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5th International Conference on Machine Learning, big data and Business Intelligence, MLBDBI 2023

作者： Yuan, Shuping Mariano, Vladimir Y. National University College of Computing and Information Technologies Manila Philippines AnHui XinHua University School of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350359930

With the continuous emergence and wide application of new medical imaging technologies, doctors can easily use them to provide key and reliable information for the diagnosis of lesions in real time. At the same time, with the continuous development of computer vision, deep learning has derived many model structures in the field of medical image segmentation. Among them, U-net network structure has become one of the important segmentation models in the field of medical image because of its clever skip connection, which can retain complete features while locating clear boundaries. Algorithms based on deep learning often rely on the training of massive data sets. In the field of cell images, dataset calibration is very difficult. Especially in multi-cell images, the position and boundary of nucleus and cytoplasm are ambiguous, and can not be correctly calibrated even by a large amount of manpower. And from the perspective of imaging principles, medical image data is complex and not informative enough. Based on the comparison between the improved fully connected U-net++ network and FCN, SegNet, U-Net and other network models, this paper proposes a data augmentation method for the selection and splicing of cell sample data from the perspective of data sampling and augmentation, which can effectively transform the weak supervised data containing part of the label into strong supervised data for training. © 2023 IEEE.

关键词： Cytology

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Knowledge Distillation Scheme for Named Entity Recognition Model Based on BERT 5

Knowledge Distillation Scheme for Named Entity Recognition M...

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5th International Conference on Machine Learning, big data and Business Intelligence, MLBDBI 2023

作者： Ye, Chengqiong Hernandez, Alexander A. National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350359930

In order to improve the applicability of named entity recognition model in industrial application scenarios, this paper studies the BERT named entity recognition task. From the perspective of lightweight model, an adaptive weight distillation method is designed to compress the BERT named entity recognition model. By adjusting the weight hyperparameter of distillation loss to an adaptive function, this method enables the model to automatically adjust the weight value according to the training process, avoiding the influence of manual parameter adjustment. Furthermore, by decreasing the middle hidden layer's dimension, this method compresses the teacher model from the width and incorporates the distillation of the middle Transformer layer of the model into the distillation process, allowing it to fully utilize the rich semantic information in the deep structure of the model. Ultimately, a light-weight, well-functioning model with a straightforward structure and few parameters is trained. By employing the suggested technique, the model's inference speed can be increased by seven times while reducing the number of model parameters to 15% of the original model with little loss of accuracy. © 2023 IEEE.

关键词： Distillation

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Research on Presentation Generation Method of Credential Selective Disclosure in Self-Sovereign Identity 7th

Research on Presentation Generation Method of Credential Sel...

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7th International Conference on computing, Control and Industrial Engineering, CCIE 2023

作者： Qi, Yu Zhang, Jiarui Zhang, Han School of Artificial Intelligence and Big Data Hefei University Hefei230601 China Beijing Academy of Blockchain and Edge Computing Beijing100081 China

ISBN: (纸本)9789819927296

Nowadays, the developing trend of digital identity is Self-Sovereign Identity (SSI). The construction and improvement of SSI management system and related standards also attract more and more attention. However, the SSI projects that have been made public are still imperfect in terms of selective disclosure of the Verifiable Credential (VC). To disclose the least information in Verifiable Presentation (VP) and increase the privacy protection of holder’s information, we propose a VC selective disclosure method for multiple types of claims. Specifically, this method is based on the W3C’s VC standard for extensions, divides claim information into five categories (number, numeric string, non-numeric string, time, and Boolean), divides the disclosure into two categories (exact value and range value), and is based on Zero-Knowledge Range Proof and Simplified Payment Verification (SPV) idea to precisely respond to the verifier’s assertion request. The proposed scheme can effectively answer all kinds of assertion requests with anti-repudiation, privacy protection, and security, which enriches and improves the VP generation method of VC selective disclosure and contributes to the further development of SSI. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic document identification systems

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Research on Time Series data Prediction Based on STP-GAN Algorithm 5

Research on Time Series Data Prediction Based on STP-GAN Alg...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Zhang, Rui Mariano, Vladimir Y. College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence AnHui XinHua University Hefei China

ISBN: (纸本)9798350357950

In this paper, we utilize the Sentiment Analysis technique in NLP field to provide the opinion influence factors for the algorithmic model by performing sentiment analysis on the text of the research report, introduce the GAN algorithm which is more compatible with the logic of the temporal data operation, and on the basis of which we add the Text Pathway, and put forward the text-assisted Generative Adversarial Network Prediction Model, STP-GAN. The model combines temporal data prediction with sentiment analysis to reduce the error of model training, and introduces the Transformer structure to improve the learning ability of temporal data features. In addition, STP-GAN utilizes BERT for fine-tuning and incorporates a lexicon to increase model adaptability. And on the basis of the proposed STP-GAN, the optimizer RMSPropW, which is more suitable for this research, is proposed, which uses the exponentially weighted moving average as the decay coefficient and utilizes weight decay to improve the training stability and training speed of the prediction model. © 2023 IEEE.

