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An Analysis on the Training Mode of Master Students in Artificial Intelligence Field for Electronic Information Professional Degree—Take Hunan Normal University as an Example 8th

An Analysis on the Training Mode of Master Students in Artif...

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8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022

作者： Fu, Weina Liu, Shuai Institute of Information Science and Engineering Hunan Normal University Hunan Changsha410081 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Changsha410081 China

ISBN: (纸本)9783031211638

The ability of independent innovation in the field of artificial intelligence is a key element to occupy the commanding heights of future technology and talent competition in China. The cultivation of artificial intelligence talents, especially the cultivation high-end talent, it is essential to promote the development of artificial intelligence industry. At present, the market for artificial intelligence talents is in great demand. There is a shortage of high-end artificial intelligence talents, the weak strength of artificial intelligence teachers in universities, and the incomplete education system for talent training and many other issues. This paper combines some measures taken by Hunan Normal University for graduate students in the field of artificial intelligence in the degree of electronic information, the necessity and feasibility of artificial intelligence training senior talents are discussed for social needs in the field of artificial intelligence, The training goals and methods of postgraduate students in the field of artificial intelligence under the professional category of electronic information major are clarified, and the construction of artificial intelligence field is planned. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Professional aspects

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The Problems and Analysis of Artificial Intelligence Specialty Construction in Universities Under the Present Situation of Artificial Intelligence Development 8th

The Problems and Analysis of Artificial Intelligence Special...

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8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022

ISBN: (纸本)9783031211638

China’s independent innovation ability in the field of artificial intelligence is a key link to occupy the commanding heights of future science and technology and talent competition. The cultivation of artificial intelligent talents is crucial to promote the development of the artificial intelligent industry. There is a large demand for artificial intelligence talents in the market, but the development of artificial intelligence in colleges and universities is still faced with many problems, such as the construction of related disciplines and majors has just started, the strength of artificial intelligence teachers in colleges and universities is weak, the talent training is mainly based on postgraduate education and the education system is not perfect. This paper analyzes the current cultivation situation of artificial intelligence major in undergraduate colleges and vocational colleges, points out the shortcomings in the construction of artificial intelligence major, and gives some suggestions. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Personnel training

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A Method for Automatic English Oral Fluency Scoring 17

A Method for Automatic English Oral Fluency Scoring

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Li, Su-Mei Zhu, Zi-Xi Li, Xiao-Ning Li, Xin-Guang Guangdong University of Foreign Studies Laboratory of Language Engineering and Computing Guangdong Guangzhou China Experimental Teaching Center Guangdong University of Foreign Studies Guangdong Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangdong Guangzhou China Guangdong University of Foreign Studies Guangdong Guangzhou China

ISBN: (纸本)9798331507398

In order to address the issue of incomplete feature extraction in traditional oral fluency scoring methods, a method based on feature fusion is proposed in the paper, which integrates traditional acoustic features and deep features of audio to complete the scoring of oral fluency. For the audio to be tested, IS10_paraling features are extracted as traditional acoustic features, and a fine-tuned wav2vec2.0 pre-trained model is used to extract deep features of the audio. These two types of features are concatenated to form fusion features, which are then input into a multilayer perceptron scoring model to obtain the oral fluency score. Experimental results on the speechocean762 dataset show that compared to scoring methods based on single types of audio features, the fusion feature-based method improves scoring performance. The multilayer perceptron scoring model based on fusion features achieves a Pearson correlation coefficient of 0.744 with human scoring, demonstrating the effectiveness of the proposed method. © 2024 IEEE.

关键词： Regression analysis

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Quality Control of Vaccine Cold Chain Transportation Under Intelligent System Monitoring - from China's Experience

Quality Control of Vaccine Cold Chain Transportation Under I...

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作者： Li, Zhaoxia Ran, Wenxue Zheng, Hanwen Xue, Yun Business School Yunnan University of Finance and Economics Kunming650000 China E-Commerce School Yunnan College of Foreign Affairs and Foreign Language Kunming650000 China School of Logistics and Management Engineering Yunnan University of Finance and Economics Kunming650000 China Yunnan Key Laboratory of Service Computing Kunming650000 China

In order to accurately assess the impact on vaccine quality, it is necessary to study the temperature change during the period when cold chain temperature monitoring data and information are missing. In this paper, the simulation experiment method is used to continuously monitor the transportation temperature data of the refrigerated truck through an intelligent system, analyze the relationship between the temperature change in the vaccine box of the refrigerated truck and in the compartment, and verify the fitting effect of the model for predicting the temperature change of each monitoring point in the vehicle. The results indicate that when temperature monitoring data and information are momentarily missing, the monitoring results are still valid. At the same time, the nonlinear model constructed based on the heat conduction formula can accurately predict the temperature changes at various monitoring points inside the vehicle, and the model fitting effect is good through testing. This study fills the theoretical gap of the lack of cold chain temperature monitoring data and information and provides a reference for the regulatory authorities to formulate regulations, manufacturers to judge the efficacy of vaccines, transportation enterprises to formulate emergency plans, and operators to deal with emergencies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Trucks

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A Multi-level Approach to Learning Early Warning based on Cognitive Diagnosis and Learning Behaviors Analysis 26

A Multi-level Approach to Learning Early Warning based on Co...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ma, Hua Zhao, Wen Jiang, Zixu Huang, Peiji Tang, Wensheng Zhang, Hongyu Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan Normal University College of Information Science and Engineering Changsha China Co. Ltd China Central South University School of Business Changsha China

ISBN: (纸本)9798350331684

Learning early warning is of great significance for coping with students' learning risks. The existing research fails in modeling the fluctuation of students' learning states and providing the multi-level early warning for students at different levels. To address them, a new approach of learning early warning is proposed to predict at-risk students in e-learning environment by combining cognitive diagnosis with learning behaviors analysis. In this approach, the students' learning process is modeled from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning performance. The convolutional neural network and long short-term memory network are used to explore the students' latent learning features. Then, the Adaboost algorithm is applied to predict students' learning performance. Based on the predicted performance, the evaluation rules are designed to provide multi-level learning early warning for students. Finally, the experiments demonstrate that the proposed method could predict at-risk students efficiently and accurately. © 2023 IEEE.

