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Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Jianxing Lin Guimin Huang JiaHao Wang School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

The pursuit of accurate and fluent English communication is the cornerstone of global academic exchange and expression. The accuracy of written English is crucial for effective discourse; however, despite advancements in grammatical error correction (GEC) technology, these systems often fall short when analyzing coherent text. This paper delves into the complexities of English writing and the challenges posed by current GEC systems, advocating for a shift toward understanding document-level context in error correction. We propose a dual-encoder architecture within an encoder-decoder model, referred to as the Context-Aware Grammatical Error Correction (CAGEC) model. The CAGEC model employs an innovative dual-encoder structure, combining the encoder of the Transformer model with a Bi-GRU (Bidirectional Gated Recurrent Unit) neural network, and integrates encoding into the decoder through attention and gating mechanisms to achieve a deep understanding of the source sentence and its context.

关键词： Accuracy Neural networks Writing Logic gates Transformers Encoding Error correction Context modeling Synthetic data Software engineering

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Online Q&A for Machine Learning Courses Based on Formula Recognition 4

Online Q&A for Machine Learning Courses Based on Formula Rec...

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4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Chen, Xiaopan Li, Minghao Zhang, Tianxiang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan University Kaifeng Henan Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9798350394610

Mathematical formulas are ubiquitous in courses such as Machine Learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. This paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection. © 2023 IEEE.

关键词： Deep learning

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Enhancing Online Teaching Effectiveness Through Hand-Raising Detection 4

Enhancing Online Teaching Effectiveness Through Hand-Raising...

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4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Dong, Zhiwei Chen, Xiaopan Shi, Yufeng Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. This paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments. © 2023 IEEE.

关键词： Students

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Nested Saturated Control of Uncertain Complex Cascade Systems Using Mixed Saturation Levels

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1163-1174页

作者： Meng Li Zhigang Zeng Wuhan National Laboratory for Optoelectronics Wuhan 430074 School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074China

This study addresses the problem of global asymptotic stability for uncertain complex cascade systems composed of multiple integrator systems and non-strict feedforward nonlinear systems. To tackle the complexity inherent in such structures, a novel nested saturated control design is proposed that incorporates both constant saturation levels and state-dependent saturation levels. Specifically, a modified differentiable saturation function is proposed to facilitate the saturation reduction analysis of the uncertain complex cascade systems under the presence of mixed saturation levels. In addition, the design of modified differentiable saturation function will help to construct a hierarchical global convergence strategy to improve the robustness of control design scheme. Through calculation of relevant inequalities, time derivative of boundary surface and simple Lyapunov function,saturation reduction analysis and convergence analysis are carried out, and then a set of explicit parameter conditions are provided to ensure global asymptotic stability in the closed-loop systems. Finally, a simplified system of the mechanical model is presented to validate the effectiveness of the proposed method.

关键词： method saturation differentiable

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Multi-level fusion with fine-grained alignment for multimodal sentiment analysis

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Journal of King Saud University Computer and Information Sciences 2025年第5期37卷 1-15页

作者： Li, Xiaoge Ma, Yanan An, Xiaochun Xing, Jinshuo Liu, Ren Ren, Yunsheng College of Computer Science Xi’an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China College of Public Administration and Policy Shandong University of Finance and Economics Jinan China

Multimodal sentiment analysis (MSA) integrates and processes data from multiple sources, like audio and text, to better understand human emotions through cross-modal interactions. The effective acquisition and integration of meaningful features for constructing richer sentiment representations remains a key challenge in MSA. Most existing methods directly obtain global representations and integrate at the utterance level from different modalities, but this ignores fine-grained representations and makes it difficult to capture intricate relationships within and between modalities. Therefore, we propose a novel method, Fine-grained Multimodal Fusion Network (MMTA). Firstly, a Fine-grained Alignment (FGA) module is introduced to align and extract word-level features to bridge heterogeneous modal gaps. FGA enables word-level alignment between audio, text, and their corresponding contextual information using the Montreal Forced Aligner (MFA). Secondly, a Multi-level Fusion module (MLF) is designed, which captures more cross-modal interaction through three stages: Local-Local Interaction, Local-Global Interaction, and Similarity-weighted Representation Adjustment. Finally, an Attention Fusion Network(AFN) module is developed to capture both inter- and intra-modal correlations, enabling the generation of consistent multimodal representations. Extensive evaluations on widely used MSA datasets, CMU-MOSI and CMU-MOSEI, indicate that our method outperforms prior baselines and validates the effectiveness of the fine-grained alignment and the multi-level fusion for improving multimodal sentiment analysis performance.

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TMMM: Transformer in Multimodal Sentiment analysis under Missing Modalities

TMMM: Transformer in Multimodal Sentiment Analysis under Mis...

