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arXiv 2021年

作者： Bhyravarapu, Sriram Hartmann, Tim A. Hoang, Hung P. Kalyanasundaram, Subrahmanyam Reddy, I. Vinod The Institute of Mathematical Sciences HBNI Chennai India CISPA Helmholtz Center for Information Security Germany Algorithms and Complexity Group TU Wien Austria Department of Computer Science and Engineering IIT Hyderabad India Department of Electrical Engineering and Computer Science IIT Bhilai India

A conflict-free coloring of a graph G is a (partial) coloring of its vertices such that every vertex u has a neighbor whose assigned color is unique in the neighborhood of u. There are two variants of this coloring, one defined using the open neighborhood and one using the closed neighborhood. For both variants, we study the problem of deciding whether the conflict-free coloring of a given graph G is at most a given number k. In this work, we investigate the relation of clique-width and minimum number of colors needed (for both variants) and show that these parameters do not bound one another. Moreover, we consider specific graph classes, particularly graphs of bounded clique-width and types of intersection graphs, such as distance hereditary graphs, interval graphs and unit square and disk graphs. We also consider Kneser graphs and split graphs. We give (often tight) upper and lower bounds and determine the complexity of the decision problem on these graph classes, which improve some of the results from the literature. Particularly, we settle the number of colors needed for an interval graph to be conflict-free colored under the open neighborhood model, which was posed as an open problem. Copyright © 2021, The Authors. All rights reserved.

关键词： Color

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Battery Capacity of Energy-Storing Quantum Systems

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Physical Review Letters 2023年第3期131卷 030402-030402页

作者： Xue Yang Yan-Han Yang Mir Alimuddin Raffaele Salvia Shao-Ming Fei Li-Ming Zhao Stefan Nimmrichter Ming-Xing Luo School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China School of Computer and Network Security Chengdu University of Technology Chengdu 610059 China Department of Physics of Complex Systems S. N. Bose National Center for Basic Sciences Kolkata 700106 India Scuola Normale Superiore I-56127 Pisa Italy School of Mathematical Sciences Capital Normal University Beijing 100048 China Max-Planck-Institute for Mathematics in the Sciences 04103 Leipzig Germany Naturwissenschaftlich-Technische Fakultät Universität Siegen Siegen 57068 Germany CAS Center for Excellence in Quantum Information and Quantum Physics Hefei 230026 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China

The quantum battery capacity is introduced in this Letter as a figure of merit that expresses the potential of a quantum system to store and supply energy. It is defined as the difference between the highest and the lowest energy that can be reached by means of the unitary evolution of the system. This function is closely connected to the ergotropy, but it does not depend on the temporary level of energy of the system. The capacity of a quantum battery can be directly linked with the entropy of the battery state, as well as with measures of coherence and entanglement.

关键词： Quantum foundations Quantum measurements Quantum theory Quantum thermodynamics

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V³H: View Variation and View Heredity for Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2020年第3期1卷 233-247页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan 430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China The Department of Electrical and Computer Engineering University of Florida Gainesville 32611 FL United States

Real data often appear in the form of multiple incomplete views. Incomplete multiview clustering is an effective method to integrate these incomplete views. Previous methods only learn the consistent information between different views and ignore the unique information of each view, which limits their clustering performance and generalizations. To overcome this limitation, we propose a novel View Variation and View Heredity approach (V³H). Inspired by the variation and the heredity in genetics, V³H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively. Then, by aligning different views based on their cluster indicator matrices, V³H integrates the unique information from different views to improve the clustering performance. Finally, with the help of the adjustable low-rank representation based on the heredity matrix, V³H recovers the underlying true data structure to reduce the influence of the large incompleteness. More importantly, V³H presents possibly the first work to introduce genetics to clustering algorithms for learning simultaneously the consistent information and the unique information from incomplete multiview data. Extensive experimental results on fifteen benchmark datasets validate its superiority over other state-of-the-arts. Impact Statement—Incomplete multiview clustering is a popular technology to cluster incomplete datasets from multiple sources. The technology is becoming more significant due to the absence of the expensive requirement of labeling these datasets. However, previous algorithms cannot fully learn the information of each view. Inspired by variation and heredity in genetics, our proposed algorithm V³H fully learns the information of each view. Compared with the state-of-the-art algorithms, V³H improves clustering

关键词： Incomplete multiview clustering view heredity view variation

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Implementation of an Intelligent Animal Monitoring System Using Wireless Sensor Network and IoT Platform

Implementation of an Intelligent Animal Monitoring System Us...

