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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Huang, Chu Zhang, Qianzhen Guo, Deke Zhao, Xiang Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9783031306747

Streaming graph analysis is gaining importance in various fields due to the natural dynamicity in many real graph applications. Prior subgraph discovery problem over streaming graphs mostly focuses on characteristics like frequency and burstiness. Persistence, as a new characteristic, is getting increasing attention. Persistent subgraph discovery highlights behaviors where a subgraph appears recurrently in many time windows, which is vital for many real-world applications (e.g., anomaly detection). While persistent subgraph discovery enjoys many interesting real-life applications, there is no off-the-shelf solution to compute the persistent pattern efficiently. In this work, we are the first to study the persistent subgraph pattern discovering problem over the streaming graph. We devise an auxiliary data structure called TFD to detect the persistent subgraph patterns in real-time with limited memory usage. TFD maps each subgraph into the corresponding bucket based on hash functions to compute the persistence of each pattern. Then we introduce optimizations to separate persistent and non-persistent patterns, further improving the effectiveness and throughput in space-scarce scenarios. Extensive experiments confirm the superiority of our proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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CPAKE: Dynamic Batch Authenticated Key Exchange with Conditional Privacy 24th

CPAKE: Dynamic Batch Authenticated Key Exchange with Condit...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xiang, Axin Tian, Youliang Xiong, Jinbo Ying, Zuobin Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guiyang550025 China Industrial School of Joint Innovation Quanzhou Vocational and Technical University Quanzhou362268 China Faculty of Data Science City University of Macau 999078 China

ISBN: (纸本)9789819615247

Authenticated key exchange (AKE) needs to be designed for realizing point-to-multipoint secure communications in blockchain networks (BNet). However, since BNet is open, untrusted and decentralized, traditional certificateless AKE schemes are difficult to overcome two technical bottlenecks, i.e., private distribution of partial keys and accurate identification of anonymous traitors. This paper proposes a conditionally private and dynamic batch AKE scheme (CPAKE) in BNet. We first develop a privacy-preserving certificateless key generation algorithm to prevent the disclosure of nodes’ privacy during partial key distribution. Then, we design a simple anonymous traitor tracing mechanism against Byzantine adversaries to accurately identify and trace the traitor’s identity. Finally, we build a dynamic batch authentication protocol with decentralized node management to ensure the final validity of honest nodes. Moreover, performance analysis indicates that our scheme reduces the signing and batch-verifying time by approximately 193.10 ms and 10.93 ms respectively over state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding 40

SharDAG: Scaling DAG-Based Blockchains Via Adaptive Sharding

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Cheng, Feng Xiao, Jiang Liu, Cunyang Zhang, Shijie Zhou, Yifan Li, Bo Li, Baochun Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab China Hong Kong University of Science and Technology Hong Kong University of Toronto Canada

ISBN: (纸本)9798350317152

Directed Acyclic Graph (DAG)-based blockchain (a.k.a distributed ledger) has become prevalent for supporting highly concurrent applications. Its inherent parallel data structure accelerates block generation significantly, shifting the bottleneck from performance to storage scalability. An intuitive solution is to apply state sharding that divides the entire ledger (i.e., transactions and states) into multiple shards. While each node only stores proportional transactions, it suffers from the challenges of storing and ensuring the processing consistency of cross-shard transactions. In this paper, we propose SharDAG, a new mechanism that leverages adaptive sharding for DAG-based blockchains to achieve high performance and strong consistency. The key idea of SharDAG is to exploit unique characteristics -silent assets -and design a lightweight processing mechanism based on avatar account caching. Furthermore, we design a Byzantine resilient cross-shard verification mechanism with a theoretically optimal number of participating nodes, which guarantees the consistency and security of avatar account aggregation. Our comprehensive evaluations on real-world workloads demonstrate that SharDAG presents up to 3.8 x throughput improvement compared to the state-of-the-art and reduces the storage overhead of cross-shard transactions. © 2024 IEEE.

