INTELLIGENT MONITORING OF A LARGE CATAMARAN FERRY

Wave load cycles, wet-deck slamming events, accelerations and motion comfort are important considerations for high-speed catamarans operating in moderate to large waves. Although developing a hull monitoring system according to classification guidelines for such vessels is broadly acceptable, the data processing requirements for outputs such as rainflow counting, filtering, probability distribution, fatigue damage estimation and warning due to slamming can be as sophisticated to implement as the system components themselves. Advanced analytics such as machine learning and deep learning data pipelines will also create more complexities for such systems, if included. This paper provides an overview of data analytics methods and cloud computing resources for remotely monitoring motions and structural responses of a 111 m high-speed catamaran. To satisfy the data processing requirements, MATLAB Reference Architectures on Amazon Web Services (AWS) were used. Such combination enabled fast parallel computing and advanced feature engineering in a time-efficient manner. A MATLAB Production Server on AWS has been set up for near real-time analytics and execution of functions developed according to the class guidelines. A case study using Long Short‑Term Memory (LSTM) networks for ship speed and Motion Sickness Incidence (MSI) is provided and discussed. Such data architecture provides a flexible and scalable solution, leading to deeper insights through big data processing and machine learning, which supports hull monitoring functions as a service

Deep learning model for automated detection of efflorescence and its possible treatment in images of brick facades

One of the most common pathologies in exposed brick facades is efflorescence, which, although they often have a similar appearance, their effects and way of solving them can range from a one-off cleaning to a repair that involves adding or replacing the material. Therefore, the novel goal of this work is to verify whether it is possible to automate this task of distinguishing what type of intervention each brick needs. To do this, the methodology followed focuses on proposing, training and validating a deep convolutional neural network with the real-time end-to-end method that simultaneously predicts multiple bounding boxes and class probabilities for those boxes. For this, images of 765 building facades will be used, of which 392 were selected, proceeding to label 4704 bricks, resulting in that the model achieved a mAP maximum at epoch 100 with 0.894, which is therefore of interest for the creation of intervention maps

Deep learning in phishing mitigation: a uniform resource locator-based predictive model

To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optimized eventually. However, the optimized methods produce gratuitous performance overhead due to the limited exploration of advanced phishing cues. Thus, a phishing uniform resource locator-based predictive model is enhanced by this work to defeat this deficiency using deep learning algorithms. This model’s architecture encompasses pre-processing of the effective feature space that is made up of 60 mutual uniform resource locator (URL) phishing features, and a dual deep learning-based model of convolution neural network with bi-directional long short-term memory (CNN-BiLSTM). The proposed predictive model is trained and tested on a dataset of 14,000 phish URLs and 28,074 legitimate URLs. Experimentally, the performance outputs are remarked with a 0.01% false positive rate (FPR) and 99.27% testing accuracy

Deteção de veículos e edifícios em imagens aéreas obtidas por drone

The need to develop software for aerial image analysis, captured by Unmanned Aerial Vehicles, has increased over the years because their use has become more prevalent in different day-to-day scenarios. Object detection, a Computer Vision technique, is one of the most explored problems in this area and consists of identifying and locating objects in images or videos, with the help of Artificial Intelligence technologies. The aim of this dissertation is to analyze the performance of Deep Learning algorithms for detecting vehicles and buildings in aerial images. Two of the main algorithms described in literature, Faster R-CNN and YOLO, the latter in the third and fifth versions, were chosen to verify which one is capable of better performance. The dataset provided by the Portuguese Military Academy, which was annotated and pre-processed, was used for the training of each algorithm and the performance of tests. The results obtained in the abovementioned dataset demonstrate that there is a considerable discrepancy between the two algorithms, both in terms of performance and speed. Faster R-CNN only proved to be superior to the two versions of YOLO in terms of training speed, as it was the algorithm that required less time for training. Among the versions of YOLO, the fifth version showed the best results.A necessidade de desenvolver software para a análise de imagem aérea, capturada por Veículos Aéreos Não Tripulados, tem vindo a aumentar ao longo dos anos devido ao facto de serem cada vez mais utilizadas em diversos cenários do dia-a-dia. A deteção de objetos, técnica da Visão Computacional, é um dos problemas mais explorados nesta área e consiste na identificação e localização de objetos em imagens ou vídeos, com o auxílio de tecnologias de Inteligência Artificial. Pretende-se com esta dissertação analisar o desempenho de algoritmos de Aprendizagem Profunda, para a deteção de veículos e edifícios em imagens aéreas. Foram escolhidos dois dos principais algoritmos descritos na literatura, Faster R-CNN e YOLO, este último na terceira e quinta versão, por forma a verificar qual apresenta melhor desempenho. Para o treino de cada algoritmo e realização de testes foi utilizado um conjunto de dados fornecido pela Academia Militar Portuguesa, o qual foi anotado e pré-processado. Os resultados obtidos, no referido conjunto de dados, demonstraram que existe uma discrepância considerável entre os dois algoritmos, tanto a nível do desempenho como do tempo de deteção. O Faster R-CNN apenas se mostrou superior em relação às duas versões do YOLO no tempo de treino, pois foi o algoritmo que precisou de menos tempo. Entre as versões do YOLO, a quinta versão foi a que apresentou melhores resultados.Mestrado em Engenharia de Computadores e Telemátic

