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A Truthful Randomized Mechanism for Heterogeneous Resource Allocation With Multi-Minded in Mobile Edge Computing

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第5期21卷 5677-5690页

作者： Liu, Xi Li, Weidong Qujing Normal Univ Sch Informat Engn Key Lab Intelligent Sensor & Syst Design Qujing 655001 Peoples R China Yunnan Univ Sch Math & Stat Kunming 650504 Peoples R China

In the context of mobile edge computing (MEC), it can be quite challenging to provide and allocate multiple resources from heterogeneous MEC servers for remote execution of tasks of mobile devices (MDs). However, obtaining more resources for tasks can save time and effort, and MDs are willing to pay higher prices for more resources. Motivated by these challenges, this study addresses the problem of heterogeneous resource allocation with multi-minded (HRAM) in MEC. Unlike other studies that have focused on MDs with single-minded demands, this study considers the multi-attribute demands of MDs. It considers cases where an MD declares multiple demands, each with a different bid attached to it. This multi-minded approach gives MDs more flexibility and control over the resources they receive, leading to increased satisfaction and better outcomes. However, MDs are self-interested and can misreport their preferences, which results in a low utilization rate of heterogeneous resources. Therefore, we have formulated this problem in an auction-based setting, and our objective is to allocate heterogeneous resources of heterogeneous MEC servers to maximize social welfare, which is the sum of MDs' valuations. We demonstrate that the HRAM problem is NP-hard, proposing a randomized mechanism consisting of a second-price auction and a fixed-price auction. This study claims that the proposed randomized mechanism is universally truthful and that the MDs have no desire to misreport their demands. Additionally, we analyze the randomized mechanism's time complexity and approximation ratio and present the experimental results to support our claim. We demonstrate that the randomized mechanism performs excellently in different environments, benefiting MDs and edge cloud providers.

关键词： Servers Task analysis Resource management Cost accounting Approximation algorithms Computational modeling Multitasking Mobile edge computing truthfulness resource allocation multi-minded algorithm design

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A Multi-Surrogate Assisted Multi-Tasking Optimization algorithm for High-Dimensional Expensive Problems

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algorithmS 2025年第1期18卷 4-4页

作者： Li, Hongyu Chen, Lei Zhang, Jian Li, Muxi Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China Northwestern Polytech Univ Ocean Inst Taicang 215400 Peoples R China Tianjin Univ Sch Mech Engn Tianjin 300354 Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) are widely used in the field of high-dimensional expensive optimization. However, real-world problems are usually complex and characterized by a variety of features. Therefore, it is very challenging to choose the most appropriate surrogate. It has been shown that multiple surrogates can characterize the fitness landscape more accurately than a single surrogate. In this work, a multi-surrogate-assisted multi-tasking optimization algorithm (MSAMT) is proposed that solves high-dimensional problems by simultaneously optimizing multiple surrogates as related tasks using the generalized multi-factorial evolutionary algorithm. In the MSAMT, all exactly evaluated samples are initially grouped to form a collection of clusters. Subsequently, the search space can be divided into several areas based on the clusters, and surrogates are constructed in each region that are capable of completely describing the entire fitness landscape as a way to improve the exploration capability of the algorithm. Near the current optimal solution, a novel ensemble surrogate is adopted to achieve local search in speeding up the convergence process. In the framework of a multi-tasking optimization algorithm, several surrogates are optimized simultaneously as related tasks. As a result, several optimal solutions spread throughout disjoint regions can be found for real function evaluation. Fourteen 10- to 100-dimensional test functions and a spatial truss design problem were used to compare the proposed approach with several recently proposed SAEAs. The results show that the proposed MSAMT performs better than the comparison algorithms in most test functions and real engineering problems.

