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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Li, Hui Hao, Yihang Lyu, Mengda Yu, Xiaojie Yang, Bo Peng, Lizhi Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China Quancheng Laboratory Jinan China

ISBN: (纸本)9783031466762

The booming of cryptocurrencies in the last decade brought about the burst of cryptomining for obtaining cryptocurrencies in recent years. Only those users with plenty of computing resources are able to gain profits according to the design of block chain. As a result, this brings out more and more criminal attacks to maliciously plunder private and public computing resources through networks. Consequently, the detection of malicious cryptomining behavior is particularly important for network security and management. In this paper, we designed Mining Vanguard, realizing the recognition of mining behavior through the detection of DNS behavior. By constructing a comprehensive feature set that includes both traditional DNS resolution features and morpheme features, we combine network characteristics with semantic characteristics, aiming to achieve early recognition. Through a large number of targeted experiments, it is verified that Mining Vanguard is promising for detecting mining behaviors on the Internet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： network security

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Deep Self-supervised Subspace Clustering with Triple Loss 30th

Deep Self-supervised Subspace Clustering with Triple Loss

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Bu, Xiaotong Dong, Jiwen Zhang, Mengjiao Feng, Guang Gao, Xizhan Niu, Sijie School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9783031533075

Deep subspace clustering (DSC) methods are widely used in various fields such as motion segmentation, image segmentation, and text mining. It uses the deep neural network to map high-dimensional features into low-dimensional latent subspace to achieve effective division of data. Nevertheless, DSC simply tends to learn representations based on auto-encoder, which can’t fully exploit the intrinsic structure of the data. In this paper, we design a novel approach called Deep self-supervised subspace clustering with triple data (DSSCT), which aims to uncover supervised information inherent in the data. Specifically, DSSCT leverages data augmentation and triple contrastive loss to obtain more effective low-dimensional representations that capture the similarity and difference among different samples. In addition, we introduce a dual self-expression matrix fusion strategy to further enhance the discriminant of the self-expression matrix used in DSSCT. To evaluate the performance of our proposed method, we conduct extensive experiments on several widely used public datasets and achieved excellent performance when compared with other state-of-art methods. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Matrix algebra

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MoCap-Video Data Retrieval with Deep Cross-Modal Learning 30th

MoCap-Video Data Retrieval with Deep Cross-Modal Learning

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Zhang, Lu Peng, Jingliang Lv, Na Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9783031533075

Cross-modal retrieval between video and motion capture (MoCap) data facilitates efficient reuse of human motion data in either skeletal or video format. For this purpose, we propose a deep cross-modal learning model for cross-modal retrieval between MoCap data and video data. First, we use a graph convolution-based network and a 3D convolution-based network to extract features from MoCap data and video data, respectively. In addition, we propose to use a pre-defined common subspace to maximize the inter-class variation and minimize the intra-class variation. Furthermore, we employ a similarity matrix to achieve the alignment between these two modalities and exploit their underlying correlations. For the purpose of experimental evaluation, due to the small amount of video data corresponding to the MoCap data in the public HDM05 dataset, we recorded a video dataset corresponding to the HDM05 motion capture dataset and performed cross-modal retrieval on it. The experimental results proved the effectiveness of the proposed scheme. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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JDC-GCN: joint diversity and consistency graph convolutional network

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Neural computing and Applications 2025年 1-17页

作者： Lin, Renjie Yao, Jie 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

In real-world scenarios, multi-view data comprises heterogeneous features, with each feature corresponding to a specific view. The objective of multi-view semi-supervised classification is to enhance classification performance by leveraging the inherent complementary and consistent information present within diverse views. Nevertheless, many existing frameworks primarily focus on assigning suitable weights to different views while neglecting the importance of consistent information. In this paper, a multi-view semi-supervised classification framework called joint diversity and consistency graph convolutional network (JDC-GCN) is proposed. Firstly, the structure of graph convolutional network is introduced to the multi-view semi-supervised classification, capable of propagating the label information over the topological structure of multi-view data. Secondly, the proposed JDC-GCN captures the complementary and consistent information from multiple views through two indispensable sub-modules, Diversity-GCN and Consistency-GCN, respectively. Finally, the attention mechanism is leveraged to dynamically adjust the weights of various views, allowing us to measure the significance of heterogeneous features and the consistent graph without introducing additional parameters. Comprehensive experiments on eight multi-view datasets are conducted to validate the effectiveness of the JDC-GCN algorithm. The results show that the proposed method exhibits superior classification performance compared to other state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Self-supervised learning

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MMCN: Multi-Modal Co-attention network for Medical Visual Question Answering 22

MMCN: Multi-Modal Co-attention Network for Medical Visual Qu...

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3rd International Conference on Control, Robotics and intelligent System, CCRIS 2022

作者： Sun, Ming Xu, Qilong Wang, Ercong Wang, Wenjun Tan, Lei Zhao, Xiu Yang Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan China

ISBN: (纸本)9781450396851

Medical visual question Answering (MedVQA) attempts to answer medical questions posed by a correlative medical image. Although it has vast potential in the medical domain, this technology is still difficult to apply in real-life. It has not been widely adopted because accurate answer prediction requires a refined understanding of medical images and question text. Existing methods directly use the whole image and the whole question for multi-modal fusion to predict the answer. However, for a question, important information only exists in a small part of the whole image and a few critical words in the question, and extra information may interfere with the answer prediction. To this end, we introduce an effective multi-modal co-attention network (MMCN) for learning essential words in the question and essential regions in the image. Each word and region is scored by the attention weighting method, which is used to indicate the importance of each word and region in the process of model reasoning. Experimental comparisons show that our MMCN is superior to the most advanced methods of the public RAD-VQA dataset. © 2022 ACM.

