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2024 7th International Conference on computer Information Science and Artificial Intelligence, CISAI 2024

作者： Shi, Zhuo Xu, Ziquan Qiu, Xia He, Haoen Luo, Xiaonan School of Art and Design Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of lmage and Graphic Intelligent Processing Guangxi Guilin China Department of Computer and Information Security University of Guilin Electronic Technology Guangxi Guilin China Department of Guangxi Key Laboratory of Image and Graphic Intelligent Processing University of Guilin Electronic Technology Guangxi Guilin China

ISBN: (纸本)9798400707254

It is difficult for children to build electronic building blocks. In order to facilitate users to build building blocks, this paper designs and implements an electronic building block auxiliary system based on machine vision. The system uses the deep learning object detection model PP-YOLOv2 to detect the building block pictures taken by the user, obtain the location information of all the two-dimensional code in the pictures, and then decode the two-dimensional code, perform perspective transformation on the pictures, and obtain the final two-dimensional code decoding information and location information. Finally, the position of the QR code identified by the building blocks on the user’s picture is crossed and compared with the position of the standard building blocks, so as to determine whether the building blocks placed by the user are correct and give the user a prompt. The system test shows that the system is not only easy to use, but also easy to maintain, and can correctly handle most of the small and medium difficulty examples in the electronic building blocks course. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Enhancing Security in Smart Homes with IoT Using Logit-Boosted Techniques 6

Enhancing Security in Smart Homes with IoT Using Logit-Boost...

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6th International Conference on computer Information Science and Application Technology, CISAT 2023

作者： Rahim, Asif Zhong, Yanru Ahmad, Tariq School of Computer and Information Security Guilin university of Electronic Technology Guilin54001 China Guangxi Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin541001 China School of Information and Communication Engineering Guilin University of Electronic Technology Guilin541001 China

ISBN: (纸本)9781510668546

The utilization and advancement of Internet of Things (IoT) gadgets have noticeably increased in recent times. Various sectors, including intelligent homes, healthcare, sports analysis, and different industries, use IoT devices. Traffic is a crucial aspect of IoT devices, and it differs from conventional device traffic in several ways. This investigation employed 41 Internet of Things (IoT) devices. A machine learning model was developed that can classify IoT devices into multiple categories based on 13 different network traffic features. The raw data that was gathered was preprocessed using techniques like standardization and scaling techniques of the dataset. Algorithms for feature selecting can be utilized to extract characteristics from textual data. Following stratification, The dataset contains 106,234 sets of features that were utilized to enhance the predictive model. This research employed various evaluation metrics to demonstrate the effectiveness of the logistic-boosted algorithms. In this research, collaborative machine learning methods were utilized as a starting point to develop an intrusion detection system (IDS) that can identify abnormal network traffic. The principal aim of this investigation is to identify attacks and abnormalities within a Smart Home IoT setting. We have introduced a new technique for constructing Logit-Boosted algorithm, namely Logi-CatBoost Classifier. Once the logitboosted algorithm was employed to classify the dataset, Logi-CBC performed the best, with an accuracy of 88.70%. We suggested Logi-CBC algorithm achieved the top precision among previously used Logit-Boosted algorithms in past studies on the same dataset. © 2023 SPIE.

关键词： Internet of things

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Multi-scale convolutional neural networks for brain disease diagnosis

Multi-scale convolutional neural networks for brain disease ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Zhongyi Gao, Libin Tong, Yinsheng Lu, Xingjin Li, Rui Xiao, Lei Li, Zuoyong Wenzhou University College of Computer Science and Aritificial Intelligence Wenzhou325035 China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou China Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9798350337488

The brain is the most sophisticated and complex organ in the human body. Nowadays, diagnosing complex and diverse brain diseases is a hot topic. Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), and others are common brain diseases. With the increased usage of Artificial Intelligence (AI) in medical image analysis, the endeavor to make AI comprehend brain images for assisting doctors in making objective diagnoses of brain diseases has gained considerable attention. Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used tool for diagnosing and analyzing brain ***, the Pearson correlation coefficient (PCC) method constructs a dynamic functional connectivity (dFC) network using a fixed window size. However, this method has limitations as it is challenging to extract potential high-level features from the dFC and determine local feature relevance. This paper presents a method for constructing dFC based on multi-scale sliding windows and proposes Multi-scale Convolutional Neural Networks (MsCNN) for learning and analyzing dFC at various scales. Finally, a deep fusion of features learned at different scales is employed for the diagnostic classification of brain *** to the rs-fMRI dataset from ADNI, the classification accuracy was 84.0% for eMCI/lMCI, 84.4% for lMCI/AD, and 58.4% for NC/eMCI/lMCI/AD. Applied to rs-fMRI data from ABIDE, the accuracy was 74.7% for ASD/NC. The proposed method exhibits robust classification performance and introduces a new approach to diagnosing other brain diseases. © 2023 IEEE.

