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Electrocardiogram (ECG) signals are recordings of the heart's electrical activity and are widely used in the medical field to diagnose various cardiac conditions and monitor heart function. The accurate classification of ECG signals is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification. To this end, after the preprocessing step, we extracted several features such as BPM, IBI, and SDNN. Then, the features are fed into a multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. Population-based metaheuristic techniques have been effectively used to address this. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms., Comment: 42 pages, 9 figures

The dataset presented in this paper presents a dataset related to three motionless activities, including driving, watching TV, and sleeping. During these activities, the mobile device may be positioned in different locations, including the pants pockets, in a wristband, over the bedside table, on a table, inside the car, or on other furniture, for the acquisition of accelerometer, magnetometer, gyroscope, GPS, and microphone data. The data was collected by 25 individuals (15 men and 10 women) in different environments in Covilhã and Fundão municipalities (Portugal). The dataset includes the sensors’ captures related to a minimum of 2000 captures for each motionless activity, which corresponds to 2.8 h (approximately) for each one. This dataset includes 8.4 h (approximately) of captures for further analysis with data processing techniques, and machine learning methods. It will be useful for the complementary creation of a robust method for the identification of these type of activities.

The network layer is central to the networking scientific area. It is around the network layer that all the data communications develop, and one of its main tasks is to allow the identification of each single interface/machine between the potentially many interfaces in a network. This seminar addresses some of the issues that are usually presented to young Computer Science Engineering students in the course of several classes, but also presents some topics that are not address in networking courses. It is mostly focused on using Internet Protocol addresses in Local Area Networks, also considering issues that belong to the Wide Area Networks, such as data aggregation. This document summarizes the content of a seminar, therefore it comprehends both teaching and researching subject. The seminar starts with a history of the evolution of the communication protocols from the early days of networks up until IPv6. It describes a new approach to define the addresses of network interfaces using Variable Length Subnet Masks, as usually this is a not an easy task for Computer Science Engineering undergraduate students. This summary also describes some of the limitations of the data communication in todays' networks, proposing some solutions, where possible, including a novel mean of connectionless data transmission by using IPv6 addresses, by extension of previously published research. The way the seminar is organized provides a history to the past of the Internet Protocol, a view of some of its well-known current limitations, and a glimpse into a possible future regarding an improved connectionless layer 3 data transfer protocol.

Computer aided diagnosis (CAD) of Breast Cancer (BRCA) images has been an active area of research in recent years. The main goals of this research is to develop reliable automatic methods for detecting and diagnosing different types of BRCA from diagnostic images. In this paper, we present a review of the state of the art CAD methods applied to magnetic resonance (MRI) and mammography images of BRCA patients. The review aims to provide an extensive introduction to different features extracted from BRCA images through texture and statistical analysis and to categorize deep learning frameworks and data structures capable of using metadata to aggregate relevant information to assist oncologists and radiologists. We divide the existing literature according to the imaging modality and into radiomics, machine learning, or combination of both. We also emphasize the difference between each modality and methods strengths and weaknesses and analyze their performance in detecting BRCA through a quantitative comparison. We compare the results of various approaches for implementing CAD systems for the detection of BRCA. Each approachs standard workflow components are reviewed and summary tables provided. We present an extensive literature review of radiomics feature extraction techniques and machine learning methods applied in BRCA diagnosis and detection, focusing on data preparation, data structures, pre processing and post processing strategies available in the literature. There is a growing interest on radiomic feature extraction and machine learning methods for BRCA detection through histopathological images, MRI and mammography images. However, there isnt a CAD method able to combine distinct data types to provide the best diagnostic results. Employing data fusion techniques to medical images and patient data could lead to improved detection and classification results., Comment: 25 pages, 7 figures, Scientific grant report

Quantified-self application is widely used in sports and health management; the type and amount of data that can be fed back to the user are growing rapidly. However, only a few studies discussed the social attributes of quantified-self data, especially in the context of cycling. In this study, we present “SocialBike,” a digital augmented bicycle that aims to increase cyclists’ motivation and social relatedness in physical activity by showing their quantified-self data to each other. To evaluate the concept through a rigorous control experiment, we built a cycling simulation system to simulate a realistic cycling experience with SocialBike. A within-subjects experiment was conducted through the cycling simulation system with 20 participants. Quantitative data were collected with the Intrinsic Motivation Inventory (IMI) and data recorded by the simulation system; qualitative data were collected through user interviews. The result showed that SocialBike increase cyclists’ intrinsic motivation, perceived competence, and social relatedness in physical activity.

