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arXiv 2021年

作者： Khan, Asad Huerta, E.A. Zheng, Huihuo Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States

MSC Codes 68T10, 85-08, 83C35, 83C57We present a deep-learning artificial intelligence model that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers. We used the NRHybSur3dq8 surrogate model to produce train, validation and test sets of = |m| = 2 waveforms that cover the parameter space of binary black hole mergers with mass-ratios q ≤ 8 and individual spins |sz{1, 2}| ≤ 0.8. These waveforms cover the time range t ∈ [−5000M, 130M], where t = 0M marks the merger event, defined as the maximum value of the waveform amplitude. We harnessed the ThetaGPU supercomputer at the Argonne Leadership Computing Facility to train our AI model using a training set of 1.5 million waveforms. We used 16 NVIDIA DGX A100 nodes, each consisting of 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, to fully train our model within 3.5 hours. Our findings show that artificial intelligence can accurately forecast the dynamical evolution of numerical relativity waveforms in the time range t ∈ [−100M, 130M]. Sampling a test set of 190,000 waveforms, we find that the average overlap between target and predicted waveforms is & 99% over the entire parameter space under consideration. We also combined scientific visualization and accelerated computing to identify what components of our model take in knowledge from the early and late-time waveform evolution to accurately forecast the latter part of numerical relativity waveforms. This work aims to accelerate the creation of scalable, computationally efficient and interpretable artificial intelligence models for gravitational wave astrophysics. Copyright © 2021, The Authors. All rights reserved.

关键词： Merging

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AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

arXiv

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arXiv 2021年

作者： Khan, Asad Huerta, E.A. Kumar, Prayush Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Chicago ChicagoIL60637 United States International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore560089 India

We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We trained AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8, that include modes up to ` ≤ 4 and (5, 5), except for (4, 0) and (4, 1), that describe binaries with mass-ratios q ≤ 8, individual spins sz{1,2} ∈ [−0.8, 0.8], and inclination angle θ ∈ [0, π]. Our probabilistic AI surrogates can accurately constrain the mass-ratio, individual spins, effective spin, and inclination angle of numerical relativity waveforms that describe such signal manifold. We compared the predictions of our AI models with Gaussian process regression, random forest, k-nearest neighbors, and linear regression, and with traditional Bayesian inference methods through the PyCBC Inference toolkit, finding that AI outperforms all these approaches in terms of accuracy, and are between three to four orders of magnitude faster than traditional Bayesian inference methods. Our AI surrogates were trained within 3.4 hours using distributed training on 1,536 NVIDIA V100 GPUs in the Summit *** Codes 68T10, 85-08, 83C35, 83C57 Copyright © 2021, The Authors. All rights reserved.

关键词： Gravity waves

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Combining dynamic and static analysis for automated grading SQL statements

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第4期5卷 179-190页

作者： Wang, Jinshui Zhao, Yunpeng Tang, Zhengyi Xing, Zhenchang School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Key Laboratory of Hunan Province for Mobile Business Intelligence Hunan University of Technology and Business Changsha410205 China Research School of Computer Science Australian National University ACT 2601 Australia

Learning and teaching Structured Query Language (SQL) is a challenge that is widely recognized within the computer science education community. Programming ac-tivities are considered to be the most important aspect of the learning process. Manually grading of SQL statements, however, is often regarded as a tedious and time-consuming ordeal. In this paper we propose an automated grading approach for SQL statements, which combines dynamic and static analysis. The proposed approach first identified the correct statements from the student SQL statements, and then grades the incorrect statements based on the similarity between the statements and the correct statements. Empir-ical evaluation of the proposal was carried out on a dataset consisting of 255 manually graded pairs of statements. Experimental results show that our approach is superior to three state-of-the-art grading approaches. Our study found that each grading approach has its particular advantages and disadvantages, and the combination of different grading approaches can help to improve the quality of grading results. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Grading

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Correction: Dynamic task offloading for resource allocation and privacy-preserving framework in Kubeedge-based edge computing using machine learning

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Cluster Computing 2024年第7期27卷 9433-9433页

作者： Lingayya, Sadananda Jodumutt, Sathyendra Bhat Pawar, Sanjay Rangrao Vylala, Anoop Chandrasekaran, Senthilkumar Shri Madhwa Vadiraja Institute of Technology and Management Bantakal Karnataka India Department of Computer Applications St Joseph Engineering College Mangaluru India Mechanical Engineering Department Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Artificial Intelligence and Data Science Jyothi Engineering College APJ Abdul Kalam Technological University Thrissur India Vellore Institute of Technology Vellore India

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Advances in machine and deep learning for modeling and real-time detection of multi-messenger sources

arXiv

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arXiv 2021年

作者： Huerta, Eliu A. Zhao, Zhizhen Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Mathematics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Statistics National Center for Supercomputing Applications Center for Artificial Intelligence Innovation University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the Universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a wide range of wavelengths and time scales. Combining and processing these datasets that vary in volume, speed and dimensionality requires new modes of instrument coordination, funding and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets, have powered the rise of artificial intelligence. This digital revolution now powers a multi-billion dollar industry, with far-reaching implications in technology and society. In this chapter we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in Multi-Messenger Astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017, to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and *** Codes 83-02, 83-04, 83-08, 85-08, 85-10, 68T07, 68T20, Copyright © 2021, The Authors. All rights reserved.

