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SSRN 2024年

作者： Kong, Lingping Velasquez, Juan D. Pant, Millie Pan, Jeng-Shyang Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Listpodu 17 Czech Republic Department of Industrial Engineering University of Chile O’Higgins 1058 Santiago Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India College of Computer Science and Engineering Shandong University of Science and Technology China School of Artificial Intelligence Nanjing University of Information Science and Technology 219 Ningliu Road China Instituto Sistemas Complejos de Ingeniería Santiago Chile Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India

Rankability is a fundamental concept that evaluates a dataset's inherent capacity to establish a meaningful ranking of its elements, with widespread applications in web search, data mining, cybersecurity, and machine *** its significance, relatively little focus has been on evaluating datasets for ranking suitability. Several rankability measures have emerged, such as comparing graphs representing datasets with complete dominance graphs using combinatorial and linear algebraic techniques. However, these approaches typically involve solving eigenvalue problems for large matrices or training regression models on extensive datasets. In this paper, we propose to adopt efficient and straightforward statistical metrics, entropy, and variance to develop a novel rankability method. Our method is designed to be fast and efficient, addressing the challenges posed by existing techniques. We conducted experiments using three football tournament datasets, and the results demonstrate a strong correlation scores (correlation coefficients) in terms of Spearman and Kendall's tau evaluations with Elo rating, a system developed by Arpad Elo in the 1950s and applied in many competitions. Specifically, the Spearman correlation between the proposed method and the Elo rating reached 0.9205 on Atlantic Coast data with a p-value of 0.0004, which is higher than the compared algorithm by 0.2424 in correlation score. © 2024, The Authors. All rights reserved.

关键词： Entropy

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Generalized Cauchy–Riemann Equations with Power-law Singularities in Coefficients of Lower Order

Generalized Cauchy–Riemann Equations with Power-law Singula...

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International Scientific Conference on Modern Methods, Problems and Applications of Operator Theory and Harmonic Analysis, OTHA 2020

作者： Soldatov, A.P. Rasulov, A.B. Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Center for Fundamental and Applied Mathematics Moscow Russia National Research University "Moscow Power Engineering Institute" Moscow Russia

ISBN: (纸本)9783030774929

The generalized Cauchy-Riemann equation uz¯+ au+ bū= f is considered with coefficients having power singularities at the origin. More exactly it is assumed that a(z) = O(|z|−α−1), α∈ , and b(z) = O(|z|−1) as z → 0. With the help of so called modified Pompeiu integral a general solution of this equation is described. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Integral equations

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Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology

Enhancing IoT Intelligence: A Transformer-based Reinforcemen...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Gaith Rjoub Saidul Islam Jamal Bentahar Mohammed Amin Almaiah Rana Alrawashdeh Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science Khalifa University Abu Dhabi UAE King Abdullah II School of Information Technology The University of Jordan Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan Department of Information and Computer Science King Fahd University of Petroleum & Minerals Dhahran KSA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents’ capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decisionmaking efficiency and adaptability. Our contributions include a detailed exploration of the transformer’s role in processing heterogeneous IoT data, a comprehensive evaluation of the framework’s performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape.

关键词： Intelligent automation Navigation Decision making Ecosystems Smart homes Reinforcement learning Transformers

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ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks

ST-GIN: An Uncertainty Quantification Approach in Traffic Da...

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International Conference on Intelligent Transportation

作者： Zepu Wang Dingyi Zhuang Yankai Li Jinhua Zhao Peng Sun Shenhao Wang Yulin Hu Department of Computer and Information Science University of Pennsylvania Department of Civil and Environmental Engineering Massachusetts Institute of Technology Department of Natural and Applied Sciences Duke Kunshan University Department of Urban and Regional Planning University of Florida Department of Information and Communication Engineering Wuhan University

Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems. However, real-world transportation data, collected from loop detectors or similar sources, often contains missing values (MVs), which can adversely impact associated applications and research. Instead of discarding this incomplete data, researchers have sought to recover these missing values through numerical statistics, tensor decomposition, and deep learning techniques. In this paper, we propose an innovative deep learning approach for imputing missing data. A graph attention architecture is employed to capture the spatial correlations present in traffic data, while a bidirectional neural network is utilized to learn temporal information. Experimental results indicate that our proposed method outperforms all other benchmark techniques, thus demonstrating its effectiveness.

