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7th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2022

作者： Wang, Linyong Wu, Ruiqi Li, Cen Zou, Zhiqiang Nanjing University of Posts and Telecommunications NanJing China College of Computer Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing NanJing China

ISBN: (纸本)9781665487115

Existing supervised deep learning model requires large amounts of labeled training data to learn new tasks. This is a limitation for many practical applications in disaster areas as well as in many other fields such as medical domain, where numerous labour and time are required to label datasets. According to our literature survey, there is few self-supervised learning model with high accuracy for image recognition about natural disasters. This paper proposed a self-supervised learning model for disaster image recognition based on MoCo model, called MoCo-Q, including image preprocessing, model training and transfer learning after fine-Tuning. After being trained by learning a couple of features of disaster images without artificial labels, the trained neural network is applied to downstream tasks, which achieves the recognition of a large number of disaster images at a high accuracy without the manual intervention and labeling. With the implement of the MoCo-Q, images of natural disasters can be quickly recognized without manual labeling. The work can provide scientific decision-making support for the government and it is of great significance for natural disaster assessment and disaster relief. Finally, experiments on real-world natural disaster datasets, CrisisMMD and ASONAM17, are performed, and we compared with the state-of-The-Art supervised method (ResNet50) that uses same datasets. The result presents that the accuracy of the MoCo-Q (91.56%) is 5.71% higher than that of the supervised learning model (85.85%). © 2022 IEEE.

关键词： Training Image recognition Neural networks Transfer learning Supervised learning Training data Manuals

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SNNLog: A Log Parsing Scheme with Siamese Network and Fixed Depth Tree in Networks

SNNLog: A Log Parsing Scheme with Siamese Network and Fixed ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Lei Sun Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

Syslog records critical information of network when the system is running, and has been used to help practitioners carry out various network maintenance and operation activities. Because of abundance of syslog, automated log analysis technology is needed to complete the above activities. Usually, log parsing is an important part of log analysis. Previous log parsing methods have good achievements, but they heavily rely on well-designed regular expressions and ignore semantic information of log. To solve these problems, we propose a novel log parsing method SNNLog, which uses Siamese Network to assess the similarity between log messages, and the similarity is used for the subsequent parsing. In addition, after parsing, SNNLog merges log similar events, reducing the misjudgment rate caused by non-numeric token variables at the beginning of messages. We evaluated on five publicly accessible log datasets and the results show that compared with SOTA algorithms such as LogMine, Spell, Drain and MoLFI, SNNLog has an F1-score of 0.999 on five datasets, and parsing accuracy of four datasets is the highest.

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Object Detection Algorithm Based on Second-Order Pooling Network and Gaussian Mixture Attention 5

Object Detection Algorithm Based on Second-Order Pooling Net...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Ma, Sugang Li, Ningbo Yang, Xiaobao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

To improve the feature representation ability of the YOLOX algorithm and obtain better detection performance, an object detection algorithm based on second-order pooling network and gaussian mixture attention is proposed. Firstly, the second-order pooling network is added after the PAFPN, and the higher-order statistical information is obtained by calculating the covariance matrix between different channels, to enhance the non-linear modeling capability. Secondly, the mixture attention based on the gaussian function is added after the SPP to model global contexts in the spatial and channel dimensions respectively, which improves the network performance with almost no extra parameters. The experimental results show that the detection accuracy of the proposed algorithm on the PASCAL VOC dataset reaches 82.6 %, which is 1.6 % higher than the YOLOX algorithm. © 2022 ACM.

关键词： Object detection

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A label-guided contrastive capsule network for few-shot text classification

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Neural Computing and Applications 2025年 1-26页

作者： Xu, Yang Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

Few-shot text classification involves transferring knowledge from a limited dataset to perform classification tasks in unseen domains. Existing metric-based meta-learning models, such as prototypical networks, have shown to be effective. However, these models fail to separate embeddings of similar classes, resulting in performance stagnation. Meanwhile, traditional text classification recently has succeeded with the support of extensive prior knowledge from large language models. However, in few-shot scenarios, it is challenging to generate discriminative sentence embeddings using pre-trained models, often leading to overfitting issues. Furthermore, random support set sampling adversely affects prototype quality, resulting in inaccurate category representations. In this paper, we propose the label-guided contrastive capsule network (LGCCN) to address these issues. LGCCN introduces label information as external knowledge for each category, comprising a label-guided contrastive clustering module (LGCCM) and a label-guided prototype enhancement module (LGPEM). LGCCM employs label information to guide a contrastive clustering process, using labels as anchors to enhance the process, yielding discriminative sentence embeddings from the pre-trained model. LGPEM iteratively generates a robust prototype with label guidance, significantly reducing the impact of random support set sampling. The proposed LGCCN was thoroughly evaluated on seven benchmark datasets from diverse application domains. Experimental results demonstrate that LGCCN outperforms the state-of-the-art (SOTA) method by 9.7% in 1-shot tasks and 1.1% in 5-shot tasks on news datasets. Additionally, on intent recognition datasets, LGCCN surpasses the SOTA with a 3.3% improvement in 1-shot tasks and a 1.4% improvement in 5-shot tasks, highlighting its superior performance in few-shot text classification. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Contrastive Learning

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data Augmentation of Thyroid Ultrasound Images Using Generative Adversarial Network

Data Augmentation of Thyroid Ultrasound Images Using Generat...

