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Electronic Technology and information Science (ICETIS), International Conference on

作者： Bowen Sun Xiaohan Yang Ying Guo Zhihao Zhao Jing Chen Hu Zhang Jibin Wang 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)9798350388343

ISBN: (纸本)9798350388350

With the rapid advancement of information technologies, the global data volume is increasing at an unprecedented speed. Data Lakehouse has become an effective solution for real-time aggregation of multimodal heterogeneous data and supporting multiple query modes. It is worth noting that due to the column-based storage commonly employed in Data Lakehouse and the resulting mechanisms such as Copy-on-Write and Merge-On-Read, data query speed, data freshness, and storage costs have become huge challenges. In this paper, an Offline Compaction Framework based on Hotspot Prediction (OCF-HP) was designed to address the above challenges. Specifically, we analyzed the Timeline data in Hudi which is the mainstream Data Lakehouse framework, then predicted the hotspot periods of data writing based on the improved LSTM model using such data, and realized automatic trigger of Hudi offline compaction tasks in reasonable non-hot periods. The experiment results show that OCF-HP provides Hudi with a more optimized offline compaction solution so that data lakehouse managers could achieve better query speed and data freshness with reducing storage costs in Hudi's read optimized mode.

关键词： Analytical models Costs Data integrity Predictive models Writing Data models Compaction

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Context-Guided and Syntactic Augmented Dual Graph Convolutional network for Aspect-Based Sentiment Analysis

Context-Guided and Syntactic Augmented Dual Graph Convolutio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jia Yi 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

Predicting the sentiment polarity of aspect terms in sentences is the goal of Aspect-Based Sentiment Analysis(ABSA) task. Graph Convolutional network(GCN) is used in majority of the ABSA task due to its ability to effectively capture the dependencies among words or entities within sentences. However, it may not work as expected when some sentences have no obvious syntactic structure. To alleviate this issue, we propose a Context-guided and Syntactic Augmented Dual Graph Convolutional network(CSADGCN) model for the ABSA task. Specifically, we propose a context-guided attention mechanism that captures both global and local information by combining self-attention and aspect-level attention, even though some sentences have no obvious syntactic structure. In addition,we augment the GCN with multiple linguistic features and utilize a biaffine attention module to capture the relationship between words. On three datasets, extensive experimental modifications reveal that our CSADGCN model performs better than the most recent baseline approach.

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Patent Recommendation Methods Based on Transformer Encoders and Regularization Strategies

Patent Recommendation Methods Based on Transformer Encoders ...

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Advanced information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

作者： Wan Zheng Qingyan Ding Na Li Yu Pan Xuecheng 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)9798350343335

ISBN: (纸本)9798350343342

Patent recommendation service has become a major demand of all walks of life in the society. Patent data collection and analysis can not only help enterprises grasp the development status of the industry, but also provide decision support for them in the research and development process of science and technology services. In view of the problem of inaccurate patent recommendation caused by the inconsistency between the search text and the patent text length and the low accuracy of traditional patent recommendation, a word and sentence dual processing model based on Transformer encoder and regularization strategy is proposed to complete the patent recommendation task from the level of sentence granularity and word granularity. The sentence granularity feature extraction module uses the transformer encoder superimposed multi-head attention method to obtain the feature information between the sentence vector groups, so that the sentence vector fuses the feature information of each sentence, and solves the problem of low recommendation accuracy caused by the inconsistent length of the patent text and the search text; The word granularity feature extraction module uses the twin network model to extract the feature information of the sentence granularity vector using the Transformer encoder using the double splicing operation, improve the model performance and model accuracy; Introduce a strategy based on Dropout and symmetric JS divergence regularization, improve the model generalization ability and accuracy. Experiments have proved that it outperforms SBERT, BERT, MGMSN and SVM models in terms of performance.

关键词： Patents Accuracy Fuses Splicing Feature extraction Transformers Search problems Vectors Data models Data mining

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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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SOD-YOLO: A New Small Object Traffic Sign Recognition network

SOD-YOLO: A New Small Object Traffic Sign Recognition Networ...

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International Conference on computer Supported Cooperative Work in Design

作者： Xunjia Yin Xiaoming Wu Xiangzhi Liu Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security 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

Traffic sign recognition is a challenging task for unmanned systems, especially the problem of detecting small targets. During traffic sign recognition (TSR), traffic sign targets are small, represented by a small number of pixels in the image, and lack sufficient detail, making it difficult to detect them using a detector. To solve these problems a new traffic sign target detection algorithm is proposed, which introduces a new convolutional module SPD-Conv to capture the feature information of small target traffic signs more efficiently and replaces the C3 module in the backbone network with a C2f module, which greatly reduces the parameters and computation of the original network and achieves a lighter weight to make the model easier to deploy. A new weighted bi-directional feature pyramid network is used to replace the original feature pyramid network. In addition, a coordinate lightweight attention module is introduced for the small target feature loss problem, and finally, a new loss function is proposed to improve the convergence speed of the model while improving the small target detection accuracy. Extensive experimental results on CCTSDB and tt-100k datasets show that our method is more versatile and superior for traffic sign small target detection compared to several state-of-the-art methods.

