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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Liu, Yu Mao, Yingling Liu, Zhenhua Ye, Fan Yang, Yuanyuan Stony Brook University Department of Electrical and Computer Engineering United States Stony Brook University Department of Applied Mathematics and Statistics United States

ISBN: (纸本)9798350334142

Mobile edge computing is becoming one of the ubiquitous computing paradigms to support applications requiring low latency and high computing capability. FPGA-based reconfigurable accelerators have high energy efficiency and low latency compared to general-purpose servers. Therefore, it is natural to incorporate reconfigurable accelerators in mobile edge computing systems. This paper formulates and studies the problem of joint task offloading, access point selection, and resource allocation in heterogeneous edge environments for latency minimization. Due to the heterogeneity in edge computing devices and the coupling between offloading, access point selection, and resource allocation decisions, it is challenging to optimize over them simultaneously. We decomposed the proposed problem into two disjoint subproblems and developed algorithms for them. The first subproblem is to jointly determine offloading and computing resource allocation decisions and is NP-hard, where we developed an algorithm based on semidefinite relaxation. The second subproblem is to jointly determine access point selection and communication resource allocation decisions, where we proposed an algorithm with a provable approximation ratio of 2.62. We conducted extensive numerical simulations to evaluate the proposed algorithms. Results highlighted that the proposed algorithms outperformed baselines and were near-optimal over a wide range of settings. © 2023 IEEE.

关键词： Resource allocation

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A Novel Scheme for the Solution of Differential Equations Through Müntz-Legendre Wavelets 13

A Novel Scheme for the Solution of Differential Equations Th...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Madhulika Arora, Ritu Kumar, Ankit Singh, Amit K. School of Applied and Life Sciences Uttaranchal University Department of Mathematics Uttarakhand Dehradun248007 India Department of Mathematics and Statistics Kanya Gurukula Campus Uttarakhand Haridwar249404 India School of Applied Sciences & Humanities Imsec Department of Mathematics Uttar Pradesh Ghaziabad201015 India Rajkiya Engineering College Department of Applied Sciences and Humanities Uttar Pradesh Ambedkar Nagar224122 India

ISBN: (纸本)9798350380583

This study aims to present a numerical wavelet-based approach for solving both lower-order and higher-order differential equations. This approach is characterized by the use of Miintz- Legendre wavelets (MLWs) for estimation. The MLW is considered by the Muntz-Legendre polynomials (MLPs), which has orthogonal properties. Firstly, MLW is introduced after which an integral operational matrix is derived. By utilizing this operational matrix, the original differential equation is reformulated into a system of algebraic equations with involved unknown coefficients associated with the Muntz-Legendre wavelets (MLWs), which are determined through further calculations to approximate the solution accurately. The effectiveness and accuracy of the proposed technique are validated using specific numerical examples. © 2024 IEEE.

关键词： Choquet integral

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Analysis of Mortality Patterns and Forecasting Future Trends: Actuarial Statistical Approaches 4

Analysis of Mortality Patterns and Forecasting Future Trends...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Hrithik, P.M. Malhan, Rakesh Rakhra, Manik Mathur, Rashmi Nandra, Richa Nagpal, Rakhi Lovely Professional University Department of Mathematics and Statistics Punjab Phagwara India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Mittal School of Business Lovely Professional University Punjab Phagwara India Dav Collage Department of Mathematics Punjab Jalandhar India

ISBN: (纸本)9798350387490

Therefore, in response to this knowledge gap, this research article employs and comparatively examines mortality models, particularly the Gompertz and Makeham. We examine and analyze these models in terms of performance and nuances for forecasting mortality rate, to give a comprehensive view of trends and expected lifespans. This is done with help of age-specific mortality data. With this in mind, we estimate the accuracy, reliability, and practical implications of the Gompertz and Makeham models in capturing the trend of mortality in multiple subgroups. Being originated from the actuarial science and the field of public health our work aims to contribute to the understanding of mortality modelling approaches and provide to the academicians, policymakers and practitioners the adequate tools for the lifespan, population health expectancy and mortality risk estimation. The following comprehensive review encourages the enhancement of mortality modelling precision, healthcare assemblies for the purpose of treatment and prophylactic measures, as well as population health management techniques, since it offers appraisal for prosaic methodologies for decision-making. © 2024 IEEE.

