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检索条件"主题词=Consensus Algorithm"

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Study of Socio-inspired Metaheuristic algorithms Through a Proposed Generic Framework 5th

Study of Socio-inspired Metaheuristic Algorithms Through a P...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Dhiman, Radhika Sood, Manu Thakur, Jawahar Department of Computer Science Himachal Pradesh University Shimla India

ISBN: (纸本)9789819763177

In recent years, the socio-inspired algorithms, one of the subclasses of metaheuristics, have been looked upon active in the research community. This novel subclass has been inspired by the human behavior and social activities/interactions exhibited by the individuals to improve themselves. Multiple socio-inspired algorithms have been proposed till date, and the research is still going on to map new concepts of human behavior into algorithms. This paper proposes a generic framework for socio-inspired algorithms to comprehend the working structure of these metaheuristic algorithms. Additionally, to prove the authenticity of the proposed framework, 10 recently developed (2019–2023) socio-inspired metaheuristic algorithms have been reviewed against this framework. The authors anticipate that this generic framework can be used as a benchmark to study the socio-inspired metaheuristic algorithms developed in future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： consensus algorithm

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The Average-Value Allocation Problem 27

The Average-Value Allocation Problem

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27th International Conference on Approximation algorithms for Combinatorial Optimization Problems, APPROX 2024 and the 28th International Conference on Randomization and Computation, RANDOM 2024

作者： Bhawalkar, Kshipra Feng, Zhe Gupta, Anupam Mehta, Aranyak Wajc, David Wang, Di Google Research Mountain View United States NYU Google Research Mountain View New York City United States Technion Haifa Israel

ISBN: (纸本)9783959773485

We initiate the study of centralized algorithms for welfare-maximizing allocation of goods to buyers subject to average-value constraints. We show that this problem is NP-hard to approximate beyond a factor of e−e1, and provide a e4−e1-approximate offline algorithm. For the online setting, we show that no non-trivial approximations are achievable under adversarial arrivals. Under i.i.d. arrivals, we present a polytime online algorithm that provides a constant approximation of the optimal (computationally-unbounded) online algorithm. In contrast, we show that no constant approximation of the ex-post optimum is achievable by an online algorithm. © Kshipra Bhawalkar, Zhe Feng, Anupam Gupta, Aranyak Mehta, David Wajc, and Di Wang.

关键词： consensus algorithm

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P-Rank+: A Scalable Efficient P-Rank Search algorithm 24

P-Rank+: A Scalable Efficient P-Rank Search Algorithm

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33rd ACM International Conference on Information and Knowledge Management, CIKM 2024

作者： Zhang, Maoyin Yu, Weiren Nanjing University of Sci. and Tech. Jiangsu China Warwick University CoventryCV4 7AL United Kingdom

ISBN: (纸本)9798400704369

P-Rank (Penetrating-Rank) is a charming measure of structural similarity between objects based on graph topology. It recursively follows the principle that "two objects are considered similar if (a) they are referenced by similar objects and (b) they reference similar objects''. The best-known algorithm for computing P-Rank employs two repeated Singular Value Decompositions (SVDs) coupled with the Woodbury matrix identity. However, this method does not scale well on billion-sized graphs. Worse yet, this algorithm only provides a linear approximation of the P-Rank model and cannot deliver accurate P-Rank values. In this paper, we propose P-Rank+, a fast and efficient algorithm for computing P-Rank similarities, which scales well on large graphs with billions of edges. P-Rank+ leverages dimensionality reduction techniques by performing only one SVD of the graph integrated with Hadamard products in the reduced subspace. Moreover, we provide provable error guarantees for P-Rank+ computation. Experiments on various datasets validate that P-Rank+ is 1 - 3 orders of magnitude faster than the best-known competitor while achieving excellent scalability on massive graphs. © 2024 Owner/Author.

关键词： consensus algorithm

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Topsis-Based Genetic algorithm for Non-Dominated Sorting of Local Search Im-NSGA-II 6

Topsis-Based Genetic Algorithm for Non-Dominated Sorting of ...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Xue, Tonglai Zhang, Zheng Gao, Tianyu Han, Fei North China University of Technology Beijing China China National General Mechanical Engineering Corporation Beijing China

ISBN: (纸本)9798350374315

Aiming at the problem of large computation of non-dominated sorting genetic algorithm of local search class, this paper proposes an improved local search NSGA-II algorithm (Im-NSGA-II) based on Topsis algorithm by adjusting adaptive crossover and genetic operators. By applying the Topsis algorithm to the population individuals, the solutions with low fitness are taken out, and the local search algorithm is applied to the set of solutions with low fitness in each genetic process. Simulation experiments on the ZDT standard test function are conducted and compared with the traditional NSGA-II algorithm, the multi-objective particle swarm algorithm MOPSO, and the multi-objective differential evolutionary algorithm MOEA/D. The results show that Im-NSGA-II is able to effectively balance the ability of global search and local exploration and accelerate the convergence of the populations compared with the other algorithms. © 2024 IEEE.