Deep Learning Models for Detecting Malware Attacks

Malware is one of the most common and severe cyber-attack today. Malware infects millions of devices and can perform several malicious activities including mining sensitive data, encrypting data, crippling system performance, and many more. Hence, malware detection is crucial to protect our computers and mobile devices from malware attacks. Deep learning (DL) is one of the emerging and promising technologies for detecting malware. The recent high production of malware variants against desktop and mobile platforms makes DL algorithms powerful approaches for building scalable and advanced malware detection models as they can handle big datasets. This work explores current deep learning technologies for detecting malware attacks on the Windows, Linux, and Android platforms. Specifically, we present different categories of DL algorithms, network optimizers, and regularization methods. Different loss functions, activation functions, and frameworks for implementing DL models are presented. We also present feature extraction approaches and a review of recent DL-based models for detecting malware attacks on the above platforms. Furthermore, this work presents major research issues on malware detection including future directions to further advance knowledge and research in this field.Comment: Revised figures 2 and 3, revised title, remove typos page 1

Data-driven Computational Social Science: A Survey

Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.Comment: 28 pages, 8 figure

Development of a deep learning-based computational framework for the classification of protein sequences

Dissertação de mestrado em BioinformaticsProteins are one of the more important biological structures in living organisms, since they perform multiple biological functions. Each protein has different characteristics and properties, which can be employed in many industries, such as industrial biotechnology, clinical applications, among others, demonstrating a positive impact. Modern high-throughput methods allow protein sequencing, which provides the protein sequence data. Machine learning methodologies are applied to characterize proteins using information of the protein sequence. However, a major problem associated with this method is how to properly encode the protein sequences without losing the biological relationship between the amino acid residues. The transformation of the protein sequence into a numeric representation is done by encoder methods. In this sense, the main objective of this project is to study different encoders and identify the methods which yield the best biological representation of the protein sequences, when used in machine learning (ML) models to predict different labels related to their function. The methods were analyzed in two study cases. The first is related to enzymes, since they are a well-established case in the literature. The second used transporter sequences, a lesser studied case in the literature. In both cases, the data was collected from the curated database Swiss-Prot. The encoders that were tested include: calculated protein descriptors; matrix substitution methods; position-specific scoring matrices; and encoding by pre-trained transformer methods. The use of state-of-the-art pretrained transformers to encode protein sequences proved to be a good biological representation for subsequent application in state-of-the-art ML methods. Namely, the ESM-1b transformer achieved a Mathews correlation coefficient above 0.9 for any multiclassification task of the transporter classification system.As proteínas são estruturas biológicas importantes dos organismos vivos, uma vez que estas desempenham múltiplas funções biológicas. Cada proteína tem características e propriedades diferentes, que podem ser aplicadas em diversas indústrias, tais como a biotecnologia industrial, aplicações clínicas, entre outras, demonstrando um impacto positivo. Os métodos modernos de alto rendimento permitem a sequenciação de proteínas, fornecendo dados da sequência proteica. Metodologias de aprendizagem de máquinas tem sido aplicada para caracterizar as proteínas utilizando informação da sua sequência. Um problema associado a este método e como representar adequadamente as sequências proteicas sem perder a relação biológica entre os resíduos de aminoácidos. A transformação da sequência de proteínas numa representação numérica é feita por codificadores. Neste sentido, o principal objetivo deste projeto é estudar diferentes codificadores e identificar os métodos que produzem a melhor representação biológica das sequências proteicas, quando utilizados em modelos de aprendizagem mecânica para prever a classificação associada à sua função a sua função. Os métodos foram analisados em dois casos de estudo. O primeiro caso foi baseado em enzimas, uma vez que são um caso bem estabelecido na literatura. O segundo, na utilização de proteínas de transportadores, um caso menos estudado na literatura. Em ambos os casos, os dados foram recolhidos a partir da base de dados curada Swiss-Prot. Os codificadores testados incluem: descritores de proteínas calculados; métodos de substituição por matrizes; matrizes de pontuação específicas da posição; e codificação por modelos de transformadores pré-treinados. A utilização de transformadores de última geração para codificar sequências de proteínas demonstrou ser uma boa representação biológica para aplicação subsequente em métodos ML de última geração. Nomeadamente, o transformador ESM-1b atingiu um coeficiente de correlação de Matthews acima de 0,9 para multiclassificação do sistema de classificação de proteínas transportadoras

A lightweight network for improving wheat ears detection and counting based on YOLOv5s

IntroductionRecognizing wheat ears plays a crucial role in predicting wheat yield. Employing deep learning methods for wheat ears identification is the mainstream method in current research and applications. However, such methods still face challenges, such as high computational parameter volume, large model weights, and slow processing speeds, making it difficult to apply them for real-time identification tasks on limited hardware resources in the wheat field. Therefore, exploring lightweight wheat ears detection methods for real-time recognition holds significant importance.MethodsThis study proposes a lightweight method for detecting and counting wheat ears based on YOLOv5s. It utilizes the ShuffleNetV2 lightweight convolutional neural network to optimize the YOLOv5s model by reducing the number of parameters and simplifying the complexity of the calculation processes. In addition, a lightweight upsampling operator content-aware reassembly of features is introduced in the feature pyramid structure to eliminate the impact of the lightweight process on the model detection performance. This approach aims to improve the spatial resolution of the feature images, enhance the effectiveness of the perceptual field, and reduce information loss. Finally, by introducing the dynamic target detection head, the shape of the detection head and the feature extraction strategy can be dynamically adjusted, and the detection accuracy can be improved when encountering wheat ears with large-scale changes, diverse shapes, or significant orientation variations.Results and discussionThis study uses the global wheat head detection dataset and incorporates the local experimental dataset to improve the robustness and generalization of the proposed model. The weight, FLOPs and mAP of this model are 2.9 MB, 2.5 * 109 and 94.8%, respectively. The linear fitting determination coefficients R2 for the model test result and actual value of global wheat head detection dataset and local experimental Site are 0.94 and 0.97, respectively. The improved lightweight model can better meet the requirements of precision wheat ears counting and play an important role in embedded systems, mobile devices, or other hardware systems with limited computing resources