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Increasing the Maximum Capacity Path in a Network and Its Application for Improving the Connection Between Two Routers

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Tsinghua science and technology 2024年第3期29卷 753-765页

作者： Adrian M.Deaconu Javad Tayyebi Department of Mathematics and Computer Science Transilvania University of BrasovBrasov 500036Romania Department of Industrial Engineering Birjand University of TechnologyBirjand 000097Iran

Abstract:This paper addresses the problem of improving the optimal value of the Maximum Capacity Path(MCP)through expansion in a flexible network,and minimizing the involved *** only condition applied to the cost functions is to be non-decreasing *** is a non-restrictive condition,reflecting the reality in practice,and is considered for the first time in the ***,the total cost of expansion is a combination of max-type cost(e.g.,for supervision)and sum-type cost(*** building infrastructures,price of materials,price of labor,etc.).For this purpose,two types of strategies are combined:(l)increasing the capacity of the existing arcs,and(l)adding potential new *** different problems are introduced and *** the problems have immediate applications in Internet routing *** first one is to extend the network,so that the capacity of an McP in the modified network becomes equal to a prescribed value,therefore the cost of modifications is minimized.A strongly polynomial-time algorithm is deduced to solve this *** second problem is a network expansion under a budget constraint,so that the capacity of an McP is maximized.A weakly polynomial-time algorithm is presented to deal with *** the special case when all the costs are linear,a Meggido's parametric search technique is used to develop an algorithm for solving the problem in strongly polynomial *** new approach has a time complexity of O(n^(4)),which is better than the time complexity of O(n4 log(n)of the previously known method from literature.

关键词： Maximum Capacity Path(MCP) network expansion Internet routing polynomial-time algorithms

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PCB Surface Defect Detection based on YOLOv8n

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2017-2025页

作者： You, Rui Wang, Zhifeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the electronic manufacturing industry, accurate detection of PCB defects is crucial as it directly impacts product quality and reliability. The primary challenges in PCB defect detection include missed detections and false alarms, particularly concerning micro-defects. This study proposes an enhanced PCB defect detection algorithm based on YOLOv8, which incorporates the Global Attention Module (GAM), Partial Convolution Layer (PConv3), and a multi-head detection strategy. The GAM improves the model’s sensitivity to micro-defects by capturing and weighting global context information through spatial and channel attention mechanisms applied to the input feature map. The PConv3 optimizes feature extraction, minimizing false alarms due to information loss. The multi-head detection strategy identifies defects at varying scales, preserving detailed information and enhancing the detection of small-sized defects. Experimental results demonstrate that the improved algorithm achieves a 3.6% increase in average precision while meeting real-time detection requirements. © (2024), (International Association of Engineers). All rights reserved.

关键词： Deep Learning Global Attention Module Multi-head Detection PCB Defect Detection PConv3 YOLOv8

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LDTR:Transformer-based lane detection with anchor-chain representation

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Computational Visual Media 2024年第4期10卷 753-769页

作者： Zhongyu Yang Chen Shen Wei Shao Tengfei Xing Runbo Hu Pengfei Xu Hua Chai Ruini Xue School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Didi Chuxing Beijing 100081China

Despite recent advances in lane detection methods,scenarios with limited-or no-visual-clue of lanes due to factors such as lighting conditions and occlusion remain challenging and crucial for automated ***,current lane representations require complex post-processing and struggle with specific *** by the DETR architecture,we propose LDTR,a transformer-based model to address these *** are modeled with a novel anchorchain,regarding a lane as a whole from the beginning,which enables LDTR to handle special lanes *** enhance lane instance perception,LDTR incorporates a novel multi-referenced deformable attention module to distribute attention around the ***,LDTR incorporates two line IoU algorithms to improve convergence efficiency and employs a Gaussian heatmap auxiliary branch to enhance model representation capability during *** evaluate lane detection models,we rely on Fr´echet distance,parameterized F1-score,and additional synthetic *** results demonstrate that LDTR achieves state-of-the-art performance on well-known datasets.

关键词： transformer lane detection anchor-chain

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Robust style injection for person image synthesis

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CAAI Transactions on Intelligence technology 2025年第2期10卷 402-414页

作者： Yan Huang Jianjun Qian Shumin Zhu Jun Li Jian Yang School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina School of Fashion and Textiles The Hong Kong Polytechnic UniversityHong KongChina

Person Image Synthesis has been widely used in fashion with extensive application *** point of this task is how to synthesise person image from a single source image under arbitrary *** methods generate the person image with target pose well;however,they fail to preserve the fine style details of the source *** address this problem,a robust style injection(RSI)model is proposed,which is a coarse-to-fine framework to synthesise target the person *** develops a simple and efficient cross-attention based module to fuse the features of both source semantic styles and target pose for achieving the coarse aligned *** adaptive instance normalisation is employed to enhance the aligned features in conjunction with source semantic ***,source semantic styles are further injected into the positional normalisation scheme to avoid the fine style details erosion caused by massive *** training losses,optimal transport theory in the form of energy distance is introduced to constrain data distribution to refine the texture style ***,the authors’model is capable of editing the shape and texture of garments to the target style *** experiments demonstrate that the authors’RSI achieves better performance over the state-of-art methods.

