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Notes on Linear Gossip Algorithms:Potentials and Challenges

Journal of Systems science & Complexity 2025年第1期38卷 98-128页

作者： GERENCSER Balazs GERENCSER Laszlo HUN-REN Alfred Renyi Institute of Mathematics Budapest 1052Hungary and Eotvos Lorand UniversityDepartment of Probability and StatisticsBudapest 1518Hungary HUN-REN SZTAKI Institute for Computer Science and ControlBudapest 1111Hungary

The authors consider the problem of reaching consensus over a communication network via asynchronous interaction between pairs of agents.A well-known method is the linear gossip algorithm due to Tsitsiklis(1984).Extension of this,allowing the selection of a strictly stationary sequence of communicating pairs,was given in Picci and Taylor(2013).Extension of the linear gossip algorithm to directed communication networks,retaining the linear dynamics,was proposed by Cai and Ishii(2012),later extended by Silvestre,et al.(2018).A definite novelty of these algorithms is that L2-convergence with exponential rate can be *** authors attend the above issues,extending the result of Picci and Taylor(2013)motivated by features of algorithms for directed *** authors present and discuss the algorithm of Silvestre,et al.(2018),together with systematic simulation results based on 5M randomly chosen parameter *** core of the proposed mathematical technology is a set of simple observations,presented with a tutorial aspect,by which the authors can conveniently establish various results on the almost sure convergence of products of strictly stationary sequences of matrices to a rank-1 matrix.

关键词： Consensus gossip algorithms Lyapunov-exponents spectral gap

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Detecting Intra- and Extra-Decade Nature of Droughts in Iran Using Empirical Mode Decomposition (EMD)

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Iranian Journal of science and Technology - Transactions of Civil Engineering 2025年 1-12页

作者： Mir, Firouzeh Mahmoudi, Peyman Khosravi, Mahmood Amir Jahanshani, Seyed Mahdi Department of Physical Geography Faculty of Geography and Environmental Planning University of Sistan and Baluchestan Zahedan Iran Department of Statistics Faculty of Mathematics Statistics and Computer Science University of Sistan and Baluchestan Zahedan Iran

The present study aims at modeling Iran’s intra- and extra-decade drought behaviors. To do so, 61 synoptic stations’ annual precipitation data were procured from Iran’s meteorology organization for 30 years (1986–2015). To identify the various drought degrees’ frequencies in the studied stations, the Standardized Precipitation Index (SPI) was applied on an annual scale. Cluster analysis was utilized for regionalizing the droughts and empirical mode decomposition, as well, was applied to detecting Iran’s decennial drought behaviors. The results indicated that Iran can be divided into five different regions based on the annual SPI values’ time-series behaviors. Counting the number of times various drought classes occur in each of the aforesaid five regions, it was concluded that the northeastern region with 11 cases of drought, the southeastern region with 6 cases of drought, and the northern strip with also 6 cases of drought are respectively the regions with the highest and the lowest frequencies of drought occurrence. Severe and very severe drought occurrences were also observed in the northwestern region (2 cases) and southwestern region (1 case) with them not being detected in the other regions on a yearly basis. The results pertinent to the behavioral detection of Iran’s intra- and extra-decade drought cycles, as well, indicated that the changes in the fivefold region’s drought in Iran substantially feature intra-decade nature so a less than 10-year alternation should be expected for the droughts in Iran. Put differently, the drought return period takes less than 10 years to occur. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Cluster analysis Drought Empirical mode decomposition (EMD) Regionalization Standardized precipitation index (SPI)

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BMWP: the first Bengali math word problems dataset for operation prediction and solving

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Discover Artificial Intelligence 2025年第1期5卷 1-15页

作者： Mondal, Sanchita Khatua, Debnarayan Mandal, Sourav Prasad, Dilip K. Sekh, Arif Ahmed School of Medical Science and Technology IIT Kharagpur West Bengal Kharagpur721302 India Department of Mathematics and Statistics Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur512213 India School of Computer Science and Engineering XIM University Odisha Puri752050 India Department of Computer Science Uit the Arctic University of Norway Tromsø9017 Norway

