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Equitable Top-k Results for Long Tail Data

Proceedings of the ACM on Management of Data 2023年第4期1卷 1-24页

作者： Md Mouinul Islam Mahsa Asadi Senjuti Basu Roy New Jersey Institute of Technology Newark NJ USA

For datasets exhibiting long tail phenomenon, we identify a fairness concern in existing top-k algorithms, that return a "fixed" set of k results for a given query. This causes a handful of popular records (products, items, etc) getting overexposed and always be returned to the user query, whereas, there exists a long tail of niche records that may be equally desirable (have similar utility). To alleviate this, we propose θ-Equiv-top-k-MMSP inside existing top-k algorithms - instead of returning a fixed top-k set, it generates all (or many) top-k sets that are equivalent in utility and creates a probability distribution over those sets. The end user will be returned one of these sets during the query time proportional to its associated probability, such that, after many draws from many end users, each record will have as equal exposure as possible (governed by uniform selection probability). θ-Equiv-top-k-MMSP is formalized with two sub-problems. (a) θ-Equiv-top-k-Sets to produce a set S of sets, each set has k records, where the sets are equivalent in utility with the top-k set; (b) MaxMinFair to produce a probability distribution over S, that is, PDF(S), such that the records in S have uniform selection probability. We formally study the hardness of θ-Equiv-top-k-MMSP. We present multiple algorithmic results - (a) An exact solution for θ-Equiv-top-k-Sets, and MaxMinFair. (b) We design highly scalable algorithms that solve θ-Equiv-top-k-Sets through a random walk and is backed by probability theory, as well as a greedy solution designed for MaxMinFair. (c) We finally present an adaptive random walk based algorithm that solves θ-Equiv-top-k-Sets and MaxMinFair at the same time. We empirically study how θ-Equiv-top-k-MMSP can alleviate a equitable exposure concerns that group fairness suffers from. We run extensive experiments using 6 datasets and design intuitive baseline algorithms that corroborate our theoretical analysis.

关键词： equal exposure fairness long tail data optimization algorithm top-k retrieval

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A New algorithm of AHP-BP Applied in Petroleum Engineering

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS & STATISTICS 2013年第9期39卷 138-145页

作者： Jing Ning Fan Honghai Zhai Yinghu Geng Zhi China Univ Petr Beijing Peoples R China

Artificial neural network has been widely used at present to solve many engineering problems. However, simple neural network model is hard to get the desired result. This paper proposes one combined model of AHP (Analytic Hierarchy Process) and BPNN (Back Propagation Neuron Network) based on data mining. This model can improve the rate of convergence and the reliability of results. The validity of this method has been demonstrated with data from an existing oilfield in north-west China for predicting the rate of penetration (ROP). Furthermore, the model can be used in post-well analysis to identify areas where potential drilling performance was not achieved, and help in identifying improvements for future projects.

关键词： Petroleum Engineering optimization algorithm Data Mining Neural Network AHP

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The nearest graph Laplacian in Frobenius norm

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NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS 2024年第6期31卷

作者： Sato, Kazuhiro Suzuki, Masato Univ Tokyo Grad Sch Informat Sci & Technol Dept Math Informat Tokyo Japan

We address the problem of finding the nearest graph Laplacian to a given matrix, with the distance measured using the Frobenius norm. Specifically, for the directed graph Laplacian, we propose two novel algorithms by reformulating the problem as convex quadratic optimization problems with a special structure: one based on the active set method and the other on direct computation of Karush-Kuhn-Tucker points. The proposed algorithms can be applied to system identification and model reduction problems involving Laplacian dynamics. We demonstrate that these algorithms possess lower time complexities and the finite termination property, unlike the interior point method and V-FISTA, the latter of which is an accelerated projected gradient method. Our numerical experiments confirm the effectiveness of the proposed algorithms.

关键词： convex quadratic optimization directed graph graph Laplacian optimization algorithm

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A Selection Process for Genetic algorithm Using Clustering Analysis

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algorithmS 2017年第4期10卷 123-123页

作者： Chehouri, Adam Younes, Rafic Khoder, Jihan Perron, Jean Ilinca, Adrian Univ Quebec Chicoutimi 555 Blvd Univ Chicoutimi PQ G7H 2B1 Canada Lebanese Univ Fac Engn Branch 3 Rafic Harriri Campus Beirut Lebanon Univ Versailles St Quentin Yvelines LISV Lab 10-12 Ave Europe F-78104 Velizy Villacoublay France Univ Quebec Rimouski WERL 300 Allee Ursulines Rimouski PQ G5L 3A1 Canada

This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGA(f)) and via an optimal partitioning K-opt (KGA(o)) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems.

