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Smartphone Addiction Analysis Model Based on C5.0 algorithm 2

Smartphone Addiction Analysis Model Based on C5.0 Algorithm

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Wang, Li Bohai University College of Educational Science Jinzhou China

ISBN: (纸本)9798350372892

Smartphone addiction has become a serious social problem, and this article studies the scientific methods for analyzing smartphone addiction. Firstly, study the C4.5 algorithm model, describe the algorithm using mathematical formulas, and design the execution process of the C 4. 5 algorithm using a flowchart. Then, based on the C4.5 algorithm model, study the C 5.0 algorithm model, introduce more new technologies, and use the Cost sensitive tree to reduce the probability of high cost misjudgments;Improve accuracy through Boosting algorithm. Finally, experimental design and result analysis were conducted, using the mobile addiction index scale and self-control scale for data collection. During the experimental process, commonly used data mining algorithms were compared and analyzed to demonstrate the rationality of choosing the C 5. 0 algorithm. It was found that constructing a smartphone addiction analysis model based on the C5.0 algorithm provides fundamental work for building a smartphone addiction analysis platform and provides theoretical guidance for better effective treatment and prevention of smartphone addiction. The research results provide fundamental work for building a smartphone addiction analysis platform, and provide theoretical guidance for effective treatment and prevention of smartphone addiction. © 2024 IEEE.

关键词： consensus algorithm

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Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints 41

Two-sided Competing Matching Recommendation Markets With Quo...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Li, Yuantong Cheng, Guang Dai, Xiaowu Department of Statistics and Data Science UCLA United States Department of Biostatistics UCLA United States

In this paper, we propose a new recommendation algorithm for addressing the problem of two-sided online matching markets with complementary preferences and quota constraints, where agents' preferences are unknown a priori and must be learned from data. The presence of mixed quota and complementary preferences constraints can lead to instability in the matching process, making this problem challenging to solve. To overcome this challenge, we formulate the problem as a bandit learning framework and propose the Multi-agent Multi-type Thompson Sampling (MMTS) algorithm. The algorithm combines the strengths of Thompson Sampling for exploration with a new double matching technique to provide a stable matching outcome. Our theoretical analysis demonstrates the effectiveness of MMTS as it can achieve stability and has a total Õ(Q√KmaxT)-Bayesian regret with high probability, which exhibits linearity with respect to the total firm's quota Q, the square root of the maximum size of available type workers √Kmax and time horizon T. In addition, simulation studies also demonstrate MMTS' effectiveness in various settings. We provide code used in our experiments https://***/Likelyt/Double-Matching. Copyright 2024 by the author(s)

关键词： consensus algorithm

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The Data-Reusing Maximum Correlation Entropy algorithm Based with Logarithmic Functions 5

The Data-Reusing Maximum Correlation Entropy Algorithm Based...

引用

5th International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Mu, Junying Mu, Zhaoqing Gao, Ying Ou, Shifeng School of Physics and Electronic Information Yantai University Shandong Province Yantai China

ISBN: (纸本)9798350368284

The data-reusing maximum correlation entropy algorithm (DRMCC) exhibits good convergence characteristics in adaptive filters when the input data are correlated. To further enhance the estimation performance of this algorithm for sparse systems, a penalty term in the form of a logarithmic function is incorporated, leading to the proposal of a logarithmic functionbased data-reusing maximum correlation entropy algorithm (DRMCCLZA). The penalty term imposes varying degrees of zero-attraction for distinct weight coefficients, enabling the algorithm to adjust the magnitude of zero-attraction in real time, contingent on the fluctuations of the weight coefficients throughout the update iteration process. The theoretical analysis of the convergence of the DRMCCLZA algorithm is presented herewith. The results of the simulation experiments demonstrate that the algorithm exhibits superior performance compared to other similar algorithms in system identification scenarios. Additionally, the algorithm demonstrates enhanced robustness in the context of sparse systems. © 2024 IEEE.