关键词： Blockchain

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First-principles investigation of adhesion strength and interfacial bonding in Mg/X(X=Ti,Zr,Hf,V,Nd,Cr,Mo,Mn,and Fe)interface

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Journal of Magnesium and Alloys 2025年第1期13卷 83-89页

作者： Longke Bao Zhifu Yao Yuhui Zhang Cuiping Wang Kaihong Zheng Rongpei Shi Xingjun Liu Fusheng Pan State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhen 518055China College of Materials Science and Engineering and Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055China Institute of New Materials Guangdong Academy of SciencesGuangzhou 510650China College of Materials Science and Engineering Xiamen UniversityXiamen 361005China College of Materials Science and Engineering Chongqing UniversityChongqing 400045China

Magnesium alloys have gained extensive applications across various industries,including aerospace,transportation,and civil construction,owing to their excellent combinations of high specific strength and stiffness[1].However,their lim-ited strength due to the lack of effective strengthening phases has hindered their broader industrial applications[2].Never-theless,it has been challenging to achieve significant strength-ening due to the restricted solubility of alloying elements in magnesium[3].Thus,more and more efforts have been made to explore the concept of secondary phase-reinforced magne-sium alloys[2,4,5],where the secondary phase acts as re-inforcing agents within the magnesium matrix,resembling a composite material.

关键词： adhesion strength interfacial bonding aluminum alloys industrial applications solubility strengthening phases alloying elements Mg X interfaces

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Research on Mining Association Rules for Classroom Concentration Based on Apriori Algorithm

Research on Mining Association Rules for Classroom Concentra...

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IEEE Information technology and Mechatronics Engineering Conference (ITOEC)

作者： Jingqiu Bai Huafeng Kong Wenhan Hu Department of Information Engineering Wuhan Business University Hubei China Major in Data Science and Big Data Technology The Main Research Direction is the Computer Application

This paper studies the correlation between students' concentration in class and learning interest, emotional state and other influencing factors. By collecting students' classroom status data, a data set suitable for data mining is constructed. Based on the idea of Apriori algorithm, the Anaconda Navigator programming tool is used to mine the association rules of students' classroom concentration. Through data mining based on Apriori algorithm, and the association rules between students' emotional state, learning interest and other factors and in-class test scores are output. The results of strong association rules show that the better students' emotional state, learning interest and other states are, the better their classroom concentration performance is. Mining the potential influencing factors of students' classroom concentration provides valuable reference theory for students to improve classroom concentration and teachers to improve classroom teaching effect, which is helpful for teachers and students to build a more efficient classroom together in the future.

关键词：

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Temporal Feature Mining in Dynamic Graph of Brain Connectivity data

Temporal Feature Mining in Dynamic Graph of Brain Connectivi...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Liu, Tao Jing, Ming Zhang, Guangwei Zhang, Li Yu, Jiguo School of Computer Science and Technology Qilu University of Technology Jinan China Big Data Institute Qilu University of Technology Jinan China Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350337020

In recent years, the graph feature mining method of brain connection data based on graph theory has been regarded as a popular and universal technology in the field of neuroscience. How to mine valuable information from brain connection data has become a research hotspot. Current research shows that the pathogenic factors of attention deficit and hyperactivity disorder (ADHD) may be caused by the abnormal connection between brain network structures. In order to find out the pathogenic factors of ADHD patients, we also carried out frequent sub-graph mining on the connectivity graph data of brain functional network. By constantly adjusting the sup-port threshold, all the subgraphs of ADHD patients and healthy control group were mined, and the differences in brain region connectivity were successfully found out. By combining the recently introduced neural document embedding model with traditional pattern mining techniques, we regard the brain network connection structure graph as the document and frequent subgraph as the atomic unit of the embedding process. By learning the mapping, each graph can be mapped to a D-dimensional continuous vector. The mapping needs to capture the similarity between the graphs. Feature vectors can be used as the direct input of graph classification in many traditional machine learning methods. Finally, support vector machine in machine learning is used to verify the accuracy of classification, and the results show that the accuracy is high. © 2023 IEEE.

关键词： ADHD brain network identification fMRI Frequent subgraph mining Graph data mining

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Research on Text Generation of Medical Intelligent Question and Answer Based on Bi-LSTM and Neural Network technology 22

Research on Text Generation of Medical Intelligent Question ...

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22nd IEEE/ACIS International Conference on Computer and Information science, ICIS 2022

作者： Huang, Keyi Ji, Fenda Lu, Wei Xiao, Yue Zhuhai College of Science and Technology Alibaba Cloud Big Data Application Academy Zhuhai China

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

Text generation is a popular research direction in the field of natural language processing as well as artificial intelligence, especially in the medical field. Text generation technology plays an extremely important role in medical intelligent question and answer systems. In previous Q&A systems, the display of generated dialogues is usually limited to Q&A and is difficult to adapt to vertical domains with strong characteristics. In this paper, to solve the task of specialized Q&A in medical field and provide users with comprehensive answers. We propose a TensorFlow architecture-based approach to medical text generation that uses sequential models in Keras to transform the task into a classification problem by treating the text generation problem as a prediction problem. By training and experimenting with the model on a large-scale Chinese medical question-and-answer dataset, the results show that our model has a good fit with applications in this specific domain. The model can aggregate medical domain knowledge, extract useful treatment information and generate medical knowledge text. At the same time, through research, it is found that splitting questions into phrases and then inputting them into the model can effectively solve the problem of repeated sentences in the generated answers. Since the model we proposed can answer patients' questions well and accurately in most cases, this research provides an improvement direction for exploring medical intelligent question answering and text generation. © 2022 IEEE.

