An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

Motor imagery (MI) is a typical BCI paradigm and has been widely applied into many aspects (e.g. brain-driven wheelchair and motor function rehabilitation training). Although significant achievements have been achieved, multiple motor imagery decoding is still unsatisfactory. To deal with this challenging issue, firstly, a segment of electroencephalogram was extracted and preprocessed. Secondly, we applied a filter bank common spatial pattern (FBCSP) with one-vs-rest (OVR) strategy to extract the spatio-temporal-frequency features of multiple MI. Thirdly, the F-score was employed to optimise and select these features. Finally, the optimized features were fed to the spiking neural networks (SNN) for classification. Evaluation was conducted on two public multiple MI datasets (Dataset IIIa of the BCI competition III and Dataset IIa of the BCI competition IV). Experimental results showed that the average accuracy of the proposed framework reached up to 90.09% (kappa: 0.868) and 81.33% (kappa: 0.751) on the two public datasets, respectively. The achieved performance (accuracy and kappa) was comparable to the best one of the compared methods. This study demonstrated that the proposed method can be used as an alternative approach for multiple MI decoding and it provided a potential solution for online multiple MI detection

Utilizing Computational Complexity to Protect Cryptocurrency against Quantum Threats: A Review

Digital currency is primarily designed on problems that are computationally hard to solve using traditional computing techniques. However, these problems are now vulnerable due to the computational power of quantum computing. For the postquantum computing era, there is an immense need to reinvent the existing digital security measures. Problems that are computationally hard for any quantum computation will be a possible solution to that. This research summarizes the current security measures and how the new way of solving hard problems will trigger the future protection of the existing digital currency from the future quantum threat

Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

An SOA-Based Framework of Computational Offloading for Mobile Cloud Computing

Mobile Computing is a technology that allows transmission of audio, video, and other types of data via a computer or any other wireless-enabled device without having to be connected to a fixed physical link. Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, connection instability, and limited computational power. In particular, the advent of connecting mobile devices to the internet offers the possibility of offloading computation and data intensive tasks from mobile devices to remote cloud servers for efficient execution. This proposed thesis develops an algorithm that uses an objective function to adaptively decide strategies for computational offloading according to changing context information. By following the style of Service-Oriented Architecture (SOA), the proposed framework brings cloud computing to mobile devices for mobile applications to benefit from remote execution of tasks in the cloud. This research discusses the algorithm and framework, along with the results of the experiments with a newly developed system for self-driving vehicles and points out the anticipated advantages of Adaptive Computational Offloading

Data fusion techniques for biomedical informatics and clinical decision support

Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visualization, analysis, detection, estimation, or classification. Data fusion can be applied at the raw-data, feature-based, and decision-based levels. Data fusion applications of different sorts have been built up in areas such as statistics, computer vision and other machine learning aspects. It has been employed in a variety of realistic scenarios such as medical diagnosis, clinical decision support, and structural health monitoring. This dissertation includes investigation and development of methods to perform data fusion for cervical cancer intraepithelial neoplasia (CIN) and a clinical decision support system. The general framework for these applications includes image processing followed by feature development and classification of the detected region of interest (ROI). Image processing methods such as k-means clustering based on color information, dilation, erosion and centroid locating methods were used for ROI detection. The features extracted include texture, color, nuclei-based and triangle features. Analysis and classification was performed using feature- and decision-level data fusion techniques such as support vector machine, statistical methods such as logistic regression, linear discriminant analysis and voting algorithms --Abstract, page iv