关键词： genetic algorithm selection process clustering k-means optimization algorithm

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Adaptive mean center of mass particle swarm optimizer for auto- localization in 3D wireless sensor networks

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第9期35卷

作者： Alhasan, Waseem Ahmad, Rami Wazirali, Raniyah Aleisa, Noura Shdeed, Weaam Abo Montfort Univ Dubai Sch Comp Sci & Informat Dubai U Arab Emirates Amer Univ Emirates Coll Comp Informat Technol Dubai 503000 U Arab Emirates Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia Syrian Virtual Univ Fac Informat Technol Damascus Syria

Wireless Sensor Networks (WSNs) have become instrumental in environmental monitoring, healthcare, agriculture, and industrial automation. In WSNs, the precise localization of sensor nodes is crucial for informed decision-making and network efficiency. This study explores localization in the context of WSNs, focusing on the 6LoWPAN and Zigbee protocols. These protocols are vital for integrating WSNs into the Internet of Things (IoT). We highlight the significance of spatial node distribution and WSNs' challenges, such as resource limitations and signal interference. We emphasize range-based methods due to their accuracy. We propose the Adaptive Mean Center of Mass Particle Swarm Optimizer (AMCMPSO) to address these. Inspired by the center of mass principle, this algorithm adapts parameters for enhanced localization on regular and irregular surfaces. AMCMPSO leverages the principle of the center of mass and mean values to enhance the efficiency of sensor node localization. The algorithm incorporates adaptive parameters, including inertia weight and acceleration coefficients, to improve search efficiency and convergence speed. Our simulations demonstrate the superior performance of AMCMPSO, with an average improvement rate of 99.86%. Moreover, the localization error is consistently below 1.34 cm, ensuring precise spatial awareness. In 3D environments, AMCMPSO consistently delivers coverage rates exceeding 87%, even in challenging scenarios.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Wireless Sensor Networks WSNs-6LoWPAN Localization Range based optimization algorithm Particle Swarm Optimizer Irregular surfaces

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A dual-band dipole array antenna with fan-beam radiation for biomedical and telecommunications applications

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RESULTS IN ENGINEERING 2024年 24卷

作者： Barzegari, Rahim Ghobadi, Changiz Nourinia, Javad Urmia Univ Dept Elect Engn Orumiyeh *** Iran

This study introduces a new technique for achieving dual-band functionality in a printed dipole antenna with integrated balun feeding without additional elements. By employing an optimization algorithm, the fundamental parameters of the antenna are extracted, resulting in an antenna design that operates effectively in two distinct frequency bands of 2.4-2.5 and 5-6 GHz for WLAN/ISM and biomedical applications. By implementing the proposed design in a two-element array structure, the antenna successfully achieved a fan-beam radiation pattern in both frequencies. The array antenna exhibits fan-beam radiation patterns and peak gains of 6.90 dB and 7.84 dB, respectively, in two frequency bands: 2.06-2.52 GHz and 5.09-5.93 GHz. According to the proposed design, the array antenna has measured half-power beamwidths (HPBWs) of 38.34 degrees and 25.14 degrees in the two frequencies specified. The overall dimensions of the antenna are 1.100 lambda x 0.563 lambda x 0.004 lambda. This research design offers advantages such as smaller dimensions, higher gain, and a more straightforward structure compared to dualband array antennas with fan beam radiation patterns.

关键词： Dual-band antenna Array antenna Fan-beam Biomedical optimization algorithm

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Extended shortest path selection for package routing of complex networks

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2017年第11期28卷 1750129-1750129页

作者： Ye, Fan Zhang, Lei Wang, Bing-Hong Liu, Lu Zhang, Xing-Yi Anhui Univ Dept Comp Sci Hefei Anhui Peoples R China Univ Sci & Technol China Dept Modern Phys Hefei Anhui Peoples R China Univ Sci & Technol China Nonlinear Sci Ctr Hefei Anhui Peoples R China Univ Derby Coll Engn & Technol Comp & Math Dept Derby England Anhui Univ Dept Comp Sci Hefei Anhui Peoples R China

