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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Teel, Andrew R. Goebel, Rafal K. Sanfelice, Ricardo G. Crisafulli, Max F. University of California Ece Department Santa BarbaraCA93106-9560 United States Loyola University Chicago Department of Mathematics and Statistics 1032 W. Sheridan Rd ChicagoIL60660 United States University of California Electrical and Computer Engineering Department Santa CruzCA95064 United States

ISBN: (纸本)9798350316339

A stochastic simulator that approximates the behavior of a hybrid system given in terms of a hybrid inclusion is considered. A hybrid inclusion combines constrained differential and difference inclusions to model the continuous (flows) and discrete (jumps) dynamics, respectively. The simulator is a stochastic discrete-time system that employs a set-valued right-hand side when approximating flows. Under mild conditions on the data defining the simulator and the hybrid system, together with a non-uniform averaging condition, it is shown that almost every sample path of each solution generated by the stochastic simulator is close to a solution of the original hybrid system on compact time domains when the step size sequence is sufficiently small and converges to zero but is not summable. A probabilistic characterization is also provided. An example is provided to illustrate the interest of the proposed framework. © 2024 IEEE.

关键词： Stochastic models

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Uncovering the Hidden Insights of the Government AI Readiness Index: Application of Fuzzy LMAW and Schweizer-Sklar Weighted Framework

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2024年第2期7卷 443-468页

作者： Nasution, Mahyuddin K. M. Elveny, Marischa Pamucar, Dragan Popovic, Milena Gušavac, Bisera Andric Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan20155 Indonesia Data Science & Artificial Intelligence Fasilkom-TI Universitas Sumatera Utara Medan20155 Indonesia University of Belgrade Faculty of Organizational Science Department of Operations Research and Statistics Jove Ilica 154 Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan School of Engineering and Technology Sunway University Selangor Malaysia

There is considerable promising in artificial intelligence (AI) and algorithms, with governments worldwide increasingly investing in this transformative technology. The potential benefits include improved performance, cost reduction, efficient management, and crime prediction and prevention, among others. The AI era holds the promise of revolutionizing various aspects of society. However, as countries prepare to leverage the power of artificial intelligence, questions arise about the validity of rankings published on the readiness of the governments for the application of AI. In this article, the weighting criteria that are analysed in the Oxford Insights AI Readiness Index are scrutinized, aiming to provide a more accurate assessment. Instead of conventional averaging, arithmetic and geometric non-linear functions are employed to analyse and assess the rank of the countries. Through clustering analysis, countries are categorized into three distinct groups based on observed criteria, offering a nuanced perspective on government AI readiness. This clustering approach not only facilitates a more effective categorization of countries based on their AI preparedness, but also accentuates the variations and similarities within each cluster, which enables deeper insights into regional trends and pinpoint targeted strategies for enhancement within each cluster. © 2024 Regional Association for Security and crisis management. All rights reserved.

关键词： Cost reduction

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

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Local RBF Method for Transformed Three Dimensional Sub-Diffusion Equations

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International Journal of Applied and Computational mathematics 2022年第3期8卷 1-21页

作者： Kamran Ahmadian, A. Salimi, M. Salahshour, S. Department of Mathematics University of Management and Technology Khyber Pakhtoon Khwa Lahore Pakistan School of Mathematical Sciences College of Science and Technology Wenzhou-Kean University Wenzhou China Department of Mathematics Near East University Nicosia TRNC Mersin 10 Turkey Department of Mathematics and Statistics St. Francis Xavier University Antigonish NS Canada Center for Dynamics Faculty of Mathematics Technische Universität Dresden Dresden 01062 Germany Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey

In this article we study the numerical solution of time fractional 3D sub-diffusion equation in Caputo sense. The time fraction sub-diffusion equation is obtained by replacing the time derivative in the standard sub-diffusion equation with time fractional derivative. Numerous physical phenomenon are modeled via sub-diffusion equation such as porous systems, polymers, and turbulent plasma etc. The local RBF method is one of most successful methods for numerical solution of integer and non-integer order PDEs, as it overcome the computational burden of the global RBF method. Our proposed numerical scheme approximates the solution in three main steps. Firstly, we use the Laplace transform to reduce the given time dependent problem to an equivalent time independent problem. Secondly, we approximate the solution of the transformed equation using the local RBF method. Finally, the solution of original problem is retrieved via inverse Laplace transform by representing it as a Bromwich integral. The Bromwich integral is then approximated using the mid point rule. The purpose of using the Laplace transform is avoiding the classical time marching procedure. One of the main benefits of local RBF method is that it only considers the nodes located in the sub-domain surrounding a local node for numerical approximation at this node. This method perform well for large scale problem and can also reduce the ill conditioning issues. The convergence and stability of the proposed numerical method are discussed. The proposed numerical method is tested against three examples. The computational results highlight the accuracy and efficiency of the method. © 2022, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： 3D sub-diffusion equation Caputo’s derivative Contour integration method Convergence Laplace transform Local RBFs method Mid point rule Stability Talbot’s contour

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Research on PM2.5 Concentration Prediction Based on HHO-LMBP Neural Network 6

Research on PM2.5 Concentration Prediction Based on HHO-LMBP...

