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arXiv 2022年

作者： Wu, Youxi Hu, Qian Li, Yan Guo, Lei Zhu, Xingquan Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Key Laboratory of Big Data Computing Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China School of Economics and Management Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China Department of Computer Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Mininglamp Academy of Sciences Mininglamp Technology Beijing100084 China Ministry of Education Hefei230009 China

A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values. Moreover, existing methods mainly neglect the order relationships between time series values. To tackle these issues, inspired by order-preserving matching, this paper proposes an Order-Preserving sequential Pattern (OPP) mining method, which represents patterns based on the order relationships of the time series data. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath the time series data. To obtain frequent trends in time series, we propose the OPP-Miner algorithm to mine patterns with the same trend (sub-sequences with the same relative order). OPP-Miner employs the filtration and verification strategies to calculate the support and uses pattern fusion strategy to generate candidate patterns. To compress the result set, we also study finding the maximal OPPs. Experiments validate that OPP-Miner is not only efficient and scalable but can also discover similar sub-sequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from https://***/wuc567/Pattern-Mining/tree/master/OPP-Miner. Copyright © 2022, The Authors. All rights reserved.

关键词： Time series

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NI-UDA: Graph adversarial domain adaptation from non-shared-and-imbalanced big data to small imbalanced applications

arXiv

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arXiv 2021年

作者： Xiao, Guangyi Xiang, Weiwei Liu, Huan Chen, Hao Peng, Shun Guo, Jingzhi Gong, Zhiguo College of Computer Science and Electronic Engineering Hunan University Chansha410082 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province School of Electrical and Information Engineering Huaihua University Hunan Huaihua418008 China Department of Computer and Information Science University of Macau Macau SAR999078 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Macau SAR999078 China

We propose a new general Graph Adversarial Domain Adaptation (GADA) based on semantic knowledge reasoning of class structure for solving the problem of unsupervised domain adaptation (UDA) from the big data with non-shared and imbalanced classes to specified small and imbalanced applications (NI-UDA), where non-shared classes mean the label space out of the target domain. Our goal is to leverage priori hierarchy knowledge to enhance domain adversarial aligned feature representation with graph reasoning. In this paper, to address two challenges in NI-UDA, we equip adversarial domain adaptation with Hierarchy Graph Reasoning (HGR) layer and the Source Classifier Filter (SCF). For sparse classes transfer challenge, our HGR layer can aggregate local feature to hierarchy graph nodes by node prediction and enhance domain adversarial aligned feature with hierarchy graph reasoning for sparse classes. Our HGR contributes to learn direct semantic patterns for sparse classes by hierarchy attention in self-attention, non-linear mapping and graph normalization. our SCF is proposed for the challenge of knowledge sharing from non-shared data without negative transfer effect by filtering low-confidence non-shared data in HGR layer. Experiments on two benchmark datasets show our GADA methods consistently improve the state-of-theart adversarial UDA algorithms, e.g. GADA(HGR) can greatly improve f1 of the MDD by 7.19% and GVB-GD by 7.89% respectively on imbalanced source task in Meal300 dataset. The code is available at https://***/gada. Copyright © 2021, The Authors. All rights reserved.

关键词： Data transfer

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A Spectrum Resource Sharing Algorithm for IoT Networks based on Reinforcement Learning 16

A Spectrum Resource Sharing Algorithm for IoT Networks based...

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16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020

作者： Shi, Zhaoyuan Xie, Xianzhong Kadoch, Michel Cheriet, Mohamed Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China École de Technologie Supérieure University of Quebec Department of Electrical Engineering Montreal Canada

ISBN: (纸本)9781728131290

Internet of Things (IoT) has attracted tremendous interest since it can improve production efficiency and system intelligence significantly. However, with the explosive growth of various types of device and data flow, IoT suffers spectrum resource scarcity for wireless applications. In this paper, we propose a solution for spectrum resource sharing in the IoT network, with the objective to facilitate the limited spectrum sharing between different kinds of sensors. To overcome the challenges of unknown dynamic IoT environment, the deep Q-learning network (DQN) is adopted. BS acts as the single agent and centrally manages all spectrum resources. First, a new reward function is designed to drive the learning process, which takes into account the different communication requirements of various sensors. In addition, to improve the learning efficiency of DQN, we compress the action space. Finally, simulation results show that compared with other algorithms, the proposed algorithm can achieve good network performance. © 2020 IEEE.