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Mechanisms for Applying Pattern Recognition algorithms in Animal Husbandry

Mechanisms for Applying Pattern Recognition Algorithms in An...

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International Conference on Innovations in Sustainable Agricultural Systems, ISAS 2024

作者： Pankina, S.I. Zhumayev, Zh. Lyutikova, M.N. Admiral Ushakov Maritime State University Novorossiysk Russia S. Yessenov Caspian University of Technologies and Engineering 30 Microdistrict Aktau130000 Kazakhstan

ISBN: (纸本)9783031706721

The development and global use of computer technologies make it possible to recognize the information being processed depending on the set of tasks. In this paper the problem of the use of modern technologies in animal husbandry is considered, allowing not only to simplify and reduce some daily operations, but also to increase the demand and quality of livestock products in general. The authors set the task of developing algorithms for pattern recognition based on the definition of external object parameters using software. The article describes the mathematical apparatus of finding the number of individuals in a herd, determining the calculation with a given error of the power of the set of elements. The main content of the study is devoted to the description and construction of the method of controlling the number of animals in the field of animal husbandry with the help of the software SimInTech, allowing to simulate in a mode «real time». The work focuses on a topical topic in animal husbandry, and further research in this area will broaden the scope of work. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： consensus algorithm

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A Simple Distributed algorithm for Sparse Fractional Covering and Packing Problems 35

A Simple Distributed Algorithm for Sparse Fractional Coverin...

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35th International Symposium on algorithms and Computation, ISAAC 2024

作者： Li, Qian Ouyang, Minghui Wang, Yuyi Shenzhen International Center For Industrial And Applied Mathematics Shenzhen Research Institute of Big Data China School of Mathematical Sciences Peking University Beijing China Lambda Lab China Railway Rolling Stock Corporation Zhuzhou Institute China

ISBN: (纸本)9783959773546

This paper presents a distributed algorithm in the CONGEST model that achieves a (1 + ϵ)approximation for row-sparse fractional covering problems (RS-FCP) and the dual column-sparse fraction packing problems (CS-FPP). Compared with the best-known (1 + ϵ)-approximation CONGEST algorithm for RS-FCP/CS-FPP developed by Kuhn, Moscibroda, and Wattenhofer (SODA’06), our algorithm is not only much simpler but also significantly improves the dependency on ϵ. © Qian Li, Minghui Ouyang, and Yuyi Wang.

关键词： consensus algorithm

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Regulation of algorithmic Collusion, Refined: Testing Pessimistic Calibrated Regret 25

Regulation of Algorithmic Collusion, Refined: Testing Pessim...

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4th ACM Symposium on Computer Science and Law, CS and LAW 2025

作者： Hartline, Jason D. Wang, Chang Zhang, Chenhao Northwestern University EvanstonIL United States

ISBN: (纸本)9798400714214

We study the regulation of algorithmic (non-)collusion amongst sellers in dynamic imperfect price competition by auditing their data as introduced by Hartline et al. [23]. We develop an auditing method that tests whether a seller’s pessimistic calibrated regret is low. The pessimistic calibrated regret is the highest calibrated regret of outcomes compatible with the observed data. This method relaxes the previous requirement that a pricing algorithm must use fully-supported price distributions to be auditable. This method is at least as permissive as any auditing method that has a high probability of failing algorithmic outcomes with non-vanishing calibrated regret. Additionally, we strengthen the justification for using vanishing calibrated regret, versus vanishing best-in-hindsight regret, as the non-collusion definition, by showing that even without any side information, the pricing algorithms that only satisfy weaker vanishing best-in-hindsight regret allow an opponent to manipulate them into posting supra-competitive prices. This manipulation cannot be excluded with a non-collusion definition of vanishing best-in-hindsight regret. We motivate and interpret the approach of auditing algorithms from their data as suggesting a per se rule. However, we demonstrate that it is possible for algorithms to pass the audit by pretending to have higher costs than they actually do. For such scenarios, the rule of reason can be applied to bound the range of costs to those that are reasonable for the domain. © 2025 Copyright held by the owner/author(s).

