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25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022

作者： Lin, Tengxiang Lin, Cheng-Kuan Chen, Zhen Cheng, Hongju College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan

ISBN: (纸本)9789811995811

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, significantly reducing service latency. In this paper, we aim to divide the task into several sub-tasks through its inherent interrelation, guided by the idea of high concurrency for synchronization, and then offload sub-tasks to other edge servers so that they can be processed to minimize the cost. Furthermore, we propose a DRL-based Multi-Task Dependency Offloading Algorithm (MTDOA) to solve challenges caused by dependencies between sub-tasks and dynamic working scenes. Firstly, we model the Markov decision process as the task offloading decision. Then, we use the graph attention network to extract the dependency information of different tasks and combine Long Short-term Memory (LSTM) with Deep Q network (DQN) to deal with time-dependent problems. Finally, simulation experiments demonstrate that the proposed algorithm boasts good convergence ability and is superior to several other baseline algorithms, proving this algorithm’s effectiveness and reliability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Research on the Security Defense Strategy of Smart City's Substitution Computer network in Big Data 5

Research on the Security Defense Strategy of Smart City's Su...

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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Ye, Yuguang Quanzhou Normal University fujian Quanzhou362000 China Fujian Provincial Key Laboratory of Data Intensive Computing fujian Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University fujian Quanzhou362000 China

ISBN: (纸本)9781665435246

With the rapid development of the information technology era, the era of big data has also arrived. While computer networks are promoting the prosperity and development of society, their applications have become more extensive and in-depth. Smart city video surveillance systems have entered an era of networked surveillance and business integration. The problems are also endless. This article discusses computer network security in the era of big data, hoping to help strengthen the security of computer networks in our country. This paper studies the computer network security prevention strategies of smart cities in the era of big data. © 2021 IEEE.

关键词： Big data

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Super-Resolution of Defocus Thread Image Based on Cycle Generative Adversarial networks 12th

Super-Resolution of Defocus Thread Image Based on Cycle Gene...

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12th IFIP TC 12 International Conference on intelligent information processing, IIP 2022

作者： Jiang, Pengfei Xu, Wangqing Li, Jinping School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-Year Jinan250022 China

ISBN: (纸本)9783031039478

The dual camera calibration measurement method can realize low-cost and high-precision bolt dimension measurement by using two microscope cameras. But the height difference between the thread crest and root exceeds the depth of field, and the thread image becomes defocus, which seriously affects the measurement accuracy. For this reason, a super-resolution method for defocus thread image based on cyclic generative adversarial networks is proposed. We collected focus thread images and defocus thread images as training data. Two encoders are used in the generation network to extract image defocus features and content features. And a sub-pixel convolution layer is added to the decoder to achieve image super-resolution. A loss function based on adversarial loss and cycle-consistent loss is constructed to realize unsupervised training of the network, thereby achieve super-resolution of defocus thread images. The experimental results show that, in the simulated defocus images, the method has superiority in image detail preservation, sharpness improvement and peak signal to noise ratio. In the bolt dimension measurement task, it can effectively reconstruct the clear thread image and thus provide the measurement accuracy to 0.01 mm. © 2022, IFIP International Federation for information processing.

关键词： Optical resolving power

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Adaptive-Propagating Heterophilous Graph Convolutional network

SSRN

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SSRN 2024年

作者： Huang, Yang Pi, Yueyang Shi, Yiqing Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China College of Photonic and Electronic Engineering Fujian Normal University Fuzhou350007 China

Graph convolutional network have significant advantages in tasks dealing with graph-structured data, but most existing approaches based on graph convolutional network usually potentially assume that nodes belonging to the same class in a graph tend to form edges, yet inter-class edges exist in many real graph-structured data. Due to the propagation mechanism of graph convolutional network, it is usually difficult to avoid the aggregation of interference information of nodes from different classes, leading to the inclusion of noise and irrelevant information in the results, thus affecting the performance of the model. We propose a new framework to address this issue, which can extend graph convolutional network to heterophilous graph-structured data. The proposed method has two main parts: Firstly, from the perspective of feature space, the heterophily of the graph-structured data is modeled so that the model can adaptively change the information propagation process according to the homophily of the edges, and mitigate the influence of inter-class information on the model. Secondly, the implicit node interaction information is captured through the feature space, which is then fused with the original interaction information to aggregate sufficient intra-class information. Extensive experiments on real-world datasets demonstrate the competitiveness of our approach among current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Graphic methods

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Any-Angle Routing Algorithm for Microfluidic Biochips Driven by Flow Path

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2025年第4期62卷 978-988页

作者： Youlin, Pan Shuai, Guo Xing, Huang Genggeng, Liu College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

Continuous-flow microfluidic biochips (CFMBs) have become a hot research topic in recent years due to their ability to perform biochemical assays automatically and efficiently. For the first time, PathDriver+ takes the requirements of the actual fluid transportation into account in the design process of CFMBs and implements the actual fluid transport and removal, and plans separate flow paths for each transport task, which have been neglected in previous work. However, PathDriver+ does not take full advantage of the flexibility of CFMBs routing because it only considers the optimization of flow channel length for the global routing in the mesh model, except for the detailed routing. In addition, PathDriver+ only considers the X architecture, while the existing work shows that the any-angle routing can utilize the routing resources more efficiently and shorten the flow channel length. To address the above issues, we propose a flow path-driven arbitrary angle routing algorithm, which can improve the utilization of routing resources and reduce the flow channel length while considering the actual fluid transportation requirements. The proposed algorithm constructs a search graph based on constrained Delaunay triangulation to improve the search efficiency of routing solutions while ensuring the routing quality. Then, a Dijkstra-based flow path routing method is used on the constructed search graph to generate a routing result with a short channel length quickly. In addition, in the routing process, channel reuse strategy and intersection optimization strategy are proposed for the flow path reuse and intersection number optimization problems, respectively, to further improve the quality of routing results. The experimental results show that compared with the latest work PathDriver+, the length of channels, the number of ports used, and the number of channel intersections are significantly reduced by 33.21%, 11.04%, and 44.79%, respectively, and the channel reuse rate is i

