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Smart World Congress (SWC), IEEE

作者： Luwen Huang Michael Kapteyn Karen E. Willcox Department of Computer Science University of Texas at Austin Oden Institute for Computational Engineering and Sciences University of Texas at Austin

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

This paper articulates the importance of graphical models and graph-based methods as fundamental enablers of Digital Twins. Graph-based representations are well known to be suited for describing complex systems where the connections between entities are as important as the entities themselves. The interconnections within and across data, models, and decisions are central to a Digital Twin’s value. Not only does a graph emphasize the scalable representation of such interrelationships, it also provides a natural mathematical setting for addressing uncertainty and complexity—arguably the two biggest barriers to scalable deployment and adoption of Digital Twins. We discuss how recent advances in theory and algorithms for large-scale knowledge graphs and graphical models can be combined in a multi-layered formulation to provide a powerful foundation for achieving scalable Digital Twins for complex systems.

关键词： Graphical models Uncertainty Knowledge graphs Ontologies Probabilistic logic Data models Digital twins Complexity theory Complex systems

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Distances Between Immersed Graphs: Metric Properties

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Matematica 2023年第1期2卷 197-222页

作者： Buchin, Maike Chambers, Erin Fang, Pan Fasy, Brittany Terese Gasparovic, Ellen Munch, Elizabeth Wenk, Carola Department of Computer Science Ruhr University Bochum Bochum 44780 Germany Department of Computer Science Saint Louis University Saint Louis MO United States Department of Computer Science Tulane University New Orleans LA United States School of Computing & amp Department of Mathematical Sciences Montana State University Bozeman MT United States Department of Mathematics Union College Schenectady NY United States Department of Computational Mathematics Science and Engineering Michigan State University East Lansing MI United States Department of Mathematics Michigan State University East Lansing MI United States

Graphs in metric spaces appear in a wide range of data sets, and there is a large body of work focused on comparing, matching, or analyzing collections of graphs in different ambient spaces. In this survey, we provide an overview of a diverse collection of distance measures that can be defined on the set of finite graphs immersed (and in some cases, embedded) in a metric space. For each of the distance measures, we recall their definitions and investigate which of the properties of a metric they satisfy. Furthermore we compare the distance measures based on these properties and discuss their computational complexity. © The Author(s) 2022.

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Optimizing Forwarding Strategies in Sparsely Delay-Tolerant Networks with Short Contact Durations 15

Optimizing Forwarding Strategies in Sparsely Delay-Tolerant ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nemera, Yadesa Bedasa Jule, Leta Tesfaye Sivakumar, S. Pavithra, J. Sampathkumar, A. Rastogi, Ravi Dambi Dollo Univeristy Department of Information Technology Ethiopia Dambi Dollo University College of Natural and Computational Science Department of Physics Dambi Dollo Ethiopia Nehru Institute of Engineering and Technology Department of Cse Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Artificial intelligence and data science Coimbatore India Dambi Dollo Univeristy Department of Computer Science Ethiopia Koneru Lakshmaiah Education Foundation Department of Cse AP Guntur India

ISBN: (纸本)9798350370249

Delay-tolerant networks (DTNs) are specific settings that experience either instability or the absence of a complete path between the source and destination nodes. The contact lifetime between pairs of network nodes is constrained by the mobility of nodes and the restricted bandwidth of peer- to-peer wireless transmission. In sparsely delay-tolerant networks, the re-establishment of contact between pairs of nodes is significantly delayed beyond the duration of the initial contact. Hence, the need for careful thought in designing a buffer control forwarding policy arises due to the limited contact period. Although forwarding algorithms have been designed extensively, much research has not been done on forwarding when there is a short contact time. When the message exchange between the pairs of network nodes is not completed during the contract period, they must wait until they are within communication range once more. To tackle the issue, we have suggested an innovative forwarding policy in buffer management that considers the duration of interaction. To assess the effectiveness of the proposed effort, we utilized the Opportunistic Network Environment (ONE) networking simulator. The suggested approach is combined with the MaxProp routing protocol buffer control forwarding policy and evaluated using two distinct metrics: bundle delivery rate and average delivery delay. In addition, we evaluated the performance of the suggested work by considering the number of nodes, buffer size, and simulation time. Across all the trials conducted in this thesis, the simulation results consistently demonstrate that our suggested method beats the existing forwarding strategy. © 2024 IEEE.

