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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Zheng Su Huiling Shi Hao Hao Wei Zhang 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

In this paper, we address the problem of recommendation-enabled caching for cloud-edge-end collaborative networks. We leverage collaborative filtering techniques to predict user content preferences and construct a content recommendation matrix. To minimize content delivery delay, we model the cache replacement process as a MDP and propose an enhanced FDDPG algorithm for optimization. Experimental results demonstrate that our approach significantly reduces average delivery latency and improves the cache hit rate compared to existing methods.

关键词： Base stations Federated learning Collaborative filtering Reinforcement learning Prediction algorithms Delays Sensors Optimization

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Machine Learning-Based Prediction of MPI Communication Time in High-Performance computing Applications

Machine Learning-Based Prediction of MPI Communication Time ...

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Zhuang Wang Yuan Zhuang Yunhui Zeng 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

The rapid advancements in high-performance computing (HPC) have made large-scale parallel computing feasible. As a commonly used parallel programming model, Message Passing Interface (MPI) plays a crucial role in HPC systems and directly affects the performance and efficiency of these applications. Therefore, modeling and predicting the performance of MPI communication is critically important. This paper utilizes a machine learning-based approach to predict MPI communication time. It uses performance analysis tools to collect real-time communication data and select critical features related to execution time. The data is categorized, then, for each type of MPI communication, a dedicated regression model is trained. Firstly, XGBoost and Artificial Neural networks (ANN) are employed for function level modeling. Then, fusion techniques are used to combine XGBoost and ANN models to improve prediction accuracy. Finally, these function level models are integrated into an application level model to predict the overall communication time. This model is evaluated using CG, FT, and CPMD. Experimental results show that the average prediction errors of the model are 12.6%, 13.2%, and 13.5%, with a minimum error of 7%.

关键词： Accuracy Computational modeling High performance computing Message passing Artificial neural networks Machine learning Predictive models Data models Supercomputers Tuning

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Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer 14

Parallel Optimization of Computational Fluid Dynamics Applic...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Liu, Anjun Liao, Yin Xu, Xihao Chen, YanYan Zhong, Hongjun Wu, Jianming Kong, Bo Raikov, Alexander Guo, Meng Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Industrial Software Jinan China Zhejiang Yuansuan Technology Co. Ltd Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Hangzhou China Qilu University of Technology Shandong Academy of Sciences Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security China Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Shandong Jinan China

ISBN: (纸本)9798350371024

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1-2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry. © 2023 IEEE.

关键词： Supercomputers

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Rail Transit Event Extraction based on the Fusion of Semantic and Local Dependency Features

Rail Transit Event Extraction based on the Fusion of Semanti...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Qiang Chen Haining Meng Yihao Bao Mingsong Yang Xinyu Tong Qin Zhao Muhammad Bilal Khan Xinhong Hei School of Computer Science and Engineering Xi’an University of Technology Xi’an China School of Computer Science and Engineering Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an University of Technology Xi’an China School of Civil Engineering and Architecture Xi’an University of Technology Xi’an China

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

Event extraction in rail transit plays a vital role in all stages of design, construction, and final acceptance. Event extraction is a key part of building event logic graph (ELG). However, the difficulty in obtaining semantic features and event local dependencies poses a challenge to accurate event extraction, which hinders the informatization and intelligent development of rail transit projects. To address these challenges, we proposes an event extraction method that fuses semantic and local dependency features. The study initially preprocesses the rail transit design codes dataset and performs sequence labeling. Then, the event trigger words are mined by combining the term frequency-inverse document frequency (TF-IDF) and minimum edit distance (MED) algorithms. Based on the semantic similarity measurement method and the trigger word mining results, the features representation of event semantic information is enhanced by using the Bidirectional Encoder Representations from Transformers (BERT) model and Bi-directional Long Short-Term Memory (BiLSTM) model. At the same time, event arguments are mined and a local dependency graph is generated based on their mining results, and then the enhanced local dependency features are obtained by using the graph convolutional network (GCN). Finally, the event semantic features and local dependency features are fused, and the conditional random field (CRF) is used to predict the labels to obtain the event extraction results. Through the comparison with five baseline methods on the datasets of rail transit design codes, it is found that the Precision, Recall, and F1 score of the proposed method are improved, with an average improvement of 9.604% on Precision, 9.342% on Recall, and 9.730% on F1 score, respectively. The contribution of the sub-modules to the overall performance is further evaluated through ablation experiments, which confirms the effectiveness and feasibility of the proposed method.

