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Creating a Mobile Application for the Study of a Person Kinematic Portrait 25

Creating a Mobile Application for the Study of a Person Kine...

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25th International Conference on Soft computing and measurements, SCM 2022

作者： Tokareva, Irina A. Alekseev, Vladimir V. Surov, Denis Mikus, Oleg A. Saint Petersburg Electrotechnical University LETI Information-Measuring Systems and Technology Dept St. Petersburg Russia

ISBN: (纸本)9781665496698

Biomechanical analysis of human movements always begins with determining various characteristics of a moving body. These characteristics may include various mechanical characteristics (for example, displacement, speed, acceleration, muscle traction, etc.). Some of these characteristics are determined experimentally, and the rest are calculated. This paper describes a mobile application for the iOS operating system designed to transmit and process the spatial parameters of a person's movement from a local MEMS-based measurement system obtained experimentally when analyzing a person's gait. © 2022 IEEE.

关键词： Application programs

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Towards adaptive, non-intrisive and convergent algorithms to solve parametric PDEs with unbounded coefficients 5

Towards adaptive, non-intrisive and convergent algorithms to...

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5th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2023

作者： Farchmin, Nando Eigel, Martin Physikalisch-Technische Bundesanstalt Abbestr. 2-12 Berlin10587 Germany Weierstrass Institute of Applied Analysis and Stochastic Mohrenstrasse 39 Berlin10117 Germany

Deriving approximations of PDEs with stochastic parameters is one of the pressing issues in both mathematics and applications. From an application point of view, e.g. in shape determination of circuits on micro chips, computing numerical approximations becomes very demanding as typically many parameters have to be reconstructed. From the mathematical side, especially when coefficients of the PDE are unbounded, solvability is difficult to prove and numerical approximations face numerous challenges. This is complicated even more as the number of stochastic parameters becomes large, which requires additional mathematical compression schemes to keep computations tractable. Furthermore, as very involved software to simulate the physical measurement process is typically required, an adaptation of the software for each problem is not an option for most practical applications. In this talk we present an adaptive scheme ready to derive an approximation to the solution of an elliptic PDE with a lognormal diffusion coefficient, which builds the functional surrogate adaptively while relying solely on a priori generated samples of the underlying forward black-box model. The algorithm steers the refinement of the spacial finite element mesh, the stochastic approximation space and the required number of reconstruction samples. We prove that the algorithm indeed converges and even show evidence that we achive the same convergence rate as similar but intrusive approaches. © 2023 UNCECOMP proceedings. All rights reserved.

关键词： Stochastic systems

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Effect of Camera Position on Knee Angle Extracted by Single RGB Camera Markerless Method of Human Posture

Effect of Camera Position on Knee Angle Extracted by Single ...

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2023 IEEE International Conference on Real-Time computing and Robotics, RCAR 2023

作者： Hu, Rui Diao, Yanan Sheng, Juyi Sun, Yue Ning, Yunkun Liu, GenYuan Wang, Yingchi Li, Guanglin Zhao, Guoru Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Research Center for Neural Engineering Shenzhen Institutes of Advanced Technology China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen China

ISBN: (纸本)9798350327182

Medical rehabilitation robots have shown strong potential for rehabilitation assessment and monitoring of the elderly. Accurate estimation of human posture based on video analysis can help the clinical application of medical rehabilitation robots. However, the current human pose estimation still needs to improve its accuracy;it is missing 3D posture information and has an unclear occlusion effect on accuracy. This paper proposes a markerless human pose measurement method based on a monocular RGB camera to find the knee joint angle using a 2D and 3D pose estimator. Eleven young people were recruited to participate in the experiments to study the effects of different camera positions on the joint angle extraction results. The results showed a camera angle of 45° was more effective in measuring sitting posture, with a standard deviation of 5.97 for the left leg. A camera angle of 90° was more effective in measuring gait, with a standard deviation of 9.34 for the left leg. In summary, the proposed method can fully use 3D posture information and consider the influence of camera angle on accuracy to improve posture estimation accuracy. The execution of this study is beneficial to promote medical rehabilitation robots to clinical applications. © 2023 IEEE.

关键词： Joints (anatomy)

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What can real information content tell us about compressing climate model data? 8

What can real information content tell us about compressing ...

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8th IEEE/acm International Workshop on Data analysis and Reduction for Big Scientific Data, DRBSD-8 2022

作者： Sather, Hayden Pinard, Alexander Baker, Allison H. Hammerling, Dorit M. Colorado School of Mines Department of Computer Science GoldenCO United States Applied Mathematics and Statistics Colorado School of Mines GoldenCO United States Computational and Information Systems Lab National Center for Atmospheric Research BoulderCO United States

ISBN: (纸本)9781665463379

The massive data volumes produced by climate simulation models create an urgent need for data reduction. Lossy compression in particular is a solution that can significantly reduce storage requirements, however, a tradeoff must be made between the amount of compression applied and the scientific integrity of the data. Determining how much compression can be applied is therefore vital for applying lossy compression. One particular metric for gauging the quality of compression is the percentage of real information present in the original data that is preserved in the compressed data. We compute bitwise real information content for several climate variables from the popular Community Earth System Model, and we investigate the amount of compression that can be applied to each of these climate variables using two popular compression algorithms designed for floating-point data while preserving 99% of the real information content. The analysis of the real information content of data after lossy compression has been applied shows a helpful visualization of how compression artifacts have been introduced to the data. Finally, we demonstrate how this real information content can be used in a straightforward manner to determine compressor settings for our data. © 2022 IEEE.

