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检索条件"机构=Key Laboratory of Computing Power Network and Information Security"

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An Adaptive Residual Coordinate Attention-Based network for Hat and Mask Wearing Detection in Kitchen Environments

An Adaptive Residual Coordinate Attention-Based Network for ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ying Zhao Xiaoming Wu Xiangzhi Liu Hao Chen Bei Qi Huomin Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In order to ensure food safety, it is required for personnel to wear hats and masks during food handling processes. To accurately detect the wearing status of kitchen staff, the ARP-YOLO model is proposed. Firstly, images are obtained from multiple kitchens and angles to construct a dataset reflecting the wearing status of hats and masks. To simulate more complex kitchen environments, Gaussian noise is added to the data and lighting conditions are adjusted for data augmentation. Lighting conditions in the kitchen can affect detection, causing the same target to exhibit different shapes and features under different lighting conditions, leading to missed detections. To address the above issues, we propose ARCA (Adaptive-Residual-Coordinate-Attention), which uses residual connections to strengthen attention to important features while preserving original features, and employs adaptive convolution reduction to reduce module parameters. To improve target localization accuracy, P2 detection layers are added in the Neck to obtain more accurate target position information. The ARP-YOLO model demonstrates significant improvements over the baseline model, with a 13.6% increase in Recall, allowing for more effective target capture and reduced missed detection risk. Additionally, mAP@0.5 has increased by 10%, enhancing target localization accuracy. The F1-Score has also increased by 7.4%, better balancing the relationship between Precision and Recall. To validate the effectiveness of the model, comparative experiments with other models are conducted, showing that ARP-YOLO model's Recall and Average Precision(AP) are higher than those of other models.

关键词： Location awareness Adaptation models Accuracy Convolution Shape Computational modeling Gaussian noise Lighting Feature extraction Personnel

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Lightweight Malicious Encrypted Traffic Detection Method Based on KL Image Compression

Lightweight Malicious Encrypted Traffic Detection Method Bas...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yuecheng Wen Xiaotong Li Haiqing Lv Menglu Wang Wenbo Zuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Timely and accurate detection of and response to potentially malicious encrypted traffic is crucial in cybersecurity strategies. Convolutional neural network-based approaches that convert traffic loads into images for analysis have made significant progress in malicious encrypted traffic detection, but they suffer from the challenges of large number of participants, large network size, and high demand for computational resources, especially when edge computing devices are limited. Therefore, there is an urgent need for a lightweight, efficient and accurate malicious encrypted traffic detection model. This paper proposes a malicious encrypted traffic detection model whose core idea is to utilize KL image compression technique. The model first converts the raw traffic data into grayscale images and applies KL transform to compress the images efficiently. Subsequently, a convolutional neural network model with lightweight design is used to classify the compressed image for fast and accurate malicious encrypted traffic detection. After validating on both the public traffic dataset and the lab’s self-collected dataset, the results indicate that our method requires just 0.0216 seconds to process a single input sample, achieving an average classification accuracy of 98%. These findings highlight its practicality and excellence in addressing the real-world challenges of malicious encrypted traffic.

关键词： Image coding Accuracy Computational modeling Transforms Telecommunication traffic Gray-scale Data models Mobile handsets Cryptography Convolutional neural networks

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DTBNet: medical image segmentation model based on dual transformer bridge

DTBNet: medical image segmentation model based on dual trans...