关键词： BERT generative adversarial model optimizer sentiment propensity analysis temporal data

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Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

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Computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu Xiangyang Luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method *** address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short *** on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each ***,the PoI similarities between long and short windows are calculated for training and ***,series of experiments is conducted based on real Internet forum *** results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed *** the length of long window is small,traditional methods perform better,and SVR is the *** the contrary,the deep learning methods show superiority,and LSTM performs *** results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

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A Complete Feature Matching Fusion Method for Palmprint Recognition 5

A Complete Feature Matching Fusion Method for Palmprint Reco...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Wang, Hongxia Mariano, Vladimir Y. College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China

ISBN: (纸本)9798350357950

As we all know, the single model extraction palmprint biometric recognition information is relatively simple, and the existing traditional palmprint recognition methods are greatly affected by the external environment. In this paper, we propose a fusion method of traditional methods and deep learning, fusing at the feature layer and matching value layer respectively. The fusion strategies are maximum fusion and summation fusion. The experimental results achieve high recognition accuracy and low Equal Error Rate on both constrained environment and unrestricted environment data sets. © 2023 IEEE.

关键词： Complete Feature Matching Fusion Feature-Level Fusion Palmprint Recognition

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Meta-transfer learning for predicting position-dependent milling stability across varying overhang lengths under limited experimental data

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Journal of Manufacturing Processes 2025年 149卷 43-56页

作者： Deng, Congying Wu, Qiang Miao, Jianguo Lu, Sheng Xie, Luofeng School of Integrated Circuits Chongqing University of Posts and Telecommunications Chongqing400065 China College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Mechanical Engineering Sichuan University Chengdu610065 China

Stability lobe diagram (SLD) serves as a pivotal reference in determining appropriate chatter-free milling parameters. The tool tip frequency response functions (FRFs), as crucial prerequisites for deriving theoretical SLDs, vary with the position and overhang length. The acquisition of tool tip FRFs under different machining positions and overhang lengths necessitates repeated time-consuming experiments or simulations. To guarantee the efficiency and accuracy in predicting SLDs, this paper puts forward a meta-transfer learning method utilizing a deep neural network to refine the prediction of stability limits with limited experimental data. Initially, a source overhang length is defined to perform the impact testing under adequate machining positions for acquiring tool tip dynamics and constructing a theoretical stability dataset. Fewer impact tests for a target overhang length are conducted under insufficient machining positions arranged through a proposed similarity-based sampling strategy, and a target stability dataset is constructed using limited tool tip FRFs. Secondly, the source dataset is updated by domain adaptation on the modal parameters with a multi-fidelity modeling. Subsequently, meta-learning is carried out on multiple meta-tasks randomly determined from the updated source dataset. The ultimately trained deep neural network during the meta-learning phase undergoes fine-tuning with the target dataset for predicting the stability limits at various machining positions. Finally, a three-axis vertical machining center is taken to conduct a detailed experimental validation. Comparative analyses indicate that the proposed methodology outperforms others in accurately predicting stability limits, even with a constrained number of labeled samples. © 2025 The Society of Manufacturing Engineers

关键词： Milling (machining)

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of Computer Science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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OSFS-Vague: Online streaming feature selection algorithm based on vague set

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1451-1466页

作者： Jie Yang Zhijun Wang Guoyin Wang Yanmin Liu Yi He Di Wu School of Physics and Electronic Science Zunyi Normal UniversityZunyiChina Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Computer Science Old Dominion UniversityNorfolkVirginiaUSA College of Computer and Information Science Southwest UniversityChongqingChina

Online streaming feature selection(OSFS),as an online learning manner to handle streaming features,is critical in addressing high-dimensional *** real big data-related applications,the patterns and distributions of streaming features constantly change over time due to dynamic data generation ***,existing OSFS methods rely on presented and fixed hyperparameters,which undoubtedly lead to poor selection performance when encountering dynamic *** make up for the existing shortcomings,the authors propose a novel OSFS algorithm based on vague set,named *** main idea is to combine uncertainty and three-way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous ***-Vague also improves the calculation method of correlation between features and ***,OSFS-Vague uses the distance correlation coefficient to classify streaming features into relevant features,weakly redundant features,and redundant ***,the relevant features and weakly redundant features are filtered for an optimal feature *** evaluate the proposed OSFS-Vague,extensive empirical experiments have been conducted on 11 *** results demonstrate that OSFS-Vague outperforms six state-of-the-art OSFS algorithms in terms of selection accuracy and computational efficiency.

关键词： feature selection online feature selection three-way decision vague set

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Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

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Research in Astronomy and Astrophysics 2024年第3期24卷 283-296页

作者： Zi-Yi You Yun-Rong Pan Zhi Ma Li Zhang Shuo Xiao Dan-Dan Zhang Shi-Jun Dang Ru-Shuang Zhao Pei Wang Ai-Jun Dong Jia-Tao Jiang Ji-Bing Leng Wei-An Li Si-Yao Li School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Big data and Computer Science Guizhou Normal UniversityGuiyang 550025China Guizhou Software Engineering Research Center Guiyang 550000China

Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research ***,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering ***,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification *** this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for *** on both High Time Resolution Universe SurveyⅡ(HTRU2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTRU2,the precision and recall rates of its parallel mode reach0.981 and 0.988 *** FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within ***,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

关键词： methods data analysis-surveys-methods numerical

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