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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Wang, Chenyu Zhang, Zhao Zhu, Zhipeng Zhou, Hongyan School of Computer Science and Software Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning 185 Qianshanzhong Road Liaoning Anshan114051 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

The classification of short-term power load data by clustering algorithm can lay a good foundation for the subsequent power load forecasting work and provide a more efficient, safe and reliable direction for the operation of power system. Density peaks clustering algorithm with k-nearest neighbors and weighted similarity can reduce the negative impact of global data on local data, avoid the collateral effect of errors and improve the accuracy of the model. It has better effect on local peak power change and global power forecast. In this paper, the density peaks clustering algorithm with k-nearest neighbors and weighted similarity are compared with DPC clustering method through simulation examples, and the effectiveness of this method is verified. © 2024 SPIE.

关键词： Clustering algorithms

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An Efficient Convolutional Multi-Scale Vision Transformer for Image Classification 2

An Efficient Convolutional Multi-Scale Vision Transformer fo...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Zhang, Ji Chen, Zhihao Ge, Yiyuan Yu, Mingxin Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing China Beijing Information Science and Technology University Computer School Beijing China Jilin University Department of Software Engineering Changchun China

ISBN: (纸本)9798350331417

This paper introduces an innovative and efficient multi-scale Vision Transformer (ViT) for the task of image classification. The proposed model leverages the inherent power of transformer architecture and combines it with the concept of multi-scale processing generally used in convolutional neural networks (CNNs). The work aims to address the limitations of conventional ViTs which typically operate at a single scale, hence overlooking the hierarchical structure in visual data. The multi-scale ViT enhances classification performance by processing image features at different scales, effectively capturing both low-level and high-level semantic information. Extensive experimental results demonstrate the superior performance of the proposed model over standard ViTs and other state-of-the-art image classification methods, signifying the effectiveness of the multi-scale approach. This research opens new avenues for incorporating scale-variance in transformer-based models for improved performance in vision tasks. © 2023 IEEE.

关键词： Classification Convolutional neural networks Multi-scale Vision Transformer

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A Fractional Fourier Based Medical Image Authentication Approach

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computers, Materials & Continua 2022年第2期70卷 3133-3150页

作者： Fayez Alqahtani Mohammed Amoon Walid El-Shafai Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Patient medical information in all forms is crucial to keep private and secure,particularly when medical data communication occurs through insecure ***,there is a bad need for protecting and securing the color medical images against impostors and *** this paper,an optical medical image security approach is *** is based on the optical bit-plane Jigsaw Transform(JT)and Fractional Fourier Transform(FFT).Different kernels with a lone lens and a single arbitrary phase code are exploited in this security approach.A preceding bit-plane scrambling process is conducted on the input color medical images prior to the JT and FFT processes to accomplish a tremendous level of robustness and *** confirm the efficiency of the suggested security approach for secure color medical image communication,various assessments on different color medical images are examined based on different statistical security ***,a comparative analysis is introduced between the suggested security approach and other conventional cryptography *** simulation outcomes acquired for performance assessment demonstrate that the suggested security approach is highly *** has excellent encryption/decryption performance and superior security results compared to conventional cryptography approaches with achieving recommended values of average entropy and correlation coefficient of 7.63 and 0.0103 for encrypted images.

关键词： Medical image security encryption JT FFT cryptosystem

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Research on Facial Landmark Detection Algorithm Based on Improved Attention Mechanism

Research on Facial Landmark Detection Algorithm Based on Imp...

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2022 International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Cui, Xi-Shuo Shi, Tian-Wei Cui, Wen-Hua Tao, Ye School of Electronic and Information Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China

ISBN: (纸本)9781510662988

In recent years, facial landmark detection has assumed an important role in various fields. However, the current facial landmark detection algorithms are still lacking in recognition accuracy. In order to solve the above problem, this paper uses Ghost bottleneck to replace the original bottleneck on the basis of the original model of PFLD model, and adds and improves the CBAM attention mechanism. The improved PFLD model increases the ability of the model to extract facial landmark and improves the accuracy of the algorithm. The improved model has high accuracy and low parametric number and improves the accuracy of facial landmark detection. It also provides a new idea for facial landmark detection task. © 2023 SPIE. All rights reserved.