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6th International Workshop on Advanced Algorithms and Control engineering, IWAACE 2022

作者： Wang, Hai-Jun Wang, Zhi-Gang Jin, Tao Qiao, Zhi-Li Zhang, Xiao-Jiao Department of Mathematics and Computer Engineering Ordos Institute of Technology Inner Mongolia Ordos017000 China Ordos Statistics Bureau Inner Mongolia Ordos017000 China

ISBN: (纸本)9781510657755

Aiming at the PM2.5 concentration in the air, which is affected by meteorological factors and atmospheric pollutants, and has the characteristics of nonlinearity and uncertainty, a prediction method of LMBP neural network based on Harris Hawk optimization algorithm is proposed. In the process of LMBP neural network weight threshold optimization process, Harris Hawk optimization algorithm (HHO) is introduced, and a LMBP initial weights and thresholds optimization method based on HHO algorithm is designed. This method utilizes the global optimization ability of HHO algorithm and effectively avoids the LMBP neural network is trapped in a local minimum worth of possibilities. The simulation results show that the prediction model based on the HHO-LMBP algorithm has higher accuracy and better stability than the DE-LMBP and LMBP algorithms. © 2022 SPIE.

关键词： Global optimization

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Design and Simulation of an Audio Signal Alerting and Automatic Control System

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Communications and Network 2023年第4期15卷 98-119页

作者： Winfred Adjardjah John Awuah Addor Wisdom Opare Isaac Mensah Ayipeh Department of Electrical and Electronic Engineering Takoradi Technical University Takoradi Ghana Department of Mathematics Statistics and Actuarial Science Takoradi Technical University Takoradi Ghana

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

关键词： Emergency Response Emergency Management Team Audio Signal Alerting Automatic Control System Uni Pro XL Manual Communication Fast Fourier Transform Magnitude Zero Crossing Rate Root Means Square

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Length-minimizing LED trees

arXiv

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arXiv 2024年

作者： Remešíková, Mariana Sarkociová Sarkoci, Peter Trnovská, Mária Department of Mathematics and Constructive Geometry Faculty of Civil Engineering Slovak University of Technology in Bratislava Radlinského 11 Bratislava81005 Slovakia Department of Applied Mathematics and Statistics Faculty of Mathematics Physics and Informatics Mlynská Dolina F1 Bratislava84248 Slovakia

In this paper, we introduce a specific type of Euclidean tree called LED (Leaves of Equal Depth) tree. LED trees can be used in computational phylogeny, since they are a natural representative of the time evolution of a set of species in a feature space. This work is focused on LED trees that are length minimizers for a given set of leaves (species) and a given isomorphism type (the hierarchical structure of ancestors). The underlying minimization problem can be seen as a variant of the classical Euclidean Steiner tree problem. Even though it has a convex objective function, it is rather non-trivial, since it has a non-convex feasible set. The main contribution of this paper is that we provide a uniqueness result for this problem. Moreover, we explore some geometrical and topological properties of the feasible set and we prove several geometrical characteristics of the length minimizers that are analogical to the properties of Steiner trees. At the end, we show a simple example of an application in historical *** Codes 05C05, 05C90, 00A69, 90C26, 49K99 © 2024, CC BY.

关键词： Trees (mathematics)

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A tensor factorization model of multilayer network interdependence

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 13587-13640页

作者： Izabel Aguiar Dane Taylor Johan Ugander Institute for Computational and Mathematical Engineering Stanford University Stanford CA School of Computing Department of Mathematics and Statistics University of Wyoming Laramie WY Department of Management Science and Engineering Institute for Computational and Mathematical Engineering Stanford University Stanford CA

Multilayer networks describe the rich ways in which nodes are related by accounting for different relationships in separate layers. These multiple relationships are naturally represented by an adjacency tensor. In this work we study the use of the nonnegative Tucker decomposition (NNTuck) of such tensors under a KL loss as an expressive factor model that naturally generalizes existing stochastic block models of multilayer networks. Quantifying interdependencies between layers can identify redundancies in the structure of a network, indicate relationships between disparate layers, and potentially inform survey instruments for collecting social network data. We propose definitions of layer independence, dependence, and redundancy based on likelihood ratio tests between nested nonnegative Tucker decompositions. Using both synthetic and real-world data, we evaluate the use and interpretation of the NNTuck as a model of multilayer networks. Algorithmically, we show that using expectation maximization (EM) to maximize the log-likelihood under the NNTuck is step-by-step equivalent to tensorial multiplicative updates for the NNTuck under a KL loss, extending a previously known equivalence from nonnegative matrices to nonnegative tensors.