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International Joint Conference on Neural networks (IJCNN)

作者： Kequan Chen Yuli Wang Jinyong Cheng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Accurate medical image segmentation [1] techniques help doctors make better diagnoses. The traditional U-Net [2] model usually uses skip connections to directly connect the corresponding layers of the encoder and decoder, which can induce the model to retain more spatial information. Therefore, a series of ways to optimise skip connections, such as the introduction of residual connections, have appeared to optimise the convergence speed of the model and to improve the model’s performance. Although skip connections have achieved remarkable success in the field of medical image segmentation, since the structure of medical images is more complex than that of general images, skip connections may cause the model to learn some irrelevant features when performing image segmentation, and the transfer of the bottom layer information to the top layer will also cause some interference, in order to better achieve the multi-scale fusion of the bottom layer features and the top layer features and to reduce the interference between them, we proposed DTBNet for medical image segmentation, which is a U-shaped network with a bridge structure over the skip connections, and is characterised by the fact that the features output from the different layers of the encoder are first processed by the bridge structure before being transmitted to the corresponding layers of the decoder. Next, we design a SE_ASPP module in the encoder that can capture information using different scales of receptive fields and adaptively adjusts the weights for each channel. We also propose a neighbour fusion module(NFM) in the encoder for fusing features from adjacent layers of the encoder to enhance the representation of features at different scales. In addition, we construct a perceptual loss function [3] to comprehensively supervise the task-aware features from the bottom to the top layer, which helps the model to better capture the structures and textures in medical images. Our method achieves sota performan

关键词： Bridges Image segmentation Semantics Neural networks Interference Medical services Transformers

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An Efficient Multi-Layer Indexing Method on Blockchain for Multimodal Data Querying

An Efficient Multi-Layer Indexing Method on Blockchain for M...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haoyu Jia Xiaoming Wu Shanshan Liu Qile Yuan Bei Qi Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the digital society era, the generation frequency of multimodal data is rapidly increasing. Blockchain, recognized as a trusted distributed database technology, provides a new solution for the trustworthy storage and efficient management of multimodal data. However, blockchain systems support only query that use transaction hash values as keywords and cannot directly leverage the content features of multimodal data, leading to generally low query efficiency. To address this issue, this paper introduces an efficient multi-layer indexing method on blockchain for multimodal data querying. It establishes an effective mapping between on-chain and off-chain data through a verifiable on-chain and off-chain collaborative storage architecture. The paper also proposes Multi-layer Bitmap Block Index (MBBI) and Cuckoo Merkel Tree (C-MT) to optimize the querying process. Experimental results demonstrate that this method not only ensures the consistency and integrity of metadata across on-chain and off-chain but also significantly enhances the efficiency of multimodal data query. This offers a feasible solution for the storage and query needs of large-scale multimodal data.

关键词： Collaboration Metadata Data retrieval Throughput Data structures Blockchains Systems support Cybernetics Indexing

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MMFI-Net: Multilevel Multimodal Feature Injection network for Infrared and Visible Image Fusion

MMFI-Net: Multilevel Multimodal Feature Injection Network fo...

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International Joint Conference on Neural networks (IJCNN)

作者： Rundong Du Jinyong Cheng Huixuan Zhao Ying Guo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Infrared and visible image fusion aims at a better transfer of feature information from both modalities into the fused image. However, almost all existing methods currently focus on the performance of only one part of a single-modal feature extraction or image reconstruction, and do not link multimodal and multilevel features well. This leads to the problem of insufficient feature information in the image reconstruction process, and the resulting fused image lacks texture details and edge information. In this paper, we propose a multilevel multimodal feature injection network (MMFI-Net). First, the DMII module is used to complement the information of the difference modals. Then, the complemented feature information is decomposed more specifically into basic and detail information by fuzzy filtering and Laplace filtering. Next, in the final image reconstruction section, we concatenate the multi-level features from the feature extraction section with the low-level feature information from the image reconstruction section. In this way, the network is better able to capture information in the image and mitigate the problem of feature information loss. Our method demonstrates significant superiority in terms of contrast, edge information, and texture details, as evidenced by experimental results on publicly available datasets.

