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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Faxiang Gao, Guanbin Na, Jing Wu, Jiande Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9789887581543

This paper solves the neural network (NN) tracking control problem for uncertain nonlinear servo system with time-varying parameters and uncertainties. To address the uncertain nonlinear function with unknown time-varying parameters (i.e., unknown nonlinear spatiotemporal function), the time-varying parameter extraction method is used to separate the time-varying parameters from uncertain nonlinear spatiotemporal function, which yields an unknown state-based boundary function. By the tools of NN and adaptive technology, an adaptive neural tracking controller is designed, which guarantees the uniformly ultimately bounded (UUB) performance of the resulting closed-loop system. The effectiveness of the designed method is verified by simulations. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Servomechanisms

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Hybrid Convolutional-Transformer Neural Network for Driver Distraction Detection

Hybrid Convolutional-Transformer Neural Network for Driver D...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Li, Penghua Mou, Qiyun Hou, Jie Tu, Yushan Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing China Chongqing University of Posts and Telecommunications College of Automation Chongqing China Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing China

ISBN: (纸本)9798350337754

Distracted driving causes a significant number of traffic accidents each year, making it a crucial area of research to monitor the driver's state and prevent accidents. Although convolutional networks are widely used for detecting driver distraction, they face several issues, such as poor generalization ability, high model capacity, and response latency. To tackle these challenges, this paper introduces a nimble and low-latency algorithm named MViTCNet, which utilizes the MobileViT module for the identification of driver distraction while operating within constrained computational resources. The MViTCNet algorithm extracts local features and constructs an original feature map, which is fused with a global feature representation processed by a Transformer to capture finer details and obtain a larger receptive field. Additionally, the classic convolutional module MBConv is employed to reduce spatial redundancy, and downsampling is used to obtain low-resolution feature maps. By stacking multiple modules to learn more complex feature representations, the network structure increases in depth and width, resulting in a better fit for complex data distributions. Experiments on an embedded hardware platform show that MViTCNet achieves an acceptable trade-off, with an accuracy of 91.04% on the StateFarm dataset and 97.80% on the LDDB dataset at a latency of 28.9±5.3 milliseconds, outperforming existing methods. © 2023 IEEE.

关键词： Convolution

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Visualizing Program Behavior: A Study of Enhanced Program Diagrams Using LLM

Visualizing Program Behavior: A Study of Enhanced Program Di...

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Frontiers in Education (FIE) Conference

作者： Ying Li Runze Yang ShiJie Gui Peng Shi Xuefei Huang Da Yang Xiaozhou Zhang Yiming Gai School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang University Hangzhou China

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This paper aims to address the difficulties faced by novice programmers in grasping code structure and execution flow, improving programming thinking, and pinpointing code errors with accuracy. It proposes providing students with program behavior diagrams based on large language models (LLMs) and visualization techniques to achieve personalized guidance. Specifically, these program behavior diagrams include programming thinking visualization diagrams and code vulnerability visualization diagrams. A programming thinking visualization diagram employs static code analysis to gather code structure information, combined with the structured chain-of-thought method to collectively optimize the LLM. This enables the LLM to explain each interpretable part of the code from top to bottom, detailing the programming concepts, and displaying them on a modularized code structure diagram. The code vulnerability visualization diagram primarily utilizes the fine-tuned LLM, optimizing it based on program analysis and clustering analysis methods to accurately identify vulnerabilities in student code and display them on a code flow diagram. Its feature is to visually display to students the error location, error information, and the impact of errors on program flow, rather than providing the programming answers. Lastly, through experiments and statistical analysis of actual teaching data, this paper serves a demonstration that the enhanced models used in the visualization diagram generation process have a noticeable effect on mainstream LLMs, and that visualization diagrams hold significant value for students at different stages of learning.