关键词： Climate models

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Hybrid Anomaly Detection and Prioritization for Network Logs at Cloud Scale 22

Hybrid Anomaly Detection and Prioritization for Network Logs...

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17th European Conference on Computer systems (EuroSys)

作者： Ohana, David Wassermann, Bruno Dupuis, Nicolas Kolodner, Elliot Raichstein, Eran Malka, Michal IBM Res Haifa Israel IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781450391627

Monitoring the health of large-scale systems requires significant manual effort, usually through the continuous curation of alerting rules based on keywords, thresholds and regular expressions, which might generate a flood of mostly irrelevant alerts and obscure the actual information operators would like to see. Existing approaches try to improve the observability of systems by intelligently detecting anomalous situations. Such solutions surface anomalies that are statistically significant, but may not represent events that reliability engineers consider relevant. We propose ADEPTUS, a practical approach for detection of relevant health issues in an established system. ADEPTUS combines statistics and unsupervised learning to detect anomalies with supervised learning and heuristics to determine which of the detected anomalies are likely to be relevant to the Site Reliability Engineers (SREs). ADEPTUS overcomes the labor-intensive prerequisite of obtaining anomaly labels for supervised learning by automatically extracting information from historic alerts and incident tickets. We leverage ADEPTUS for observability in the network infrastructure of IBM Cloud. We perform an extensive real-world evaluation on 10 months of logs generated by tens of thousands of network devices across 11 data centers and demonstrate that ADEPTUS achieves higher alerting accuracy than the rule-based log alerting solution, curated by domain experts, used by SREs daily.

关键词： Anomaly Detection Log analysis Reliability Machine Learning Deep Learning Cloud computing AIOps

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Use of measurements in Worst-Case Execution Time Estimation for Real-Time systems 11

Use of Measurements in Worst-Case Execution Time Estimation ...

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11th Brazilian Symposium on computing systems Engineering (SBESC)

作者： Santillo Costa, Jessica Junia de Oliveira, Romulo Silva Arcaro, Luis Fernando Fed Univ Santa Catarina UFSC Dept Automat & Syst DAS Florianopolis SC Brazil

ISBN: (纸本)9781665443111

Real-time systems are computational systems that are subject to temporal requirements. The increase in processing and efficiency needs has created demand for the use of processors with ever higher levels of complexity. In order to ensure compliance with deadlines, the Worst-Case Execution Time (WCET) of each task should be estimated. The main contribution of this work is an experimental evaluation of the applicability of measurement-Based Probabilistic Timing analysis (MBPTA), based on the Extreme Value Theory, in the specific case of an operating system based on the ARINC 653 specification, running on a processor with complex acceleration elements (e.g. cache memory and superscalar pipeline). Although not perfect, the application of EVT proved to be an adequate alternative, with satisfactory results, to the usual practice of using the highest observed value with a safety margin.

关键词： MBPTA WCET real-time systems

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The Quantum Program Dependence Graph and its uses in Quantum Software Development

The Quantum Program Dependence Graph and its uses in Quantum...

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Quantum Software Engineering (Q-SE), International Workshop on

作者： Haibo Yu Jianjun Zhao Kyushu Sangyo University Japan Kyushu University Japan

ISBN: (数字)9798331526214

ISBN: (纸本)9798331526221

Quantum software is increasingly recognized as a critical component of quantum computing systems. As resources for quantum software design grow, the need for specialized tools and techniques to support quantum software development becomes more pressing. In this paper, we introduce the quantum program dependence graph (QPDG), a novel dependence analysis model specifically tailored for quantum software. The QPDG operates directly on the source code of quantum programs, capturing both classical and quantum-specific dependencies—namely, entanglement, no-cloning, temporal, and measurement dependencies—thus providing detailed insights into program dependencies at the code level. We further discuss key applications of QPDG, including quantum program slicing, comprehension, debugging, testing, maintenance, reuse, and complexity metrics, demonstrating its potential to enhance various aspects of quantum software engineering.

关键词： Quantum computing Quantum entanglement Source coding Debugging Software Maintenance Complexity theory Software engineering Testing Software development management

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Security analysis of 5G handover in commercial networks utilizing a formal method 11

Security analysis of 5G handover in commercial networks util...