关键词： Internet of things

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CoIL: Coordinate-based internal learning for imaging inverse problems

arXiv

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arXiv 2021年

作者： Sun, Yu Liu, Jiaming Xie, Mingyang Wohlberg, Brendt Kamilov, Ulugbek S. Department of Computer Science and Engineering Washington University St. LouisMO63130 United States Department of Electrical and Systems Engineering Washington University St. LouisMO63130 United States Los Alamos National Laboratory Theoretical Division Los AlamosNM87545 United States

We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for continuous representation of measurements. Unlike traditional DL methods that learn a mapping from the measurements to the desired image, CoIL trains a multilayer perceptron (MLP) to encode the complete measurement field by mapping the coordinates of the measurements to their responses. CoIL is a self-supervised method that requires no training examples besides the measurements of the test object itself. Once the MLP is trained, CoIL generates new measurements that can be used within a majority of image reconstruction methods. We validate CoIL on sparse-view computed tomography using several widely-used reconstruction methods, including purely model-based methods and those based on DL. Our results demonstrate the ability of CoIL to consistently improve the performance of all the considered methods by providing high-fidelity measurement fields. © 2021, CC BY.

关键词： Mapping

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Editorial: special issue on “The security, privacy, and digital forensics of mobile networks and mobile cloud”

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Digital Communications and Networks 2019年第4期5卷 203-204页

作者： Lei Chen Guofei Gu Wenjia Li Rami J.Haddad the Department of Information Technology Allen E. Paulson College of Engineering and Computing at Georgia Southern University USA the Department of Computer Science & Engineering at Texas A&M University Computer Science at New York Institute of Technology (NYIT) since the Department of Electrical and Computer Allen E. Paulson College of Engineering and Computing at Georgia Southern University the Optical Networks and Smart Grid Applications (ONSmart) Laboratory GSU

To many people, smartphones and other mobile devices have been an indispensable part of their daily lives. With personal financial and medical information and job-related data processed on mobile devices,systems and networks as well as the connected mobile cloud, it is critically important that secure mobile operating systems, secure mobile applications, secure smartphone devices, secure cellular networks and secure mobile cloud all keep up with the ever-growing mobile usage.

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δ-Calculus:A New Approach to Quantifying Location Privacy

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computers, Materials & Continua 2020年第6期63卷 1323-1342页

作者： Lihua Yin Ran Li Jingquan Ding Xiao Li Yunchuan Guo Huibing Zhang Ang Li Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Institute of Information Engineering CASBeijing100093China Xinjiang Technical Institute of Physics&Chemistry Chinese Academy ScienceUrumqi830011China Key laboratory of Speech Language Information Processing of Xinjiang Urumqi830046China University of Chinese Academy of Sciences Beijing100049China Guilin University of Electronic Technology Guilin541004China Department of Electrical and Computer Engineering Duke UniversityDurham27708USA

With the rapid development of mobile wireless Internet and high-precision localization devices,location-based services(LBS)bring more convenience for people over recent *** LBS,if the original location data are directly provided,serious privacy problems *** a response to these problems,a large number of location-privacy protection mechanisms(LPPMs)(including formal LPPMs,FLPPMs,etc.)and their evaluation metrics have been proposed to prevent personal location information from being leakage and quantify privacy ***,existing schemes independently consider FLPPMs and evaluation metrics,without synergizing them into a unifying *** this paper,a unified model is proposed to synergize FLPPMs and evaluation *** detail,the probabilistic process calculus(calledδ-calculus)is proposed to characterize obfuscation schemes(which is a LPPM)and integrateα-entropy toδ-calculus to evaluate its privacy ***,we use two calculus moving and probabilistic choice to model nodes’mobility and compute its probability distribution of nodes’locations,and a renaming function to model privacy *** formally defining the attacker’s ability and extending relative entropy,an evaluation algorithm is proposed to quantify the leakage of location ***,a series of examples are designed to demonstrate the efficiency of our proposed approach.

关键词： Location privacy δ-calculus relative entropy

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Rapid TAURUS for relaxation-based color magnetic particle imaging

arXiv

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arXiv 2021年

作者： Arslan, Tunç Alper Özaslan, A. Kurt, Semih Muslu, Yavuz Saritas, Emine Ulku Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Bilkent University Ankara06800 Turkey Neuroscience Program Sabuncu Brain Research Center Bilkent University Ankara06800 Turkey Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI United States Department of Radiology University of Wisconsin-Madison MadisonWI United States Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Science for Life Laboratory Stockholm Sweden

Magnetic particle imaging (MPI) is a rapidly developing medical imaging modality that exploits the non-linear response of magnetic nanoparticles (MNPs). Color MPI widens the functionality of MPI, empowering it with the capability to distinguish different MNPs and/or MNP environments. The system function approach for color MPI relies on extensive calibrations that capture the differences in the harmonic responses of the MNPs. An alternative calibration-free x-space-based method called TAURUS estimates a map of the relaxation time constant, τ, by recovering the underlying mirror symmetry in the MPI signal. However, TAURUS requires a back and forth scanning of a given region, restricting its usage to slow trajectories with constant or piecewise constant focus fields (FFs). In this work, we propose a novel technique to increase the performance of TAURUS and enable τ map estimation for rapid and multi-dimensional trajectories. The proposed technique is based on correcting the distortions on mirror symmetry induced by time-varying FFs. We demonstrate via simulations and experiments in our in-house MPI scanner that the proposed method successfully estimates high-fidelity τ maps for rapid trajectories that provide orders of magnitude reduction in scanning time, while preserving the calibration-free property of TAURUS. Copyright © 2021, The Authors. All rights reserved.