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Leveraging AI Technologies in Libraries through Experimentation-Driven Frameworks

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Internet Reference Services Quarterly 2023年第4期27卷 211-222页

作者： Varun Gupta Chetna Gupta a Multidisciplinary Research Centre for Innovations in SMEs (MrciS) Gisma University of Applied Sciences Potsdam Germanyb Department of Economics & Business Administration University of Alcala Alcalá de Henares Madrid Spain c Department of Computer Science & Engineering Jaypee Institute of Information Technology Noida India

This column aims to explore the frameworks to help libraries foster digital innovation by leveraging AI technologies through continuous experimentation to innovate their services for their patrons. Additionally, the column seeks to highlight the benefits and interplay between the frameworks, providing insights for librarians interested in implementing AI solutions and driving technological advancements in library settings. The column reports two frameworks - The Need-Based Experimentation (NBE) Framework and the Curiosity-Based Experimentation (CBE) Framework based on the author’s professional experiences and empirical observations of 10 university libraries’ experimentation-driven AI technology adoption practices. The NBE framework focuses on experimenting with AI technologies that have the functional capability to address the library’s current business needs. In contrast, the CBE framework explores AI technologies out of curiosity, aiming to gain practical experiences and uncover potential future applications, aligned with the librarian’s interests. These frameworks guide librarians to effectively experiment with AI technology based on their motivations and goals. To the best of the authors’ knowledge, there is no experimentation-driven framework for adopting AI technologies to assist libraries do so strategically. The adoption of AI should be influenced by carefully planned, ongoing experiments, the results of which should be deployed in real to inform adoption decisions.

关键词： Artificial intelligence curiosity-based experimentation (CBE) framework experimentation libraries need-based experimentation (NBE) framework technology adoption

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Integrability and Linearizability of a Family of Three-Dimensional Polynomial Systems

arXiv

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arXiv 2024年

作者： Huang, Bo Mastev, Ivan Romanovski, Valery LMIB School of Mathematical Sciences Beihang University Beijing100191 China Center for Applied Mathematics and Theoretical Physics MariborSI-2000 Slovenia Faculty of Natural Science and Mathematics University of Maribor MariborSI-2000 Slovenia Faculty of Electrical Engineering and Computer Science University of Maribor MariborSI-2000 Slovenia

We investigate the local integrability and linearizability of a family of three-dimensional polynomial systems with the matrix of the linear approximation having the eigenvalues 1, ζ, ζ2, where ζ is a primitive cubic root of unity. We establish a criterion for the convergence of the Poincaré–Dulac normal form of the systems and examine the relationship between the normal form and integrability. Additionally, we introduce an efficient algorithm to determine the necessary conditions for the integrability of the systems. This algorithm is then applied to a quadratic subfamily of the systems to analyze its integrability and linearizability. Our findings offer insights into the integrability properties of three-dimensional polynomial systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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An equivalent reformulation and multi-proximity gradient algorithms for a class of nonsmooth fractional programming

arXiv

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arXiv 2023年

作者： Zhou, Junpeng Zhang, Na Li, Qia School of Computer Sciences and Engineering Sun Yat-Sen University Guangzhou510006 China Department of Applied Mathematics College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Computer Sciences and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Guangzhou510006 China

In this paper, we consider a class of structured fractional programs, where the numerator part is the sum of a block-separable (possibly nonsmooth nonconvex) function and a locally Lipschitz differentiable (possibly nonconvex) function, while the denominator is a convex (possibly nonsmooth) function. We first present a novel reformulation for the original problem and show the relationship between optimal solutions, critical points and KL exponents of these two problems. Inspired by the reformulation, we propose a flexible framework of multi-proximity gradient algorithms (MPGA), which computes the proximity operator with respect to the Fenchel conjugate associated with the convex denominator of the original problem rather than evaluating its subgradient as in the existing methods. Also, MPGA employs a nonmonotone linear-search scheme in its gradient descent step, since the smooth part in the numerator of the original problem is not globally Lipschitz differentiable. Based on the framework of MPGA, we develop two specific algorithms, namely, cyclic MPGA and randomized MPGA, and establish their subsequential convergence under mild conditions. Moreover, the sequential convergence of cyclic MPGA with the monotone line-search (CMPGA ML) is guaranteed if the extended objective associated with the reformulated problem satisfies the Kurdyka-Lojasiewicz (KL) property and some other mild assumptions. In particular, we prove that the corresponding KL exponents are 12 for several special cases of the fractional programs, and so, CMPGA ML exhibits a linear convergence rate. Finally, some preliminary numerical experiments are performed to demonstrate the efficiency of our proposed algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Gradient methods

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Dynamics of Multi-Strain Malware Epidemics over Duty-Cycled Wireless Sensor Networks 4

Dynamics of Multi-Strain Malware Epidemics over Duty-Cycled ...

