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arXiv 2021年

作者： Liu, Yixin Li, Zhao Pan, Shirui Gong, Chen Zhou, Chuan Karypis, George The Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia The Alibaba Group Hangzhou310000 China The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100093 China The Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets. © 2021, CC BY.

关键词： Anomaly detection

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Identification of switched linear systems: Persistence of excitation and numerical algorithms

arXiv

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arXiv 2021年

作者： Mu, Biqiang Chen, Tianshi Cheng, Changming Bai, Er-Wei Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiaotong University Shanghai China Department of Electrical and Computer Engineering University of Iowa Iowa CityIA52242 United States

This paper investigates two issues on identification of switched linear systems: persistence of excitation and numerical algorithms. The main contribution is a much weaker condition on the regressor to be persistently exciting that guarantees the uniqueness of the parameter sets and also provides new insights in understanding the relation among different subsystems. It is found that for uniquely determining the parameters of switched linear systems, the minimum number of samples needed derived from our condition is much smaller than that reported in the literature. The secondary contribution of the paper concerns the numerical algorithm. Though the algorithm is not new, we show that our surrogate problem, relaxed from an integer optimization to a continuous minimization, has exactly the same solution as the original integer optimization, which is effectively solved by a block-coordinate descent algorithm. Moreover, an algorithm for handling unknown number of subsystems is proposed. Several numerical examples are illustrated to support theoretical analysis. Copyright © 2021, The Authors. All rights reserved.

关键词： Integer programming

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A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation 2

A Time Effect based Collaborative Filtering Approach for Use...

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2019 2nd International Conference on computer Information science and Artificial Intelligence, CISAI 2019

作者： Chen, Yuxi Zhang, Xiaotong Zhao, Qing Akhtar, Faheem School of Statistics and Mathematics Country Central University of Finance and Economics Beijing100081 China Harpur College of Arts and Sciences Binghamton University-SUNY New York13902 United States Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Vrije Universiteit Amsterdam1081 HV Netherlands School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing100124 China Department of Computer Science Sukkur IBA University Sukkur65200 Pakistan

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect;next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative filtering

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Low-rank reduced biquaternion tensor ring decomposition and tensor completion

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Applied mathematics and Computation 2025年 504卷

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Artificial Intelligence at Sun Yat-sen University the Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Mathematics University of Manitoba MB Canada

We define the reduced biquaternion tensor ring (RBTR) decomposition and provide a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR ranks with total variation (TV) regularization to optimize the process. Numerical experiments on color image and video completion tasks indicate the advantages of our method. © 2025 Elsevier Inc.

关键词： Numerical methods

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Towards the hierarchical state machine oriented proteus systems programming language

Towards the hierarchical state machine oriented proteus syst...

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Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2020

作者： Tellier, Daniel Millman, Meyer McClelland, Brian Go, Kate Beatrix Balayan, Alice Munje, Michael J. Dewey, Kyle Ho, Nhut Havelund, Klaus Ingham, Michel Department of Computer Science California State University Northridge NorthridgeCA91330 United States Department of Electrical and Computer Engineering California State University Northridge NorthridgeCA91330 United States Department of Mechanical Engineering California State University Northridge NorthridgeCA91330 United States Laboratory for Reliable Software Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Systems Engineering Division Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

ISBN: (纸本)9781624106088

In space applications, hierarchical state machines (HSMs) are often used for writing simulation and control software, including that of the Curiosity rover. At the Jet Propulsion Lab (JPL), multiple programming languages have been developed specifically for writing HSM-based software, and these have been used in practice. However, we observe that the existing languages developed have significant issues with one or more of usability, performance, and safety, making them problematic for HSM-based development. To address these concerns, we are taking lessons learned from these languages and developing a new programming language named Proteus. Proteus builds HSM support directly into the language, and permits complex HSMs to be defined which communicate with each other. Proteus is designed with a look and feel similar to C/C++, making it usable and overall approachable for use by JPL systems engineers. Proteus itself compiles to C++, allowing it to fit easily into existing development toolchains, and making it amenable to embedded real-time systems. To ensure that Proteus will be of use to its target audience, it is being developed iteratively through a series of prototypes which are regularly evaluated by key stakeholders at JPL, allowing us to refine its design if we go off course. While Proteus is still very young in its development, we demonstrate its basic viability on an example utilizing multiple independent HSMs communicating with each other, and relevant execution traces. In the future, we plan to apply Proteus to larger HSMs taken from real space applications, and many additional relevant features are planned. © 2020 The MITRE Corporation. All Rights Reserved.