关键词： Forecasting

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MMT: Transformer for Multi-modal Multi-label Self-supervised Learning

MMT: Transformer for Multi-modal Multi-label Self-supervised...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2023

作者： Wang, Jiahe Li, Jia Liu, Xingrui Gao, Xizhan Niu, Sijie Dong, Jiwen School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789819708543

We proposed a novel network, called Transformer, for Multi-modal Multi-label Self-Supervised Learning (MMT) in the context of video classification. Our approach tackles the input challenges arising from different modalities by incorporating auxiliary tasks that exploit the natural synchronization and correlation between video, audio, and text. To bridge the semantic gap existing among modalities, we perform alignment in both high-dimensional and low-dimensional feature spaces for video-audio and video-text modalities, respectively. Moreover, we introduce a contrastive loss to enhance the proximity of semantically related samples, effectively catering to the specific requirements of multi-modal learning. The experimental results demonstrate that our method achieves competitive performance in various video classification tasks. This demonstrates the effectiveness of our proposed Transformer network for MMT, showcasing its potential in advancing multi-modal learning research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supervised learning

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VANet: A New network for Multi-modal Self-supervised Learning from Video and Audio

VANet: A New Network for Multi-modal Self-supervised Learnin...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2023

作者： Liu, Xingrui Zhang, Chen Feng, Zeming Dong, Jiwen Niu, Sijie Gao, Xizhan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789819708543

A new network for Multi-modal Self-Supervised Learning from Video and Audio (VANet) is proposed for video action recognition. To overcome the input issues of different modalities, we establish auxiliary tasks by exploiting the natural synchronization and correlation between video and audio, and align them at both the segment-level representation and the video/audio-level representation. In this work, we introduce a contrastive loss for joint embedding learning of the two input modalities, which is improved to enhance the proximity of semantically relevant samples and meet the special needs of multi-modal learning. In addition, we identify and remove false negative samples from the negative set. Our method achieves competitive results in video action recognition tasks and demonstrates that joint multi-modal learning is superior to single modality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supervised learning

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MARL-DCF: A Dual-Channel Fusion Routing Algorithm based on Multi-agent Reinforcement Learning 24th

MARL-DCF: A Dual-Channel Fusion Routing Algorithm Based on ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Zhenyu Sun, Runyuan Liang, Zhifeng Liu, Bo Sun, Zhenxiang 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

ISBN: (纸本)9789819615414

Emerging applications have imposed stringent demands on Internet quality of service (QoS). To ensure that various data streams within different Internet services receive the appropriate QoS, advancements in flow classification technology, software-defined networking (SDN), and programmable network devices have enabled the network to identify user demands and precisely control fine-grained traffic routing quickly. Although significant progress has been made with these technologies, optimizing the transmission paths of business flows to meet diverse QoS requirements in real-time remains a challenge. To address this issue, this paper proposes a dual-channel fusion routing algorithm based on multi-agent reinforcement learning (MARL-DCF). Implemented within an SDN architecture, the algorithm was experimentally validated in a Mininet environment using real network topologies and traffic trajectories. The results demonstrate that MARL-DCF can optimize routing paths according to the network environment, meet various QoS demands, and exhibit strong adaptability and reliability in complex scenarios such as link failures and traffic fluctuations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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Constructing High-Resolution Microstructure Image for Cement Hydration Using Real-ESRGAN

Constructing High-Resolution Microstructure Image for Cement...

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2023 International Conference on networks, Communications and intelligent computing, NCIC 2023

作者： Han, Yingying Zhang, Liangliang Wang, Lin Yang, Bo University of Jinan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan China Quan Cheng Laboratory Shandong Jinan China

ISBN: (纸本)9798350371949

Observing the microstructure images of cement can help us understand cement hydration and assist in designing such materials. Through the use of microscopic imaging equipment, such as scanning electron microscopy, images of cement microstructure can be captured. However, in the actual observation process, due to the limitations of the imaging equipment, it is common to adjust the equipment focus and observe different positions, but only high-resolution images of partial area can be obtained. It will increase economic costs and manual repeat scanning costs for obtaining whole sample vision. This paper studies the construction of high-resolution microstructure image for cement hydration based on Real-ESRGAN. This method utilizes the adaptability, parallelism and fault-tolerance of intelligent computing, which provide us a potential and low-cost way to obtain high-resolution microstructure image compared to microscopic imaging equipment. Experiment results demonstrate that the proposed method presents a good performance in constructing high-resolution microstructure image of cement hydration. © 2023 IEEE.

关键词： Cements

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A Graph Convolutional network for Point Cloud Completion 22

A Graph Convolutional Network for Point Cloud Completion

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5th International Conference on Control and Computer Vision, ICCCV 2022

作者： Tan, Lei Wang, Daole Yin, Miao Sun, Ming Zhao, Xiuyang Kong, Xiangyu Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan China

ISBN: (纸本)9781450397315

In this paper, we propose a graph convolutional network for point cloud completion (GCNC), predicting the missing region. The local information is embedded into the global features by a graph neural network module, which comprehensively captures relations among points through a graph-based context aggregation. On the other hand, the skip connection effectively utilizes the local structural details of incomplete point clouds during the inference of multi-scale missing parts, which preserves the detailed structure of the complete point cloud. Extensive experiments on the ShapeNet and ModelNet40 benchmark show that the proposed approach outperforms the previous baselines, highlighting its effectiveness. Our quantitative and qualitative analysis confirms the effectiveness of each part of the method. © 2022 ACM.

关键词： Graph neural networks

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