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4th International Balkan Conference on Communications and Networking, BalkanCom 2021

作者： Fedorov, Dmitriy Tabarak, Yrys Dadlani, Aresh Kumar, Muthukrishnan Senthil Kizheppatt, Vipin Nazarbayev University Department of Electrical and Computer Engineering Nur-Sultan Kazakhstan PSG College of Technology Department of Applied Mathematics and Computational Sciences Coimbatore India Birla Institute of Technology and Science Pilani Department of Electrical and Electronics Engineering Goa India

ISBN: (纸本)9781665402583

Insights on the salient features of malicious software spreading over large-scale wireless sensor networks (WSNs) in low-power Internet of Things (IoT) are not only essential to project, but also mitigate the persistent rise in cyber threats. While the analytical findings on single malware spreading dynamics are well-established, the interplay among multiple malware strains with heterogeneous infection rates in power-limited WSNs yet remain unexplored. Inspired by compartmental modeling in epidemiology, we present the mean-field approximation for a novel stochastic epidemic model of two mutually exclusive malware strains spreading over WSNs with sleep/awake modes of energy consumption. Referred as the susceptible-infected by strain 1 or by strain 2-susceptible with duty cycles (SI1I2SD), we then derive the basic reproduction number to characterize the sufficient conditions for the existence and stability of the infection-free and endemic equilibrium states. Simulation results show the predictive capability of the proposed model for energy-efficient WSNs evolving as random geometric graphs against uniformly connected networks. © 2021 IEEE.

关键词： Wireless sensor networks

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Byzantine-Resilient Decentralized Multi-Armed Bandits

arXiv

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arXiv 2023年

作者： Zhu, Jingxuan Koppel, Alec Velasquez, Alvaro Liu, Ji The Department of Applied Mathematics and Statistics Stony Brook University United States J.P. Morgan AI Research United States Department of Computer Science University of Colorado Boulder United States Department of Electrical and Computer Engineering Stony Brook University United States

In decentralized cooperative multi-armed bandits (MAB), each agent observes a distinct stream of rewards, and seeks to exchange information with others to select a sequence of arms so as to minimize its regret. Agents in the cooperative setting can outperform a single agent running a MAB method such as Upper-Confidence Bound (UCB) independently. In this work, we study how to recover such salient behavior when an unknown fraction of the agents can be Byzantine, that is, communicate arbitrarily wrong information in the form of reward mean-estimates or confidence sets. This framework can be used to model attackers in computer networks, instigators of offensive content into recommender systems, or manipulators of financial markets. Our key contribution is the development of a fully decentralized resilient upper confidence bound (UCB) algorithm that fuses an information mixing step among agents with a truncation of inconsistent and extreme values. This truncation step enables us to establish that the performance of each normal agent is no worse than the classic single-agent UCB1 algorithm in terms of regret, and more importantly, the cumulative regret of all normal agents is strictly better than the non-cooperative case, provided that each agent has at least 3f + 1 neighbors where f is the maximum possible Byzantine agents in each agent’s neighborhood. Extensions to time-varying neighbor graphs, and minimax lower bounds are further established on the achievable regret. Experiments corroborate the merits of this framework in practice. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Fog Computing Security Assessment for Device Authentication in the Internet of Things 22

Fog Computing Security Assessment for Device Authentication ...

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22nd IEEE International Conference on High Performance Computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on Data science and Systems, HPCC-SmartCity-DSS 2020

作者： Al Harbi, Saud Halabi, Talal Bellaiche, Martine Polytechnique Montreal Department of Computer Engineering Quebec Canada University of Winnipeg Department of Applied Computer Science Manitoba Canada Polytechnique Montreal Department of Computer Engineering Quebec Canada

ISBN: (纸本)9781728176499

The Fog is an emergent computing architecture that will support the mobility and geographic distribution of Internet of Things (IoT) nodes and deliver context-Aware applications with low latency to end-users. It forms an intermediate layer between IoT devices and the Cloud. However, Fog computing brings many requirements that increase the cost of security management. It inherits the security and trust issues of Cloud and acquires some of the vulnerable features of IoT that threaten data and application confidentiality, integrity, and availability. Several existing solutions address some of the security challenges following adequate adaptation, but others require new and innovative mechanisms. These reflect the need for a Fog architecture that provides secure access, efficient authentication, reliable and secure communication, and trust establishment among IoT devices and Fog nodes. The Fog might be more convenient to deploy decentralized authentication solutions for IoT than the Cloud if appropriately designed. In this short survey, we highlight the Fog security challenges related to IoT security requirements and architectural design. We conduct a comparative study of existing Fog architectures then perform a critical analysis of different authentication schemes in Fog computing, which confirms some of the fundamental requirements for effective authentication of IoT devices based on the Fog, such as decentralization, less resource consumption, and low latency. © 2020 IEEE.

关键词： Fog

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