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Algorithm, Image Processing and Machine Vision (AIPMV), International Conference on

作者： Aokun Mei Hua Huo Big Data Analysis Laboratory of Henan Medical Big Data and Computing Intelligence Engineering Technology Research Center School of Information Engineering Henan University of Science and Technology Luoyang China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Insect fine-grained image classification is an application scenario in fine-grained image classification. It not only has the characteristics of small inter-class differences and large intra-class differences, but also has the difficulty that some categories have multiple life-stage forms, which makes the general fine-grained image classification model difficult to play a role in insect scenes. To this end, based on the Vision Transformer, we design a fine-grained classification network for insect images based on abnormal clustering and cross slicing, called ACCS-Trans. In the first stage of the model, we use segmentation and clustering operations to distinguish the special morphology of insects in those few-shot life stages. The model can avoid the interference of few-sample abnormal morphology on the class feature extraction of the current class during training. The second stage is the cross slicing module, which uses the anchor box of the image sample segmentation region in the first stage to cut the original sample image to form the main target image, which is used as the information supplement area of the original image. Finally, the image is divided into doubling patch groups by two vertical patch operations. In the third stage, we concatenate the multiplication patch group and input it into the Vision Transformer network for class feature extraction. We fully experiment with ACCS-Trans on two insect image datasets. Compared with the current mainstream fine-grained image classification models, ACCS- Trans achieves state-of-the-art effects on both datasets, and we do ablation experiments for each module. The effect of each module on our ACCS- Trans is analyzed. The excellent performance of our ACCS- Trans in insect scenes is verified in these experiments, these provide new ideas for the task of fine-grained classification of insect images.

关键词： Training Image segmentation Computer vision Visualization Insects Image edge detection Morphology Transformers Feature extraction Image classification

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An Efficient Storage Architecture Based on Blockchain and Distributed database for Public Security big data

An Efficient Storage Architecture Based on Blockchain and Di...

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作者： Liao, Duoyue Dong, Xinhua Xu, Zhigang Han, Hongmu Yan, Zhongzhen Sun, Qing Li, Qi Institute of Public Security Big Data School of Computer Science Hubei University of Technology Wuhan430068 China

For the massive multi-source and heterogeneous public security big data, a distributed, secure and large-scale storage platform is urgently needed. As one of the most popular distributed ledger technologies, blockchain satisfies the needs of distribution and security, but there are still deficiencies in the storage of massive data. This paper provides an in-depth analysis of the advantages and disadvantages of blockchain and distributed databases in various aspects, and combines the characteristics of distributed databases to improve the performance of blockchain in terms of storage and query. HBase is proposed to extend the data storage layer of Hyperledger Fabric. The hash of the bulk data is first calculated and the complete data is stored into HBase by calling chaincode to store only the hash onto the chain. A hash-based rowkey construction has also been designed in an attempt to improve the efficiency of reading data in the blockchain. Finally, this architecture is applied to realize the storage of massive public security big data and the results demonstrate an ideal case of a 6% increase in throughput, 5% reduction in block size, and 13% reduction in transaction latency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Int-GNN: A User Intention Aware Graph Neural Network for Session-Based Recommendation 48

Int-GNN: A User Intention Aware Graph Neural Network for Ses...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xu, Guangning Yang, Jinyang Guo, Jinjin Huang, Zhichao Zhang, Bowen Harbin Institute of Technology Computer Science & Technology Shenzhen China JD Intelligent Cities Research JD iCity Beijing China Shenzhen Technology University College of Big Data and Internet Shenzhen China

ISBN: (纸本)9781728163277

Session-Based Recommendation (SBR) is a spotlight research problem. Although many efforts have been made, challenges still exist. The key to unlocking this shackle is the user intention, an intuitive but hard-to-model concept in the anonymous session. Unlike previous research, we suggest mining potential user intention by counting the number of item occurrences in a user session and considering the long interval between item re-interactions. Beyond these, we take user preference, a biased user intention, into account in the prediction stage. Forming these together, we propose a model named user Intention aware Graph Neural Network (Int-GNN) aiming at capturing user intention. Extensive experiments have been conducted on three real-world datasets, and the results show the superiority of our method. The code is available on GitHub: https://***/xuguangning1218/IntGNN_ICASSP2023 © 2023 IEEE.

关键词： Graph neural networks

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Integration of Cross-Organizational Business Processes with Blockchain: Identifying Key Activities for On-Chain Execution Based on Behavioral Profiles 6

Integration of Cross-Organizational Business Processes with ...

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6th International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Lei, Jiaxin Wang, Lili College of Mathematics and Big Data Anhui University of Science&Technology Anhui Huainan232001 China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Anhui Huainan232001 China

ISBN: (纸本)9781837242672

Business Process Management (BPM) is widely applied within enterprises to standardize and streamline processes, and blockchain offers an effective solution for managing inter-organizational business processes. Lots of throughput and gas are required when deploying smart contracts for normative process execution and storing data on chains. However, a complete normative process on the chain requires a lot of gas and operation and maintenance costs, and also limits the flexibility of process execution and modification. To address these challenges, the paper proposes an improved approach about how to convert the full process into on-chain. Firstly, key activities of business processes suitable for on-chain implementation are identified by constructing a matrix of behavior profile, adapting frequency analysis, and executable sequence analysis. Secondly, these key activities are then converted into smart contracts and deployed on the blockchain. Finally, we enhance intelligent management in BPM by employing Intelligent Information Systems (IIS) for real-time monitoring, process prediction, and automated decision-making to implement an experiment. Experimental results show that the proposed method can be adapted to most business scenarios, improves the flexibility of BPM and blockchain integration, and reduces the application cost of blockchain in business processes. Meanwhile, it takes into account the security transparency of blockchain and the execution performance of business processes. © The Institution of Engineering & technology 2024.

