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GTK:A Hybrid-Search Algorithm of Top-Rank-k Frequent Patterns Based on Greedy Strategy

Computers, Materials & Continua 2020年第6期63卷 1445-1469页

作者： Yuhang Long Wensheng Tang Bo Yang Xinyu Wang Hua Ma Hang Shi Xueyu Cheng Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan UniversityChangsha410082China Clayton State University MorrowGA 30260USA

Currently,the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding *** the existing algorithms,although a level-wise-search could fully mine the target patterns,it usually leads to the delay of high rank patterns generation,resulting in the slow growth of the support threshold and the mining *** at this problem,a greedy-strategy-based top-rank-k frequent patterns hybrid mining algorithm(GTK)is proposed in this *** this algorithm,top-rank-k patterns are stored in a static doubly linked list called RSL,and the patterns are divided into short patterns and long *** short patterns generated by a rank-first-search always joins the two patterns of the highest rank in RSL that have not yet been *** the basis of the short patterns satisfying specific conditions,the long patterns are extracted through *** reduce redundancy,GTK improves the generation method of subsume index and designs the new pruning strategies of *** algorithm also takes the use of reasonable pruning strategies to reduce the amount of computation to improve the computational *** datasets and synthetic datasets are adopted in experiments to evaluate the proposed *** experimental results show the obvious advantages in both time efficiency and space efficiency of GTK.

关键词： Top-rank-k frequent patterns greedy strategy hybrid-search

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A Short Flows Fast Transmission Algorithm Based on MPTCP Congestion Control 6th

A Short Flows Fast Transmission Algorithm Based on MPTCP Con...

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Zhong, Hua Dong, PingPing Tang, WenSheng Yang, Bo Xie, JingYun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030578831

With the development of cloud computing and big data technology, traffic in the network presents new features. Real-time online interactive applications are increasing, mainly including web services, instant messaging, online commerce, advertising systems. They have very stringent latency requirements. However, the data traffic generated by data backup, movie download, data mining require high bandwidth. When large and small flows are transmitted concurrently, a series of problems will arise. MPTCP treats long and short flows equally. In this way, packets of long flows will preempt bandwidth at the bottleneck link, fill the router’s whole queue buffer. This will cause the short flows transmission timeout. To solve these problems, we propose a novel algorithm named MPTCP-SPTA (MPTCP short flows fast transmission algorithm). The algorithm give high priority to short flows based on the congestion control algorithm, then introduce the Explicit Congestion Notification mechanism(ECN) in MPTCP. Through extensive network simulator (NS-3) simulations, we demonstrate that MPTCP-SPTA outperforms TCP and MPTCP in three typical network topology, which can significantly reduce the completion time of the small flow and effectively improving the total throughput of the network. © 2020, Springer Nature Switzerland AG.

关键词： Web services

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Parameterized Algorithms for Fixed-Order Book Drawing with Bounded Number of Crossings per Edge 14th

Parameterized Algorithms for Fixed-Order Book Drawing with B...

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14th International Conference on Combinatorial Optimization and Applications, COCOA 2020

作者： Liu, Yunlong Chen, Jie Huang, Jingui Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

ISBN: (纸本)9783030648428

Given a graph G= (V, E) and a fixed linear order of V, the problem fixed-order book drawing with bounded number of crossings per edge asks whether there is a k-page book drawing of G such that the maximum number of crossings per edge is upper-bounded by an integer b. This problem was posed by Bhore et al. (GD 2019;J. Graph Algorithms Appl. 2020) and thought to be interesting for further investigation. In this paper, we study the fixed-parameter tractable algorithms for this problem. More precisely, we show that this problem parameterized by both the bounded number b of crossings per edge and the vertex cover number τ of the graph admits an algorithm running in time (b+2)O(τ3)·|V|, and this problem parameterized by both the bounded number b of crossings per edge and the pathwidth κ of the vertex ordering admits an algorithm running in time (b+2)O(κ2)·|V|. Our results provide a specifical answer to Bhore et al.’s question. © 2020, Springer Nature Switzerland AG.

关键词： Parameter estimation

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An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch

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Journal of Bionic Engineering 2024年第3期21卷 1541-1566页

作者： keyu Zhong Fen Xiao Xieping Gao Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education Hunan Normal UniversityChangsha410081China School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China School of Computer Science Xiangtan UniversityXiangtan411105China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China

Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific *** many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching *** address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED *** proposed algorithm first stores non-dominated optimal solutions found so far into an ***,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index *** mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant ***,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto *** this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching *** proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance ***,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED ***,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs

关键词： Dynamic economic emission dispatch Multi-objective optimization Golden jackal Euclidean distance index

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APFA:Ameliorated Pathfinder Algorithm for Engineering Applications

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Journal of Bionic Engineering 2024年第3期21卷 1592-1616页

Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food *** algorithm has the characteristics of a simple structure and high ***,PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation *** paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimization ***,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the ***,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst ***,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence *** three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the *** performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic *** numerical experimental results demonstrated the superiority of APFA.

