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作者： Yu Wang Mingle Zhou Chunjing Wang Xingheng Mei Yumei Li 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)

The big data industry is a major opportunity to promote the development of the digital economy. This paper analyzes the status quo and problems of the big data industry in Shandong Province, combines the connotation of the big data industry chain,and puts forward countermeasure suggestions for the high-quality development of the big data industry in Shandong Province in four aspects, namely, infrastructure construction,data factor market cultivation, and data application development.

关键词： Big data industry Shandong province Big data industry chain High-quality development

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A Deep Exploration of BitLocker Encryption and security Analysis 20

A Deep Exploration of BitLocker Encryption and Security Anal...

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20th IEEE International Conference on Communication Technology, ICCT 2020

作者： Tan, Cheng Zhang, Lijun Bao, Liang Science and Technology on Communication Security Laboratory Chengdu China Security Ministry of Public Security Key Lab of Information Network Shanghai China

ISBN: (纸本)9781728181417

Due to the popularity of Windows system, BitLocker is widely used as a built-in disk encryption tool. As a commercial application, the design of BitLocker has to consider a capability of disaster recovery, which helps a user to recover data stored on encrypted disk when a regular access is not available. In this case, it will inevitably lead to some security risks when using BitLocker. We have a deep exploration of BitLocker encryption mechanism in this paper. We present the decryption method of encrypted VMK in case of system partition encryption and non-system partition encryption, respectively. VMK is the core key in BitLocker, with which the encrypted partition or the entire disk can be further decrypted. As for security analysis on BitLocker, we firstly make a difficulty analysis of brute force cracking on BitLocker keys, and then we analyze a possible threat caused by key theft. Based on this, we propose a few countermeasures about BitLocker usage. Additionally, we give some suggestions about security enhancement of BitLocker encryption. © 2020 IEEE.

关键词： Cryptography

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges

Unleashing the Potential of Decentralized Federated Learning...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Haibo Cheng Wenjian Liu Youyang Qu Kexue Fu Qinyuan Wang Faculty of Data Science City University of Macau Macau 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

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL’s current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling.

关键词： Surveys Data privacy Systematics Federated learning Medical services Data models Servers security Reliability Systematic literature review

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A Scientific Workflow Engine Design and Implementation

A Scientific Workflow Engine Design and Implementation

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第四届公共管理与智能社会国际学术会议

作者： Zhichao Zhang Meng Guo Tingting Guo Key Laboratory of' Computing Power Network and Information Security of Ministry of Education Shandong Computer Science Center (National Supercomputing 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 Inspur General Software Co.Ltd. Shandong Carbon Manager Group Co.Ltd.

This paper designs and implements a scientific workflow engine that follows the BPMN 2.0, enriching existing research on scientific workflow engine specification and theory. The engine is implemented using the Java language and adopts a layered architecture, including a process virtual machine, a BPMN model engine and a scientific workflow model engine from the bottom up, which has good scalability and is able to provide powerful support for the design and operation of scientific workflows so as to process scientific research data more efficiently.

关键词： Scientific workflow workflow workflow engine BPMN 2.0

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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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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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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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Leveraging High-Order Similar Nodes and Structural Correlation for Attention-Based Graph Embedding

Leveraging High-Order Similar Nodes and Structural Correlati...

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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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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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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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