Techniques for the reverse engineering of banking malware

Malware attacks are a significant and frequently reported problem, adversely affecting the productivity of organisations and governments worldwide. The well-documented consequences of malware attacks include financial loss, data loss, reputation damage, infrastructure damage, theft of intellectual property, compromise of commercial negotiations, and national security risks. Mitiga-tion activities involve a significant amount of manual analysis. Therefore, there is a need for automated techniques for malware analysis to identify malicious behaviours. Research into automated techniques for malware analysis covers a wide range of activities. This thesis consists of a series of studies: an anal-ysis of banking malware families and their common behaviours, an emulated command and control environment for dynamic malware analysis, a technique to identify similar malware functions, and a technique for the detection of ransomware. An analysis of the nature of banking malware, its major malware families, behaviours, variants, and inter-relationships are provided in this thesis. In doing this, this research takes a broad view of malware analysis, starting with the implementation of the malicious behaviours through to detailed analysis using machine learning. The broad approach taken in this thesis differs from some other studies that approach malware research in a more abstract sense. A disadvantage of approaching malware research without domain knowledge, is that important methodology questions may not be considered. Large datasets of historical malware samples are available for countermea-sures research. However, due to the age of these samples, the original malware infrastructure is no longer available, often restricting malware operations to initialisation functions only. To address this absence, an emulated command and control environment is provided. This emulated environment provides full control of the malware, enabling the capabilities of the original in-the-wild operation, while enabling feature extraction for research purposes. A major focus of this thesis has been the development of a machine learn-ing function similarity method with a novel feature encoding that increases feature strength. This research develops techniques to demonstrate that the machine learning model trained on similarity features from one program can find similar functions in another, unrelated program. This finding can lead to the development of generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra. Further, this research examines the use of API call features for the identi-fication of ransomware and shows that a failure to consider malware analysis domain knowledge can lead to weaknesses in experimental design. In this case, we show that existing research has difficulty in discriminating between ransomware and benign cryptographic software. This thesis by publication, has developed techniques to advance the disci-pline of malware reverse engineering, in order to minimize harm due to cyber-attacks on critical infrastructure, government institutions, and industry.Doctor of Philosoph

Automatic Rural Road Centerline Extraction from Aerial Images for a Forest Fire Support System

In the last decades, Portugal has been severely affected by forest fires which have caused massive damage both environmentally and socially. Having a well-structured and precise mapping of rural roads is critical to help firefighters to mitigate these events. The traditional process of extracting rural roads centerlines from aerial images is extremely time-consuming and tedious, because the mapping operator has to manually label the road area and extract the road centerline. A frequent challenge in the process of extracting rural roads centerlines is the high amount of environmental complexity and road occlusions caused by vehicles, shadows, wild vegetation, and trees, bringing heterogeneous segments that can be further improved. This dissertation proposes an approach to automatically detect rural road segments as well as extracting the road centerlines from aerial images. The proposed method focuses on two main steps: on the first step, an architecture based on a deep learning model (DeepLabV3+) is used, to extract the road features maps and detect the rural roads. On the second step, the first stage of the process is an optimization for improving road connections, as well as cleaning white small objects from the predicted image by the neural network. Finally, a morphological approach is proposed to extract the rural road centerlines from the previously detected roads by using thinning algorithms like the Zhang-Suen and Guo-Hall methods. With the automation of these two stages, it is now possible to detect and extract road centerlines from complex rural environments automatically and faster than the traditional ways, and possibly integrating that data in a Geographical Information System (GIS), allowing the creation of real-time mapping applications.Nas últimas décadas, Portugal tem sido severamente afetado por fogos florestais, que têm causado grandes estragos ambientais e sociais. Possuir um sistema de mapeamento de estradas rurais bem estruturado e preciso é essencial para ajudar os bombeiros a mitigar este tipo de eventos. Os processos tradicionais de extração de eixos de via em estradas rurais a partir de imagens aéreas são extremamente demorados e fastidiosos. Um desafio frequente na extração de eixos de via de estradas rurais é a alta complexidade dos ambientes rurais e de estes serem obstruídos por veículos, sombras, vegetação selvagem e árvores, trazendo segmentos heterogéneos que podem ser melhorados. Esta dissertação propõe uma abordagem para detetar automaticamente estradas rurais, bem como extrair os eixos de via de imagens aéreas. O método proposto concentra-se em duas etapas principais: na primeira etapa é utilizada uma arquitetura baseada em modelos de aprendizagem profunda (DeepLabV3+), para detetar as estradas rurais. Na segunda etapa, primeiramente é proposta uma otimização de intercessões melhorando as conexões relativas aos eixos de via, bem como a remoção de pequenos artefactos que estejam a introduzir ruído nas imagens previstas pela rede neuronal. E, por último, é utilizada uma abordagem morfológica para extrair os eixos de via das estradas previamente detetadas recorrendo a algoritmos de esqueletização tais como os algoritmos Zhang-Suen e Guo-Hall. Automatizando estas etapas, é então possível extrair eixos de via de ambientes rurais de grande complexidade de forma automática e com uma maior rapidez em relação aos métodos tradicionais, permitindo, eventualmente, integrar os dados num Sistema de Informação Geográfica (SIG), possibilitando a criação de aplicativos de mapeamento em tempo real

Multi-level analysis of Malware using Machine Learning

Multi-level analysis of Malware using Machine Learnin