The automatic recognition of Activities of Daily Living (ADL) with a multi-sensor mobile device that can acquire different types of sensors' data, and rely on the use of machine learning methods to handle the recognition of ADL with reliable accuracy. This paper focuses on the literature review of the existing methods to make the identification of ADL in order to assess the efficiency of the different methods for the identification of ADL and their environments using off-the-shelf mobile devices. Data acquired from several sensors can be used for the identification of ADL, where the motion, magnetic and location sensors handle the recognition of activities with movement, and the acoustic sensors handle the recognition of activities related with the environment. Therefore, the main purpose of this study is to present a review of the machine learning methods already used on this field, relating them with the accuracy and number of ADL recognized.

Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors data, reports an accuracy of 89.51%., arXiv admin note: substantial text overlap with arXiv:1711.00124, arXiv:1711.00104; text overlap with arXiv:1711.00096

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%.

Most mobile devices include motion, magnetic, acoustic, and location sensors. They allow the implementation of a framework for the recognition of Activities of Daily Living (ADL) and its environments, composed by the acquisition, processing, fusion, and classification of data. This study compares different implementations of artificial neural networks, concluding that the obtained results were 85.89% and 100% for the recognition of standard ADL. Additionally, for the identification of standing activities with Deep Neural Networks (DNN) respectively, and 86.50% for the identification of the environments with Feedforward Neural Networks. Numerical results illustrate that the proposed framework can achieve robust performance from the data fusion of off-the-shelf mobile devices.

O assumir de responsabilidades pela preparação das demonstrações financeiras do grupo económico Infraventus por parte do mestrando, despoletou a necessidade de avançar para o presente projeto. Em face dos parcos conhecimentos teóricos sobre o tema e de que todo o processo operacional associado à elaboração das demonstrações financeiras consolidadas se mostrava moroso e revestido de algumas lacunas, importava por isso a busca de novas soluções. O grupo Infraventus, que se dedica principalmente ao negócio das energias renováveis, engloba um conjunto de cerca de 40 empresas, as quais se inserem numa estrutura de participações que se mostra distante das estruturas piramidais dos grupos usualmente encontradas, dificultando os trabalhos de consolidação e acarretando a produção de informação intempestiva. Deste modo, este trabalho tem como objetivo encontrar uma solução que permita tornar o processo de elaboração das contas consolidadas mais eficiente e, assim, obter-se informação tempestiva e, por conseguinte, mais útil. Para se alcançar o objetivo pretendido, começa-se por fazer um estudo teórico sobre a temática da consolidação de contas, procurando o enquadramento para as problemáticas que poderão surgir dentro daquele grupo económico. Seguidamente, efetua-se o estudo do caso, em ambiente real, procurando-se encontrar formas de potenciar, não só a informação já existente como também os recursos informáticos disponíveis. Suportando-se no ERP utilizado na empresa (software “Primavera”), juntamente com uma ferramenta informática de uso corrente, como é o Microsoft Excel, procurou-se mostrar que é possível obter as demonstrações financeiras consolidadas de forma tendencialmente automatizada. Esta automatização permite mitigar as tarefas rotineiras e de reduzido valor acrescentado, tornando o processo mais eficiente, de forma a convergir para a satisfação de um dos requisitos da informação financeira que é o da tempestividade e por conseguinte, a sua utilidade.

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COVID-19 showed the need for community support networks to help vulnerable individuals that were forced to stay in their homes during extended periods. While the pandemic is slowly passing, there is still a need for this type of help, especially for elderly who nowadays want to live longer in their own home. Several social engagement platforms are already offering support for this type of services. However, some became rather inactive, while others are successful but not always in the context of offering help to vulnerable elderly. In this paper we present 16 design recommendations to consider when designing (or to improve) social engagement platforms focused on volunteerism for helping older people. These recommendations are based on the evaluation of 10 digital social engagement platforms that allow asking for assistance and/or volunteering to provide help.