关键词： Buffer Management Forwarding Policy Contact Duration DTN Sparse DTN

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Organization of Storage and Processing of Experimental Results from the Continuous Process of Styrene-Butadiene Copolymerization Using Databases

Organization of Storage and Processing of Experimental Resul...

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Mikhailova, Tatiana Mikhailov, Vladimir Mustafina, Svetlana Ufa University of Science and Technology Department of Mathematical and Computer Modeling Ufa Russia Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan Russia

ISBN: (纸本)9798331511241

The article presents an approach to storing and processing the results of industrial, laboratory, and computational experiments based on databases. The approach is presented on the example of the process of copolymerization of butadiene with styrene, which proceeds continuously in a cascade of series-connected ideal mixing reactors. A conceptual and logical database model of the industrial process under study has been created. The constructed structure can be adapted for other copolymerization processes, depending on the type of process, its formulation, and the process flow conditions. The presented physical model is implemented as a database and has been tested in the Microsoft SQL Server environment. © 2025 IEEE.

关键词： Data reduction

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Organization of Storage and Processing of Experimental Results from the Continuous Process of Styrene-Butadiene Copolymerization Using Databases

Organization of Storage and Processing of Experimental Resul...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Tatiana Mikhailova Vladimir Mikhailov Svetlana Mustafina Department of Mathematical and Computer Modeling Ufa University of Science and Technology Ufa Russia Institute of Computational Mathematics and Information Technologies Kazan Federal University Kazan Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

关键词： Industries Adaptation models Databases Organizations mathematical models Kinetic theory Recording Servers Polymers Inductors

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Exploiting Gaussian Noise Variance for Dynamic Differential Poisoning in Federated Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Hossain, Md Tamjid Badsha, Shahriar La, Hung Islam, Shafkat Khalil, Ibrahim Texas A&M University-San Antonio Department of Computational Engineering Mathematical Sciences United States General Motors United States University of Nevada Department of Computer Science and Engineering Reno United States Purdue University Department of Computer Science United States RMIT University School of Computing Technologies Australia

The emerging field of Federated Learning (FL) is reshaping privacy-preserved data analysis and decision support mechanisms within several critical infrastructure (CIs) sectors such as autonomous transportation, energy, and healthcare. To shield sensitive operational and client data from privacy attackers, Differential Privacy (DP) has been proposed to integrate on top of the FL process. Yet, we identify that integrating Gaussian noise for achieving DP guarantee can inadvertently create a new vector for differential model poisoning attacks in FL. Moreover, exploiting the variance in Gaussian noise enables attackers to camouflage their activities within the legitimate noise of the system, a significant yet largely overlooked security flaw in the differentially private federated learning (DPFL) framework. Addressing this research gap, we introduce a novel adaptive model poisoning through episodic loss memorization (α-MPELM) technique. This method enables attackers to dynamically inject adversarial noise into the differentially private local model parameters. The technique has a dual purpose: hindering the optimal convergence of the global FL model and simultaneously avoiding detection by the anomaly detectors. Our evaluation of the α-MPELM attack reveals its capability to deceive Norm, Accuracy, and Mix anomaly detection algorithms, surpassing the conventional random malicious device (RMD) attacks with attack accuracy improvements of 6.8%, 12.6%, and 13.8%, respectively. Additionally, we introduce a reinforcement learning-based DP level selection strategy, rDP, as an effective countermeasure against α-MPELM attack. Our empirical findings confirm that this defense mechanism steadily progresses to an optimal policy. © 2020 IEEE.