关键词： Students

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Simple and Efficient Knowledge Graph Attention Network for Recommendation

Simple and Efficient Knowledge Graph Attention Network for R...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Li, Yangding Fu, Shaobin Feng, Hao Zeng, Yangyang Wang, Jinghao Jiang, Zhihao Zhang, Lvyun Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University School of Information Science and Engineering Changsha China Shenzhen Lazada Software Technology Company Limited Shenzhen China Hechi University School of Big Data and Computer Science Guangxi China

ISBN: (纸本)9798350312492

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive. © 2023 IEEE.

关键词： Recommender systems

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p-Norm Broad Learning for Negative Emotion Classification in Social Networks

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Big Data Mining and Analytics 2022年第3期5卷 245-256页

作者： Guanghao Chen Sancheng Peng Rong Zeng Zhongwang Hu Lihong Cao Yongmei Zhou Zhouhao Ouyang Xiangyu Nie Laboratory of Language Engineering and Computing Guangdong University of Foreign StudiesGuangzhou 510006China Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices South China Normal UniversityGuangzhou 511400China School of Computer Science and Software Zhaoqing UniversityZhaoqing 526000China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China School of Computing University of LeedsLeeds LS29JTUnited Kingdom

Negative emotion classification refers to the automatic classification of negative emotion of texts in social *** existing methods are based on deep learning models,facing challenges such as complex structures and too many *** meet these challenges,in this paper,we propose a method for negative emotion classification utilizing a Robustly Optimized BERT Pretraining Approach(RoBERTa)and p-norm Broad Learning(p-BL).Specifically,there are mainly three contributions in this ***,we fine-tune the RoBERTa to adapt it to the task of negative emotion ***,we employ the fine-tuned RoBERTa to extract features of original texts and generate sentence ***,we adopt p-BL to construct a classifier and then predict negative emotions of texts using the *** with deep learning models,p-BL has advantages such as a simple structure that is only 3-layer and fewer parameters to be ***,it can suppress the adverse effects of more outliers and noise in data by flexibly changing the value of ***,we conduct extensive experiments on the public datasets,and the experimental results show that our proposed method outperforms the baseline methods on the tested datasets.

关键词： social networks negative emotion RoBERTa broad learning p-norm

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Modality Unified Attack for Omni-Modality Person Re-Identification

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5577-5587页

作者： Bian, Yuan Liu, Min Yi, Yunqi Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning based person re-identification (re-id) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnerable to adversarial examples (AEs), leaving the robustness of multi-modality re-id models unexplored. Due to the lack of knowledge about the specific type of model deployed in the target black-box surveillance system, we aim to generate modality unified AEs for omni-modality (single-, cross- and multi-modality) re-id models. Specifically, we propose a novel Modality Unified Attack method to train modality-specific adversarial generators to generate AEs that effectively attack different omni-modality models. A multi-modality model is adopted as the surrogate model, wherein the features of each modality are perturbed by metric disruption loss before fusion. To collapse the common features of omnimodality models, Cross Modality Simulated Disruption approach is introduced to mimic the cross-modality feature embeddings by intentionally feeding images to non-corresponding modality-specific subnetworks of the surrogate model. Moreover, Multi Modality Collaborative Disruption strategy is devised to facilitate the attacker to comprehensively corrupt the informative content of person images by leveraging a multi modality feature collaborative metric disruption loss. Extensive experiments show that our MUA method can effectively attack the omni-modality re-id models, achieving 55.9%, 24.4%, 49.0% and 62.7% mean mAP Drop Rate, respectively. © 2005-2012 IEEE.

关键词： Feature extraction Perturbation methods Measurement Generators Collaboration Robustness Identification of persons Data mining Closed box Training

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Design and Analysis of a Modified ZNN with Dynamic Parameter for Time Varying Generalized Sylvester Equation

Design and Analysis of a Modified ZNN with Dynamic Parameter...

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2022 International Conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2022

作者： Zhang, Yongsheng Chen, Jifeng He, Yongjun Peng, Hao Li, Ping College of Information and Mechatronics Engineering Hunan International Economics University Changsha410205 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410205 China

ISBN: (数字)9781665498586

ISBN: (纸本)9781665498586

The time varying generalized Sylvester equation (TVGSE) is widely used in many domains such as mathematics, engineering and control system. Differing from the zeroing neural network (ZNN) and varying parameter ZNN (VPZNN), a novel neural network algorithm based on a novel time-varying parameter, named modified VPZNN (MVPZNN), is presented and studied for handling TVGSE in this work. In particular, the proposed MVPZNN possesses faster convergence capacity when activated by the linear and sign-bi-power activation function, respectively. Numerical simulation indicates that MVPZNN has superiority in convergence rate in coping with TVGSE compared with fixed parameter ZNN and other VPZNN models. © 2022 IEEE.

关键词： Neural networks

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Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese

arXiv

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arXiv 2024年

作者： Zhang, Jingshen Chen, Xinglu Qiu, Xinying Wang, Zhimin Feng, Wenhe School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Faculty of Chinese Language and Culture Guangdong University of Foreign Studies Guangzhou China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques with lexcial simplification. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification. © 2024, CC BY-NC-SA.

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