关键词： YOLO Adaptation models Accuracy Target recognition Computational modeling Detectors Feature extraction

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A Multi-Scale Infrared and Visible Image Fusion network Based on Context Perception

A Multi-Scale Infrared and Visible Image Fusion Network Base...

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International Conference on computer Supported Cooperative Work in Design

作者： Huixuan Zhao Jinyong Cheng Rundong Du 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

Existing image fusion methods often fail to fully utilize the contextual information of the images, resulting in blurry and distorted fusion results. In addition, although the existing multi-scale feature extraction methods can consider the information of the source image from multiple perspectives, they tend to ignore the local information. These limitations degrade the performance and effectiveness of image fusion techniques in practical applications. To address above issues, we propose a fusion network architecture called MIA-Nest, consisting of encoder network, fusion network, and decoder network. Specifically, we introduce the Squeeze-and-Excitation Blocks(SEBs) into the encoder network, so that it can take local information into account while extracting multi-scale features. At the same time, in order to allow the fusion network to integrate the context information of the extracted features, we introduce Global Context Blocks (GCBs), so as to enrich the details in the fusion result and enable it to contain more information. Finally, the decoder network reconstructs the fusion image from the fused features obtained by the fusion network. Experimental results demonstrate that our proposed method significantly improves the quality and detail preservation capability of the fused images.

关键词： Federated learning computer architecture network architecture Feature extraction Decoding Data mining Image fusion

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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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GNN-ASG: A Double Feature Selection-based Adversarial Sample Generation Method in Industrial Control System

GNN-ASG: A Double Feature Selection-based Adversarial Sample...

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International Conference on computer Supported Cooperative Work in Design

作者： Lijuan Xu Zhiang Yao Dawei Zhao Xin Li Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security 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

Due to the unique constraints of industrial control data, industrial control adversarial sample attacks are particularly challenging. Existing methods strive to conduct adversarial sample attacks under the conditions of satisfying industrial control data constraints, however, the results are not ideal. Therefore, this study proposes a new adversarial sample generation method GNN-ASG based on double feature selection. GNN-ASG uses data constraints to ensure the rationality of generated data, and uses Graph Deviation network (GDN) and Autoencoder to improve the quality and versatility of adversarial samples. A new adversarial sample evaluation metrics Adversarial Sample Attack Impact Rate (ASAIR) is proposed to address the problem that existing evaluation metrics are difficult to accurately judge the effectiveness of adversarial sample attacks. This method considers the principle and application environment of adversarial samples, and fully demonstrates the practical effect of adversarial samples. In a comprehensive experiment conducted on three public datasets, GNN-ASG achieves an impressive ASAIR of 21.83%, higher than existing methods of 13.94%. This paper demonstrates the versatility and effectiveness of GNN-ASG by comparing its performance with three state-of-the-art adversarial sample generation methods on four anomaly detection models. GNN-ASG can maximally reduce the F1-Score of the detection model by 0.7605.

关键词： Measurement Federated learning Industrial control Feature extraction Anomaly detection Glass box

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CNN-Based Forensic Method on Contrast Enhancement with JPEG Post-Processing

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computers, Materials & Continua 2021年第12期69卷 3205-3216页

作者： Ziqing Yan Pengpeng Yang Rongrong Ni Yao Zhao Hairong Qi Institute of Information Science Beijing Jiaotong UniversityBeijing100044China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong UniversityBeijing100044China Electrical Engineering and Computer Science Department University of Tennessee KnoxvilleKnoxville37996TNUSA

As one of the most popular digital image manipulations,contrast enhancement(CE)is frequently applied to improve the visual quality of the forged images and conceal traces of forgery,therefore it can provide evidence of tampering when verifying the authenticity of digital *** enhancement forensics techniques have always drawn significant attention for image forensics community,although most approaches have obtained effective detection results,existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG *** detection of forgery on contrast adjustments in the presence of JPEG post processing is still a challenging *** this paper,we propose a new CE forensic method based on convolutional neural network(CNN),which is robust to JPEG *** proposed network relies on a Xception-based CNN with two preprocessing ***,unlike the conventional CNNs which accepts the original image as its input,we feed the CNN with the gray-level co-occurrence matrix(GLCM)of image which contains CE fingerprints,then the constrained convolutional layer is used to extract high-frequency details in GLCMs under JPEG compression,finally the output of the constrained convolutional layer becomes the input of Xception to extract multiple features for further *** results show that the proposed detector achieves the best performance for CE forensics under JPEG post-processing compared with the existing methods.

关键词： Contrast enhancement forensics convolutional neural networks robust forensics JPEG compression

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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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