关键词： Face recognition

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Systematic analysis of artificial intelligence in the era of industry 4.0

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Journal of Management Analytics 2023年第1期10卷 89-108页

作者： Weiru Chen Wu He Jiayue Shen Xin Tian Xianping Wang Department of Information Technology&Decision Science Old Dominion UniversityNorfolkVAUSA Department of Computer&Information Science Arkansas Tech UniversityRussellvilleARUSA Department of Engineering Technology SUNY Polytechnic InstituteUticaNYUSA College of Computing and Software Engineering Kennesaw State UniversityMariettaGAUSA Computer Information Technology&Graphics Department Purdue University NorthwestHammondINUSA

Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily *** the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry *** study conducts a systematic literature review to study the state-of-the-art on AI in industry *** paper describes the development of industries and the evolution of *** paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry *** findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of *** the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.

关键词： Industry 4.0 AI deep learning neural network supervised learning unsupervised learning reinforced learning

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Legal Text Retrieval with Contrastive Representation Learning and Evolutionary Data Augmentation 13

Legal Text Retrieval with Contrastive Representation Learnin...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhou, Youhua Yan, Xueming Huang, Han Yan, Haowen Chen, Minghao School of Software Engineering South China University of Technology Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350308365

Legal text retrieval holds significant importance in the audit field, posing a challenge as a semantic matching problem. Despite the success of text semantic matching methods, particularly with the advent of large language models, these approaches face challenges when applied to domain-specific tasks, like legal text retrieval. Specifically, issues arise due to the concentrated distribution of data within the specific domain and the insufficient number of training samples. To address these challenges, this paper introduces a text semantic matching model tailored for the task of legal text retrieval, leveraging contrastive learning and evolutionary algorithms. A contrastive learning-based embedding model, which learns semantic representations in a feature space, is used to minimize the distance between matched text pairs and maximize the distance between unmatched text pairs. Additionally, an evolutionary algorithm-based sample augmentation model is introduced to augment the sample set and enhance the representational capabilities of the samples. The efficacy of the proposed method is evaluated in the context of legal text retrieval in the auditing field, and the experimental results reveal promising outcomes, with the proposed method achieving a Hits@l accuracy of 53.09%, a 2.99% improvement over the best baseline model. The Hits@20 accuracy reaches 75.15%, representing a 2.69% enhancement compared to the state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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A Two-Stream Word-level information Fusion BERT Model for Event Element Entity Recognition 12

A Two-Stream Word-level Information Fusion BERT Model for Ev...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Zhang, Yan Li, Kaiwen Zhang, Jinghui Zhou, Jingya School of Information Engineering Shanghai Donghai Vocational& Technical Colledge Shanghai China School of Software Engineering Southeast University Nanjing China School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9798331511074

This paper primarily explores methods for enhancing the performance of event element entity recognition tasks by enriching the positional information of vocabulary and focusing on document context. It proposes and implements an event element entity recognition algorithm based on vocabulary-enhanced dual-stream encoders named Two-Stream Word-level information Fusion BERT, TSWLIF-BERT. A word-level information fusion layer that fully integrates the vocabulary and its positional information was designed and implemented.: (1) Designed and implemented a word-level information fusion layer that fully integrates both the vocabulary and their positional information, deeply integrating the vocabulary and positional information in the pre-trained model, thereby obtaining an enhanced vocabulary model (Word-level information Fusion BERT, WLIF-BERT).(2) Based on the model WLIF-BERT, designed a dual-stream encoder that considers cross-sentence information with a global encoder and focuses on sentence-internal information with a local encoder, fully modeling the contextual information.(3) Used masked conditional random fields to solve the problem of illegal sequence label transitions in models based on conditional random fields. Experiments show that this method significantly improves performance in event element entity recognition tasks. © 2024 IEEE.