关键词： multilayer networks social networks stochastic blockmodels Tucker decomposition link prediction

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Collaborative Pareto Set Learning in Multiple Multi-Objective Optimization Problems

Collaborative Pareto Set Learning in Multiple Multi-Objectiv...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chikai Shang Rongguang Ye Jiaqi Jiang Fangqing Gu School of Mathematics and Statistics Guangdong University of Technology China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Pareto Set Learning (PSL) is an emerging research area in multi-objective optimization, focusing on training neural networks to learn the mapping from preference vectors to Pareto optimal solutions. However, existing PSL methods are limited to addressing a single Multi-objective Optimization Problem (MOP) at a time. When faced with multiple MOPs, this limitation results in significant inefficiencies and hinders the ability to exploit potential synergies across varying MOPs. In this paper, we propose a Collaborative Pareto Set Learning (CoPSL) framework, which learns the Pareto sets of multiple MOPs simultaneously in a collaborative manner. CoPSL particularly employs an architecture consisting of shared and MOP-specific layers. The shared layers are designed to capture commonalities among MOPs collaboratively, while the MOP-specific layers tailor these general insights to generate solution sets for individual MOPs. This collaborative approach enables CoPSL to efficiently learn the Pareto sets of multiple MOPs in a single execution while leveraging the potential relationships among various MOPs. To further understand these relationships, we experimentally demonstrate that shareable representations exist among MOPs. Leveraging these shared representations effectively improves the capability to approximate Pareto sets. Extensive experiments underscore the superior efficiency and robustness of CoPSL in approximating Pareto sets compared to state-of-the-art approaches on a variety of synthetic and real-world MOPs. Code is available at https://***/ckshang/CoPSL.

关键词： Training Codes Neural networks Collaboration Focusing Pareto optimization Benchmark testing

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Prediction of BRIC Stock Price Using ARIMA,SutteARIMA,and Holt-Winters

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Computers, Materials & Continua 2022年第1期70卷 523-534页

作者： Ansari Saleh Ahmar Pawan Kumar Singh Nguyen Van Thanh Nguyen Viet Tinh Vo Minh Hieu Department of Statistics Faculty of Mathematics and Natural ScienccesUniversitas Negeri MakassarMakassar90223Indonesia School of Humanities and Social Sciences Thapar Institute of Engineering and TechnologyPatiala147004India Faculty of Commerce Van Lang UniversityHo Chi Minh City70000Vietnam

The novel coronavirus has played a disastrous role in many countries *** outbreak became a major epidemic,engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and *** paper identifies two different models to capture the trend of closing stock prices in Brazil(BVSP),Russia(***),India(BSESN),and China(SSE),i.e.,(BRIC)*** predict the stock prices for three daily time periods,so appropriate preparations can be undertaken to solve these ***,we compared the ARIMA,SutteARIMA and Holt-Winters(H-W)methods to determine the most effective model for predicting *** stock closing price of BRIC country data was obtained from Yahoo *** data dates from 01 November 2019 to 11 December 2020,then divided into two categories-training data and test *** data covers 01 November 2019 to 02 December *** days(03December 2020 to 11December 2020)of datawas tested to determine the accuracy of the models using training data as a *** measure the accuracy of the models,we obtained the means absolute percentage error(MAPE)and mean square error(MSE).Prediction model Holt-Winters was found to be the most suitable for forecasting the Brazil stock price(BVSP)while MAPE(0.50)and MSE(579272.65)with Holt-Winters(smaller than ARIMA and SutteARIMA),model SutteARIMA was found most appropriate to predict the stock prices of Russia(***),India(BSESN),and China(SSE)when compared to ARIMA and *** andMSE with SutteARIMA:Russia(MAPE:0.7;MSE:940.20),India(MAPE:0.90;MSE:207271.16),and China(MAPE:0.72;MSE:786.28).Finally,Holt-Winters predicted the daily forecast values for the Brazil stock price(BVSP)(12 December to 14 December 2020 i.e.,115757.6,116150.9 and 116544.1),while SutteARIMA predicted the daily forecast values of Russia stock prices(***)(12 December to 14 December 2020 i.e.,3238.06,3241.54 and 3245.01),India stock price(BSESN)(

关键词： SutteARIMA Holt-Winters ARIMA stock price COVID-19

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