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Cyber Resilience (ICCR), International Conference on

作者： Ateeq Ur Rehman Jehangir Arshad Muhammad Tariq Sadiq Abdul Rehman Munir Ahmad Mohammad Kamrul Hasan Hussam Al Hamadi Tauqeer Faiz Department of Electrical Engineering GC University Lahore Pakistan Department of Electrical & Computer Engineering Lahore Campus COMSATS University Islamabad Lahore Pakistan School of Architecture Technology and Engineering University of Brighton Brighton United Kingdome NCBAE School of Computer Science Lahore Pakistan Faculty of Information Science and Technology Center for Cyber Security Universiti Kebansaan Malaysia (UKM) Bangi Selangor Malaysia College of Engineering and IT University of Dubai School of IT Skyline University College Sharjah UAE Faculty of Engineering Computing and Science (FECS) - Swinburne University of Technology Australia

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

Meat and dairy products are negatively impacted by a lack of technology in the livestock industry in developing countries. To cater for this challenge, the Internet of Things (IoT), Node-MCU, and intelligent wireless sensor nodes are deployed to create a new smart dairy monitoring system. A cow collar with a temperature sensor, a GPS module, and an environmental parameter regularization system are included. Data from modules is stored in a separate database using an innovative IoT -based front end. All the deployed WS nodes can determine whether the environment is stable or not at any given time. Sensors built inside cow collars can assess vital indications like temperature and pulse rate, as well as the animal's exact location. The design of the Cow collar also has a feature that automatically alerts the owner. The plug-and-play technology offered is designed to be easy to adapt. When a farm has many animals, automation decreases the need for human involvement and so lowers labor expenses. The exploitation of remote monitoring systems improves the health of the animals and hence yields a better amount of dairy. This study also includes a detailed comparison of the proposed implementation with current systems to demonstrate its originality. Other applications, such as smart monitoring of zoo animals and poultry, may be derived from the proposed technology.

关键词： Temperature sensors Wireless communication Temperature measurement Wireless sensor networks Animals Databases Cows

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An artificial intelligence and blockchain technology-based data management framework for multicenter randomized controlled trials

引用

science Bulletin 2025年第6期70卷 856-860页

作者： Dongyuan Yun Xiaohang Wu Xi Chen Yahan Yang Yuanjun Shang Shaopeng Liu Dinesh Visva Gunasekeran Duoru Lin Lixue Liu Lanqin Zhao Wenben Chen Jingjing Chen Ling Jin Yuxuan Wu Jiaming Hong Weiling Hu Zhenzhe Lin Carol Y.Cheung Xiang Chen Peichen Xie Zhenzhen Liu Changhai Ding Patrick Yu-Wai-Man Keming Yu Daniel Shu Wei Ting Yizhi Liu Zibin Zheng Gansen Zhao Zhihua Xia Tien Yin Wong Haotian Lin on behalf of Advanced Technologies Improve Clinical Trial(ATICT)Group State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510000China School of Biomedical Engineering Sun Yat-sen UniversityGuangzhou 510000China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou 510000China Singapore Eye Research Institute Singapore National Eye CenterSingapore999002Singapore School of Medical Information Engineering Guangzhou University of Chinese MedicineGuangzhou 510000China Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China Zhongshan School of Medicine Sun Yat-sen UniversityGuangzhou 510000China Clinical Research Center Zhujiang HospitalSouthern Medical UniversityGuangzhou 510000China Cambridge Center for Brain Repair and MRC Mitochondrial Biology Unit Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCB20XYUnited Kingdom UCL Institute of Ophthalmology University College LondonLondonWC1E 6BTUnited Kingdom School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510000China School of Computer Science South China Normal UniversityGuangzhou 510000China College of Cyber Security Jinan UniversityGuangzhou 510000China Duke-NUS Medical School Singapore999002Singapore Tsinghua Medicine Tsinghua UniversityBeijing 100010China Hainan Eye Hospital and Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityHaikou 570100China Centre for Precision Medicine and Department of Genetics and Biomedical Informatics Zhongshan School of MedicineSun Yat-sen UniversityGuangzhou 510000China 不详