关键词： Location awareness Analytical models Codes Accuracy Statistical analysis Large language models Education data visualization Optimization methods Programming profession

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ProgMate: An intelligent Programming Assistant Based on LLM

ProgMate: An Intelligent Programming Assistant Based on LLM

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Frontiers in Education (FIE) Conference

作者： Ying Li Runze Yang Xiantao Zhang Peng Shi Da Yang Xuefei Huang School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing International Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This study addresses the challenges faced in personalized tutoring within large-scale programming courses, such as significant ability gaps among students, limited available resources, among others. For these reasons, we proposed an intelligent programming assistant, ProgMate, based on large language models (LLMs). Benefitting from the robust understanding and learning capabilities of LLM, ProgMate not only comprehensively monitors the learning process, but can also surpass human teaching in aspects such as intelligent assignment grading, identification of knowledge gaps, and assessment of learning abilities. ProgMate embodies a new 4-A digital teaching paradigm, characterized by its ability to provide precise guidance with anything, to anyone, anywhere, at any time, with features like “omnipresence”, “adaptive guidance” and “customization”. It facilitates the organic integration of collective teaching and individualized guidance, continuous learning, and long-term development, as well as resource efficiency and precision nurturing, offering a viable path and empirical support for the digital transformation of future education.

关键词： Adaptive systems Navigation Large language models Digital transformation Education Educational technology Programming profession Monitoring

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype Learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. The DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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Research on Frequency of Radial Vibration of Elastic Thin Spherical Shell

Research on Frequency of Radial Vibration of Elastic Thin Sp...

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2023 International Congress on Ultrasonics, ICU Beijing 2023

作者： Haidao, Zhang Long, Xu Fengming, Li College of Science China Jiliang University Zhejiang Hangzhou310018 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

The ultrasonic frequency radial vibration of an elastic thin spherical shell composed of isotropic materials is studied, the frequency equation of its vibration is deduced, and the equivalent circuit is obtained. The finite element software COMSOL is used to verify the obtained analytical theory, and the calculated results are basically consistent with the theoretical calculated values. The relevant conclusions provide references for the application of spherical shell structure in related fields. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Shells (structures)

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A Hierarchical Adaptive Multi-Task Reinforcement Learning Framework for Multiplier Circuit Design 41

A Hierarchical Adaptive Multi-Task Reinforcement Learning Fr...

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Zhihai Wang, Jie Zuo, Dongsheng Ji, Yunjie Xia, Xilin Ma, Yuzhe Hao, Jianye Yuan, Mingxuan Zhang, Yongdong Wu, Feng CAS Key Laboratory of Technology in GIPAS MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Microelectronics Thrust Hong Kong University of Science and Technology Guangzhou China Noah's Ark Lab Huawei Technologies China College of Intelligence and Computing Tianjin University China

Multiplier design-which aims to explore a large combinatorial design space to simultaneously optimize multiple conflicting objectives-is a fundamental problem in the integrated circuits industry. Although traditional approaches tackle the multi-objective multiplier optimization problem by manually designed heuristics, reinforcement learning (RL) offers a promising approach to discover high-speed and area-efficient multipliers. However, the existing RL-based methods struggle to find Pareto-optimal circuit designs for all possible preferences, i.e., weights over objectives, in a sample-efficient manner. To address this challenge, we propose a novel hierarchical adaptive (HAVE) multi-task reinforcement learning framework. The hierarchical framework consists of a meta-agent to generate diverse multiplier preferences, and an adaptive multi-task agent to collaboratively optimize multipliers conditioned on the dynamic preferences given by the meta-agent. To the best of our knowledge, HAVE is the first to well approximate Pareto-optimal circuit designs for the entire preference space with high sample efficiency. Experiments on multipliers across a wide range of input widths demonstrate that HAVE significantly Pareto-dominates state-of-the-art approaches, achieving up to 28% larger hypervolume. Moreover, experiments demonstrate that multipliers designed by HAVE can well generalize to large-scale computation-intensive circuits. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model 13

Structure-constrain Source Free Domain Adaptation with Regul...

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13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Yang, Dan Peng, Xin Su, Cheng Tan, Dayu Chen, Chaoyang Zhong, Weimin Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Anhui Hefei230601 China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350361674

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. Then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness. © 2024 IEEE.

关键词： Domain Knowledge

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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

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Computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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