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IEEE International Ultrasonics Symposium (IEEE IUS)

作者： Liang, Junzhao Chen, Junying South China Univ Technol Sch Software Engn Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou Peoples R China

ISBN: (纸本)9780738112091

Ultrasound (US) has been investigated as a common method of computer aided diagnosis because of its low-cost, harmless and real-time scanning. Also the rapid development of deep learning segmentation and classification models alleviates the influence of low signal-to-noise ratio and artifacts of ultrasonic imaging. However, due to the privacy issues of medical data, it is not easy to acquire sufficient data for deep learning model training. In recent years, generative adversarial networks (GANs) are widely used in data augmentation. However, GANs suffer from the problem of mode collapse in the training process then generate images with a limited variety. On the other hand, variational auto-encoder (VAE) is free from mode collapse but it generates blurred images. In this work, we study an auto-encoding generative adversarial network combining the advantages of GAN and VAE to generate realistic images for medical thyroid ultrasound image augmentation. Experiment results show that the generated images can simulate realistic ultrasound features and thyroid tissues for augmentation and help training a U-Net model to get better segmentation results.

关键词： Generative adversarial network data augmentation ultrasound thyroid

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State-of-Charge Estimation of Lithium Battery Based on Deep Residual Shrinkage Networks and a Variant Long Short Term Memory Neural Network

State-of-Charge Estimation of Lithium Battery Based on Deep ...

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Chinese Control and Decision Conference, CCDC

作者： Penghua Li Yihui Zhang Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China

Accurate state-of-charge (SOC) estimation, which is critical to ensuring the safe and reliable operation of battery management systems in electric vehicles, is still a challenging task due to sophisticated battery dynamics and ever-changing ambient conditions. This paper proposes a method combining DRSN and ASTLSTM neural networks to estimate lithium batteries' state of charge (SOC). The proposed method can not only utilize the historical information of the data but also eliminate unimportant features by inserting soft thresholding into the deep architectures. To reveal the performance of the proposed method, the results are compared with several other methods from the literature. The comparative experimental results show that the proposed method achieves a lower root mean square error (RMSE) of 0.83% with normal data and RMSE of 0.98% with noisy data, respectively.

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An Exact Potential Game-Based End-Edge-Cloud Collaborative Task Offloading Approach 19

An Exact Potential Game-Based End-Edge-Cloud Collaborative T...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Shen, Yue Liu, Bowen Dai, Fei Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shenzhen China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350346558

With the development of Industrial Internet of Things(IIoT), massive industrial devices produce a large number of delay-sensitive and computing-intensive tasks. Offloading tasks under the end-edge-cloud collaboration mode can improve the efficiency of completing tasks. However, on the one hand, tasks need to be finished before corresponding deadlines and pay for consuming resources, so delay and cost constitute the main factors affecting task offloading decisions. On the other hand, limited real-time resources in the system may not meet the needs of all devices, so we should ensure that as many devices can complete tasks on time as possible while optimizing task delay and cost of each device. To tackle the challenges, we propose an end-edge-cloud collaborative task offloading approach based on exact potential game. Aiming at optimizing delay-cost of each device and task completion rate of the whole system, the end-edge-cloud collaborative task offloading problem is modeled as an exact potential game and solved. Extensive experiments show that the approach can improve the task completion rate of the system while optimizing the delay-cost of each device. © 2022 IEEE.

关键词： Edge computing

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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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UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism 5

UAV Visual Tracking Algorithm Based on Feature Fusion of the...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Ma, Sugang Zhang, Zixian Zhao, Zhixian Yang, Xiaobao Hou, Zhiqiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene. © 2022 ACM.

关键词： Unmanned aerial vehicles (UAV)

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Hierarchical Clustering Combination Method Using Intuitionistic Fuzzy Similarity in Software Module Clustering

Hierarchical Clustering Combination Method Using Intuitionis...

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International Conference on Networking and Network Applications (NaNA)

作者： Hong Xia Bo Wang Yongkang Zhang Yanping Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi 'an Key Laboratory of Big Data and Intelligent Computing Xi'an Shaanxi China

In order to solve the problem that the similarity method used in software module clustering can produce arbitrary decision, and the description matrix of dendrogram generated by base clustering in hierarchical clustering combination does not consider more characteristics of entities, a hierarchical clustering combination method based on intuitionistic fuzzy similarity (HCC-IFS) was proposed. In the process of entity similarity calculation, the intuitionistic fuzzy set theory and similarity calculation method are used to provide membership and non-membership attributes for each entity, so as to improve the merging efficiency and solve arbitrary decision problems. In the description matrix representation of base clustering, the absolute and relative features obtained from the dendrogram are used to construct the description matrix, and the generated description matrix is combined. Finally, the proposed algorithm is verified by experiments. Experimental results show that HCC-IFS can produce better clustering results and improve the recovery quality of software architecture.

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