关键词： Actuarial Science Age-Specific Mortality Gompertz Model Life Expectancy Makeham Model Mortality Dynamics Mortality Forecasting Mortality Modeling Mortality Rates Mortality Risk Assessment Mortality Trends Population Demographics Population Health Planning Public Health

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An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

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Computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

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A computational supervised neural network procedure for the fractional SIQ mathematical model

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European Physical Journal: Special Topics 2023年第5期232卷 535-546页

作者： Mukdasai, Kanit Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Singkibud, Peerapongpat Sadat, R. Ali, Mohamed R. Department of Mathematics Faculty of Science Khon Kaen University Khon Kaen 40002 Thailand Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Applied Mathematics and Statistics Faculty of Science and Liberal Arts Rajamangala University of Technology Isan Nakhon Ratchasima 30000 Thailand Department of Mathematics Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University in Egypt New Cairo 11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Benha Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. © 2022, The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature.

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A new locally t-diagnosable structure under the PMC model with an application to matching composition networks

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Discrete Applied mathematics 2025年 374卷 1-15页

作者： Chen, Meirun Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen Fujian China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City Taiwan

The PMC model is the test-based diagnosis in which a node performs the diagnosis by testing the neighbor nodes via the links between them. If we concentrate on the status of some nodes then instead of doing the global test, Hsu and Tan proposed the concept of local diagnosis and two structures to diagnose a node under the PMC model. To better evaluate the local diagnosability of a node, we propose a new structure and the related algorithm to diagnose a node under the PMC model in this paper. Applying the two structures proposed by Hsu and Tan, and the new structure we propose in this paper, we determine the accurate value of the local diagnosability of each node in matching composition networks. Simulation results are presented, showing the performance of our algorithm. It shows that even if the failure probability of a node is 0.4, our algorithm can still determine the state of a node with the accuracy above 0.9. © 2025 Elsevier B.V.

关键词： Diagnosability Local-diagnosability Matching composition networks PMC model

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Robust Correlation Clustering Problem with Locally Bounded Disagreements

Robust Correlation Clustering Problem with Locally Bounded D...

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作者： Ji, Sai Li, Min Liang, Mei Zhang, Zhenning Institute of Mathematics Hebei University of Technology Tianjin300401 China School of Mathematics and Statistics Shandong Normal University Jinan250358 China College of Statistics and Data Science Beijing University of Technology Beijing100124 China Beijing University of Technology Department of Operations Research and Information Engineering Beijing100124 China

Min-max disagreements are an important generalization of the correlation clustering problem (CorCP). It can be defined as follows. Given a marked complete graph G=(V, E), each edge in the graph is marked by a positive label '+' or a negative label '-' based on the similarity of the connected vertices. The goal is to find a clustering C of vertices V, so as to minimize the number of disagreements at the vertex with the most disagreements. Here, the disagreements are the positive cut edges and the negative non-cut edges produced by clustering C. This paper considers two robust min-max disagreements: min-max disagreements with outliers and min-max disagreements with penalties. Given parameter δ\in(0,1/14), we first provide a threshold-based iterative clustering algorithm based on LP-rounding technique, which is a (1/δ, 7/(1-14δ))-bi-criteria approximation algorithm for both the min-max disagreements with outliers and the min-max disagreements with outliers on one-sided complete bipartite graphs. Next, we verify that the above algorithm can achieve an approximation ratio of 21 for min-max disagreements with penalties when we set parameter δ=1/21. © 1996-2012 Tsinghua University Press.