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11th International Conference on Information systems and computing Technology, ISCTech 2023

作者： Nie, Shiyue Li, Song Xue, Liang Zhang, Lu Academy of Military Sciences National Innovation Institute of Defense Technology Beijing China Academy of Military Sciences Institute of War Studies Beijing China Peking University Beijing China China Electronic Engineering Design Institute Beijing China

ISBN: (纸本)9798350342406

The fifth-generation (5G) of mobile network is accelerating and expanding globally. The 5G Mobile subscribers pursue continuous and secure cellular services, even when they are on the move. Handover is the key to address this issue. This paper analyzes the security of 5G handover procedures in commercial networks utilizing a formal method, which has identified 5 vulnerabilities in the signaling process of 5G handover. Firstly, we abstract the state transfer system for 5G handover from the collected commercial network signaling messages, then we extract from the 5G security specifications the security properties that should be achieved by 5G handover and establish the appropriate channel model as well as the threat model. Finally, we use the TAMARIN prover to check whether the 5G handover model satisfies the combed security properties. We find that attackers could still launch attacks by exploiting (1) insufficiently validated measurement report messages and Radio Resource Control (RRC) reconfiguration messages;(2) inadequately protected registration messages, UE capability transfer messages, and identity request messages. Therefore, well-known security risks, such as Denial-of-Service (DoS) attacks, relay attacks, and power drain attacks, still apply to 5G handover in commercial networks. © 2023 IEEE.

关键词： Denial-of-service attack

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FlexScience'22: 12th Workshop on AI and Scientific computing at Scale using Flexible computing Infrastructures 22

FlexScience'22: 12th Workshop on AI and Scientific Computing...

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31st International Symposium on High-Performance Parallel and Distributed computing (HPDC)

作者： Costan, Alexandru Nicolae, Bogdan Sato, Kento INSA Rennes IRISA Rennes France Argonne Natl Lab Lemont IL USA RIKEN Ctr Kobe Hyogo Japan

ISBN: (纸本)9781450391993

Scientific computing applications generate enormous datasets that are continuously increasing exponentially in both complexity and volume, making their analysis, archival, and sharing one of the grand challenges of modern big data analytics. Supported by the rise of artificial intelligence and deep learning, such enormous datasets are becoming valuable resources even beyond their original scope, opening new opportunities to learn patterns and extract new knowledge at large scale, potentially without human intervention. However, this leads to an increasing complexity of the workflows that combine traditional HPC simulations with big data analytics and AI applications. An initial wave that opened this direction was the shift from compute-intensive to data-intensive, which saw several ideas from big data analytics (in-situ processing, shipping computations close to data, complex and dynamic workflows) fused with the tightly coupled patterns addressed by the AI and the high performance computing ecosystems. In a quest to keep up with the complexity of the workflows, the design and operation of the infrastructures capable of running them efficiently at scale has evolved accordingly. Extreme heterogeneity at all levels (combinations of CPUs and accelerators, various types of memories and local storage and network links, parallel file systems and object stores, etc.) is now the norm. ideas pioneered by cloud and edge computing (aspects related to elasticity, multi-tenancy, geo-distributed processing, stream computing) are also beginning to be adopted in the HPC ecosystem (containerized workflows, on-demand jobs to complement batch jobs, streaming of experimental data from instruments directly to supercomputers, etc.). Thus, modern scientific applications need to be integrated into an entire Compute Continuum from the edge all the way to supercomputers and large data-centers using flexible infrastructures and middlewares. The 12th workshop on AI and Scientific computing at

关键词： computing continuum scientific computing cloud computing edge processing artificial intelligence

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SciTrust: Evaluating the Trustworthiness of Large Language Models for Science 24

SciTrust: Evaluating the Trustworthiness of Large Language M...

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proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and analysis

作者： Emily Herron Junqi Yin Feiyi Wang Oak Ridge National Laboratory Oak Ridge Tennessee

ISBN: (纸本)9798350355543

This work presents SciTrust, a comprehensive framework for assessing the trustworthiness of large language models (LLMs) in scientific contexts, with a focus on truthfulness, accuracy, hallucination, and sycophancy. The framework introduces four novel open-ended benchmarks in Computer Science, Chemistry, Biology, and Physics, and employs a multi-faceted evaluation approach combining traditional metrics with LLM-based evaluation. SciTrust was applied to five LLMs, including one general-purpose and four scientific models, revealing nuanced strengths and weaknesses across different models and benchmarks. The study also evaluated SciTrust's performance and scalability on high-performance computing systems. Results showed varying performance across models, with Llama3-70B-Instruct performing strongly overall, while Galactica-120B and SciGLM-6B excelled among scientific models. SciTrust aims to advance the development of trustworthy AI in scientific applications and establish a foundation for future research on model robustness, safety, and ethics in scientific contexts. We have open-sourced our framework, including all associated scripts and datasets, at https://***/herronej/SciTrust.

关键词： High Performance computing

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