关键词： Gravity waves

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Probing nuclear physics with supernova gravitational waves and machine learning

arXiv

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arXiv 2023年

作者： Mitra, Ayan Orel, Daniil Abylkairov, Y. Sultan Shukirgaliyev, Bekdaulet Abdikamalov, Ernazar Center for Astrophysical Surveys National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Physics Nazarbayev University 53 Kabanbay Batyr ave Astana010000 Kazakhstan Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Kazakh-British Technical University 59 Tole Bi Street Almaty050000 Kazakhstan Department of Computer Science Nazarbayev University 53 Kabanbay Batyr ave Astana010000 Kazakhstan Energetic Cosmos Laboratory Nazarbayev University 53 Kabanbay Batyr ave Astana010000 Kazakhstan Heriot-Watt International Faculty Zhubanov University 263 Zhubanov brothers str Aktobe030000 Kazakhstan Fesenkov Astrophysical Institute 23 Observatory str Almaty050020 Kazakhstan Department of Computation and Data Science Astana IT University 55/11 Mangilik El ave Astana010000 Kazakhstan

Core-collapse supernovae are sources of powerful gravitational waves (GWs). We assess the possibility of extracting information about the equation of state (EOS) of high density matter from the GW signal. We use the bounce and early post-bounce signals of rapidly rotating supernovae. A large set of GW signals is generated using general relativistic hydrodynamics simulations for various EOS models. The uncertainty in the electron capture rate is parametrized by generating signals for six different models. To classify EOSs based on the GW data, we train a convolutional neural network (CNN) model. Even with the uncertainty in the electron capture rates, we find that the CNN models can classify the EOSs with an average accuracy of about 87 percent for a set of four distinct EOS models. © 2023, CC BY.

关键词： Gravity waves

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On the security of a three factor remote user authentication scheme using fuzzy extractor 14th

On the security of a three factor remote user authentication...

引用

14th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018

作者： Chen, Chien-Ming Huang, Yanyu Deng, Xiaoting Wu, Tsu-Yang Shenzhen China College of Computer Science and Engineering Shandong University of Technology Shandong China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou 350118 China Fujian University of Technology Fuzhou 350118 China

ISBN: (纸本)9783030037444

A secure authenticated key exchange protocol is an important key to establish a secure wireless communication. Various research have been conducted to study the efficiency and security of these authenticated key exchange protocol. A recent work by Giri et al. proposed a three factor remote user authentication scheme using Fuzzy Extractor for single server environment. However we found that their protocol is still vulnerable against an online password guessing attack. We also found that their protocol does not provide the perfect forward secrecy. To solve such problems, we propose a simple but effective improvement. © Springer Nature Switzerland AG 2019.

关键词： Authentication

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Cover patches: A general feature extraction strategy for spoofing detection

Cover patches: A general feature extraction strategy for spo...

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作者： Cai, Guorong Su, Songzhi Leng, Chengcai Wu, Jipeng Wu, Yundong Li, Shaozi Computer Science and Engineering College Jimei University Xiamen361021 China Department of Intelligence Science and Technology Xiamen University Xiamen360005 China Department of Mathematics Northwest University Xi'an City710127 China Fujian Collaborative Innovation Center for Big Data Applications in Governments Fuzhou350003 China

Face anti-spoofing has attracted many attentions in security applications, such as mobile payment and entrance guard. Until now, face anti-spoofing technique is still a challenging task. Mainstream image-based spoofing algorithms usually use global motion or texture information to distinguish whether an input face is live or fake. However, the performance of these methods are sensitive in light changes, or images acquired from different sensors. The main reason is that spoofed face image always has slight different texture in local areas, such as landmark or salient region of face. To this end, this paper proposes a novel multi-patches feature extraction strategy to detect spoofing. First, a set of patches with specific combination scheme is selected to cover the face image. Second, features such as hand-crafted Gray Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), or deep features are extracted from these patches. Third, all features are combined as the global descriptor of the face image, then fed into an SVM classifier to verify the anti-spoofing detection. Experimental results show that the proposed strategy can effectively enhance the performance, concerning with the accuracy of spoofed face detection in four widely used anti-spoofing databases. © 2018 John Wiley & Sons, Ltd.

关键词： Local binary pattern

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A spatial correlation based partial coverage scheduling scheme in wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2020年第2期5卷 34-43页

作者： Zhang, Jing Nian, Hongrui Ye, Xiucai Ji, Xiaorong He, Yao College of Information Science and Engineering Institute of Artificial Intelligence Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology 33th Xueyuan Road University Town Minhou FuzhouFujian350118 China College of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Department of Computer Science University of Tsukuba Tsukuba3058573 Japan

Wireless Sensor Networks are formed by a mass of various sensors, they are adopted in lots of applications. Because of the existence of spatial correlation in WSNs, the information from some geographic independence sensors are more significant than that from high correlation ones. Thus, it is unnecessary to transmit information from every sensor to the sink node. This paper focus on the coverage area for data monitoring in WSNs. To construct the virtual backbone network, we propose an efficient connected dominating set construction method based on clique, by taking advantage of the high spatial correlation characteristics of clique construction. Furthermore, by minimizing the number of sensors to activate, we propose a Spatial Correlation based partial Coverage Scheduling Scheme (SCCS) to prolong the network lifetime. The corresponding experiment indicate the efficiency and the feasibility of the proposed SCCS scheme. Moreover, this algorithm is able to guarantee better performance by comparing to the existing schemes. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Scheduling

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Correction to: development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

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Journal of Cloud Computing 2023年第1期12卷 1-1页

作者： Kumar, Ankit Khan, Surbhi Bhatia Pandey, Saroj Kumar Shankar, Achyut Maple, Carsten Mashat, Arwa Malibari, Areej A. Department of Computer Engineering & Applications GLA University Mathura India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Data Science School of Science Engineering and Environment University of Salford Manchester Byblos UK WMG University of Warwick Coventry UK Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK Faculty of Computing and Information Science King Abdulaziz University Rabigh Saudi Arabia Department of Industrial and Systems Engineering College of Engineering Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

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