Solving math word problems of varying complexities is one of the most challenging and exciting research questions in artificial intelligence (AI), particularly in natural language processing (NLP) and machine learning (ML). Foundational language models such as GPT must be evaluated for intelligence, and solving word problems is a key method for this assessment. These problems become especially difficult when presented in low-resource regional languages such as Bengali. Word problem solving integrates the cognitive domains of language processing, comprehension, and transformation into real-world solutions. During the past decade, advances in AI and machine learning have significantly progressed in addressing this complex issue. Although researchers worldwide have primarily utilized datasets in English and some in Chinese, there has been a lack of standard datasets for low-resource languages such as Bengali. In this pioneering study, we introduce the first Bengali Math Word Problem Benchmark Data Set (BMWP), comprising 8653 word problems. We detail the creation of this dataset and the benchmarking methods employed. Furthermore, we investigate operation prediction from Bengali word problems using state-of-the-art deep learning (DL) techniques. We implemented and compared various standard DL-based neural network architectures, achieving an accuracy of 92±2%. The data set and the code will be available at https://***/SanchitaMondal/BMWP. © The Author(s) 2025.

关键词： Recurrent neural networks

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QUANTUM MARKOV DECISION PROCESSES: GENERAL THEORY, APPROXIMATIONS, AND CLASSES OF POLICIES

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SIAM Journal on Control and Optimization 2025年第2期63卷 1156-1183页

作者： Saldi, Naci Sanjari, Sina Yuksel, Serdar Department of Mathematics Bilkent University Ankara Turkey Department of Mathematics and Computer Science Royal Military College KingstonON Canada Department of Mathematics and Statistics Queen's University KingstonON Canada

In this paper, the aim is to develop a quantum counterpart to classical Markov decision processes (MDPs). First, we provide a very general formulation of quantum MDPs with state and action spaces in the quantum domain, quantum transitions, and cost functions. Once we formulate the quantum MDP (q-MDP), our focus shifts to establishing the verification theorem that proves the sufficiency of Markovian quantum control policies and provides a dynamic programming principle. Subsequently, a comparison is drawn between our q-MDP model and previously established quantum MDP models (referred to as QOMDPs) found in the literature. Furthermore, approximations of q-MDPs are obtained via finite-action models, which can be formulated as QOMDPs. Finally, classes of open-loop and classical-state-preserving closed-loop policies for q-MDPs are introduced, along with structural results for these policies. In summary, we present a novel quantum MDP model aiming to introduce a new framework, algorithms, and future research avenues. We hope that our approach will pave the way for a new research direction in discrete-time quantum control. © 2025 Society for Industrial and Applied Mathematics.

关键词： Cost functions

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MCDM model using Jaccard and cosine similarity-driven aggregation operators in (n, m)-rung orthopair fuzzy environment: a case study on government medical facilities in Indian states

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International Journal of Information Technology (Singapore) 2025年第1期17卷 225-236页

作者： Arya, Priyanshu Pal, A.K. Department of Mathematics Statistics and Computer Science G. B. Pant University of Agriculture and Technology Uttarakhand Pantnagar 263145 India

This paper introduces a new Multiple Criteria Decision Making (MCDM) method based on the (n, m)-rung orthopair fuzzy sets (n, m-ROFSs) framework, enhanced with Jaccard and cosine similarity measures. This innovative approach effectively captures complex nuances and uncertainties in decision-making processes. The method involves several key contributions likewise, the proposal of new Jaccard (JSM) and cosine similarity measures (CSM) within the n, m-ROFSs environment, a novel weight determination method, and a new MCDM method tailored for n, m-ROFSs. The robustness of the method is demonstrated through a detailed examination of its mathematical properties and the development of a new aggregation operator, followed by sensitivity and comparison analyses. The practical utility of the proposed MCDM method is illustrated by evaluating and ranking the ten most densely populated states in India based on government-provided medical facilities. This study highlights the effectiveness and real-world applicability of the developed approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cosine similarity Jaccard similarity MCDM n, m-ROFSs