关键词： Trajectories

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SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors

arXiv

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arXiv 2021年

作者： Liang, Xin Zhao, Kai Di, Sheng Li, Sihuan Underwood, Robert Gok, Ali M. Tian, Jiannan Deng, Junjing Calhoun, Jon C. Tao, Dingwen Chen, Zizhong Cappello, Franck Department of Computer Science Missouri University of Science and Technology RollaMO65409 United States Department of Computer Science & Engineering University of California Riverside RiversideCA92521 United States Facebook Inc. Menlo ParkCA94025 United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL60439 United States Cerebras Systems SunnyvaleCA94085 United States Department of Computer Science Washington State University PullmanWA99164 United States School of Computing Clemson University ClemsonSC29634 United States Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

Today’s scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Error-bounded lossy compressor has been considered one of the most effective solutions to the above problem. In practice, however, the best-fit compression method often needs to be customized/optimized in particular because of diverse characteristics in different datasets and various user requirements on the compression quality and performance. In this paper, we develop a novel modular, composable compression framework (namely SZ3), which involves three significant contributions. (1) SZ3 features a modular abstraction for the prediction-based compression framework such that the new compression modules can be plugged in easily. (2) SZ3 supports multi algorithm predictors and can automatically select the best-fit predictor for each data block based on the designed error estimation criterion. (3) SZ3 allows users to easily compose different compression pipelines on demand, such that both compression quality and performance can be significantly improved for their specific datasets and requirements. (4) In addition, we evaluate several lossy compressors composed from SZ3 using the real-world datasets. Specifically, we leverage SZ3 to improve the compression quality and performance for different use-cases, including GAMESS quantum chemistry dataset and Advanced Photon Source (APS) instrument dataset. Experiments show that our customized compression pipelines lead to up to 20% improvement in compression ratios under the same data distortion compared with the state-of-the-art approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressors

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Atomically smooth films of CsSb: a chemically robust visible light photocathode

arXiv

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arXiv 2023年

作者： Parzyck, C.T. Pennington, C.A. DeBenedetti, W.J.I. Balajka, J. Echeverria, E. Paik, H. Moreschini, L. Faeth, B.D. Hu, C. Nangoi, J.K. Anil, V. Arias, T.A. Hines, M.A. Schlom, D.G. Galdi, A. Shen, K.M. Maxson, J.M. Laboratory of Atomic and Solid State Physics Department of Physics Cornell University IthacaNY14853 United States Cornell Laboratory for Accelerator-Based Sciences and Education Cornell University IthacaNY14853 United States Department of Chemistry and Chemical Biology Cornell University IthacaNY14853 United States Cornell University IthacaNY14853 United States School of Electrical & Computer Engineering University of Oklahoma NormanOK73019 United States Center for Quantum Research and Technology University of Oklahoma NormanOK73019 United States Department of Materials Science and Engineering Cornell University IthacaNY14853 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States Leibniz-Institut für Kristallzüchtung Max-Born-Straße 2 Berlin12489 Germany Department of Industrial Engineering University of Salerno SA Fisciano84084 Italy

Alkali antimonide semiconductor photocathodes provide a promising platform for the generation of high brightness electron beams, which are necessary for the development of cutting-edge probes including x-ray free electron lasers and ultrafast electron diffraction. However, to harness the intrinsic brightness limits in these compounds, extrinsic degrading factors, including surface roughness and contamination, must be overcome. By exploring the growth of CsxSb thin films monitored by in situ electron diffraction, the conditions to reproducibly synthesize atomically smooth films of CsSb on 3C-SiC (100) and graphene coated TiO2 (110) substrates are identified, and detailed structural, morphological, and electronic characterization is presented. These films combine high quantum efficiency in the visible (up to 1.2% at 400 nm), an easily accessible photoemission threshold of 550 nm, low surface roughness (down to 600 pm on a 1 µm scale), and a robustness against oxidation up to 15 times greater then Cs3Sb. These properties suggest that CsSb has the potential to operate as an alternative to Cs3Sb in electron source applications where the demands of the vacuum environment might otherwise preclude the use of traditional alkali antimonides. © 2023, CC BY-NC-SA.

关键词： Electronic structure

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Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

Franklin Open

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Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

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