21 research outputs found

    Aspects of Terahertz Reflection Spectroscopy

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    The Fifteenth Marcel Grossmann Meeting

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    The three volumes of the proceedings of MG15 give a broad view of all aspects of gravitational physics and astrophysics, from mathematical issues to recent observations and experiments. The scientific program of the meeting included 40 morning plenary talks over 6 days, 5 evening popular talks and nearly 100 parallel sessions on 71 topics spread over 4 afternoons. These proceedings are a representative sample of the very many oral and poster presentations made at the meeting.Part A contains plenary and review articles and the contributions from some parallel sessions, while Parts B and C consist of those from the remaining parallel sessions. The contents range from the mathematical foundations of classical and quantum gravitational theories including recent developments in string theory, to precision tests of general relativity including progress towards the detection of gravitational waves, and from supernova cosmology to relativistic astrophysics, including topics such as gamma ray bursts, black hole physics both in our galaxy and in active galactic nuclei in other galaxies, and neutron star, pulsar and white dwarf astrophysics. Parallel sessions touch on dark matter, neutrinos, X-ray sources, astrophysical black holes, neutron stars, white dwarfs, binary systems, radiative transfer, accretion disks, quasars, gamma ray bursts, supernovas, alternative gravitational theories, perturbations of collapsed objects, analog models, black hole thermodynamics, numerical relativity, gravitational lensing, large scale structure, observational cosmology, early universe models and cosmic microwave background anisotropies, inhomogeneous cosmology, inflation, global structure, singularities, chaos, Einstein-Maxwell systems, wormholes, exact solutions of Einstein's equations, gravitational waves, gravitational wave detectors and data analysis, precision gravitational measurements, quantum gravity and loop quantum gravity, quantum cosmology, strings and branes, self-gravitating systems, gamma ray astronomy, cosmic rays and the history of general relativity

    Multimedia Forensics

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    This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

    From decoding turbulence to unveiling the fingerprint of climate change: Klaus Hasselmann—Nobel Prize Winner in Physics 2021

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    This open access book serves as a reference for the key elements and their significance of Klaus Hasselmann's work on climate science and on ocean wave research, all based on a rigorous and deeply physical thinking. It summarizes the original articles (mostly from the 1970 and 1980s; some of which are hard to find nowadays) and brings them in a present-day context. From 1975 until 2000, he was (founding) Director of the Max Planck Institute of Meteorology, which he made to one of the world-leading academic institutions. He first made the issue of anthropogenic climate change accessible to analysis and prediction and later transformed climate science into a significant factor in forming public policy. The book is written by co-workers and colleagues of Klaus Hasselmann, who—many under his immediate supervision—joined him in this effort. With this background, they present the key achievements and assess the significance of these for the present state of knowledge and scientific practice

    Recent Advances in Indoor Localization Systems and Technologies

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    Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

    From Decoding Turbulence to Unveiling the Fingerprint of Climate Change

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    This open access book serves as a reference for the key elements and their significance of Klaus Hasselmann's work on climate science and on ocean wave research, all based on a rigorous and deeply physical thinking. It summarizes the original articles (mostly from the 1970 and 1980s; some of which are hard to find nowadays) and brings them in a present-day context. From 1975 until 2000, he was (founding) Director of the Max Planck Institute of Meteorology, which he made to one of the world-leading academic institutions. He first made the issue of anthropogenic climate change accessible to analysis and prediction and later transformed climate science into a significant factor in forming public policy. The book is written by co-workers and colleagues of Klaus Hasselmann, who—many under his immediate supervision—joined him in this effort. With this background, they present the key achievements and assess the significance of these for the present state of knowledge and scientific practice

    Real-Time Machine Learning for Quickest Detection

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    Safety-critical Cyber-Physical Systems (CPS) require real-time machine learning for control and decision making. One promising solution is to use deep learning to discover useful patterns for event detection from heterogeneous data. However, deep learning algorithms encounter challenges in CPS with assurability requirements: 1) Decision explainability, 2) Real-time and quickest event detection, and 3) Time-eficient incremental learning. To address these obstacles, I developed a real-time Machine Learning Framework for Quickest Detection (MLQD). To be specific, I first propose the zero-bias neural network, which removes decision bias and preferabilities from regular neural networks and provides an interpretable decision process. Second, I discover the latent space characteristic of the zero-bias neural network and the method to mathematically convert a Deep Neural Network (DNN) classifier into a performance-assured binary abnormality detector. In this way, I can seamlessly integrate the deep neural networks\u27 data processing capability with Quickest Detection (QD) and provide real-time sequential event detection paradigm. Thirdly, after discovering that a critical factor that impedes the incremental learning of neural networks is the concept interference (confusion) in latent space, and I prove that to minimize interference, the concept representation vectors (class fingerprints) within the latent space need to be organized orthogonally and I invent a new incremental learning strategy using the findings, I facilitate deep neural networks in the CPS to evolve eficiently without retraining. All my algorithms are evaluated on real-world applications, ADS-B (Automatic Dependent Surveillance Broadcasting) signal identification, and spoofing detection in the aviation communication system. Finally, I discuss the current trends in MLQD and conclude this dissertation by presenting the future research directions and applications. As a summary, the innovations of this dissertation are as follows: i) I propose the zerobias neural network, which provides transparent latent space characteristics, I apply it to solve the wireless device identification problem. ii) I discover and prove the orthogonal memory organization mechanism in artificial neural networks and apply this mechanism in time-efficient incremental learning. iii) I discover and mathematically prove the converging point theorem, with which we can predict the latent space topological characteristics and estimate the topological maturity of neural networks. iv) I bridge the gap between machine learning and quickest detection with assurable performance