关键词： computer vision image reconstruction virtual try‐on

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An Improve Grey Wolf Optimizer Algorithm for Traveling Salesman Problems

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第6期51卷 602-612页

作者： Xu, Zhinan Zhang, Xiaoxia School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

The Traveling Salesman Problem (TSP) seeks the shortest closed tour that visits each city once and returns to the starting city. This problem is NP-hard, so it is not easy to solve using conventional methods. The grey wolf optimization (GWO) algorithm has shown outstanding per- formance in many practical applications. However, it is inclined towards premature convergence. This paper proposes an im- proved GWO (I-GWO) algorithm, which hybridizes GWO with genetic algorithms (GA) for the TSP. The main feature of the I-GWO algorithm is that it can make full use of the advantages of the GWO algorithm and the GA algorithm to make up for their respective shortcomings. Moreover, to make the GWO suitable for solving the TSP, both the 2-opt operator strategy and hamming distance h ave been designed to implement the discrete GWO directly. Additionally, to increase the diversity of solutions by expanding the search space, we present a new population update strategy with crossover and mutation operations in the next iteration. Meanwhile, the integration of the Simulated Annealing (SA) algorithm into the Improved Grey Wolf Optimizer (I-GWO) enhances its local search capabilities. Experimental results show that the I-GWO algorithm competes with established optimal methods for solving the TSP, suggesting its potential for different TSP variants and logistic transport domains. © (2024), (International Association of Engineers). All rights reserved.

关键词： Traveling salesman problem

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Improved Road Damage Detection Algorithm Based on YOLOv8n

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第11期51卷 1720-1730页

作者： Li, Xudong Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

This paper introduces an advanced road damage detection algorithm that effectively addresses the shortcomings of existing models, including limited detection performance and large parameter sizes, by utilizing the YOLOv8n model. Key enhancements are integrated into the proposed algorithm to bolster its efficacy. First, the ConvNeXt V2 backbone network is integrated to improve the extraction of contextual features, thereby enhancing the effectiveness of road damage detection. Second, the algorithm employs a C2f_GhostNetV2 block structure to strengthen feature representation while simultaneously reducing computational costs. Additionally, PConv is utilized in the neck region to optimize spatial feature extraction, thereby minimizing redundant computations. The experimental results indicate that the proposed algorithm performs effectively on the Chinese subset of the RDD2022 dataset. Specifically, detection accuracy improved by 1.9%, recall by 1.8%, and mAP@0.5 by 2.1%, while the number of parameters decreased by 24% compared to the initial model. The optimized algorithm increases FPS by 4, meeting the dual requirements of mobile devices for accuracy and real-time object detection. © (2024), (International Association of Engineers). All rights reserved.

关键词： Feature extraction

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Multi-lesion Segmentation of Fundus Images using Improved UNet++

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第10期51卷 1587-1595页

作者： Jiang, Haoyan Zhao, Ji School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Diabetes retinopathy (DR) is one of the complications of diabetes. Early diagnosis of retinopathy is helpful to avoid vision loss or blindness. The difficulty of this task lies in the significant differences in the size and shape of lesions between different DR samples, with a higher proportion of small lesions. We propose a new multi-disease segmentation method based on UNet++ to improve the segmentation accuracy of DR lesions. We chose Resnet50 as the backbone network and introduced a new hybrid residual module to replace the original residual module. At the same time, to compensate for the loss of information in DR small lesions during the feature extraction process, we introduce the Across Feature Map Attention (AFMA) is an auxiliary branch that enhances the segmentation accuracy of small-scale lesions. Finally, in response to the difficulty in extracting DR lesions in shallow models, the model abandoned the deep supervision structure in UNet++. In addition, we use a weighted mixed loss function to train the model. We conducted experiments on IDRID and DDR public datasets, simultaneously segmenting four typical DR lesions. The results on intersection over union (IOU) and dice similarity coefficient (Dice) showed that our method achieved competitive performance compared to other research methods. © (2024), (International Association of Engineers). All rights reserved.

关键词： Semantic Segmentation

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Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

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computers, Materials & Continua 2024年第4期79卷 1205-1222页

作者： Jungpil Shin Md.AlMehedi Hasan Md.Maniruzzaman Taiki Watanabe Issei Jozume School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science&Engineering Rajshahi University of Engineering&TechnologyRajshahi6204Bangladesh

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

关键词： Person identification leap motion hand gesture random forest support vector machine

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Reinforcement Learning Approach for Improving Platform Performance in Two-Sided Mobility Markets 27

Reinforcement Learning Approach for Improving Platform Perfo...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Ghasemi, Farnoud Tabatabaei, Seyed Hassan Ghanadbashi, Saeedeh Kucharski, Rafal Golpayegani, Fatemeh Jagiellonian University Faculty of Mathematics and Computer Science Poland School of Computer Engineering Iran University of Science and Technology Tehran Iran School of Computer Science University College Dublin Dublin Ireland

ISBN: (纸本)9798331505929

Two-sided mobility markets, with platforms like Uber and Lyft, are complex systems by nature due to intricate, non-linear interactions between the platform and the involved parties including travelers and drivers. These interactions give rise to phenomena underlying market evolution, mainly cross-side network effects. Currently, such platforms rely on rule-based (RB) strategies with a constant commission rate to grow and achieve sustainability in terms of market share and profitability. However, the constant commission rate significantly constrains the platform's ability to leverage network effects, leading to inefficient growth. In this study, a Reinforcement Learning-based (RLB) strategy is proposed to improve the platform performance through strategic levers. We employ a Deep Q-Network (DQN) within an agent-based framework, enabling the platform to adjust the commission rate on a day-to-day basis while learning the complex, non-linear interactions in the market. The results show that the RL-based strategies successfully generate and control the essential cross-side network effects in the market enhancing the platform performance via dynamic commission rate. Our results indicate 12% improvement in the platform revenue with the RL-based strategy in comparison to the rule-based strategy without significantly compromising the platform market share which can essentially impact the platform's viability in the long term. © 2024 IEEE.

关键词： Deep reinforcement learning

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