关键词： Brain diseases Classification Diagnostic Multi-scale convolutional neural networks (MsCNN) Resting-state functional magnetic resonance imaging (rs-fMRI)

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： image registration

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Competition on robust deep learning

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National Science Review 2023年第6期10卷 13-15页

作者： Yinpeng Dong Chang Liu Wenzhao Xiang Hang Su Jun Zhu Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Institute of Image Communication and Networks Engineering in the Department of Electronic Engineering (EE) Shanghai Jiao Tong University Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Peng Cheng Laboratory Pazhou Laboratory (Huangpu)

PROBLEM In recent years,the rapid development of artificial intelligence (AI) technology,especially machine learning and deep learning, is profoundly changing human production and *** various fields,such as robotics,face recognition,autonomous driving and healthcare,AI is playing an important ***,although AI is promoting the technological revolution and industrial progress,its security risks are often *** studies have found that the wellperforming deep learning models are extremely vulnerable to adversarial examples [1-3].The adversarial examples are crafted by applying small,humanimperceptible perturbations to natural examples,but can mislead deep learning models to make wrong *** vulnerability of deep learning models to adversarial examples can raise security and safety threats to various realworld applications.

关键词： robustness adversarial example adversarial training deep learning

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Enhanced YOLOv7-Based Construction Site Environmental Detection Method 4

Enhanced YOLOv7-Based Construction Site Environmental Detect...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Liang, Siheng Huang, Wenming Xiao, Yannan Guilin University of Electronic Technology School of Computer and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guilin University of Electronic Technology School of Computer and Information Security Guilin China

ISBN: (纸本)9798350343618

This paper proposes an enhanced approach utilizing the YOLOv7 model for automated construction site environment detection and management. The method leverages high-resolution surveillance cameras and computer vision techniques to achieve this goal. The proposed method starts with data collection of the construction site environment. Then produce accurate bounding boxes. These bounding boxes serve as input to an improved YOLOv7 network model, enabling feature extraction and generating high-scoring feature maps with associated weights. Utilizing these weights, image segmentation is performed to obtain the desired detection heads. Experimental results demonstrate the effectiveness of the method in identifying and annotating various environmental features within the construction site, leading to significant improvements in detection accuracy. Compared to similar techniques, this method offers faster detection speed, higher accuracy, and reduced error rates. The proposed construction site environment detection method provides an efficient and automated solution for on-site management in the construction industry, with practical applications and value. © 2023 IEEE.

关键词： Construction industry

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Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security intelligent agent reinforcement learning Offensive and defensive game

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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An English Pronunciation Quality Evaluation Model Based on Multi-dimensional Features 2

An English Pronunciation Quality Evaluation Model Based on M...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Li, Yiqun Huang, Guimin Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

As a universal language, English has been paid more and more attention, among which oral English learning is very important. In this paper, the two key technologies of pronunciation error detection and quality evaluation are studied, both of them are effectively integrated, aiming to build a model for L2 learners' English pronunciation quality evaluation. This paper mainly studies two different methods of pronunciation error detection. Based on the speech recognition framework, the standard score is compared with the threshold to judge the correctness of phoneme pronunciation, and the phoneme-dependent threshold is set to improve the maximum Precision to 0.44. By judging the correct pronunciation and confusing phoneme, the accuracy of pronunciation error detection is improved to 81.26%. This paper proposes the fusion algorithm from multi-dimensions of speech fluency and intonation respectively, and a newly designed feature called word duration ratio, which significantly improve the correlation of pronunciation quality evaluation to 0.746. © Published under licence by IOP Publishing Ltd.

关键词： Speech recognition

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Chinese image captioning via double decoding based on visual prompts 5

Chinese image captioning via double decoding based on visual...

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5th International Conference on computer Vision and Data Mining, ICCVDM 2024

作者： Tan, Weiguo Xiao, Yannan Wen, Yayuan Deng, Zhenrong Huang, Wenming School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guangxi Guilin China College of Electronic Engineering Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9781510682863

The significant progress of vision-language pre-trained models (VLMs) and large language models (LLMs) have provided a feasible new mode for image captioning, which relies on VLMs to process images and then utilizes LLMs to generate captions, simplifying the caption generation process and making it lighter. Based on this mode, to address the issues of deviation between the generated captions and the content of image expressions, incomplete descriptive information, and resource hungry in previous Chinese image captioning models, we propose a Chinese image captioning model via double decoding based on Visual Prompts, DDVP. This model employs the CLIP model as the encoder, the GPT2 model as the decoder, and introduces Visual Prompts, which are keywords related to image content. The model adopts a double decoding approach, first decoding to generate Visual Prompts and then decoding to generate the final captions based on the Visual Prompts. Through evaluation, we have demonstrated that our model achieves competitive results on the AIC-ICC dataset, and while maintaining fluency, the generated captions of DDVP can also cover the information in the image more comprehensively and accurately. © 2024 SPIE.

关键词： Visual languages

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