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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jing-Hong Wang Chang-Xin Li Jia-Teng Yang College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang China Hebei Polytechnic Institute Shijiazhuang China

Graph Attention network (GAT) is a popular method for graph embedding as they effectively leverage attention mechanisms to aggregate first-order neighborhood node information. However, they have limitations in their ability to consider high-order neighborhood nodes and structural information. Directly incorporating high-order neighborhood node information into traditional GAT may result in over-smoothing. To address this challenge, in this paper, we propose a joint attention graph embedding model based on similar networks and structural correlation. secondly, we introduce the concept of structural correlation to the original graph attentional method, and consider not only node content feature but also joint node topological structure feature when calculating the attention score. Finally, the graph attention mechanism module performs feature extraction to generate embedding representations of the nodes. The proposed algorithm achieved an accuracy of 85.7%, 74.30%, and 84.10% on the Cora, Citeseer, and Pubmed datasets respectively in node classification tasks. This represents an improvement of 2.70%, 3.94%, and 2.60% respectively compared to the traditional graph attention model. It can be seen that the proposed method can obtain a better representation of node embedding.

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Ultra-high definition video quality assessment method based on deep learning

Ultra-high definition video quality assessment method based ...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Wei Li Can He MingLe Zhou HengYu Zhang WenLong Liu Hisense Video Technology Co. Ltd Qing Dao China 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

With the development of display technology, people’s demand for high-definition video is increasing, and the requirements for video quality are also increasing. Nowadays, a large number of videos have emerged, and how to distinguish the quality of videos faces many challenges. Nowadays, deep learning is developing rapidly, and many video quality assessment (VQA) methods have emerged. These VQA methods can efficiently evaluate video quality and save a lot of manpower and material resources. To achieve the evaluation of UHD video quality, we propose an inter-frame downsampling UHD video quality assessment model (IFDU-VQA) based on deep learning. This model is a no-reference (NR) evaluation model that adopts a dual-branch structure: one branch uses a recursive network to deeply extract video inter-frame features, and combines inter-frame downsampling technology with a multi-information fusion (MIF) module to achieve different time and frequency information. Effective fusion; the other branch applies convolutional neural networks (CNN) to extract features from individual video frames. By fusing the outputs of the two branches, regression and pooling modules are used to calculate the final quality score of the video. Compared with multiple existing models, experimental results show that IFDU-VQA demonstrates superior performance on multiple standard data sets.

关键词： Deep learning Ultra-high definition video Time-frequency analysis Feature extraction Data models Quality assessment Convolutional neural networks Data mining Video recording Standards

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A Legal News Summarisation Model Based on RoBERTa, T5 and Dilated Gated CNN

A Legal News Summarisation Model Based on RoBERTa, T5 and Di...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Weijian Qin Xudong Luo School of Computer Science and Engineering Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining and Security Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education of PRC Guilin China

Summarising legal news is crucial for monitoring judicial sentiments and managing emergencies, by distilling accurate, concise and comprehensive insights from extensive, complex legal texts to enhance the efficiency of managing public opinions. However, current methods are limited in handling long-form texts. To address this, we propose a novel two-phase sentence-level summarisation model. It first converts paragraphs into sentence vectors using RoBERTa and average pooling layer, then selects the more relevant sentences using a Dilated Gated CNN layer. These extracted sentences are treated as a corpus for the T5 PEGASUS model to generate refined summaries. Experiments show our model significantly outperforms baselines on ROUGE metrics by effectively capturing relevant information from lengthy, intricate legal opinions to produce high-quality summaries.

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A Multi-Path Orchestration Method for Cloud-Edge Data Transmission Based on DRL

A Multi-Path Orchestration Method for Cloud-Edge Data Transm...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhihui Wang Xubing Dou Shuyun Li Huan Ma Xinchang Zhang Maoli 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 Shandong Normal University Jinan China QuFu normal university Jining China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In addressing the multi-path problem of cloud-edge data transmission, this paper introduces a multi-path orchestration method based on deep reinforcement learning (DRL) within the context of software-defined networks (SDN), termed Deep Q-Routing (DQR). This method establishes optimal data transmission paths between the cloud and multiple edges, based on available bandwidth and transmission delay, while avoiding path congestion to improve bandwidth utilization and reduce transmission delay. The optimal routing in SDN is defined through the design of state space sets, reward functions, and neural networks. Simulation experiments were conducted to evaluate the DQR method. Compared to existing routing methods, the proposed DQR method, which includes a reward function with weighted parameters, demonstrates superior overall network performance.

关键词： Cloud computing Scalability Neural networks Distributed databases Bandwidth Routing Deep reinforcement learning Delays Data communication Software defined networking

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

arXiv

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arXiv 2024年

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2024, CC BY-NC-SA.

关键词： Speech recognition

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Research on the Path of Industrial Intelligence Promoting the Upgrading of Manufacturing Industry

Research on the Path of Industrial Intelligence Promoting th...