    Resilient and Scalable Android Malware Fingerprinting and Detection

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    Malicious software (Malware) proliferation reaches hundreds of thousands daily. The manual analysis of such a large volume of malware is daunting and time-consuming. The diversity of targeted systems in terms of architecture and platforms compounds the challenges of Android malware detection and malware in general. This highlights the need to design and implement new scalable and robust methods, techniques, and tools to detect Android malware. In this thesis, we develop a malware fingerprinting framework to cover accurate Android malware detection and family attribution. In this context, we emphasize the following: (i) the scalability over a large malware corpus; (ii) the resiliency to common obfuscation techniques; (iii) the portability over different platforms and architectures. In the context of bulk and offline detection on the laboratory/vendor level: First, we propose an approximate fingerprinting technique for Android packaging that captures the underlying static structure of the Android apps. We also propose a malware clustering framework on top of this fingerprinting technique to perform unsupervised malware detection and grouping by building and partitioning a similarity network of malicious apps. Second, we propose an approximate fingerprinting technique for Android malware's behavior reports generated using dynamic analyses leveraging natural language processing techniques. Based on this fingerprinting technique, we propose a portable malware detection and family threat attribution framework employing supervised machine learning techniques. Third, we design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. We leverage graph analysis techniques to generate relevant, actionable, and granular intelligence that can be used to identify the threat effects induced by malicious Internet activity associated to Android malicious apps. In the context of the single app and online detection on the mobile device level, we further propose the following: Fourth, we design a portable and effective Android malware detection system that is suitable for deployment on mobile and resource constrained devices, using machine learning classification on raw method call sequences. Fifth, we elaborate a framework for Android malware detection that is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. We also evaluate the portability of the proposed techniques and methods beyond Android platform malware, as follows: Sixth, we leverage the previously elaborated techniques to build a framework for cross-platform ransomware fingerprinting relying on raw hybrid features in conjunction with advanced deep learning techniques

    Propagation Modelling for Urban Source Localization and Navigation

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    Fingerprinting Mobile Browsers

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    Nowadays, billions of people access the Internet on mobile phones and a significant portion of the traffic comes from browsers. Mobile browsers could be used as a gateway to access the underlying resources of mobile devices for fingerprinting purposes. Browsers include APIs to access the underlying hardware and software resources, such as sensors, audio and media devices, battery, and so on. The growing number of APIs have created new opportunities for browser fingerprinting mechanisms. However, the widely used browser fingerprint systems are designed for the desktop environment and the identifying information gathered using these systems do not include the unique features of mobile phones such as device sensors. The goal of this thesis is to explore additional fingerprintable metrics in the mobile context and analyze their contribution in fingerprinting browsers. In this thesis, we investigated time evolution of browser's features fingerprints and fingerprinting in the wild in the context of mobile devices. In time evolution of feature's fingerprinting, we have examined the change in permission requirements of browsers over time and evolution of browser's features fingerprints for both Google Chrome and Firefox. In our experiment, we have seen that permission requirements have increased over time, e.g. Firefox 4.0 requires only four permissions, while Firefox 55.0 requires 24 permissions. In evolution of browser's features, we have seen fingerprints that are related to media, audio, WebGL, and canvas elements of the browser show a frequent change across versions. In addition, we have seen, for both Chrome and Firefox, the user agent string is unique for each version and media devices for Chrome is unique for each version as well in our dataset. In fingerprinting in the wild, we have collected fingerprints from 134 browsing sessions of which 96 were unique. From the gathered dataset, we have calculated the identifying information, entropy, contribution of each browser's feature in our test. The result shows that IP address, user agent, and media devices are the highest entropy contributors. In addition, we have observed that the maximum possible entropy gain in our dataset, 6.58 bits, can be obtained by joining only media devices and user agent strings. To sum up, in our experiment, we have acquired additional fingerprintable metrics form modern APIs, such as sensors, audio and media devices, and battery. In time evolution of browser feature's fingerprint experiments, we have seen that modern API feature's fingerprints show frequent change across versions. Similarly, in fingerprinting in the wild experiments, these APIs are among the highest entropy contributors
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