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Prediction of global spread of covid-19 pandemic: A review and research challenges

TechRxiv

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TechRxiv 2020年

作者： Shah, Saloni Mulahuwaish, Aos Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Department of Computer Science and Information Systems Saginaw Valley State University MI United States Department of Software Engineering Salahaddin University Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wolverhampton United Kingdom Department of Software Engineering Faculty of Engineering Koya University Kurdistan Iraq

Since the initial reports of the Coronavirus surfacing in Wuhan, China;the novel virus currently without a cure has spread like a wildfire across the globe. The virus spread exponentially across all inhabited continent;catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyse and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modelling tools to latest AI technology;the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response – whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance including lockdowns. On one hand, this paper shows what accuracy of research brings to enable fighting this disease;while on the other hand it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt in compiling different research methods and comparing their accuracy in predicting the spread of COVID-19. © 2020, CC BY.

关键词： Viruses

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A Consensus Privacy Metrics Framework for Synthetic Data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing

arXiv

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arXiv 2022年

作者： Faroughi, Salah A. Pawar, Nikhil M. Fernandes, Célio Raissi, Maziar Das, Subasish Kalantari, Nima K. Mahjour, Seyed Kourosh Geo-Intelligence Laboratory Ingram School of Engineering Texas State University San MarcosTX78666 United States Associate Laboratory of Intelligent Systems Institute for Polymers and Composites Polymer Engineering Department School of Engineering University of Minho Campus of Azurém Guimarães4800-058 Portugal Department of Applied Mathematics University of Colorado Boulder BoulderCO610101 United States Artificial Intelligence in Transportation Lab Ingram School of Engineering Texas State University San MarcosTX78666 United States Computer Science and Engineering Department Texas A&M University College StationTX77843 United States

Recent breakthroughs in computing power have made it feasible to use machine learning and deep learning to advance scientific computing in many fields, including fluid mechanics, solid mechanics, materials science, etc. Neural networks, in particular, play a central role in this hybridization. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data is sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multi-physics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers are presented. This critical review provides researchers and engineers with a solid starting point to comprehend how to integrate different layers of physics into neural networks. © 2022, CC BY.

关键词： Fluid mechanics

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On PID control for synchronization of complex dynamical network with delayed nodes

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science China(Technological sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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Connected Cows: Utilizing Fog and Cloud Analytics toward Data-Driven Decisions for Smart Dairy Farming

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IEEE Internet of Things Magazine 2019年第4期2卷 32-37页

作者： Taneja, Mohit Jalodia, Nikita Malone, Paul Byabazaire, John Davy, Alan Olariu, Cristian Emerging Networks Laboratory Telecommunications Software and Systems Group Waterford Institute of Technology (WIT) Ireland Department of Computing and Mathematics Emerg.ing Networks Lab Research Unit in Telecommunications Software and Systems Group WIT Agri-Tech School of Computer Science University College Dublin Ireland Department of Computing and Mathematics School of Science and Computing Waterford Institute of Technology Innovation Exchange IBM Ireland

The Internet of Things (IoT) is about connecting people, processes, data, and things, and is changing the way we monitor and interact with things. An active incorporation of information and communication technology coupled with sophisticated data analytics approaches has the potential to transform some of the oldest industries in the world, including dairy farming. It presents a great opportunity for verticals such as the dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. Dairy farms have all the constraints of a modern business-they have a fixed production capacity, a herd to manage, expensive farm labor, and other varied farm-related processes to take care of. In this technology-driven era farmers look for assistance from smart solutions to increase profitability and to help manage their farms well. We present an end-To-end IoT application system with fog assistance and cloud support that analyzes data generated from wearables on cows' feet to detect anomalies in animal behavior that relate to illness such as lameness. The solution leverages behavioral analytics to generate early alerts toward the animals' well being, thus assisting the farmer in livestock monitoring. This in turn also helps in increasing productivity and milk yield by identifying potential diseases early on. The project specializes in detecting lameness in dairy cattle at an early stage, before visible signs appear to the farmer or an animal expert. Our trial results in a real-world smart dairy farm setup, consisting of a dairy herd of 150 cows in Ireland, demonstrate that the designed system delivers a lameness detection alert up to three days in advance of manual observation. © 2018 IEEE.

关键词： Productivity

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