Randomized controlled trials(RCTs)provide optimal evidence of the effectiveness and safety of a new drug,a new medical device,or a new therapeutic strategy with the necessary scientific design[1].Traditional electronic data collection(EDC)systems for clinical trials primarily focus on data entry,validation,and compliance ***,their reliance on centralized architectures introduces vulnerabilities in data security and integrity。

关键词： Artificial Intelligence Multicenter Randomized Controlled Trials Data security scientific design traditional Blockchain Technology clinical trials randomized controlled trials rcts provide centralized architectures

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Intelligent decoding method for Ground-penetrating radar based on improved YOLOv5

Intelligent decoding method for Ground-penetrating radar bas...

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computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： ZeYu Chen YongKang Wang HongLiang Liu Chuan Wang XinBo Jiang Ning Zhang Zhen Huang Lei Jiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) JiNan China Technological Innovation Department Shandong Hi-Speed Group Co Ltd JiNan China School of Qilu Transportation Shandong University JiNan China No.1 Engineering Development Co. Ltd. of China Railway 14Th Bureau Group RiZhao China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Ground-penetrating radar (GPR) is a non-invasive detection technique that effectively gathers information about subsurface materials. In tunneling projects, GPR can provide a fast and accurate forecast of the geological conditions in front of tunnel boring, which provides an important reference basis for tunnel construction. Currently, the interpretation of GPR images relies heavily on manual expertise, resulting in low efficiency and minimal automation. This creates an urgent need for effective and accurate identification of poor geological conditions depicted in radar images. In this study, we present the GPR-YOLOv5 model, which is designed to detect unfavorable geologic features ahead of the tunnel face from GPR images. We enhance this model with the CA attention mechanism, GSConv module, ghostC3 module, and the VariFocal loss function to improve decoding accuracy while minimizing computational load. Additionally, we integrate this algorithm with established modern object detection techniques, and our experimental results demonstrate its effectiveness.

关键词： YOLO Accuracy Ground penetrating radar Attention mechanisms Geology Computational modeling Radar imaging Tunneling Decoding Load modeling

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Problems in NP can Admit Double-Exponential Lower Bounds when Parameterized by Treewidth or Vertex Cover

arXiv

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arXiv 2023年

作者： Foucaud, Florent Galby, Esther Khazaliya, Liana Li, Shaohua Inerney, Fionn Mc Sharma, Roohani Tale, Prafullkumar Université Clermont Auvergne CNRS Mines Saint-Étienne Clermont Auvergne INP LIMOS Clermont-Ferrand63000 France Department of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden Technische Universität Wien Vienna Austria CISPA Helmholtz Center for Information Security Saarbrücken Germany University of Bergen Bergen Norway Indian Institute of Science Education and Research Pune Pune India

Treewidth serves as an important parameter that, when bounded, yields tractability for a wide class of problems. For example, graph problems expressible in Monadic Second Order (MSO) logic and Quantified SAT or, more generally, Quantified CSP, are fixed-parameter tractable parameterized by the treewidth of the input's (primal) graph plus the length of the MSO-formula [Courcelle, information & Computation 1990] and the quantifier rank [Chen, ECAI 2004], respectively. The algorithms generated by these (meta-)results have running times whose dependence on treewidth is a tower of exponents. A conditional lower bound by Fichte, Hecher, and Pfandler [LICS 2020] shows that, for Quantified SAT, the height of this tower is equal to the number of quantifier alternations. These types of lower bounds, which show that at least double-exponential factors in the running time are necessary, exhibit the extraordinary level of computational hardness for such problems, and are rare in the current literature: there are only a handful of such lower bounds (for treewidth and vertex cover parameterizations) and all of them are for problems that are #NP-complete, (Equation presented)-complete, (Equation presented)-complete, or complete for even higher levels of the polynomial hierarchy. Our results demonstrate, for the first time, that it is not necessary to go higher up in the polynomial hierarchy to achieve double-exponential lower bounds: we derive double-exponential lower bounds in the treewidth (tw) and the vertex cover number (vc), for natural, important, and well-studied NP-complete graph problems. Specifically, we design a technique to obtain such lower bounds and show its versatility by applying it to three different problems: Metric Dimension, Strong Metric Dimension, and Geodetic Set. We prove that these problems do not admit 22o(tw) ·nO(1)-time algorithms, even on bounded diameter graphs, unless the ETH fails (here, n is the number of vertices in the graph). In fact, for Strong

关键词： Parameterization

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Efficient and secure two-party distributed signing protocol for the gost signature algorithm 6th

Efficient and secure two-party distributed signing protocol ...