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AP-CFL: Clustered Federated Learning through Dynamic Clustering and Adaptive Participation in Heterogeneous IoT

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IEEE Internet of Things Journal 2025年第10期12卷 13671-13682页

作者： Cao, Yulin Ma, Jianping He, Zaobo Li, Yingshu Qinghai Normal University College of Computer Qinghai810000 China Qinghai Normal University College of Mathematics and Statistics Qinghai810000 China Ji'nan University College of Information Science and Technology Guangzhou510632 China Georgia State University Department of Computer Science Atlanta30303 United States

In the advancement of collaborative intelligence within the Internet of Things (IoT), Federated Learning (FL) enables clients to collaboratively train a global model without centralizing raw data. However, the non-independent and identically distributed (non-IID) nature of data among clients often leads to divergent local training objectives, deteriorating the performance of the aggregated global model. To address this challenge, we propose AP-CFL, a novel clustered federated learning algorithm that incorporates affinity propagation to dynamically discover the clustering structure of clients without the need to predefine the number of clusters. Specifically, AP-CFL calculates the mean of absolute differences of pairwise cosine similarity to effectively cluster clients based on similarities in their data distributions. Knowledge sharing is enhanced by decoupling each cluster model into a globally shared encoder and a cluster-specific classifier, and the local training objectives are modified to improve the generalization capacity of the shared encoder. Additionally, a robust strategy is introduced to manage partial client participation by employing a time and data importance index, which mitigates the adverse effects of model staleness and maintains the integrity of the clustering structure. Extensive experiments on diverse real-world datasets demonstrate that AP-CFL outperforms existing FL baselines in non-IID settings, effectively improving model quality and convergence stability. © 2014 IEEE.

关键词： Federated learning

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Enabling Secure Data Aggregation Scheme for Smart Grids based on Local Differential Privacy 24

Enabling Secure Data Aggregation Scheme for Smart Grids base...

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2nd International Conference on Electronics, computers and Communication Technology, CECCT 2024

作者： He, Yixuan Mei, Lin Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology Jiangsu Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710193

Existing data aggregation schemes for privacy protection in smart grids face limitations in efficiency and application, often relying on trusted third parties or incurring significant overhead. To address these issues, this paper introduces a multidimensional and multisubset data aggregation scheme leveraging local differential privacy, aimed at enabling fine-grained data analysis while safeguarding privacy. The scheme effectively protects user privacy by locally perturbing data from randomly selected dimensions while utilizing the Chinese Remainder Theorem to compress multidimensional data, thereby optimizing the transmission. Theoretical and experimental results demonstrate that our scheme offers strong privacy, flexibility, and utility, achieving a relative error of aggregation results no more than 0.05 under optimal conditions. Additionally, it outperforms existing approaches in terms of both computational and communication overhead. © 2024 Copyright held by the owner/author(s).

关键词： Differential privacy

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Availability assessment of repairable linear consecutive k-out-of-n: F systems incorporating uncertainty and multiple failure modes

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Life Cycle Reliability and Safety Engineering 2025年 1-10页

作者： Khati, Amisha Singh, S.B. Department of Mathematics Statistics and Computer Science G. B. Pant University of Agriculture and Technology Pantnagar India Department of Applied Science and Humanities RIT Roorkee India

This study evaluates the interval-valued availability of a linear consecutive k-out-of-n: F system while considering uncertainty, where the probability that the system will perform as intended is not known with precision. The proposed system is susceptible to µ number of different failure modes and undergoes periodic inspections. Each failure mode has a randomly assigned failure time, and upon failure due to the mth failure mode, an immediate corrective repair is carried out, taking an arbitrary length of time ψm (m = 1, 2,…, µ). By integrating these factors, this research establishes theorems describing point and steady-state availability bounds. Subsequently, it conducts a sensitivity assessment to evaluate how variations in the inspection period affect the system’s availability. The findings are demonstrated with an instance of an oil pipeline system. This study offers valuable insights into the reliability and availability modelling of complex systems under uncertainty, with applications across diverse engineering domains. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2025.