关键词： Large dataset

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Convolutional Neural Network for Image Classification Research - Based on VGG16

Convolutional Neural Network for Image Classification Resear...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Ruoxi Yang College of Big Data Zhuhai College of Science and Technology Zhuhai China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In recent years, convolutional neural networks (CNNs) have increasingly become the predominant approach for image classification, demonstrating remarkable performance in this domain. Notably, VGG16 has gained widespread adoption due to its deep architecture and robust feature extraction capabilities. Consequently, this paper investigates the efficacy of employing the VGG16 convolutional neural network model for image classification on the CIFAR-10 dataset. The architecture of the VGG16 model and its distinctive application characteristics are first introduced, followed by an exploration of key steps including data preprocessing, model training, and hyperparameter tuning. 99.4% accuracy on the training set and 90.6% accuracy on the test set were attained after the training procedure was optimized. To improve the model's classification performance even more, this paper identifies limitations inherent in the current framework and proposes optimization strategies such as incorporating attention mechanisms and transfer learning. These strategies provide valuable insights for future applications of deep learning techniques on small-scale image datasets.

关键词： Training Accuracy Attention mechanisms Computational modeling Transfer learning data models Convolutional neural networks Tuning Optimization Image classification

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Make an Image Move: Few-Shot Based Video Generation Guided by CLIP 27th

Make an Image Move: Few-Shot Based Video Generation Guided b...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Yonglong Yu, Cheng Li, Nannan Deng, Fuqin Ge, Ruiquan Wang, Changmiao School of Computer Science and Engineering Macau University of Science and Technology Macau China Chongqing University of Technology Chongqing China Wuyi University Jiangmen China Hangzhou Dianzi University Hangzhou China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9783031781711

The recent surge in interest surrounding text-to-video diffusion models highlights their capability to generate videos with both consistency and diversity. Current methods focus on leveraging large-scale datasets to align with training videos, while certain approaches explore the potential of zero-shot generation. Few-shot generative models adapt text-to-image model with temporal layers. They can capture the temporal motion and appearance with acceptable computational resources. However, the prevalent few-shot based approaches, which employ a singular prompt for training videos, typically result in inadequate control over complex backgrounds and multiple objects. To overcome this limitation, we introduce a novel component: the dual cross-attention layer. This component leverages CLIP for image feature extraction. The image feature and text feature will be processed independently in dual cross-attention layer, aiming to achieve image reference and enrich the training videos with additional information. This dual cross-attention mechanism empowers the diffusion model to learn both image and text information effectively, facilitating the generation of higher quality videos. Furthermore, we propose an innovative sampling method to enhance temporal consistency and stability of generative videos. Compared to other text-to-video generative models, our framework demonstrates superior efficiency in generating high quality videos with diverse styles. The code is available at https://***/FatLong666/MAIM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Video analysis

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An Optimal Wind Turbine Control Based on Improved Chaotic Sparrow Search Algorithm with Normal Cloud Model

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 108-125页

作者： Yang, Qi-Peng Wang, Chia-Hung Dao, Thi-Kien Nguyen, Trong-The Zhao, Qi-Gen Chen, Shu-Meng College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam

This study suggests a Chaotic Sparrow Search Algorithm (CSSA) based on the Sparrow Search Algorithm (SSA) for a wind turbine generator unit’s pitch controller optimal parameters. The optimal parameters set of a wind turbine pitch controller is a complex problem that needs to be tuned to archive a smooth quality performance of the outcome power operations. The CSSA is carried out with Tent chaotic mapping updating equations and a standard cloud model to avoid the original SSA algorithm drawbacks and increase quality performance to solve the optimal controller parameters. The experiments compare the CSSA against other algorithms on testing function, and the optimal pitch controller parameters demonstrate that the CSSA can solve smoothing the output power of the wind turbine unit, reducing the effect of wind speed fluctuations on the power grid. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Wind Turbine Generators

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