The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Random Walk, or finding the shortest path (SP) and rarely considering the local load information such as SP and Distance Vector Routing. Flow-based Routing mainly considers load balance and still cannot achieve best optimization. Thus, in this paper, we propose a novel dynamic routing strategy on complex network by incorporating the local load information into SP algorithm to enhance the traffic flow routing optimization. It was found that the flow in a network is greatly affected by the waiting time of the network, so we should not consider only choosing optimized path for package transformation but also consider node congestion. As a result, the packages should be transmitted with a global optimized path with smaller congestion and relatively short distance. Analysis work and simulation experiments show that the proposed algorithm can largely enhance the network flow with the maximum throughput within an acceptable calculating time. The detailed analysis of the algorithm will also be provided for explaining the efficiency.

关键词： Routing algorithm traffic flow optimization algorithm complex network

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Rolling bearing fault feature selection based on standard deviation and random forest classifier using vibration signals

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ADVANCES IN MECHANICAL ENGINEERING 2023年第4期15卷

作者： Imane, Moussaoui Rahmoune, Chemseddine Benazzouz, Djamel Univ Mhamed Bougara Solid Mech & Syst Lab LMSS Boumerdes Algeria Univ Mhamed Bougara Fac Technol Solid Mech & Syst Lab LMSS Boumerdes 35000 Algeria

The precise identification of faults is vital for ensuring the reliability of the bearing's performance, and thus, the functionality of rotary machinery. The focus of our study is on the role that feature selection plays in improving the accuracy of predictive models used for diagnosis. The study combined the Standard Deviation (STD) parameter with the Random Forest (RF) classifier to select relevant features from vibration signals obtained from bearings operating under various conditions. We utilized three databases with different bearings' health states operating under distinct conditions. The results of the study were promising, indicating that the proposed method was not only effective but also consistent, even under time-varying conditions.

关键词： Feature selection standard deviation random forest optimization algorithm bearing fault diagnosis

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Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing

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FUTURE INTERNET 2024年第4期16卷 117页

作者： Tang, Maolin Li, Wei Queensland Univ Technol Sch Comp Sci Brisbane Qld 4000 Australia Cent Queensland Univ Sch Engn & Technol Rockhampton Qld 4702 Australia

Wireless communication tower placement arises in many real-world applications. This paper investigates a new emerging wireless communication tower placement problem, namely, continuous space wireless communication tower placement. Unlike existing wireless communication tower placement problems, which are discrete computational problems, this new wireless communication tower placement problem is a continuous space computational problem. In this paper, we formulate the new wireless communication tower placement problem and propose a hybrid simulated annealing algorithm that can take advantage of the powerful exploration capacity of simulated annealing and the strong exploitation capacity of a local optimization procedure. We also demonstrate through experiments the effectiveness of this hybridization technique and the good performance and scalability of the hybrid simulated annulling in this paper.

关键词： communication tower placement optimization algorithm simulated annealing hybrid intelligent algorithm

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An optimal algorithm for mmWave 5G wireless networks based on neural network

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HELIYON 2023年第6期9卷 e17580页

作者： Chen, Liang Sefat, Shebnam M. Kim, Ki-Il Jilin Prov Inst Educ ChangChun 130022 Peoples R China Independent Univ Dept Comp Sci Dhaka Bangladesh Islamic Univ Islamic Univ Ctr Sci Res Najaf Iraq Chungnam Natl Univ Dept Comp Sci & Engn Daejeon 34134 South Korea

Fifth generation (5G) wireless networks are based on the use of spectrum blocks above 6 GHz in the millimeter wave (mmWave) range to increase throughput and reduce the overall level of interference in very busy frequency bands below 6 GHz. With the global deployment of the first commercial installations of 5G, the availability of multi-Gbps wireless connections in the mmWave frequency band becomes closer to reality and opens up some unique uses for 5G. Although, mmWave communication is expected to enable high-power radio links and broadband wireless intranet, its main challenges are inherent poor propagation conditions and high transmitter-receiver coordination requirement, which prevent it from realizing its full potential. When smart reflective surfaces are used in mmWave communication, channel state information becomes complex and imprecise. In this study, a hybrid intelligent reflecting surface consisting of a large number of passive components and a small number of RF circuits is proposed as a solution. Then, an improved deep neural network (DNN)-based technique is proposed to estimate the effective channel. The proposed technique provides better channel estimation performance according to the simulation results and improves the quality of service.

关键词： mmWave communication 5G networks optimization algorithm Channel estimation

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