal sent by the mobile node and their signal strengths, to estimate the best local values for the log-distance model parameters. Also, since our solution depends highly on the selected anchor nodes to use on the position computation, we propose a novel method for choosing the three best anchor nodes. Our method is based on several data analysis executed on a large-scale, Bluetooth-based, real-world experiment and it chooses not only the nearest anchor but also the ones that benefit our least-square-based position computation. Our solution achieves a position estimation error of 3 m, which is 17% better than a fixed-parameters model from the literature.

A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory.

Smartphones have emerged as a revolutionary technology for monitoring everyday life, and they have played an important role in Human Activity Recognition (HAR) due to its ubiquity. The sensors embedded in these devices allows recognizing human behaviors using machine learning techniques. However, not all solutions are feasible for implementation in smartphones, mainly because of its high computational cost. In this context, the proposed method, called HAR-SR, introduces information theory quantifiers as new features extracted from sensors data to create simple activity classification models, increasing in this way the efficiency in terms of computational cost. Three public databases (SHOAIB, UCI, WISDM) are used in the evaluation process. The results have shown that HAR-SR can classify activities with 93% accuracy when using a leave-one-subject-out cross-validation procedure (LOSO).

Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview.

The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people's lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users' physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.

MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors.

Human activity recognition (HAR) through sensors embedded in smartphones has allowed for the development of systems that are capable of detecting and monitoring human behavior. However, such systems have been affected by the high consumption of computational resources (e.g., memory and processing) needed to effectively recognize activities. In addition, existing HAR systems are mostly based on supervised classification techniques, in which the feature extraction process is done manually, and depends on the knowledge of a specialist. To overcome these limitations, this paper proposes a new method for recognizing human activities based on symbolic representation algorithms. The method, called "Multivariate Bag-Of-SFA-Symbols" (MBOSS), aims to increase the efficiency of HAR systems and maintain accuracy levels similar to those of conventional systems based on time and frequency domain features. The experiments conducted on three public datasets showed that MBOSS performed the best in terms of accuracy, processing time, and memory consumption.

Human Activity Recognition Based on Symbolic Representation Algorithms for Inertial Sensors.

Mobile sensing has allowed the emergence of a variety of solutions related to the monitoring and recognition of human activities (HAR). Such solutions have been implemented in smartphones for the purpose of better understanding human behavior. However, such solutions still suffer from the limitations of the computing resources found on smartphones. In this sense, the HAR area has focused on the development of solutions of low computational cost. In general, the strategies used in the solutions are based on shallow and deep learning algorithms. The problem is that not all of these strategies are feasible for implementation in smartphones due to the high computational cost required, mainly, by the steps of data preparation and the training of classification models. In this context, this article evaluates a new set of alternative strategies based on Symbolic Aggregate Approximation (SAX) and Symbolic Fourier Approximation (SFA) algorithms with the purpose of developing solutions with low computational cost in terms of memory and processing. In addition, this article also evaluates some classification algorithms adapted to manipulate symbolic data, such as SAX-VSM, BOSS, BOSS-VS and WEASEL. Experiments were performed on the UCI-HAR, SHOAIB and WISDM databases commonly used in the literature to validate HAR solutions based on smartphones. The results show that the symbolic representation algorithms are faster in the feature extraction phase, on average, by 84.81%, and reduce the consumption of memory space, on average, by 94.48%, and they have accuracy rates equivalent to conventional algorithms.

Development of trairão (Hoplias lacerdae) fingerlings in darkness - DOI: 10.4025/actascibiolsci.v28i1.1058 / Desenvolvimento de alevinos de trairão (Hoplias lacerdae) na ausência de luz - DOI: 10.4025/actascibiolsci.v28i1.1058

Sixty-six trairão (Hoplias lacerdae) fingerlings (average weight of 2.0±0.5 g and total length of 5.8±0.2 cm), trained to accept dry rations, were allotted to six 15-L aquariums, with aeration and controlled temperature (24.0±0.5ºC), in a density of 0.7 juveniles/L, aiming to evaluate the effects of darkness on fish productive performance. The treatments consisted of two photoperiods: 12 hours light: 12 hours dark (12L:12D) and 0 hour light: 24 hours dark (0L:24D), with three replicates. Fingerlings were fed ad libitum a commercial extruded diet (42% CP), twice a day. The aquariums were cleaned daily for excrement withdrawal through siphoning, exchanging » total volume. At the end of the experiment (30 days), weight gain, feed:gain ratio and survival and cannibalism rates were evaluated. The results showed that darkness did not affect the productive performance of trairão juveniles

Sessenta e seis alevinos de trairão (Hoplias lacerdae) (peso médio de 2,0±0,5 g e comprimento total de 5,8±0,2 cm) condicionados à aceitação de rações secas foram distribuídos em 6 aquários de 15 L de água com temperatura de 24,0±0,5ºC, densidade de estocagem de 0,7 juvenis/L e aeração constante, com o objetivo de avaliar a ausência de luz no desempenho produtivo dos peixes. Os tratamentos consistiram dos fotoperíodos: 12 horas luz:12 horas escuro (12L:12E) e 0 horas luz:24 horas escuro (0L:24E), com 3 repetições cada. Os alevinos foram alimentados duas vezes ao dia, com ração comercial extrusada (42% proteína bruta). Diariamente, os aquários foram sifonados com a troca de 25% do volume total de água. Ao final de 30 dias, foram avaliados ganho de peso, conversão alimentar e taxas de sobrevivência e de canibalismo. Os resultados demonstraram que a ausência de luz não influenciou o desempenho produtivo dos alevinos de trairão

A variável ocupaçäo: um alerta para o clínico geral / Occupation: an importante world for hte general practitioner

A análise dos dados oferecidos pelo Instituto Nacional de Previência Social-INPS mostra que o número de acidentes, e principalmente das doenças do trabalho, näo exprime as reais condiçöes de trabalho no Brasil. É enfatizada a importância da relaçäo entre saúde e o trabalho para o diagnóstico e tratamento das doenças e apresentado aos médicos da área clínica um roteiro para elaboraçäo da história ocupacional dos pacientes