关键词： multi-tasking optimization algorithm algorithm design dynamic ensemble of surrogates decision space partition expensive optimization problem

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Optimal Harvest-Then-Transmit Scheduling for Throughput Maximization in Time-Varying RF Powered Systems

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第11期42卷 3140-3156页

作者： Shan, Feng Luo, Junzhou Jin, Qiao Cao, Liwen Wu, Weiwei Ling, Zhen Dong, Fang Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Jiangsu Peoples R China

Energy harvesting is a promising technique to address the energy hunger problem for thousands of wireless devices. In Radio Frequency (RF) energy harvesting systems, a wireless device first harvests energy and then transmits data with this energy, hence the 'harvest-then-transmit' (HTT) principle is widely adopted. We must carefully design the HTT schedule, i.e., schedule the timing between harvesting and transmission, and decide the data transmission power such that the throughput can be maximized with the limited harvested energy. Distinct from existing work, we assume energy harvested from RF sources is time-varying, which is more practical but more difficult to handle. We first discover a surprising result that the optimal transmission power is independent of the transmission time, but solely depends on the RF harvesting power, for a simple case when the energy harvesting is stable. We then obtain an optimal offline HTT-scheduling for the general case that allows the RF harvesting power to vary with time. To the best of our knowledge, it is the first optimal HTT-scheduling algorithm that achieves maximum data throughput for time-varying RF powered systems. Finally, an efficient online heuristic algorithm is designed based on the offline optimality properties. Simulations show that the proposed online algorithm has superior performance, which achieves more than 90% of the offline maximum throughput in most cases.

关键词： Wireless communication networks algorithm design wireless power transfer harvest-then-transmit radio frequency energy harvesting time-varying wireless power Wireless communication networks algorithm design wireless power transfer harvest-then-transmit radio frequency energy harvesting time-varying wireless power

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High order expansion method for Kuiper's Vn statistic in goodness-of-fit test

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STATISTICS AND COMPUTING 2025年第4期35卷 1-19页

作者： Zhang, Hong-Yan Feng, Zhi-Qiang Liu, Haoting Lin, Rui-Jia Zhou, Yu Hainan Normal Univ Sch Informat Sci & Technol 99 Rd LongKun South Haikou 571158 Peoples R China Univ Sci & Technol Beijing Sch Automat 30 Rd Xueyuan Beijing 100083 Peoples R China Hainan Normal Univ Informat Network & Data Ctr 99 Rd LongKun South Haikou 571158 Peoples R China

Kuiper's Vn statistic, a measure for comparing the difference of ideal distribution and empirical distribution, is of great significance in the goodness-of-fit test. However, Kuiper's formulae for computing the cumulative distribution function, false positive probability, and the upper tail quantile of Vn cannot be applied to the case of small sample capacity n since the approximation error is On-1. In this work, our contributions lie in three perspectives: firstly the approximation error is reduced to On-(k+1)/2 where k is the expansion order with the high order expansion for the exponent of the differential operator;secondly, a novel high order formula with approximation error On-3 is obtained by massive calculations;thirdly, the fixed-point algorithms are designed for solving the Kuiper pair of critical values and upper tail quantiles based on the novel formula. The high order expansion method for Kuiper's Vn statistic is applicable for various applications where there are more than five samples of data. The principles, algorithms, and code for the high order expansion method are attractive for the goodness-of-fit test.

关键词： Goodness-of-fit Methods Kuiper's statistic Quantile estimation algorithm design High order expansion (HOE)

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Fixed-point algorithms for solving the critical value and upper tail quantile of Kuiper's statistics

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HELIYON 2024年第7期10卷 e28274页

作者： Zhang, Hong-Yan Sun, Wei Chen, Xiao Lin, Rui-Jia Zhou, Yu Hainan Normal Univ Sch Informat Sci & Technol 99 Rd LongKun South Haikou 571158 Peoples R China Xiamen Haicang Yunjing Expt Middle Sch 99 Rd Yunmei Xiamen 361026 Peoples R China Hainan Normal Univ Informat Network & Data Ctr 99 Rd LongKun South Haikou 571158 Peoples R China

Kuiper's statistic is a good measure for the difference of ideal distribution and empirical distribution in the goodness -of -fit test. However, it is a challenging problem to solve the critical value and upper tail quantile, or simply Kuiper pair, of Kuiper's statistics due to the difficulties of solving the nonlinear equation and reasonable approximation of infinite series. In this work, the contributions lie in three perspectives: firstly, the second order approximation for the infinite series of the cumulative distribution of the critical value is used to achieve higher precision;secondly, the principles and fixed-point algorithms for solving the Kuiper pair are presented with details;finally, finally, a mistake about the critical value c n a for ( a, n ) = (0 . 01 , 30) in Kuiper's distribution table has been labeled and corrected where n is the sample capacity and a is the upper tail quantile. The algorithms are verified and validated by comparing with the table provided by Kuiper. The methods and algorithms proposed are enlightening and worth of introducing to the college students, computer programmers, engineers, experimental psychologists and so on.