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International Joint Conference on Neural Networks (IJCNN)

作者： Huanyu Wu Siyang Li Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The cross-task gap presents a significant challenge for multimodal models because of the differences in input-output workflows. For instance, multimodal pre-trained transformers may encounter uni-modal data during testing. To mitigate the gap, this paper introduces a Transformer in Multimodal Sentiment analysis under Missing Modalities (TMMM) aims to perform well using missing-modal data during testing. TMMM uses a missing multimodal training approach to prevent accuracy degradation in testing. At the same time, a new network architecture allows the model to reconstruct missing modalities during testing. Classification token fusion and Mixture-of-Experts structures further enhance the model’s performance. A pre-training method utilizing contrastive learning, which can construct negative samples with positive samples, is proposed to overcome insufficient labeled data. Our experiments demonstrated the effectiveness of TMMM on two datasets with no modalities missing, i.e., it consistently achieved the highest classification accuracy and Macro-F1, which outperformed the best state-of-the-art baseline on each dataset by about 2% and 2.5%. Additionally, TMMM usually performs better than other baselines on datasets with missing modalities during testing.

关键词： Training Sentiment analysis Accuracy Contrastive learning Network architecture Transformers Brain modeling

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intelligent Test Paper Generation Algorithm Integrating Normal Distribution and Parallel Genetic Algorithm

Intelligent Test Paper Generation Algorithm Integrating Norm...

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intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Chuangye Liu Guimin Huang Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Genetic algorithms have been widely used in intelligent test paper generation systems. However, traditional genetic algorithms cannot ensure that the difficulty of test questions is normally distributed, and are prone to falling into local optimal solutions. To address the above problems, we proposed an intelligent test paper generation algorithm that combines normal distribution and parallel genetic algorithms. In the population initialization stage, we use the cumulative distribution function (CDF) of the normal distribution to initialize the population. In the subsequent genetic operations, we designed a directed mutation mechanism and added KL divergence as a penalty number in the design of the fitness function to ensure that the difficulty of the test questions is normally distributed. At the same time, in order to speed up the efficiency of the algorithm and escape from the local optimal solution, an adaptive migration strategy was introduced. Experimental results show that the difficulty of the test questions generated by this algorithm is normally distributed, and the algorithm performance is better than other algorithms.

关键词： Human computer interaction Heuristic algorithms Gaussian distribution Genetics Genetic algorithms Distribution functions Convergence

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Dermoscopic image Classification Using Attention Mechanism and Ensemble Learning Approaches

Dermoscopic Image Classification Using Attention Mechanism a...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Huang, Shanchuan Lei, Hongwei Jin, Liuhan Yang, Jinzhu Jiang, Tao Yao, Yudong Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Stevens Institute of Technology Department of Electrical and Computer Engineering United States Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threatening if left untreated. Early detection of the disease is important for the treatment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. Results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better performance. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings. © 2023 IEEE.

关键词： Attention mechanism Ensemble learning image classification Late fusion Skin Cancer

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Cooperative Target-Surrounding Control of Unmanned Surface Vessels Based on MADDPG 49

Cooperative Target-Surrounding Control of Unmanned Surface V...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Li, Taoman Zhu, Fei Gan, Zihan Zhou, Zexing Wang, Xiaotao Lu, Renzhi China Ship Scientific Research Center Wuxi214082 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China Wuhan University of Science and Technology Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan430081 China

ISBN: (纸本)9798350331820

This article proposes a multi-agent deep reinforce-ment learning algorithm to control a fleet of unmanned surface vessels (USVs) that encircle and capture sea targets. First, a simulation environment for USVs is established based on a dynamic model;two-dimensional control variables are used to control movements in three directions. Second, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed to achieve intelligent control of the USVs, using a reward function based on certain prior knowledge. Finally, centralized training and decentralized execution are used to complete the offline learning of multiple agents, and continuous action decisions are made based on the observations of the USV sensors. Simulations demonstrate that the method can capture stationary moving sea targets with any number of multi-agents under disturbances, and exhibits strong robustness and practicality. © 2023 IEEE.

关键词： Reinforcement learning

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Design of Virtual Experiment Online Teaching System for Economics and Management in Colleges and Universities Based on Virtualization Technology 8th

Design of Virtual Experiment Online Teaching System for Econ...

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

作者： Zhang, Jianhua Luo, Yingji Yang, Qiuhui Guangxi University of Finance and Economics Nanning530007 China Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou543002 China

ISBN: (纸本)9783031211607

The traditional experimental teaching method is single, and students lack learning initiative and creativity, which leads to the problem of insufficient teaching quality. Therefore, this paper proposes a virtual experiment online teaching system of university economics and management based on virtualization technology. Firstly, optimize the system hardware structure and improve the allowable effect of system equipment configuration. Then, the three-tier structure of customer layer, application service layer and data layer is used to optimize the system software functions, and simplify the working principle and multi-layer architecture of the online virtual experiment system based on B/S mode, so as to achieve the online teaching goal of economic and management virtual experiment in colleges and universities. The experimental results show that the system has high practicability, can effectively improve the teaching quality, and fully meet the expected requirements of the research. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： E-learning

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