关键词： Federated learning

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SPARSE RECOVERY OF ELLIPTIC SOLVERS FROM MATRIX-VECTOR PRODUCTS

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SIAM Journal on Scientific Computing 2024年第2期46卷 A998-A1025页

作者： Schäfer, Florian Owhadi, Houman Computational Science and Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Computing + Mathematical Sciences California Institute of Technology PasadenaCA91125 United States

In this work, we show that solvers of elliptic boundary value problems in d dimensions can be approximated to accuracy ϵ from only (log(N) logd(N/ϵ)) matrix-vector products with carefully chosen vectors (right-hand sides). The solver is only accessed as a black box, and the underlying operator may be unknown and of an arbitrarily high order. Our algorithm (1) has complexity (N log2(N) log2d(N/ϵ)) and represents the solution operator as a sparse Cholesky factorization with (N log(N) logd(N/ϵ)) nonzero entries, (2) allows for embarrassingly parallel evaluation of the solution operator and the computation of its log-determinant, (3) allows for (log(N) logd(N/ϵ)) complexity computation of individual entries of the matrix representation of the solver that, in turn, enables its recompression to an (N logd(N/ϵ)) complexity representation. As a byproduct, our compression scheme produces a homogenized solution operator with near-optimal approximation accuracy. By polynomial approximation, we can also approximate the continuous Green's function (in operator and Hilbert-Schmidt norm) to accuracy ϵ from (log1+d(ϵ 1)) solutions of the PDE. We include rigorous proofs of these results. To the best of our knowledge, our algorithm achieves the best known trade-off between accuracy ϵ and the number of required matrix-vector products. © 2024 Society for Industrial and Applied Mathematics Publications. All rights reserved.

关键词： Principal component analysis

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Existence of solutions to the generalized periodic fractional boundary value problem

Existence of solutions to the generalized periodic fractiona...

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作者： Fečkan, Michal Marynets, Kateryna Wang, JinRong Department of Mathematical Analysis and Numerical Mathematics Comenius University in Bratislava Bratislava Slovakia Mathematical Institute of Slovak Academy of Sciences Bratislava Slovakia Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Mathematics Guizhou University Guiyang China

We study a boundary value problem for a Caputo-type fractional differential equation subjected to periodic boundary conditions. For an auxiliary problem with the simplified right-hand side, we explicitly construct its unique solution. In addition, based on the theory of the topological index, we prove existence of at least one solution to the original problem. © 2023 The Authors. mathematical Methods in the Applied sciences published by John Wiley & Sons Ltd.

关键词： Boundary value problems

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On the Cycle Structure of Some Nonlinear Feedback Shift Registers

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Chinese Journal of Electronics 2025年第4期23卷 801-804页

作者： Zhongxiao Wang Hong Xu Wenfeng Qi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute Zhengzhou China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In this paper, we partly determine the cycle structure of two types of Nonlinear feedback shift registers (NFSRs). Based on these results, the cycle structure of a class of NFSRs with symmetric feedback functions can be completely characterized. Furthermore, an alternative proof of Kjeldsen's results is presented. Compared with the original proof based on abstract algebra theory, ours is straightforward and easy to understand.

关键词： Abstract algebra Shift registers Cryptography

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Towards Automatic Dataset Discovery From Scientific Publications

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IEEE Access 2025年

作者： Kumar, Sandeep Ghosal, Tirthankar Ekbal, Asif Indian Institute of Technology Department of Computer Science and Engineering Patna India Oak Ridge National Laboratory National Center for Computational Sciences United States

Datasets are a crucial artifact in research, and there is always a high demand for good datasets. Due to rapid scientific progress and exponential growth in the scientific literature, there has been a proportionate increase in the number of datasets. However, many datasets and research studies are left unexplored and under-utilized as these are not discoverable easily, leading to duplicate efforts. Building good datasets is costly in terms of time, money, and human effort. Hence, automated tools to facilitate the search and discovery of datasets are crucial to the scientific community. In this work, we investigate a deep neural network-based architecture to automate the dataset discovery from scientific publications. In this paper, we perform two tasks namely dataset mention extraction and entity linking. Our method outperforms the earlier ones and achieves an F1 score of 56.24 in extracting dataset mentions from research papers on a popular corpus of social science publications. Our approach also outperforms the prior research and achieves a precision score of 88.63 in linking research papers to a dataset knowledge base for another popular corpus of social science publications. We hope that this system will further promote data sharing, offset the researchers' workload in identifying the right dataset and increase the reusability of datasets. © 2013 IEEE.

关键词： Discovering Datasets Extracting Dataset References Mining Publications Natural Language Processing

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