关键词： Visualization Filtering Image edge detection Neural networks Feature extraction Image reconstruction Image fusion

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Student Behavior Detection Method Based on Improved YOLOv5 Algorithm

Student Behavior Detection Method Based on Improved YOLOv5 A...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Haitao Hu Meihong Yang Ying Guo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

When using computer vision technology to identify students in the classroom, it will be affected by the inconsistency of the seating layout in different classrooms, the complexity of the background, as well as the existence of back row students with smaller portraits, mutual occlusion and other problems resulting in poor recognition of student behavior and other problems. In order to solve this problem, this paper firstly takes the real classroom video of a university, summarizes five common student behaviors, intercepts the video frame by frame, labels the data, and constructs the dataset, and then proposes a target detection technique based on the improved YOLOv5 algorithm, which improves the YOLOv5 algorithm by replacing the SPP module in the original network with the BasicRFB module and introducing the SACA attention mechanism. algorithm, which enhances the feature extraction capability of the model and ensures the detection speed at the same time; in the post-processing stage of the model, Soft-NMS is used to replace the original NMS non-maximal value suppression method, which reduces the probability of the model leakage detection. Finally, through the ablation experiment, it is verified that the improved method proposed in this paper has better detection effect for real classroom scenarios, which can help teachers grasp the learning status of students in time and optimize the teaching methods accordingly, and at the same time, it provides solid support for the dynamic evaluation of classroom teaching.

关键词： YOLO Computer vision Attention mechanisms Accuracy Heuristic algorithms Education Layout

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TIBS: A Deep-Learning Model for network Intrusion Detection for SDN Environments

TIBS: A Deep-Learning Model for Network Intrusion Detection ...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Yilin Zhang Xiaoming Wu Huomin Dong Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center (National Supercomputer Center in Jinan) Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Software-Defined networking (SDN) is a modern network strategy that replaces traditional network structures. Its management efficiency and convenience are significantly higher than traditional networks. However, it should be pointed out that the centralized control mechanism adopted in SDN may increase the risk of single points of failure, making it more susceptible to different types of network attacks, such as Distributed Denial of Service (DDoS), DoS, PortScan and Brute Force etc. The identification and defense of these attacks are complex and require effective preventive measures. network intrusion detection technology increasingly plays an irreplaceable role in network security. Therefore, this paper proposes an intrusion detection method for SDN environments. The system covers multiple stages, each with unique functions and roles. First, it is proposes an imbalanced data oversampling method based on improved Auxiliary Classifier Generative Adversarial networks (ACGAN) to solve the problem of data imbalance. At the same time, an intrusion detection model TIBS based on Transformer and Inception-BiGRU-SA network is proposed. First, the Encoder part of Transformer is used to capture the connection globally and perform preliminary feature extraction on the input data. Secondly, the improved Inception module is used to extract multi-scale features. Extract, use the self-attention (SA) to weight the extracted spatial features of different scales, use bidirectional gated recurrent unit(BiGRU) to improve the model's ability to extract temporal features, and use the Softmax activation function for final classification. The experiment uses the CIC-IDS-2017 dataset and CIC-DDoS-2019 dataset. The experimental results show that the proposed method has high accuracy and robustness in intrusion detection tasks, and can better identify various types of attack behaviors than traditional methods. And provides more accurate prediction results. Therefore, this method has potential

关键词： Accuracy Computational modeling network intrusion detection network security Feature extraction Transformers Data models

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Collaborative Computation Model Based on Dependency Types and Constituent Trees for Aspect-Based Sentiment Analysis