关键词： consensus algorithm

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Robust Average consensus under Byzantine Attacks

Robust Average Consensus under Byzantine Attacks

引用

IEEE Conference on Decision and Control

作者： Liwei Yuan Hideaki Ishii Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China Department of Information Physics and Computing The University of Tokyo Tokyo Japan

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We study the problem of average consensus in multi-agent systems where some of the agents may malfunction. The object of robust average consensus is for non-faulty agents to converge to the average value of their initial values despite the erroneous effects from adversarial agents. To this end, we propose a surplus-based consensus algorithm that can achieve robust average consensus under Byzantine attacks in the multi-agent networks with directed topologies. The key idea is to equip each normal agent with a running-sum variable so that it can record the effects from/to neighbors across iterations. Moreover, compared to the existing secure broadcast and retrieval approach where each agent keeps track of the initial values of all agents in the network, our algorithm saves massive storage especially for large-scale networks as each agent only requires the values and the correct detection of neighbors. Finally, numerical examples are given for verifying the effectiveness of our algorithm.

关键词： Economics Network topology consensus algorithm Topology Multi-agent systems

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Cooperative Voltage Control of Distributed Photovoltaic in Distribution Network Considering Communication Delay

Cooperative Voltage Control of Distributed Photovoltaic in D...

引用

2024 Boao New Power System International Forum - Power System and New Energy Technology Innovation Forum, NPSIF 2024

作者： Liu, Bin Shao, Dian Tan, Zhukui Hu, Caixia Electric Power Research Institute CSPG Guizhou Power Grid Co. Ltd. Guiyang China College of Electrical Engineering Zhejiang University Hangzhou China Guizhou Electronic Technology College Guiyang China

ISBN: (纸本)9798331504557

Traditional reactive power-voltage coordinated regulation methods have been proven technically and economically viable in PV distribution systems. However, challenges such as the uncertainty of distributed photovoltaic output and limitation in communication network undermine the effectiveness of these conventional approaches. To address the issue of voltage violations in distribution systems with high PV penetration, this paper introduces an enhanced dynamic consensus algorithm. This algorithm coordinates distributed inverters along with the inherent distributed PV for real-time voltage control. Moreover, to account for delay in imperfect communication systems, the paper develops a mathematical model coupling the distributed algorithm with the stochastic communication process. By incorporating virtual communication buses, the continuous and unpredictable delay in actual communication systems is approximated into a discrete and finite mathematical model. © 2024 IEEE.

关键词： consensus algorithm

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Identifying Anomalous Edges with Link Sampling and consensus

Identifying Anomalous Edges with Link Sampling and Consensus

引用

Asilomar Conference on Signals, Systems & Computers

作者： Abdullah Karaaslanli Panagiotis A. Traganitis Selin Aviyente Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Graphs and graph structured data are ubiquitous in many applications as they readily represent datasets that lie in irregular, yet structured, domains. Due to their popularity, a plethora of methods have been developed to learn from graphstructured data, which have been shown to be effective in many real-world applications including biology, finance, and social sciences, among others. However, these methods generally assume that the observed graph is free of corruption. This assumption does not hold in cases where the graph includes structural contamination, such as anomalous edges, which can degrade learning performance. This paper presents a method to identify anomalous edges that can be employed prior to learning methods to mitigate their effects. The proposed method employs link prediction (LP) to assign likelihood scores to the observed edges. As LP is not anomaly aware, we combine LP with ideas from sampling and consensus algorithms. LP is applied to subgraphs which tend to have fewer anomalies. Edge anomaly scores are then obtained by judiciously combining LP prediction results across subgraphs. Preliminary results indicate the effectiveness of the proposed method.

关键词： Learning systems Crowdsourcing Computers Image edge detection Social sciences Finance consensus algorithm Sampling methods Detection algorithms Contamination

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Submodular Framework for Structured-Sparse Optimal Transport 41

Submodular Framework for Structured-Sparse Optimal Transport

引用

41st International Conference on Machine Learning, ICML 2024

作者： Manupriya, Piyushi Jawanpuria, Pratik Gurumoorthy, Karthik S. Jagarlapudi, SakethaNath Mishra, Bamdev Department of Computer Science and Engineering IIT Hyderabad India Microsoft India Walmart Global Tech India

