2,696 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    DIVAS: An LLM-based End-to-End Framework for SoC Security Analysis and Policy-based Protection

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    Securing critical assets in a bus-based System-On-Chip (SoC) is imperative to mitigate potential vulnerabilities and prevent unauthorized access, ensuring the integrity, availability, and confidentiality of the system. Ensuring security throughout the SoC design process is a formidable task owing to the inherent intricacies in SoC designs and the dispersion of assets across diverse IPs. Large Language Models (LLMs), exemplified by ChatGPT (OpenAI) and BARD (Google), have showcased remarkable proficiency across various domains, including security vulnerability detection and prevention in SoC designs. In this work, we propose DIVAS, a novel framework that leverages the knowledge base of LLMs to identify security vulnerabilities from user-defined SoC specifications, map them to the relevant Common Weakness Enumerations (CWEs), followed by the generation of equivalent assertions, and employ security measures through enforcement of security policies. The proposed framework is implemented using multiple ChatGPT and BARD models, and their performance was analyzed while generating relevant CWEs from the SoC specifications provided. The experimental results obtained from open-source SoC benchmarks demonstrate the efficacy of our proposed framework.Comment: 15 pages, 7 figures, 8 table

    Laser Technologies for Applications in Quantum Information Science

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    Scientific progress in experimental physics is inevitably dependent on continuing advances in the underlying technologies. Laser technologies enable controlled coherent and dissipative atom-light interactions and micro-optical technologies allow for the implementation of versatile optical systems not accessible with standard optics. This thesis reports on important advances in both technologies with targeted applications ranging from Rydberg-state mediated quantum simulation and computation with individual atoms in arrays of optical tweezers to high-resolution spectroscopy of highly-charged ions. A wide range of advances in laser technologies are reported: The long-term stability and maintainability of external-cavity diode laser systems is improved significantly by introducing a mechanically adjustable lens mount. Tapered-amplifier modules based on a similar lens mount are developed. The diode laser systems are complemented by digital controllers for laser frequency and intensity stabilisation. The controllers offer a bandwidth of up to 1.25 MHz and a noise performance set by the commercial STEMlab platform. In addition, shot-noise limited photodetectors optimised for intensity stabilisation and Pound-Drever-Hall frequency stabilisation as well as a fiber based detector for beat notes in the MHz-regime are developed. The capabilities of the presented techniques are demonstrated by analysing the performance of a laser system used for laser cooling of Rb85 at a wavelength of 780 nm. A reference laser system is stabilised to a spectroscopic reference provided by modulation transfer spectroscopy. This spectroscopy scheme is analysed finding optimal operation at high modulation indices. A suitable signal is generated with a compact and cost-efficient module. A scheme for laser offset-frequency stabilisation based on an optical phase-locked loop is realised. All frequency locks derived from the reference laser system offer a Lorentzian linewidth of 60 kHz (FWHM) in combination with a long-term stability of 130 kHz peak-to-peak within 10 days. Intensity stabilisation based on acousto-optic modulators in combination with the digital controller allows for real-time intensity control on microsecond time scales complemented by a sample and hold feature with a response time of 150 ns. High demands on the spectral properties of the laser systems are put forward for the coherent excitation of quantum states. In this thesis, the performance of active frequency stabilisation is enhanced by introducing a novel current modulation technique for diode lasers. A flat response from DC to 100 MHz and a phase lag below 90° up to 25 MHz are achieved extending the bandwidth available for laserfrequency stabilisation. Applying this technique in combination with a fast proportional-derivative controller, two laser fields with a relative phase noise of 42 mrad for driving rubidium ground state transitions are realised. A laser system for coherent Rydberg excitation via a two-photon scheme provides light at 780 nm and at 480 nm via frequency-doubling from 960 nm. An output power of 0.6 W at 480 nm from a single-mode optical fiber is obtained . The frequencies of both laser systems are stabilised to a high-finesse reference cavity resulting in a linewidth of 1.02 kHz (FWHM) at 960 nm. Numerical simulations quantify the effect of the finite linewidth on the coherence of Rydberg Rabi-oscillations. A laser system similar to the 480 nm Rydberg system is developed for spectroscopy on highly charged bismuth. Advanced optical technologies are also at the heart of the micro-optical generation of tweezer arrays that offer unprecedented scalability of the system size. By using an optimised lens system in combination with an automatic evaluation routine, a tweezer array with several thousand sites and trap waists below 1 Όm is demonstrated. A similar performance is achieved with a microlens array produced in an additive manufacturing process. The microlens design is optimised for the manufacturing process. Furthermore, scattering rates in dipole traps due to suppressed resonant light are analysed proving the feasibility of dipole trap generation using tapered amplifier systems

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices

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    Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.). This required the development of specific solutions and development tools for what is now referred to as TinyML. In this dissertation, we focus on improving the deployment and performance of TinyML applications, taking into consideration the aforementioned challenges, especially memory requirements. This dissertation contributed to the construction of the Edge Learning Machine environment (ELM), a platform-independent open-source framework that provides three main TinyML services, namely shallow ML, self-supervised ML, and binary deep learning on constrained devices. In this context, this work includes the following steps, which are reflected in the thesis structure. First, we present the performance analysis of state-of-the-art shallow ML algorithms including dense neural networks, implemented on mainstream microcontrollers. The comprehensive analysis in terms of algorithms, hardware platforms, datasets, preprocessing techniques, and configurations shows similar performance results compared to a desktop machine and highlights the impact of these factors on overall performance. Second, despite the assumption that TinyML only permits models inference provided by the scarcity of resources, we have gone a step further and enabled self-supervised on-device training on microcontrollers and tiny IoT devices by developing the Autonomous Edge Pipeline (AEP) system. AEP achieves comparable accuracy compared to the typical TinyML paradigm, i.e., models trained on resource-abundant devices and then deployed on microcontrollers. Next, we present the development of a memory allocation strategy for convolutional neural networks (CNNs) layers, that optimizes memory requirements. This approach reduces the memory footprint without affecting accuracy nor latency. Moreover, e-skin systems share the main requirements of the TinyML fields: enabling intelligence with low memory, low power consumption, and low latency. Therefore, we designed an efficient Tiny CNN architecture for e-skin applications. The architecture leverages the memory allocation strategy presented earlier and provides better performance than existing solutions. A major contribution of the thesis is given by CBin-NN, a library of functions for implementing extremely efficient binary neural networks on constrained devices. The library outperforms state of the art NN deployment solutions by drastically reducing memory footprint and inference latency. All the solutions proposed in this thesis have been implemented on representative devices and tested in relevant applications, of which results are reported and discussed. The ELM framework is open source, and this work is clearly becoming a useful, versatile toolkit for the IoT and TinyML research and development community

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Chatbots for Modelling, Modelling of Chatbots

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    Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informåtica. Fecha de Lectura: 28-03-202

    Cybersecurity: Past, Present and Future

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    The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-
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