关键词： information fusion

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Enhancing Sales Forecasting Accuracy in the Presence of Missing Data 17

Enhancing Sales Forecasting Accuracy in the Presence of Miss...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Tang, Jun Sun, Jingpeng Guo, Bing Shen, Yan Dai, Shengxin Wang, Peng College of Computer Science Sichuan University Chengdu China College of Computer Science University of Birmingham Dubai United Arab Emirates School of Computer Science Chengdu University of Information Technology Chengdu China College of Software Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350351637

Accurate sales forecasting serves as a critical foundation for informed business decision-making and numerous methodologies have been advanced by scholars predominantly rely on complete datasets, however, in practice, uncontrollable variables often result in the presence of missing data, diminishing the applicability of established techniques, so the sales forecasting research based on missing data is of great significance. Some researchers have resorted to employing deletion or interpolation techniques to pre-process incomplete data before conducting sales forecasts, but such completion methods tend to introduce distributional biases, subsequently leading to forecast inaccuracies. In light of these challenges, this study introduces a novel computational framework combining Convolutional Neural Network (CNN), Self-Attention, and Sequence-to-Sequence (Seq2Seq) models, where global features are first extracted via the synergistic application of CNN and Self-Attention mechanisms, then the extracted features are seamlessly integrated with the original dataset, supplanting conventional interpolation strategies, and finally the amalgamated data are inputted into the Seq2Seq model for sales prediction. The proposed methodology is empirically evaluated using sales data from a real-world company, demonstrating commendable predictive performance and underscoring the efficacy of the proposed approach in enabling accurate sales forecasts within the context of missing data scenarios. © 2024 IEEE.

关键词： Data accuracy

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A Multi-feature Co-distillation Algorithm Based on Human Skeletal Point Behavior Recognition 24

A Multi-feature Co-distillation Algorithm Based on Human Ske...

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2nd International Conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Pan, Xuefeng Tian, Zhiqiang School of Computer Wuhan Qingchuan University Wuhan China School of Information Wuhan Software Engineering Vocational College Wuhan Open University Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798400712883

Depth model-based behavior estimation of human skeletal points is widely used in the field of behavior recognition. In order to improve the accuracy of behavior recognition, the complexity and computation of the designed network model are increasing, while the mobile platforms have limited arithmetic power and memory, and thus cannot be deployed. To solve the above problems, the AGCN network, a skeletal point-based behavior recognition model, is clipped to compress the network size so that it can support common mobile platforms;and a multi-feature co-distillation method is proposed to efficiently migrate the performance of the original network to the target network, so that the performance of the clipped lightweight network tends to be close to that of the original network. The model training, testing, visualization, and ablation experiments are completed on the NTU-RGB+D60 data set. The experimental results show that the algorithm after lightweighting (clipping network and multi-feature co-distillation), can significantly reduce the computational amount of behavior recognition. © 2024 Copyright held by the owner/author(s).

关键词： Distillation

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A Derivative Matrix-Based Covert Communication Method in Blockchain

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computer Systems science & engineering 2023年第7期46卷 225-239页

作者： Xiang Zhang Xiaona Zhang Xiaorui Zhang Wei Sun Ruohan Meng Xingming Sun School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Hydrology and Water Resources Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Key Laboratory of Hydrometeorological Disaster Mechanism andWarning ofMinistry ofWater Resources Nanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) NanjingUniversity of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

The data in the blockchain cannot be tampered with and the users are anonymous,which enables the blockchain to be a natural carrier for covert ***,the existing methods of covert communication in blockchain suffer from the predefined channel structure,the capacity of a single transaction is not high,and the fixed transaction behaviors will lower the concealment of the communication ***,this paper proposes a derivation matrix-based covert communication method in *** uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into multiple layers to make full use of the redundancy of ***,to solve the problem of the lack of concealment caused by the fixed transaction behaviors,divide the rectangular matrix into square blocks with overlapping regions and then encrypt different blocks sequentially to make the transaction behaviors of the channel addresses match better with those of the real ***,the linear congruence algorithm is used to generate random sequence,which provides a random order for blocks encryption,and thus enhances the security of the encryption *** results show that this method can effectively reduce the abnormal transaction behaviors of addresses while ensuring the channel transmission efficiency.

关键词： Covert communication blockchain concealment security capacity derivation

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