Cryptographic accumulators are efficient data structures used for membership testing in various applications, including resource-constrained devices like IoT systems. This paper explores the performance of an ECC-base...
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The article provides a brief overview of human-computer interaction methods and systems, and the research made in this area. We have designed the architecture of the system and tested a method of controlling a compute...
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Authentication is a crucial process that verifies the identity of an individual or system seeking access to resources or services. Password-based authentication systems, which are the most common and widely used, can ...
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The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for pro...
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Fully Bayesian approaches to sequential decision-making assume that problem parameters are generated from a known prior. In practice, such information is often lacking. This problem is exacerbated in setups with parti...
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Fully Bayesian approaches to sequential decision-making assume that problem parameters are generated from a known prior. In practice, such information is often lacking. This problem is exacerbated in setups with partial information, where a misspecified prior may lead to poor exploration and performance. In this work we prove, in the context of stochastic linear bandits and Gaussian priors, that as long as the prior is sufficiently close to the true prior, the performance of the applied algorithm is close to that of the algorithm that uses the true prior. Furthermore, we address the task of learning the prior through metalearning, where a learner updates her estimate of the prior across multiple task instances in order to improve performance on future tasks. We provide an algorithm and regret bounds, demonstrate its effectiveness in comparison to an algorithm that knows the correct prior, and support our theoretical results empirically. Our theoretical results hold for a broad class of algorithms, including Thompson Sampling and Information Directed Sampling.
The metaverse integrates digital and physical realities into a parallel virtual environment, which has become a key direction for the future of the digital world. With the development of this paradigm, the need for hu...
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ISBN:
(数字)9798350395518
ISBN:
(纸本)9798350395525
The metaverse integrates digital and physical realities into a parallel virtual environment, which has become a key direction for the future of the digital world. With the development of this paradigm, the need for human-computer interaction (HCI) systems within the metaverse has become increasingly important, but the complexity and uniqueness of the interactions also pose a series of challenges. To better understand and solve problems in metaverse HCI systems, this paper proposes an approach based on a combination of problem framing (PF) and model-drive (MDE). The approach describes the entities of the metaverse HCI system and the special interactions between the two worlds by extending the meta-model of the PF. This paper also discusses the application scenarios of this extended model, demonstrating the feasibility of our approach through a case study.
We will present our industrial experience deploying software and heterogeneous hardware platforms to support end-to-end workflows in the power systems design engineering space. Such workflows include classical physics...
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ISBN:
(纸本)9783031236051;9783031236068
We will present our industrial experience deploying software and heterogeneous hardware platforms to support end-to-end workflows in the power systems design engineering space. Such workflows include classical physics-based High Performance Computing (HPC) simulations, GPU-based ML training and validation, as well as pre- and post-processing on commodity CPU systems. The software architecture is characterized by message-oriented middleware which normalizes distributed, heterogenous compute assets into a single ecosystem. Services provide enterprise authentication, data management, version tracking, digital provenance, and asynchronous event triggering, fronted by a secure API, Python SDK, and monitoring GUIs. With the tooling, various classes of workflows from simple, unitary through complex multi-modal workflows are enabled. The software development process was informed by and uses several national laboratory software packages whose impact and opportunities will also be discussed. Utilizing this architecture, automated workflow processes focused on complex and industrially relevant applications have been developed. These leverage the asynchronous triggering and job distribution capabilities of the architecture to greatly improve design capabilities. The physics-based workflows involve simple Python-based pre-processing, proprietary Linux-based physics solvers, and multiple distinct HPC steps each of which required unique inputs and provided distinct outputs. Post-processing via proprietary Fortran and Python scripts are used to generate training data for machine learning algorithms. Physics model results were then provided to machine learning (ML) algorithms on GPU compute nodes to optimize the machine learning models based on design criteria. Finally, the ML optimized results were validated by running the identified designs through the physics-based workflow.
Concerns with image security exist in every sector that uses digital photographs. Historically, public safety and forensics have depended on images from the crime scene, biometric photos, suspect photos, and other sou...
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Effective strategies for designing and managing coastal defenses against erosion require a multidisciplinary approach. From a regulatory perspective, the Italian Dm 152/2022 "End of Waste" governs the reuse ...
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Museums are filled with abundance of rich history, culture and amazing architectural grandeur. But yet many people would much rather prefer to watch a documentary or read a book than to visit a museum. With the aim of...
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