65 research outputs found

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Optimizing Hash-Based Signatures in Java

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    Hash-based signature schemes are an extensively studied and well-understood choice for quantum-safe digital signatures. However, certain operations, most notably the key generation, can be comparably expensive. It is, therefore, essential to use well-optimized implementations. This thesis aims to explore, implement, and evaluate optimization strategies for hashbased signature implementations in Java. These include the use of special hardware features like vector instructions and hardware acceleration for hash functions as well as the parallelization of the key generation. Overall, we are able to reduce the time required for an XMSS key generation with SHA-2 by up to 96.4% (on four CPU cores) compared to the unmodified BouncyCastle implementation. For SPHINCS+ with the Haraka hash function family, we achieve a reduction of up to 95.7% on only one CPU core. Furthermore, we investigate the use of two scheme variants WOTS-BR and WOTS+C proposed in the literature for verification-optimized signatures. We improve the existing theoretical analysis of both, provide a comparison and experimentally validate our improved theoretical analysis

    Recent Advances in Embedded Computing, Intelligence and Applications

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    The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

    Applications in Electronics Pervading Industry, Environment and Society

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    This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    ECOS 2012

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    The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology

    Intelligent Sensors for Human Motion Analysis

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    The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

    Smart and Secure Augmented Reality for Assisted Living

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    Augmented reality (AR) is one of the biggest technology trends which enables people to see the real-life surrounding environment with a layer of virtual information overlaid on it. Assistive devices use this match of information to help people better understand the environment and consequently be more efficient. Specially, AR has been extremely useful in the area of Ambient Assisted Living (AAL). AR-based AAL solutions are designed to support people in maintaining their autonomy and compensate for slight physical and mental restrictions by instructing them on everyday tasks. The discovery of visual attention for assistive aims is a big challenge since in dynamic cluttered environments objects are constantly overlapped and partial object occlusion is also frequent. Current solutions use egocentric object recognition techniques. However, the lack of accuracy affects the system's ability to predict users’ needs and consequently provide them with the proper support. Another issue is the manner that sensitive data is treated. This highly private information is crucial for improving the quality of healthcare services. However, current blockchain approaches are used only as a permission management system, while the data is still stored locally. As a result, there is a potential risk of security breaches. Privacy risk in the blockchain domain is also a concern. As major investigation tackles privacy issues based on off-chain approaches, there is a lack of effective solutions for providing on-chain data privacy. Finally, the Blockchain size has been shown to be a limiting factor even for chains that store simple transactional data, much less the massive blocks that would be required for storing medical imaging studies. To tackle the aforementioned major issues, this research proposes a framework to provide a smarter and more secure AR-based solution for AAL. Firstly, a combination of head-worn eye-trackers cameras with egocentric video is designed to improve the accuracy of visual attention object recognition in free-living settings. A heuristic function is designed to generate a probability estimation of visual attention over objects within an egocentric video. Secondly, a novel methodology for the storage of large sensitive AR-based AAL data is introduced in a decentralized fashion. By leveraging the power of the IPFS (InterPlanetary File System) protocol to tackle the lack of storage issue in the Blockchain. Meanwhile, a blockchain solution on the Secret Network blockchain is developed to tackle the existent lack of privacy on smart contracts, which provides data privacy at both transactional and computational levels. In addition, is included a new off-chain solution encapsulates a governing body for permission management purposes to solve the problem of the lost or eventual theft of private keys. Based on the research findings, that visual attention-object detection approach is applicable to cluttered environments which presents a transcend performance compared to the current methods. This study also produced an egocentric indoor dataset annotated with human fixation during natural exploration in a cluttered environment. Comparing to previous works, this dataset is more realistic because it was recorded in real settings with variations in terms of objects overlapping regions and object sizes. With respect to the novel decentralized storage methodology, results indicate that sensitive data can be stored and queried efficiently using the Secret Network blockchain. The proposed approach achieves both computational and transactional privacy with significantly less cost. Additionally, this approach mitigates the risk of permanent loss of access to the patient on-chain data records. The proposed framework can be applied as an assistive technology in a wide range of sectors that requires AR-based solution with high-precision visual-attention object detection, efficient data access, high-integrity data storage and full data privacy and security
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