关键词： Kuiper's statistic Upper tail quantile Fixed-point algorithm Numerical approximation algorithm design STEM education

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An Efficient algorithm for Sorting and Duplicate Elimination by Using Logarithmic Prime Numbers

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BIG DATA AND COGNITIVE COMPUTING 2024年第9期8卷 96-96页

作者： Yeh, Wei-Chang Forghani-elahabad, Majid Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 300044 Taiwan Fed Univ ABC Ctr Math Comp & Cognit BR-09280560 Santo Andre SP Brazil

Data structures such as sets, lists, and arrays are fundamental in mathematics and computer science, playing a crucial role in numerous real-life applications. These structures represent a variety of entities, including solutions, conditions, and objectives. In scenarios involving large datasets, eliminating duplicate elements is essential to reduce complexity and enhance performance. This paper introduces a novel algorithm that uses logarithmic prime numbers to efficiently sort data structures and remove duplicates. The algorithm is mathematically rigorous, ensuring correctness and providing a thorough analysis of its time complexity. To demonstrate its practicality and effectiveness, we compare our method with existing algorithms, highlighting its superior speed and accuracy. An extensive experimental analysis across one thousand random test problems shows that our approach significantly outperforms two alternative techniques from the literature. By discussing the potential applications of the proposed algorithm in various domains, including computer science, engineering, and data management, we illustrate its adaptability through two practical examples in which our algorithm solves the problem more than 3x104 and 7x104 times faster than the existing algorithms in the literature. The results of these examples demonstrate that the superiority of our algorithm becomes increasingly pronounced with larger problem sizes.

关键词： efficient data sorting duplicate elimination logarithmic prime numbers algorithm design time complexity

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Sudoku puzzle generation using mathematical programming and heuristics: Puzzle construction and game development

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 282卷

作者： Ates, Tugce Cavdur, Fatih Bursa Uludag Univ Dept Ind Engn Gorukle Campus TR-15059 Bursa Turkiye

In this study, first, a new mathematical programming formulation for generating Sudoku puzzles is proposed. It is possible to generate specially-configured puzzle instances using the proposed formulation which is flexible enough to control not only the numbers of the Sudoku matrix entries shown in each column, row and sub-matrix, but also the times each number appears by setting up the corresponding model parameters accordingly. The initially developed non-linear program with a quadratic constraint is reformulated as a linear-integer program by using appropriate variate transformations. The resulting mathematical program is then solved to generate Sudoku puzzles and its computational performance is analyzed through computational experiments. It is noted that the formulation is fast enough to generate Sudoku puzzles in reasonable time periods using a commercial solver on a personal computer. The study then discusses how to ensure the uniqueness of a solution for a puzzle instance generated by a hybrid approach that integrates the mathematical program with a heuristic algorithm. In the final part of the study, the idea of the proposed hybrid approach is extended and a backtracking algorithm-based puzzle generation procedure is designed and implemented by developing a standalone mobile-web game application.

关键词： Logic puzzles Sudoku Mathematical programming algorithm design Game development

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An Analysis of Robustness of Non-Lipschitz Networks

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JOURNAL OF MACHINE LEARNING RESEARCH 2023年第1期24卷 1-43页

作者： Balcan, Maria-Florina Avrim, Avrim Blum Dravyans, Dravyansh Sharma Zhang, Hongyang Carnegie Mellon Univ 5000 Forbes Ave Pittsburgh PA 15213 USA Toyota Technol Inst Chicago 6045 S Kenwood Ave Chicago IL 60637 USA Univ Waterloo 200 Univ Ave Waterloo ON N2L 3G1 Canada

Despite significant advances, deep networks remain highly susceptible to adversarial attack. One fundamental challenge is that small input perturbations can often produce large movements in the network's final-layer feature space. In this paper, we define an attack model that abstracts this challenge, to help understand its intrinsic properties. In our model, the adversary may move data an arbitrary distance in feature space but only in random low-dimensional subspaces. We prove such adversaries can be quite powerful: defeating any algorithm that must classify any input it is given. However, by allowing the algorithm to abstain on unusual inputs, we show such adversaries can be overcome when classes are reasonably well-separated in feature space. We further provide strong theoretical guarantees for setting algorithm parameters to optimize over accuracy-abstention tradeoffs using data-driven methods. Our results provide new robustness guarantees for nearest-neighbor style algorithms, and also have application to contrastive learning, where we empirically demonstrate the ability of such algorithms to obtain high robust accuracy with low abstention rates. Our model is also motivated by strategic classification, where entities being classified aim to manipulate their observable features to produce a preferred classification, and we provide new insights into that area as well.