关键词： High level synthesis

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TFGDA: exploring topology and feature alignment in semi-supervised graph domain adaptation through robust clustering 24

TFGDA: exploring topology and feature alignment in semi-supe...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Jun Dan Weiming Liu Chunfeng Xie Hua Yu Shunjie Dong Yanchao Tan Zhejiang University Queen Mary University of London Dalian University of Technology Shanghai Jiao Tong University Fuzhou University and Engineering Research Center of Big Data Intelligence Ministry of Education and Fujian Key Laboratory of Network Computing and Intelligent Information Processing

ISBN: (纸本)9798331314385

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods.

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Control Logic Routing for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2025年第4期62卷 950-962页

作者： Cai, Huayang Huang, Xing Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

With the advancement of electronic design automation, continuous-flow microfluidic biochips have become one of the most promising platforms for biochemical experiments. This chip manipulates fluid samples in milliliters or nanoliters by utilizing internal microvalves and microchannels, and thus automatically performs basic biochemical experiments, such as mixing and detection. To achieve the correct bioassay function, the microvalves deployed inside the chip are usually managed by a multiplexer-based control logic, and valves receive control signals from a core input through the control channel for accurate switching. Since biochemical reactions typically require high sensitivity, the length of control paths connecting each valve needs to be reduced to ensure immediate signal propagation, and thus to reduce the signal propagation delay. In addition, to reduce the fabrication cost of chips, a vital issue to be addressed in the logic architecture design is how to effectively reduce the total channel length within the control logic. To address the above issues, we propose a deep reinforcement learning-based control logic routing algorithm to minimize the signal propagation delay and total control channel length, thereby automatically constructing an efficient control channel network. The algorithm employs the dueling deep Q-network architecture as the agent of the deep reinforcement learning framework to evaluate the tradeoff between signal propagation delay and total channel length. Besides, the diagonal channel routing is implemented for the first time for control logic, thus fundamentally improving the efficiency of valve switching operations and reducing the fabrication cost of the chip. The experimental results demonstrate that the proposed algorithm can effectively construct a high-performance and low-cost control logic architecture. © 2025 Science Press. All rights reserved.

关键词： Deep reinforcement learning

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Multi-Scale Structure-Guided Graph Generation for Multi-View Semi-Supervised Classification

SSRN

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SSRN 2024年

作者： Wu, Yilin Chen, Zhaoliang Zou, Ying Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Department of Computer Science Hong Kong Baptist University Hong Kong

Graph convolutional network has emerged as a focal point in machine learning because of its robust graph processing capability. Most existing graph convolutional network-based approaches are designed for single-view data, yet in many practical scenarios, data is represented through multiple views. Although the ability of graph convolutional network is undeniable, the quality of graph directly affects its performance. Moreover, the process of graph generation with traditional nearest neighbor in traditional multi-view learning actually amplifies the influence of different information on performance across these views. To tackle the aforementioned challenges, this paper proposes a multi-scale graph generation deep learning framework, called multi-scale semi-supervised graph generation based multi-view classification, consisting of two modules: edge sampling and path sampling. The former aims to generate an adjacency graph by selecting edges based on the maximum likelihood among graphs from different views. Meanwhile, the latter seeks to construct adjacency graphs according to the characteristics of paths within the graphs. Extensive experimental results robustly demonstrate the superior performance of our proposed framework, compared to other state-of-the-art multi-view semi-supervised approaches. © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

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Computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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IDENTIFYMIX: AN EFFICIENT TWO-STAGE LEARNING APPROACH TO COMBATING LABEL NOISE

Journal of Applied and Numerical Optimization

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Journal of Applied and Numerical Optimization 2023年第2期5卷 237-253页

作者： Tong, Kai Ke, Xiao Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou350003 China

Deep neural networks require correct label annotation during supervised learning. It is inevitable, however, that some labels are noisy during the labeling process. A deep neural network retains incorrect labels during training, resulting in a degradation of performance. Therefore, it is essential to identify samples with potentially correct labels. In state-of-the-art methods, small-loss samples are chosen for subsequent training through a sample selection strategy. Howerver, it typically ignores the imbalance in noise ratios between mini-batches when performing sample selection within each minibatch. Further, numerous valuable samples with high losses are discarded, which adversely affects the generalization performance of the model, particularly under conditions of high noise ratios. To this end, this paper proposes IdentifyMix, an effective two-stage learning approach for noisy robust learning that combines an unique sample selection strategy and the semi-supervised learning technique. By observing how the dynamics of network training are changing, AUM (Area Under the Margin) provides a criterion that is applied in this research to identify mislabeled data. Moreover, by combining semi-supervised learning with contrastive learning and data augmentation, it is possible to extract more useful information from mislabeled samples. Experiments on several synthetic and real-world noise benchmarks demonstrate the effectiveness of IdentifyMix compared with state-of-the-art methods. © 2023 Journal of Applied and Numerical Optimization.

关键词： Image classification

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