关键词： Rails Codes Semantics Bidirectional control Knowledge graphs Feature extraction Transformers Conditional random fields Logic Labeling

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Cross-Chain Identity Authentication Protocol based on Group Signature and Zero-Knowledge Proof

Cross-Chain Identity Authentication Protocol based on Group ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Shujiang Xu Duanzhen Li Lianhai Wang Miodrag J. Mihaljević Shuhui Zhang Wei Shao Qizheng Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan PR China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan PR China Serbian Academy of Sciences and Arts Mathematical Institute Belgrade Serbia

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Cross-chain interaction plays a crucial role in enhancing asset circulation and data sharing across diverse blockchain systems. Cross-chain identity authentication is the primary pre-requisite of cross-chain security interaction. However, existing cross-chain identity authentication protocol generally has issues such as insufficient decentralization, poor universality, and low authentication efficiency. These issues compromise the reliability of cross-chain interactions. Based on group signature and zero-knowledge proof, this paper proposes a cross-chain identity authentication protocol to address these concerns. The protocol employs Decentralized Identifiers(DIDs) as the global identity identifier of users, utilizes zero-knowledge proof to provide privacy for the verification of users' identities when joining the group, and then constructs the users' transaction signatures through group signatures to hide the users' identities. This approach not only reduces the risk of users' privacy leakage but also facilitates mutual recognition of identities among cross-chain systems. Finally, the security analysis and experimental analysis shows the correctness and efficiency of the proposed scheme.

关键词： Privacy Protocols Federated learning Authentication Blockchains Reliability

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Cerebrovascular segmentation from mesoscopic optical images using Swin Transformer

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Journal of Innovative Optical Health Sciences 2023年第4期16卷 120-133页

作者： Yuxin Li Qianlong Zhang Hang Zhou Junhuai Li Xiangning Li Anan Li Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048P.R.China School of Computer Science Chengdu University of Information Technology Chengdu 610225P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan 430074 P.R.China HUST-Suzhou Institute for Brainsmatics Suzhou 215123 P.R.China

Vascular segmentation is a crucial task in biomedical image processing,which is significant for analyzing and modeling vascular networks under physiological and pathological *** advances in fluorescent labeling and mesoscopic optical techniques,it has become possible to map the whole-mouse-brain vascular networks at capillary ***,segmenting vessels from mesoscopic optical images is a challenging *** problems,such as vascular signal discontinuities,vessel lumens,and background fluorescence signals in mesoscopic optical images,belong to global semantic information during vascular *** vascular segmentation methods based on convolutional neural networks(CNNs)have been limited by their insufficient receptive fields,making it challenging to capture global semantic information of vessels and resulting in inaccurate segmentation ***,we propose SegVesseler,a vascular segmentation method based on Swin *** adopts 3D Swin Transformer blocks to extract global contextual information in 3D *** approach is able to maintain the connectivity and topology of blood vessels during *** evaluated the performance of our method on mouse cerebrovascular datasets generated from three different labeling and imaging *** experimental results demonstrate that the segmentation effect of our method is significantly better than traditional CNNs and achieves state-of-the-art performance.

关键词： Vascular segmentation Swin Transformer mesoscopic optical imaging fMOST

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GHTO: Improving Byzantine Robustness in Distributed Machine Learning Through Gradient Heterogeneity Twofold Optimization

GHTO: Improving Byzantine Robustness in Distributed Machine ...