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作者： Wenhao Liu Delong Han Mengmeng Sun Qianlong 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Qilu University of Technology (Shandong Academy of Sciences)

With the deepening development of a new round of global technological revolution and industrial transformation, the new generation of information technology is constantly breaking through. At the same time, the international environment is becoming increasingly complex, and competition in technology and industry is becoming more intense. Currently, China has shifted to a stage of high-quality development and is in a critical period of transforming its development mode, optimizing its economic structure,and transforming its growth momentum. As an important manufacturing province in China,Shandong has a complete range of industries and a complete structure. However, there are also constraints such as outdated traditional industries, surplus low-end manufacturing, and insufficient technological innovation. Therefore, promoting the digital and intelligent transformation and upgrading of the manufacturing industry has become an inevitable choice for industrial development. This article studies the current situation and practical difficulties of industrial intelligence empowering the development of the manufacturing industry, explores paths suitable for the upgrading of Shandong's manufacturing industry,and has certain reference value for promoting the intelligent transformation and highquality development of Shandong's manufacturing industry, and accelerating the construction of an advanced manufacturing strong province.

关键词： Industrial Intelligence Manufacturing Industry Industrial Upgrading Shandong Province

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Robust Outlier Rejection for 3D Registration with Variational Bayes

arXiv

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arXiv 2023年

作者： Jiang, Haobo Dang, Zheng Wei, Zhen Xie, Jin Yang, Jian Salzmann, Mathieu CVLab EPFL Switzerland PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier feature representations. In this paper, we develop a novel variational non-local network-based outlier rejection framework for robust alignment. By reformulating the non-local feature learning with variational Bayesian inference, the Bayesian-driven long-range dependencies can be modeled to aggregate discriminative geometric context information for inlier/outlier distinction. Specifically, to achieve such Bayesian-driven contextual dependencies, each query/key/value component in our non-local network predicts a prior feature distribution and a posterior one. Embedded with the inlier/outlier label, the posterior feature distribution is label-dependent and discriminative. Thus, pushing the prior to be close to the discriminative posterior in the training step enables the features sampled from this prior at test time to model high-quality long-range dependencies. Notably, to achieve effective posterior feature guidance, a specific probabilistic graphical model is designed over our non-local model, which lets us derive a variational low bound as our optimization objective for model training. Finally, we propose a voting-based inlier searching strategy to cluster the high-quality hypothetical inliers for transformation estimation. Extensive experiments on 3DMatch, 3DLoMatch, and KITTI datasets verify the effectiveness of our method. Code is available at https://***/Jiang-HB/VBReg. Copyright © 2023, The Authors. All rights reserved.

关键词： Bayesian networks

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A Fault Diagnostic Method for Rolling Bearings Based on CAR-SNN Model

A Fault Diagnostic Method for Rolling Bearings Based on CAR-...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Qiuting Li Xiuqing Wang Wenxia Du College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China College of China Combustion Engineering Hebei Normal University Shijiazhuang China

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual network (ResNet), and Spiking Neural networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings.

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Unified Density-Aware Image Dehazing and Object Detection in Real-World Hazy Scenes 15th

Unified Density-Aware Image Dehazing and Object Detection in...

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15th Asian Conference on computer Vision, ACCV 2020

作者： Zhang, Zhengxi Zhao, Liang Liu, Yunan Zhang, Shanshan Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High -Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783030695378

It is an important yet challenging task to detect objects on hazy images in real-world applications. The major challenge comes from low visual quality and large haze density variations. In this work, we aim to jointly solve the image dehazing and the object detection tasks in real hazy scenarios by using haze density as prior knowledge. Our proposed Unified Dehazing and Detection (UDnD) framework consists of three parts: a residual-aware haze density classifier, a density-aware dehazing network, and a density-aware object detector. First, the classifier exploits the residuals of hazy images to accurately predict density levels, which provide rich domain knowledge for the subsequent two tasks. Then, we design respectively a High-Resolution Dehazing network (HRDN) and a Faster R-CNN-based multi-domain object detector to leverage the extracted density information and tackle hazy object detection. Experiments demonstrate that UDnD performs favorably against other methods for object detection in real-world hazy scenes. Also, HRDN achieves better results than state-of-the-art dehazing methods in terms of PSNR and SSIM. Hence, HRDN can conduct haze removal effectively, based on which UDnD is able to provide high-quality detection results. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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BIFLC: A Blockchain and IPFS-Based Multi-Consensus Federated Learning Framework

BIFLC: A Blockchain and IPFS-Based Multi-Consensus Federated...

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International Conference on computer Supported Cooperative Work in Design

作者： Jing Li Jufeng Sun Fengyong Li Xingkai Yang Yuwei Wang College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China

Federated learning is an efficient technology that implements distributed model training among multiple data sources with local data, and can realize data privacy protection and data sharing computing .However, existing federated learning models may involve a large number of external attacks that can reconstruct the original training data using the acquired model, resulting in possible global model or user privacy data attacks. To address the above problem, we propose a new decentralized multiconsensus federated learning model by combining blockchain and interplanetary file system (IPFS), named as BIFLC. To be specific, we firstly design an on-chain consensus process based on a blockchain hybrid consensus mechanism by introducing a proof- of-work (PoW) and a proof-of-stake (PoS) mechanism, which can ensure the integrity of the on-chain consensus process and provide a chained data hash index for data. Furthermore, we introduce the interplanetary file system to reduce the cost of storing data on the chain and employ its distributed content delivery mechanism to save bandwidth. Extensive experiments demonstrate that our proposed scheme has higher accuracy and lower IPFS transmission time.

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