关键词： adversarial machine learning abstention nearest-neighbor algorithms data-driven algorithm design strategic classification

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Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations

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INFORMATION SYSTEMS RESEARCH 2024年第1期35卷 339页

作者： Bi, Xuan Yang, Mochen Adomavicius, Gediminas Univ Minnesota Carlson Sch Management Informat & Decis Sci Minneapolis MN 55455 USA

We consider a marketplace where a recommender system provider (the firm) offers incentives to acquire prospective consumers by leveraging information that a market intermediary collects about these consumers. We investigate a model of consumer acquisition that incorporates several factors affecting acquisition decisions, including the value that a consumer contributes to the recommender system, the cost of participation to the consumer (e.g., privacy loss), and the value that a consumer can derive from the system due to network externality created by existing consumers. Our model is dynamic in nature, where the firm iteratively decides the next acquisition target based on previously realized acquisition outcomes. We propose flexible data-driven procedures to estimate some of the key parameters in the model using consumers' data collected by the market intermediary, for example their historical consumption data or the consumption data of other similar consumers. We also design an algorithm to compute the dynamic acquisition sequence and the corresponding incentives to offer. We conduct simulation-based empirical evaluations on two canonical recommendation tasks: movie recommendation based on numerical ratings and product offer recommendation based on browsing (clicking) behaviors and benchmark our acquisition model with random acquisition sequences with respect to (i) firm utility, (ii) recommender system performance, and (iii) consumer surplus. We find nuanced relationships between the firm's choice of incentive strategies and acquisition outcomes. Specifically, neither a constant strategy (setting the same incentive for all consumers) nor a fully greedy strategy (extracting all cumulative network externality) is optimal on all acquisition outcomes. Under a moderately greedy strategy, where the firm only partially extracts the cumulative network externality from consumers, the dynamic acquisition sequence can outperform random sequences on three acquisition outcomes

关键词： consumer acquisition recommender systems network externality personalization algorithm design

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Intelligent recognition algorithm of connection relation of substation secondary wiring drawing based on D-LLE algorithm

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DISCOVER APPLIED SCIENCES 2024年第1期7卷 1-12页

作者： Liu, Hongzhi Yang, Bojie Kang, Fang Li, Qian Zhang, Hongyang State Grid Shandong Elect Power Co Econ & Technol Res Inst Jinan 250021 Shandong Peoples R China

The accurate recognition of the connection relationship of the substation secondary wiring drawings is a key link in the operation and maintenance of the power system, but the traditional manual recognition method has the problems of time-consuming, labor-intensive and error-prone. Therefore, this paper proposes an intelligent recognition algorithm for substation secondary wiring drawings based on D-LLE algorithm. The composition of the substation secondary layout diagram is analyzed, and the connection relationship feature image is preprocessed, including smoothing and contrast enhancement, to improve the image quality. D-LLE algorithm (Denoised Locally Linear Embedding) is used to effectively extract the connection features in the drawings, which improves the feature expression ability and recognition accuracy. The improved Canny operator is used to extract the feature edge of the connection relationship, and the feature recognition algorithm is used to realize the intelligent recognition of the connection relationship. The experimental results show that the completeness of connection information extraction of substation secondary wiring drawings obtained by the proposed algorithm is between 98.56% and 99.98%, and the mean absolute error of intelligent recognition drawings connection relationship is always lower than 0.01. It shows that the proposed algorithm can accurately and quickly identify the connection relationship in the substation secondary wiring drawings, significantly improve the recognition efficiency, and has strong robustness and versatility, which can provide strong support for the intelligent operation and maintenance of the power system.

关键词： D-LLE algorithm Substation Secondary wiring drawings Connection relation Intelligent recognition algorithm design

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