关键词： consensus algorithm

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What Constitutes a Less Discriminatory algorithm? 25

What Constitutes a Less Discriminatory Algorithm?

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4th ACM Symposium on Computer Science and Law, CS and LAW 2025

作者： Laufer, Benjamin Raghavan, Manish Barocas, Solon Cornell University New YorkNY United States MIT CambridgeMA United States Microsoft Research New YorkNY United States

ISBN: (纸本)9798400714214

Disparate impact doctrine offers an important legal apparatus for targeting discriminatory data-driven algorithmic decisions. A recent body of work has focused on conceptualizing one particular construct from this doctrine: the less discriminatory alternative, an alternative policy that reduces disparities while meeting the same business needs of a status quo or baseline policy. However, attempts to operationalize this construct in the algorithmic setting must grapple with some thorny challenges and ambiguities. In this paper, we attempt raise and resolve important questions about less discriminatory algorithms (LDAs). How should we formally define LDAs, and how does this interact with different societal goals they might serve? And how feasible is it for firms or plaintiffs to computationally search for candidate LDAs? We find that formal LDA definitions face fundamental challenges when they attempt to evaluate and compare predictive models in the absence of held-out data. As a result, we argue that LDA definitions cannot be purely quantitative, and must rely on standards of "reasonableness." We then raise both mathematical and computational constraints on firms’ ability to efficiently conduct a proactive search for LDAs, but we provide evidence that these limits are "weak" in a formal sense. By defining LDAs formally, we put forward a framework in which both firms and plaintiffs can search for alternative models that comport with societal goals. © 2025 Copyright held by the owner/author(s).

关键词： consensus algorithm

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Towards Scalable and Practical Batch-Dynamic Connectivity 51st

Towards Scalable and Practical Batch-Dynamic Connectivity

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51st International Conference on Very Large Data Bases, VLDB 2025

作者： De Man, Quinten Dhulipala, Laxman Karczmarz, Adam Lącki, Jakub Shun, Julian Wang, Zhongqi University of Maryland United States University of Warsaw IDEAS NCBR Poland Google Research United States MIT CSAIL United States

We study the problem of dynamically maintaining the connected components of an undirected graph subject to edge insertions and deletions. We give the first parallel algorithm for the problem that is work-efficient, supports batches of updates, runs in poly logarithmic depth, and uses only linear total space. The existing algorithms for the problem either use super-linear space, do not come with strong theoretical bounds, or are not parallel. On the empirical side, we provide the first implementation of the cluster forest algorithm, the first linear-space and poly logarithmic update time algorithm for dynamic connectivity. Experimentally, we find that our algorithm uses up to 19.7× less space and is upto 6.2× faster than the level-set algorithm of Holm, de Lichtenberg, and Thor up, arguably the most widely-implemented dynamic connectivity algorithm with strong theoretical guarantees. © 2025, VLDB Endowment, All rights reserved.

关键词： consensus algorithm

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Analysis of Short Video Platform algorithms Based on Causal Inference: The Formation and Decoupling of the Information Cocoon Effect

Analysis of Short Video Platform Algorithms Based on Causal ...

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2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Cheng, Jingya School of Culture and Communication Guangdong Business and Technology University Zhaoqing China

ISBN: (纸本)9798331504205

Short video platforms push content to users through recommendation algorithms, which greatly improves user experience, but also triggers the information cocoon effect. This paper explores the impact of the recommendation algorithm of short video platforms on the information cocoon effect based on the theory of causal inference, and proposes algorithm design and model simulation methods to decouple the relationship between the information cocoon effect and content diversity. First, this paper constructs a causal inference model to identify the formation mechanism of the information cocoon effect in the recommendation algorithm. Then, by introducing a diversity constraint factor, adjustments are made in the recommendation algorithm to increase the probability that users receive different types of content, thereby reducing the generation of the information cocoon effect. The model simulation results show that without affecting the user experience, the optimized recommendation algorithm improves the information diversity index by 15.7%, while the intensity of the information cocoon effect is reduced by 22.4%. This study shows that the optimization of the recommendation algorithm based on causal inference can effectively alleviate the information cocoon effect in the short video platform, improve the breadth of user information acquisition, and has high practical value. © 2025 IEEE.

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