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Creators/Authors contains: "Mohamed, A."

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  6. Abstract

    Scholars and the public conceive of extraterrestrial life through the lens of life as we know it on Earth. However, assumptions based on centuries of study around heredity and evolution on Earth may not apply to life truly independent forms of life, and some perspectives accepted or ruled out in the nineteenth century may need to be re-evaluated for life outside of Earth. In honor of the 200th birthday of Mendel, and to provide raw material for the creativity of storytellers, filmmakers, and the public, this thought experiment essay revisits a handful of classic concepts and approaches, as well as some unusual forms of life on Earth, to posit whether different types of genetics and evolution may exist in truly independent extraterrestrial forms. While fundamental evolutionary processes like natural selection and genetic drift are likely to still apply at least similarly in independent life forms, inheritance may be quite radically different from that envisioned by Mendel and others since.

     
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  7. Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days. 
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