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Evolving Safety Protocols: Deep Learning-Enabled Detection of Personal Protective Equipment 1

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4th International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Alahmid, Mustafa Bhimani, Kishankumar Saradva, Khushbu Ghildiyal, Sushil Saeedi, Sayed Aryan Ali, Saraa Yan, Han Filippov, Evgeny Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Laboratory on AI for Computational Biology Faculty of Computer Science HSE University Moscow Russia Department of Computer Science and Engineering Indian Institute of Technology Ropar Punjab India Laboratory of Methods for Big Data Analysis Faculty of Computer Science HSE University Moscow Russia Deggendorf Institute of Technology Bavaria Germany

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative AI model. The designed model detects the usage of personal protective equipment (PPE) (Personal protective equipment. Geneva: World Health Organization, 2020 [18]) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API (Sai and Sasikala in Object detection and count of objects in image using tensor flow object detection API, pp 542–546, 2019 [22]) to modify and train a model for dual purposes: PPE detection and face recognition. The state-of-the-art of this research is to substantially enhance safety compliance by addressing the prevalent issue of PPE non-compliance. To emphasis this, we have developed a pioneering software prototype that synergizes PPE detection with a face recognition-based clock-in system. This prototype demonstrates impressive object detection metrics with a mean average precision (mAP) of 0.9 for vests and 0.85 for helmets. Moreover, it exhibited efficient face recognition with a successful threshold range of 17–20%. The implementation of AI in our system promises significant enhancements to worker safety, while concurrently reducing the financial burden associated with big hazards and accidents. Beyond the development and performance of the system, this paper provides a thorough exploration of the encountered challenges, potential real-world applications (particularly in employee monitoring and clock-in systems), and the future implications of this study on research and practical applications in the field of AI-integrated safety compliance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software prototyping

ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare 35

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ESCAPED: Efficient Secure and Private Dot Product Framework ...

35th AAAI Conference on Artificial Intelligence, AAAI 2021

作者： Ünal, Ali Burak Akgün, Mete Pfeifer, Nico Methods in Medical Informatics Department of Computer Science University of Tuebingen Germany Translational Bioinformatics University Hospital Tuebingen Tuebingen Germany Statistical Learning in Computational Biology Max Planck Institute for Informatics Saarbrücken Germany

ISBN: (纸本)9781713835974

Training sophisticated machine learning models usually requires many training samples. Especially in healthcare settings these samples can be very expensive, meaning that one institution alone usually does not have enough. Merging privacy-sensitive data from different sources is usually restricted by data security and data protection measures. This can lead to approaches that reduce data quality by putting noise onto the variables (e.g., in Ε-differential privacy) or omitting certain values (e.g., for k-anonymity). Other measures based on cryptographic methods can lead to very time-consuming computations, which is especially problematic for larger multi-omics data. We address this problem by introducing ESCAPED, which stands for Efficient SeCure And PrivatE Dot product framework. ESCAPED enables the computation of the dot product of vectors from multiple sources on a third-party, which later trains kernel-based machine learning algorithms, while neither sacrificing privacy nor adding noise. We have evaluated our framework on drug resistance prediction for HIV-infected people and multi-omics dimensionality reduction and clustering problems in precision medicine. In terms of execution time, our framework significantly outperforms the best-fitting existing approaches without sacrificing the performance of the algorithm. Even though we only present the benefit for kernel-based algorithms, our framework can open up new research opportunities for further machine learning models that require the dot product of vectors from multiple sources. Copyright © 2021, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Sensitive data

University leagues alongside rankings

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Quality and Quantity 2023年第1期57卷 721-736页

作者： Banász, Zsuzsanna Kosztyán, Zsolt T. Csányi, Vivien V. Telcs, András Department of Economics University of Pannonia Egyetem str. 10 Veszprém Hungary MTA-PE Budapest Ranking Research Group Eötvös Loránd Research Network (ELKH) Egyetem str. 10 Veszprém Hungary Department of Quantitative Methods University of Pannonia Egyetem str. 10 Veszprém Hungary Institute of Advanced Studies Koszeg Koszeg Hungary Research Centre of Faculty of Business and Economics University of Pannonia Egyetem str. 10. Veszprém Hungary Head of Department of Computational Sciences MTA Wigner Research Center for Physics Budapest Hungary Department of Computer Science and Information Theory Budapest University of Technology and Economics Budapest Hungary

University rankings can both orient and disorient potential students. In rankings, universities with very different characteristics are compared, which makes interpretation difficult. We propose the application of a clustering method, which creates groups of universities that are close to each other with respect to a subset of indicators, but the indicators also show homogeneity with respect to the universities in that group. We call such groups leagues. These leagues are defined by the data themselves and are not based on subjective criteria. We demonstrate our proposition using one member of the family of the two-way clustering method, namely, biclustering. The case we present is based on the Round University Ranking (RUR) 2020 dataset. The use of leagues could provide better guidance not only for potential applicants but also for university funding organizations and policy-makers. Our case study led to a somewhat surprising observation. In the top league (based on the RUR data and indicators), the three most important indicators measure reputation, not scientific or educational performance. © 2022, The Author(s).

