17,238 research outputs found

    Spectrum Sensing Security in Cognitive Radio Networks

    Get PDF
    This thesis explores the use of unsupervised machine learning for spectrum sensing in cognitive radio (CR) networks from a security perspective. CR is an enabling technology for dynamic spectrum access (DSA) because of a CR's ability to reconfigure itself in a smart way. CR can adapt and use unoccupied spectrum with the help of spectrum sensing and DSA. DSA is an efficient way to dynamically allocate white spaces (unutilized spectrum) to other CR users in order to tackle the spectrum scarcity problem and improve spectral efficiency. So far various techniques have been developed to efficiently detect and classify signals in a DSA environment. Neural network techniques, especially those using unsupervised learning have some key advantages over other methods mainly because of the fact that minimal preconfiguration is required to sense the spectrum. However, recent results have shown some possible security vulnerabilities, which can be exploited by adversarial users to gain unrestricted access to spectrum by fooling signal classifiers. It is very important to address these new classes of security threats and challenges in order to make CR a long-term commercially viable concept. This thesis identifies some key security vulnerabilities when unsupervised machine learning is used for spectrum sensing and also proposes mitigation techniques to counter the security threats. The simulation work demonstrates the ability of malicious user to manipulate signals in such a way to confuse signal classifier. The signal classifier is forced by the malicious user to draw incorrect decision boundaries by presenting signal features which are akin to a primary user. Hence, a malicious user is able to classify itself as a primary user and thus gains unrivaled access to the spectrum. First, performance of various classification algorithms are evaluated. K-means and weighted classification algorithms are selected because of their robustness against proposed attacks as compared to other classification algorithm. Second, connection attack, point cluster attack, and random noise attack are shown to have an adverse effect on classification algorithms. In the end, some mitigation techniques are proposed to counter the effect of these attacks

    TV White Spaces: A Pragmatic Approach

    Get PDF
    190 pages The editors and publisher have taken due care in preparation of this book, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information contained herein. Links to websites imply neither responsibility for, nor approval of, the information contained in those other web sites on the part of ICTP. No intellectual property rights are transferred to ICTP via this book, and the authors/readers will be free to use the given material for educational purposes.  e ICTP will not transfer rights to other organizations, nor will it be used for any commercial purposes. ICTP is not to endorse or sponsor any particular commercial product, service or activity mentioned in this book. This book is released under the Attribution-NonCommercial-NoDerivatives Š.ĂŸ International license. For more details regarding your rights to use and redistribute this work, see http://creativecommons.org/licenses/by-nc-nd/4.0/

    Intrinsic Motivation Systems for Autonomous Mental Development

    Get PDF
    Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology. Key words: Active learning, autonomy, behavior, complexity, curiosity, development, developmental trajectory, epigenetic robotics, intrinsic motivation, learning, reinforcement learning, values

    The Opportunity of Data-Driven Services for Viral Genomic Surveillance

    Get PDF
    The recent COVID-19 pandemic has posed novel challenges to the big data and knowledge management community. The unprecedented availability of viral genomes on public databases has made possible the data-driven exploration of viruses' evolution (especially of SARS-CoV-2, the virus responsible for the disease). Properties of data and knowledge in the genomic and virological domain may fuel data science methods for the identification and possible prediction of critical phenomena, such as the emergence of variants with improved transmissibility/virulence and recombined strains. A number of tools have been produced to explore the variants' trends or suggest hypotheses on the evolutionary mechanisms of the virus. In this perspective, we elaborate on plausible directions of this field of research, which are still applicable to the SARS-CoV-2 virus but may become even more relevant in the context of new outbreaks (e.g., monkeypox, malaria, diphtheria). Expressly, we point to 1) data-driven identification of mutations or variants with potential impact; 2) data-driven identification of recombination events - creating opportunities to overcome selective pressure and adapt to new environments and hosts (e.g., livestock or humans). These directions can be framed within genomic surveillance measures, characterized by the possibility of tracking viruses by using their genome, which is collected, sequenced, and submitted to public databases by laboratories around the world. If successful, genomic surveillance substantially supports the understanding of novel viral pathogens and of their dangerousness in terms of prevalence, infectivity, and transmissibility; the implemented services can be of great utility to decision-makers in healthcare. Here, we draw current trends, challenges, and future directions of data-driven services for genomic surveillance

    The Digitalisation of African Agriculture Report 2018-2019

    Get PDF
    An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

    Psychiatric genetics and the structure of psychopathology

    Get PDF
    For over a century, psychiatric disorders have been defined by expert opinion and clinical observation. The modern DSM has relied on a consensus of experts to define categorical syndromes based on clusters of symptoms and signs, and, to some extent, external validators, such as longitudinal course and response to treatment. In the absence of an established etiology, psychiatry has struggled to validate these descriptive syndromes, and to define the boundaries between disorders and between normal and pathologic variation. Recent advances in genomic research, coupled with large-scale collaborative efforts like the Psychiatric Genomics Consortium, have identified hundreds of common and rare genetic variations that contribute to a range of neuropsychiatric disorders. At the same time, they have begun to address deeper questions about the structure and classification of mental disorders: To what extent do genetic findings support or challenge our clinical nosology? Are there genetic boundaries between psychiatric and neurologic illness? Do the data support a boundary between disorder and normal variation? Is it possible to envision a nosology based on genetically informed disease mechanisms? This review provides an overview of conceptual issues and genetic findings that bear on the relationships among and boundaries between psychiatric disorders and other conditions. We highlight implications for the evolving classification of psychopathology and the challenges for clinical translation
    • 

    corecore