关键词： Smart contract

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Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning 8

Defect Screening on Nuclear Power Plant Concrete Structures:...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Huang, Wenlian Zhu, Guanming Huang, Qiang Chen, Zhuangzhuang Chen, Jie Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results. © 2023 IEEE.

关键词： Defects

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Dynamics of a discrete predator-prey model with Holling-II functional response

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International Journal of Biomathematics 2021年第8期14卷 253-272页

作者： Yuqing Liu Xianyi Li Department of Big Data Science School of ScienceZhejiang University of Science and TechnologyHangzhou 310023P.R.China

In this paper,we use a semidiscretization method to derive a discrete predator–prey model with Holling type II,whose continuous version is stated in[*** and ***,Stability and Hopf bifurcation of a predator-prey model,*** Probl.129(2019)1–11].First,the existence and local stability of fixed points of the system are investigated by employing a key *** we obtain the sufficient conditions for the occurrence of the transcritical bifurcation and Neimark–Sacker bifurcation and the stability of the closed orbits bifurcated by using the Center Manifold theorem and bifurcation ***,we present numerical simulations to verify corresponding theoretical results and reveal some new dynamics.

关键词： Discrete predator-prey system semidiscretization method transcritical bifurcation Neimark-Sacker bifurcation

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Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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data Lake Development Status and Outlook 3

Data Lake Development Status and Outlook

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3rd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2023

作者： Li, Guonong Hu, Wenyu You, Tongzhen School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China

ISBN: (纸本)9781510668867

To adapt to the increasing demand for big data storage and analysis, and to solve the difficulties and pain points of data Warehouse technology, it is necessary to build an efficient and high-quality data storage architecture. In this thesis, the evolution, technical background, architecture, and the current status of domestic and international research of the data Lake concept were presented, data Lake and data Warehouse were compared and their advantages and disadvantages were summarized. The feasibility of data Lakehouse and ‘data Lake + data Middle-end’ was discussed, and the problems that should be solved in the construction of ‘data Lake + data Middle-end’ were prospected. Finally, the challenges faced by the further development of data Lake technology were analyzed, and some ideas to solve these problems were put forward. © 2023 SPIE.

关键词： Lakes

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A Classifier-Ensemble-Based Surrogate-Assisted Evolutionary Algorithm for Distributed data-Driven Optimization

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 711-725页

作者： Guo, Xiao-Qi Wei, Feng-Feng Zhang, Jun Chen, Wei-Neng South China University of Technology School of Computer Science and Engineering Guangzhou510006 China South China University of Technology State Key Laboratory of Subtropical Building of Big Data and Computational Intelligence Guangzhou510006 China Hanyang University ERICA School of Electrical and Engineering Ansan15588 Korea Republic of Nankai University College of Artificial Intelligence Tianjin300071 China

Surrogate-assisted evolutionary algorithms (SAEAs) have achieved effective performance in solving complex data-driven optimization problems. In the Internet of Things environment, the data of many problems are collected and processed in distributed network nodes and cannot be transmitted. As each local node can only access and build surrogate models based on partial data, local models are usually not accurate and even conflicting. To address these challenges, this article proposes a classifier-ensemble-based surrogate-assisted evolutionary algorithm (CESAEA) with the following features. First, the local nodes in CESAEA train classifiers as surrogate models based on their own data to classify candidates into several levels according to their fitness quality. The classifiers are less sensitive to the partial and biased data than regression models in local nodes. Second, the central node in CESAEA ensembles the local surrogates to form a global classifier with a relaxation condition to guide the evolutionary optimizer to generate promising candidates. The relaxation condition helps to overcome the problem of local model inconsistency. Overall, CESAEA is composed of local classifier construction, global classifier ensemble, classifier-assisted evolutionary optimization and local regression-assisted selection. As only classifiers are allowed to transmit from local nodes to the central node, the mapping relationship between decision vector and objective is hidden and thus data privacy is protected. The experimental results on benchmark functions as well as distributed feature selection problems verify the effectiveness of CESAEA compared to several state-of-the-art approaches. © 1997-2012 IEEE.

关键词： Linear programming

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Tuning-up Learning Parameters for Deep Convolutional Neural Network:A Case Study for Hand-Drawn Sketch Images

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Journal of Electronic science and technology 2022年第3期20卷 305-318页

作者： Shaukat Hayat Kun She Muhammad Mateen Parinya Suwansrikham Muhammad Abdullah Ahmed Alghaili the School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054 the School of Big Data&Software Engineering Chongqing UniversityChongqing 400044 the College of Information Science and Engineering Hunan UniversityChangsha 410082

Several recent successes in deep learning(DL),such as state-of-the-art performance on several image classification benchmarks,have been achieved through the improved ***(HPs)tuning is a key factor affecting the performance of machine learning(ML)*** state-of-the-art DL models use different HPs in different ways for classification tasks on different *** manuscript provides a brief overview of learning parameters and configuration techniques to show the benefits of using a large-scale handdrawn sketch dataset for classification *** analyzed the impact of different learning parameters and toplayer configurations with batch normalization(BN)and dropouts on the performance of the pre-trained visual geometry group 19(VGG-19).The analyzed learning parameters include different learning rates and momentum values of two different optimizers,such as stochastic gradient descent(SGD)and *** analysis demonstrates that using the SGD optimizer and learning parameters,such as small learning rates with high values of momentum,along with both BN and dropouts in top layers,has a good impact on the sketch image classification accuracy.

关键词： Deep learning(DL) hand-drawn sketches learning parameters

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