Collaborative Computation Model Based on Dependency Types an...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jia Yi Xiaoming Wu Yunfeng Dong Bei Qi Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Aspect-based sentiment analysis(ABSA) is a fine-grained sentiment classification task that aims to identify the sentiment polarity of specific aspects in a sentence. Graph convolutional networks(GCN) have recently been widely applied for modeling the associations between aspects and opinion words. However, most studies treat the relationships between all words in the graph equally through dependency analysis, without considering their dependency types, which may lead to the inability to distinguish important relationships. On the other hand, dependency trees can only reveal relationships between words and cannot simulate complex sentence relationships, such as conditional, parallel, and contrast relationships, which are crucial for capturing more accurate sentiment relationships for different aspects. To address these challenges, this paper proposes a Collaborative computation model based on Dependency Types and Constituent trees(CDTC) for aspect-based sentiment analysis. Specifically, collaborative computation of dependency type graph convolutional networks and syntax encoders enhances task efficiency. Utilizing dependency types helps distinguish important relationships, while the phrase segmentation and hierarchical structure of constituent trees provide richer syntactic information. Extensive experiments conducted on three datasets demonstrate that our proposed CDTC model achieves state-of-the-art performance.

关键词： Analytical models Sentiment analysis Graph convolutional networks Federated learning Computational modeling Semantics Collaboration

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An Efficient Energy Consumption Prediction Framework for High Performance computing Cluster Jobs

An Efficient Energy Consumption Prediction Framework for Hig...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Yantao Lou Jibin Wang Shoupeng Feng Xian Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Public Security of Shandong Province Shandong Intelligent Public Security Technology Innovation Center Jinan China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Energy consumption prediction is a crucial approach to enhance the operational efficiency of HPC (High Performance computing) clusters. Existing predicting methods for the energy consumption mainly rely on time series mode, focusing on the instantaneous power during job execution. However, in practical applications, due to the large scale of HPC clusters, the prediction models need to process a vast amount of data, often resulting in poor performance. Moreover, it's challenging to provide effective predictions for jobs with significant power fluctuations. Thus we propose a more efficient energy consumption prediction framework for HPC cluster jobs, achieving two main objectives: On one hand, it uses a data encoding module to pre-encode the sampled data of energy consumption related indicators, simplifying the data that the time series prediction model needs to process and improving prediction efficiency. On the other hand, the prediction target is set to the power distribution range in a future period of time, rather than the instantaneous power at some specific time points, which improves the usability of the prediction results. Test results show that the proposed prediction framework can achieve more markable performance improvement while ensuring the effectiveness of the predictions.

关键词： Energy consumption Computational modeling High performance computing Time series analysis power distribution Predictive models Feature extraction

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YOLO-RDD: A road defect detection algorithm based on YOLO

YOLO-RDD: A road defect detection algorithm based on YOLO

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International Conference on Computer Supported Cooperative Work in Design

作者： Jiabin Pei Xiaoming Wu Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Road maintenance is an indispensable part of the transportation system, ensuring road safety and maximizing road efficiency. The rapid and accurate acquisition of road surface information is crucial for effective road maintenance management. With the development of artificial intelligence, computer vision-based road defect detection methods are becoming popular. In this study, we propose a universal road defect detection model named YOLO-RDD, using You Only Look Once version 8 as the basic framework. Firstly, we introduce a new feature extraction and fusion approach to enhance the interaction between shallow and deep feature information, enabling multi-scale defect detection. Secondly, inspired by dynamic snake convolution, as cracks are the major road defects, we propose a new feature extraction module called DSC-C2f to adapt to the elongated and continuous morphology of cracks. Finally, we establish cross-layer connections between the backbone, neck, and prediction stages and integrate a coordinate attention module that considers inter-channel relationships and long-range positional dependencies. This allows the model to selectively focus on relevant parts of shallow and deep semantic information. We evaluate our model using the dataset provided by the crowdsensing-based road damage detection challenge (CRDDC2022) and further assess its performance on a publicly available crack and sealed crack dataset. The experiments demonstrated that our model achieved the best performance compared to the state-of-the-art (SOTA) models. Compared to the baseline model YOLOv8, our model achieved improvements of 1.2% in Precision, 1.9% in Recall, 1.9% in mAP@0.5, 0.8% in mAP@0.5:0.95, and 1.6% in F1-Score.

关键词： YOLO Cross layer design Roads Semantics Transportation Surface morphology Feature extraction

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