关键词： Pathfinder algorithm Swarm intelligent Metaheuristic Engineering problems

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A NEURAL MULTIGRID SOLVER FOR HELMHOLTZ EQUATIONS WITH HIGH WAVENUMBER AND HETEROGENEOUS MEDIA

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SIAM Journal on Scientific computing 2025年第3期47卷 C655-C679页

作者： Cui, Chen Jiang, Kai Shu, Shi Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan 411105 China

In this paper, we propose a deep learning-enhanced multigrid solver for high-frequency and heterogeneous Helmholtz equations. By applying spectral analysis, we categorize the iteration error into characteristic and noncharacteristic components. We eliminate the noncharacteristic components by a multigrid wave cycle, which employs carefully selected smoothers on each grid. We diminish the characteristic components by a learned phase function and the approximate solution of an advection-diffusion-reaction (ADR) equation, which is solved using another multigrid V-cycle on a coarser scale, referred to as the ADR cycle. The resulting solver, termed Wave-ADR-NS, enables the handling of error components with varying frequencies and overcomes constraints on the number of grid points per wavelength on coarse grids. Furthermore, we provide an efficient implementation using differentiable programming, making Wave-ADR-NS an end-to-end Helmholtz solver that incorporates parameters learned through semisupervised training. Wave-ADR-NS demonstrates robust generalization capabilities for both in-distribution and out-of-distribution velocity fields of varying difficulty. Comparative experiments with other multigrid methods validate its superior performance in solving heterogeneous 2D Helmholtz equations with wavenumbers exceeding 2000. © 2025 Society for Industrial and Applied Mathematics Publications. All rights reserved.

关键词： advection-diffusion-reaction equation deep learning-enhanced multigrid solver eikonal equation high-frequency and heterogeneous Helmholtz equation iterative error analysis

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CLIMS++: Cross language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of Computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs

arXiv

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arXiv 2025年

作者： Wang, Xiaoqin Ma, Xusen Hou, Xianxu Ding, Meidan Li, Yudong Chen, Junliang Chen, Wenting Peng, Xiaoyang Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China AIAC Xi’an Jiaotong-Liverpool University China Tsinghua University China The Hong Kong Polytechnic University Hong Kong City University of Hong Kong Hong Kong Sun Yat-sen University China

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in various tasks. However, effectively evaluating these MLLMs on face perception remains largely unexplored. To address this gap, we introduce FaceBench, a dataset featuring hierarchical multi-view and multi-level attributes specifically designed to assess the comprehensive face perception abilities of MLLMs. Initially, we construct a hierarchical facial attribute structure, which encompasses five views with up to three levels of attributes, totaling over 210 attributes and 700 attribute values. Based on the structure, the proposed FaceBench consists of 49,919 visual question-answering (VQA) pairs for evaluation and 23,841 pairs for fine-tuning. Moreover, we further develop a robust face perception MLLM baseline, Face-LLaVA, by training with our proposed face VQA data. Extensive experiments on various mainstream MLLMs and Face-LLaVA are conducted to test their face perception ability, with results also compared against human performance. The results reveal that, the existing MLLMs are far from satisfactory in understanding the fine-grained facial attributes, while our Face-LLaVA significantly outperforms existing open-source models with a small amount of training data and is comparable to commercial ones like GPT-4o and Gemini. The dataset will be released at https://***/CVI-SZU/FaceBench. Copyright © 2025, The Authors. All rights reserved.

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Parallel finite element computation of incompressible magnetohydrodynamics based on three iterations

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Applied Mathematics and Mechanics(English Edition) 2022年第1期43卷 141-154页

作者： Qili TANG Yunqing HUANG Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtanHunan Province411105China

Based on local algorithms,some parallel finite element(FE)iterative methods for stationary incompressible magnetohydrodynamics(MHD)are *** approaches are on account of two-grid skill include two major phases:find the FE solution by solving the nonlinear system on a globally coarse mesh to seize the low frequency component of the solution,and then locally solve linearized residual subproblems by one of three iterations(Stokes-type,Newton,and Oseen-type)on subdomains with fine grid in parallel to approximate the high frequency *** error estimates with regard to two mesh sizes and iterative steps of the proposed algorithms are *** numerical examples are implemented to verify the algorithm.

关键词： local and parallel algorithm finite element(FE)method iteration stationary incompressible magnetohydrodynamics(MHD)

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Exploiting Multi-Decision and Deep Refinement for Ultrasound Image Segmentation 48

Exploiting Multi-Decision and Deep Refinement for Ultrasound...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Wenjing Li, Xuanya Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc Beijing100085 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781728163277

In this paper, we propose a novel convolutional neural network (MDR-Net) for ultrasound image segmentation by exploiting multi-decision and deep refinement of the target. Our MDR-Net consists of two main parts, i.e., a multi-decision module (MDM) and a deep refinement module (DRM). Specifically, the MDM effectively addresses the issue of inconspicuous target regions in ultrasound images by combining multi-scale features and multi-receptive field self-attention to enhance the discriminative representation of features and diagnose feature points multiple times. In addition, to alleviate the problem of blurred boundaries and severe speckle noise, the DRM progressively fuses multi-scale features and makes the fused features interact with higher-level features to refine the target details step by step. Finally, we evaluate the proposed method on two publicly available datasets, namely BUSI and UDIAT. We achieve a Dice of 0.8265 and 0.8827 on the two datasets, which are at least 2% and 1.24% higher than other state-of-the-art ultrasound image segmentation methods. © 2023 IEEE.

关键词： Convolutional neural network Deep refinement Multi-decision Ultrasound image segmentation

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