关键词： Interval valued point availability Interval valued steady state availability Linear consecutive k-out-of n: F system Multiple failure modes Sensitivity analysis Uncertainty

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Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach

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Neutrosophic Sets and Systems 2025年 81卷 479-502页

作者： Abdo, Doaa A. Salama, A.A. Abdelmegaly, Alaa A. Mahmoud, Hanan Khadari Mahdi Applied statistics and insurance department faculty of commerce Mansoura university Mansoura Egypt Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Higher Institute of Advanced Management Sciences and Computers Al-Buhayrah Egypt Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura Egypt

This study tackles the problem of missing data in migrant datasets by introducing a new framework that combines machine learning techniques with neutrosophic sets. These sets, which can represent uncertainty and ambiguity, are well-suited for managing the complex nature of missing information in sensitive fields like migration research. We test the effectiveness of KNN, SVM, decision tree, random forest, and Ada Boost algorithms on a migrant dataset, comparing their results using different imputation methods (mean/mode, model-based imputer (simple tree), and random values). Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. Our results underscored the potential of neutrosophic set-based machine learning for addressing missing data issues across various fields. © 2025, University of New Mexico. All rights reserved.

关键词： Adversarial machine learning

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Intelligent classification of flue-cured tobacco based on improved network model of six channel ResNet

Journal of Biotech Research

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Journal of Biotech Research 2025年 20卷 98-110页

作者： Wang, Shiyu Zhuang, Runjie Lv, Wanping Lin, Tianzhen Wen, Yongxian College of Computer and information Science Fujian Agriculture and Forestry University Fujian Fuzhou China Institute of Statistics and Applications Fujian Agriculture and Forestry University Fujian Fuzhou China

Flue-cured tobacco is the most important raw material for cigarette production, and efficient and accurate intelligent grading of flue-cured tobacco leaves is of great significance to the acquisition and correct use of tobacco. Currently, there are few studies on the classification of flue-cured tobacco by using both front and back images of tobacco leaves, while the back image of flue-cured tobacco leaves has enriched information. To harness the back information of these leaves and enhance grading accuracy, a revolution ResNet network model, Evo-ResNet, was proposed in this study based on front and back images of flue-cured tobacco leaves. Evo-ResNet had six channel with added Squeeze-and-Excitation (SE) attention mechanism. Six grades pictures of Cuibi 1 and Yunyan 87 tobacco species were collected using mobile phone, respectively. Evo-ResNet was compared with three-channel models of ResNet, GoogLeNet, VGGNet, and AlexNet with only front images of flue-cured tobacco leaves and both front and back images of flue-cured tobacco leaves. The results showed that the loss of train set and the accuracy of validation set had better results while the front and back pictures of flue-cured tobacco leaves were used meantime. For the test set, the accuracy, recall, precision, and F1-score of Evo-ResNet were better than that of other three-channel network models, expect for GoogleNet. The accuracy, recall, precision, and F1-score value of Cuibi 1 were 95.83%, 95.83%, 96.16%, and 95.78%, respectively, while those of Yunyan 87 were 99.07%, 99.07%, 99.10% and 99.08%, respectively. There was better generalization ability for Evo-ResNet. Because of the convenience for obtaining image of flue-cured tobacco by mobile phone, the proposed Evo-ResNet network model had better applicability in practice, which provided a new idea for the application of intelligent classification of flue-cured tobacco leaves in purchasing practice of flue-cured tobacco. © (2025), (Bio Tech System). All rights

关键词： Flues

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