Unbalanced optimal transport (UOT) has recently gained much attention due to its flexible framework for handling un-normalized measures and its robustness properties. In this work, we explore learning (structured) sparse transport plans in the UOT setting, i.e., transport plans have an upper bound on the number of non-sparse entries in each column (structured sparse pattern) or in the whole plan (general sparse pattern). We propose novel sparsity-constrained UOT formulations building on the recently explored maximum mean discrepancy based UOT. We show that the proposed optimization problem is equivalent to the maximization of a weakly submodular function over a uniform matroid or a partition matroid. We develop efficient gradient-based discrete greedy algorithms and provide the corresponding theoretical guarantees. Empirically, we observe that our proposed greedy algorithms select a diverse support set and we illustrate the efficacy of the proposed approach in various applications. Copyright 2024 by the author(s)

关键词： consensus algorithm

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Making Old Things New: A Unified algorithm for Differentially Private Clustering 41

Making Old Things New: A Unified Algorithm for Differentiall...

引用

41st International Conference on Machine Learning, ICML 2024

作者： la Tour, Max Dupré Henzinger, Monika Saulpic, David McGill University Montreal Canada Klosterneuburg Austria CNRS IRIF Université Paris Cité Paris France

As a staple of data analysis and unsupervised learning, the problem of private clustering has been widely studied under various privacy models. Centralized differential privacy is the first of them, and the problem has also been studied for the local and the shuffle variation. In each case, the goal is to design an algorithm that computes privately a clustering, with the smallest possible error. The study of each variation gave rise to new algorithms: the landscape of private clustering algorithms is therefore quite intricate. In this paper, we show that a 20-year-old algorithm can be slightly modified to work for any of these models. This provides a unified picture: while matching almost all previously known results, it allows us to improve some of them and extend it to a new privacy model, the continual observation setting, where the input is changing over time and the algorithm must output a new solution at each time step. Copyright 2024 by the author(s)

关键词： consensus algorithm

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A Fast Sampling-Based Parallel Dbscan algorithm for Massive Datasets 21

A Fast Sampling-Based Parallel Dbscan Algorithm for Massive ...

引用

21st International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Gefeng, Wang Shibin, Zhang Lili, Yan Zhiying, Lv Yan, Chang Zhi, Qin School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China

ISBN: (纸本)9798331519254

Clustering is a type of data mining algorithm aimed at discovering hidden patterns in the data, and spatial clustering is an important type of it. Density-based spatial clustering of applications with noise (DBSCAN) is a clustering algorithm that can discover clusters of arbitrary shapes and has robustness to noise. DBSCAN has been widely studied and applied due to its superior clustering quality, but its high complexity limits its performance on massive-scale datasets. This paper proposes a sampling-based grid-structured DBSCAN algorithm with linear complexity that can be processed in parallel. The algorithm first clusters the sampled dataset to discover the main features of the dataset;then it establishes grids and indexes to accelerate neighbor point queries;finally, through specially designed decoupled algorithm structure, it achieves lock-free parallel computation. Experiments show that this algorithm outperforms the state-of-the-art DBSCAN algorithm under various tested datasets and parameter conditions. © 2024 IEEE.

关键词： consensus algorithm

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Blockchain Model Testing and Implementation Based on Improved PBFT consensus 11

Blockchain Model Testing and Implementation Based on Improve...

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11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Zhang, Qianyu Su, Jun Ma, Zhaohui Zhang, Yucheng Yang, Jie Zhan, Jun Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781665426053

The main study focuses on the shortcomings of the traditional PBFT consensus algorithm and the DPOS consensus algorithm, and then combines the two algorithms to form a new consensus algorithm from the perspective of optimising the consensus performance of the blockchain, which can achieve several advantages in terms of node reduction view switching, node dynamics, bandwidth overhead reduction, transaction throughput and lower system latency performance. The advantages of the improved PBFT algorithm are illustrated by the experimental data on throughput, system latency of account creation and bandwidth overhead, which are compared to illustrate the effect of the improved PBFT algorithm.

关键词： Blockchain consensus algorithm PBFT consensus algorithm DPOS consensus algorithm

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