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Caiyun Li Ming Yang Jian Cao Xiaoming Wu Yunpeng He Xin Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Evayinfo Technology Co. Ltd Jinan P. R. China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Heterogeneous datasets induce local gradient variance and global gradient variance in distributed learning, collectively referred to as gradient heterogeneity. Gradient heterogeneity increases the system's vulnerability to Byzantine attacks, affecting performance. To address this issue, the paper proposes a Gradient Heterogeneity Twofold Optimization (GHTO) algorithm to enhance the Byzantine robustness of the system in non-independent and identically distributed environments. Specifically, a Local Gradient Correction (LGC) algorithm is proposed, which aims to reduce the local gradient variance. By further integrating LGC with momentum and bucketing techniques to achieve dual optimization of local and global gradient variance. Experimental results demonstrate the proposed LGC algorithm diminishes local gradient variance, and the GHTO method optimizes both local and global gradient variance, which further strengthens the robustness of the system against Byzantine attacks.

关键词： Computer aided instruction Machine learning algorithms Distance learning Machine learning Robustness Optimization Resilience

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Efficient Caching in Cyber-Physical-Social Systems Based on Social-Aware and Popularity Prediction

Efficient Caching in Cyber-Physical-Social Systems Based on ...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Wei Zhang Xukun Sun Hao Hao Huiling Shi 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng laboratory Jinan China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

Cyber-Physical-Social Systems (CPSS) aim to enhance societal efficiency by integrating intelligent, interactive systems. To support CPSS, data is often gathered from the cloud, but non-real-time cloud-user connections affect Quality-of-Service (QoS). To solve this, we propose offloading storage to the edge, caching popular content near users. We select cluster heads using local clustering, social intimacy, and betweenness centrality. Then, we group users using the Pairwise Constrained K-Means-Monotonic (PCKMM) algorithm based on distance, relationships, and content preferences. Using unsupervised recurrent federated learning (URFL), we predict content popularity and adjust clusters dynamically. Content is cached with a popularity-driven greedy strategy, reducing access latency.

关键词： Measurement Federated learning Social networking (online) Heuristic algorithms Interactive systems Clustering algorithms Quality of service Prediction algorithms Minimization Sensors

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PWPH:Proactive Deepfake Detection Method Based on Watermarking and Perceptual Hashing

PWPH:Proactive Deepfake Detection Method Based on Watermarki...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jian Li Shuanshuan Li Bin Ma Chunpeng Wang Linna Zhou Fei Wang Yule Wang Miaomiao Yang 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 School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The popularity of Deepfake technology has raised the challenge of recognizing real and fake faces. While detection methods already exist, most of them are passive forensics and face challenges of generalizability and migration. Currently, some research attempts to protect the original image by priorly inserting invisible information. However, there are still shortcomings in terms of image quality and information robustness due to information embedding, i.e., watermarking. Therefore, we employ the robustness of perceptual hash coding and combine it with information hiding techniques to propose a proactive Deepfake detection solution, referred to as PWPH in this paper. Our approach is simple and efficient: first, the image containing a face is divided into two parts: FA (face area), and NFA (non-face area). A perceptual hash code is generated from the non-face area (NFA). Then, the hash codes are embedded as watermarks into the FA. At the extraction stage, we use the same method as the encoder to retrieve the embedded watermark from FA. The watermark is then compared with the hash code generated from the NFA of the detected image. The extracted watermark is sensitive to distortion and may vanish during Deepfake processing. Experimental results validate that our method, requiring just one encoder and decoder, enables active detection and source tracking. Furthermore, its efficacy in typical Deepfake scenarios such as face swapping and expression reconstruction is confirmed through comparison with prior arts.

关键词： Learning systems Image quality Deepfakes Codes Face recognition Watermarking Robustness Decoding Faces Image reconstruction

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

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Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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