关键词： Approximation algorithms

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ONLINE NONPARAMETRIC SUPERVISED LEARNING FOR MASSIVE DATA

arXiv

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arXiv 2024年

作者： Chaouch, Mohamed Al-Hamed, Omama M. Statistics Program Department of Mathematics and Statistics College of Arts and Sciences Qatar University Doha Qatar

Despite their benefits in terms of simplicity, low computational cost and data requirement, parametric machine learning algorithms, such as linear discriminant analysis, quadratic discriminant analysis or logistic regression, suffer from serious drawbacks including linearity, poor fit of features to the usually imposed normal distribution and high dimensionality. Batch kernel-based nonparametric classifier, which overcomes the linearity and normality of features constraints, represent an interesting alternative for supervised classification problem. However, it suffers from the "curse of dimension". The problem can be alleviated by the explosive sample size in the era of big data, while large-scale data size presents some challenges in the storage of data and the calculation of the classifier. These challenges make the classical batch nonparametric classifier no longer applicable. This motivates us to develop a fast algorithm adapted to the real-time calculation of the nonparametric classifier in massive as well as streaming data frameworks. This online classifier includes two steps. First, we consider an online principle components analysis to reduce the dimension of the features with a very low computation cost. Then, a stochastic approximation algorithm is deployed to obtain a real-time calculation of the nonparametric classifier. The proposed methods are evaluated and compared to some commonly used machine learning algorithms for real-time fetal well-being monitoring. The study revealed that, in terms of accuracy, the offline (or Batch), as well as, the online classifiers are good competitors to the random forest algorithm. Moreover, we show that the online classifier gives the best trade-off accuracy/computation cost compared to the offline classifier. © 2024, CC BY-NC-ND.

关键词： Big data

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IPH2O: Island Parallel-Harris Hawks Optimizer-Based CLSTM for Stock Price Movement Prediction

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Annals of Data Science 2024年第6期11卷 1959-1974页

作者： Joel, Linda Parthasarathy, S. Venkatesan, P. Nandhini, S. Department of Mathematics SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai89 India Research and Statistics Sri Ramachandra Medical College and RI Sri Ramachandra University Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Stock price movement forecasting is the process of predicting the future price of a financial and company stock from chaotic data. In recent years, many financial institutions and academics have shown interest in stock market forecasting. The accurate and successful predictions of the future price of stock yield a substantial profit. However, the current approaches are a major challenge due to the dynamic, chaotic, high-noise, non-linear, highly complex, and nonparametric characteristics of stock data. Furthermore, it is not sufficient to consider only the target firms' information because the stock prices of the target firms may be influenced by their related firms. Significant profits can be made by correct forecasting of stock prices, while poor forecasts can cause huge problems. Thus, we propose a novel Island Parallel-Harris Hawks Optimizer (IP-HHO)-optimized Convolutional Long Short Term Memory (ConvLSTM) with an autocorrelation model to predict stock price movement. Then, using the IP-HHO algorithm, the hyperparameters of ConvLSTM are optimized to minimize the Mean Absolute Percentage Error (MAPE). Four different types of financial time series datasets are utilized to validate the performance of the evaluation measures such as root mean square error, MAPE, Index of Agreement, accuracy, and F1 score. The results show that the IP-HHO-optimized ConvLSTM model outperforms others by improving the prediction rate accuracy and effectively minimizing the MAPE rate by 19.62%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Profitability

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Wigner non-negative states that verify the Wigner entropy conjecture

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Physical Review A 2024年第1期110卷 012228-012228页

作者： Qipeng Qian Christos N. Gagatsos Program in Applied Mathematics The Department of Electrical and Computer Engineering The Wyant College of Optical Sciences The

We present further progress, in the form of analytical results, on the Wigner entropy conjecture set forth by Van Herstraeten and Cerf [Phys. Rev. A 104, 042211 (2021)] and Hertz et al. [J. Phys. A: Math. Theor. 50, 385301 (2017)]. Said conjecture asserts that the differential entropy defined for non-negative, yet physical, Wigner functions is minimized by pure Gaussian states while the minimum entropy is equal to 1+lnπ. We prove this conjecture for the qubits formed by Fock states |0〉 and |1〉 that correspond to non-negative Wigner functions. In particular, we derive an explicit form of the Wigner entropy for those states lying on the boundary of the set of Wigner non-negative qubits. We then consider general mixed states and derive a sufficient condition for the conjecture's validity. Lastly, we elaborate on the states which are in accordance with our condition.

关键词： Quantum foundations Quantum information theory

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