引用

6th International Symposium on security and Privacy in Social Networks and Big Data, SocialSec 2020

作者： Zhang, Yunru Luo, Min Choo, Kim-Kwang Raymond Li, Li He, Debiao Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Science and Engineering Wuhan University Wuhan430072 China Department of Information Systems and Cyber Security and the Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX78249 United States

ISBN: (纸本)9789811590306

Mobile devices, such as Android and iOS devices, are often used for electronic/mobile commerce (e.g. payments using WeChat Pay and Bitcoin wallet). Hence, ensuring the security of a user’s private key stored on the device is crucial. To reduce the risk of private key leakage, a number of (t, n) threshold secret sharing protocols have been proposed in the literature. However, (t, n) threshold secret sharing protocols are generally not designed to mitigate key reconstruction attacks. Hence, in this paper we propose an efficient and secure two-party distributed signing protocol for the GOST signature algorithm, a Russian cryptographic standard algorithm. This allows us to separate a single private key to two mobile devices and generate a valid signature without reconstructing the entire private key. We then prove that our protocol is secure under non-standard assumption. Moreover, we implement our protocol using MIRACL Cryptographic SDK and evaluate its performance on two Android devices and two personal computers. © Springer Nature Singapore Pte Ltd 2020.

关键词： Android (operating system)

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

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Proceedings of the 37th International Conference on Neural information Processing Systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

关键词：

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SWIN-TRANSFORMER-YOLOV5 FOR REAL-TIME WINE GRAPE BUNCH DETECTION

arXiv

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arXiv 2022年

作者： Lu, Shenglian Liu, Xiaoyu He, Zixuan Liu, Wenbo Zhang, Xin Karkee, Manoj Guangxi Key Lab of Multisource Information Mining & Security College of Computer Science & Engineering Guangxi Normal University Guilin541004 China Center for Precision and Automated Agricultural Systems Department of Biological Systems Engineering Washington State University ProsserWA99350 United States Department of Agricultural and Biological Engineering Mississippi State University Mississippi State MS39762 United States

Precise canopy management is critical in vineyards for premium wine production because maximum crop load does not guarantee the best economic return for wine producers. Therefore, the wine grape growers need to keep tracking the number of grape bunches during the entire growth season for the optimal crop load per vine. Manual counting grape bunches can be highly labor-intensive, inefficient, and error prone. In this research, an integrated detection model, Swin-transformer-YOLOv5 or Swin-T-YOLOv5, was proposed for real-time wine grape bunch detection to inherit the advantages from both YOLOv5 and Swin-transformer. The research was conducted on two different grape varieties of Chardonnay (always white berry skin) and Merlot (white or white-red mix berry skin when immature;red when matured) from July to September in 2019. To verify the superiority of Swin-T-YOLOv5, its performance was compared against several commonly used object detectors, including Faster R-CNN, YOLOv3, YOLOv4, and YOLOv5. All models were assessed under different test conditions, including two different weather conditions (sunny and cloudy), two different berry maturity stages (immature and mature), and three different sunlight directions/intensities (morning, noon, and afternoon) for a comprehensive comparison. Additionally, the predicted number of grape bunches by Swin-T-YOLOv5 was further compared with ground truth values, including both in-field manual counting and manual labeling during the annotation process. Results showed that the proposed Swin-T-YOLOv5 outperformed all other studied models for grape bunch detection, with up to 97% of mean Average Precision (mAP) and 0.89 of F1-score when the weather was cloudy. This mAP was approximately 44%, 18%, 14%, and 4%greater than Faster R-CNN, YOLOv3, YOLOv4, and YOLOv5, respectively. Swin-T-YOLOv5 achieved its lowest mAP (90%) and F1-score (0.82) when detecting immature berries, where the mAP was approximately 40%, 5%, 3%, and 1% greater than the s

关键词： Object detection

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