关键词： Biclustering Leagues Rankings Universities

Asymptotic Relaxation of Moment Equations for a Multi-species, Homogeneous BGK Model

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arXiv 2023年

作者： Habbershaw, Evan Glasby, Ryan S. Haack, Jeffrey R. Hauck, Cory D. Wise, Steven M. Department of Mathematics The University of Tennessee KnoxvilleTN37996 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Physics and Methods Group Los Alamos National Laboratory Los AlamosNM87545 United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Multi-species BGK models describe the dynamics of rarefied gases with constituent particles of different elements or compounds with potentially non-trivial velocity distributions. In this paper, moment equations for the bulk velocities, energies, and temperatures of a spatially homogeneous multi-species BGK model are examined. A key challenge in analyzing these equations is the fact that the collision frequencies are allowed to depend on the species temperatures, which allows for more realistic simulations of dilute gas flow. Therefore, a positive lower bound is established for the species temperatures. With this lower bound, a global existence and uniqueness of solutions to the coupled velocity-energy ODE system is established. The lower bound also enables a proof of exponential decay to a unique steady-state solution. Numerical results are presented to demonstrate how the bulk velocities and temperatures relax for large *** Codes 37A60, 34A12, 82C40 © 2023, CC BY.

关键词： Velocity distribution

ASYMPTOTIC EXPECTED T-FUNCTIONALS OF RANDOM POLYTOPES WITH APPLICATIONS TO Lp SURFACE AREAS

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arXiv 2022年

作者： Hoehner, Steven Li, Ben Roysdon, Michael Thäle, Christoph Department of Mathematics & Computer Science Longwood University United States Department of Mathematical Methods in Physics Warsaw University Poland Institute for Computational and Experimental Research in Mathematics United States Department of Pure Mathematics Tel Aviv University Israel Faculty of Mathematics Ruhr University Bochum Germany

An asymptotic formula is proved for the expected T -functional of the convex hull of independent and identically distributed random points sampled from the Euclidean unit sphere in n according to an arbitrary positive continuous density. As an application, the approximation of the sphere by random polytopes in terms of Lp surface area differences is *** Codes 52A22, 52A27, 60D05 (Primary) 52A20, 52B11 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

Exploring the limits of data augmentation for retinal vessel segmentation

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arXiv 2021年

作者： Uysal, Enes Sadi Bilici, M. Şafak Zaza, B. Selin Özgenç, M. Yiǧit Boyar, Onur Department of Computer Engineering Yildiz Technical University Istanbul Turkey Department of Industrial Engineering Yildiz Technical University Istanbul Turkey Department of Quantitative Methods Marmara University Istanbul Turkey Department of Computational Science and Engineering Boǧaziçi University Istanbul Turkey

Retinal Vessel Segmentation is important for the diagnosis of various diseases. The research on retinal vessel segmentation focuses mainly on the improvement of the segmentation model which is usually based on U-Net architecture. In our study, we use the U-Net architecture and we rely on heavy data augmentation in order to achieve better performance. The success of the data augmentation relies on successfully addressing the problem of input images. By analyzing input images and performing the augmentation accordingly we show that the performance of the U-Net model can be increased dramatically. Results are reported using the most widely used retina dataset, DRIVE. © 2021, CC BY.

关键词： Diagnosis

DPA-2:a large atomic model as a multitask learner

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npj computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

Storm Surge Modeling as an Application of Local Time-stepping in MPAS-Ocean

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arXiv 2022年

作者： Lilly, Jeremy R. Capodaglio, Giacomo Petersen, Mark R. Brus, Steven R. Engwirda, Darren Higdon, Robert L. Oregon State University Department of Mathematics CorvallisOR97331-4605 United States Computational Physics and Methods Group Los Alamos National Laboratory NM87545 United States Fluid Dynamics and Solid Mechanics Group Los Alamos National Laboratory NM87545 United States Mathematics and Computer Science Division Argonne National Laboratory IL60439 United States

This paper presents the first scientific application of local time-stepping (LTS) schemes in the Model for Prediction Across Scales-Ocean (MPAS-O). We use LTS schemes in a single-layer, global ocean model that predicts the storm surge around the eastern coast of the United States during Hurricane Sandy. The variable-resolution meshes used are of unprecedentedly high resolution in MPAS-O, containing cells as small as 125 meters wide in Delaware Bay. It is shown that a particular, third-order LTS scheme (LTS3) produces sea-surface height (SSH) solutions that are of comparable quality to solutions produced by the classical four-stage, fourth-order Runge-Kutta method (RK4) with a uniform time step on the same meshes. Furthermore, LTS3 is up to 35% faster in the best cases, showing that LTS schemes are viable for use in MPAS-O with the added benefit of substantially less computational cost. The results of these performance experiments inform us of the requirements for efficient mesh design for LTS schemes. In particular, we see that for LTS to be efficient on a given mesh, it is important to have enough cells using the coarse time-step relative to those using the fine time-step, typically at least 1:5. © 2022, CC BY-SA.

关键词： Hurricanes

Topical Issue Scientific Machine Learning (1/2)

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GAMM Mitteilungen 2021年第1期44卷

作者： Benner, Peter Klawonn, Axel Stoll, Martin Computational Methods in Systems and Control Theory Max Planck institute for Dynamics of Complex Technical Systems Magdeburg Germany Fakultät für Mathematik Otto-von-Guericke Universität Magdeburg Germany Department of Mathematics and Computer Science University of Cologne Germany Center for Data and Simulation Science University of Cologne Germany Chair of Scientific Computing Department of Mathematics TU Chemnitz Germany