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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jiaying Fan Shuping Pan Xinbo Jiang Shengtao Zhang Hongliang Liu Chuan Wang Jianghua Li Chenglin Ke Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Technological Innovation Department Shandong Hi-Speed Group Co Ltd JiNan China School of Qilu Transportation Shandong University JiNan China China Railway 14th Bureau Group First Engineering Development Co. Ltd RiZhao China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Target detection technology is an effective method to detect tunnel operation safety. Although the field has made some progress in detection models, the lack of datasets and detection models has hindered its development. We produce and propose Tunnel Processes data (TP), a challenging dataset of high-precision real-world tunnel process target detection that reduces the limitations of research in this field. TP has a high resolution: 1920×1080, 1734images,9 categories. TP is the first tunnel process data set under construction to date, and its fidelity exceeds that of other tunnel process datasets in the field of target detection. In addition, we also propose a YOLO-TP benchmark model to make up for the lack of a high-precision tunnel process image target detection baseline model. Experiments on TP datasets demonstrate their validity, and experiments on the dataset presented in this paper demonstrate the validity of the benchmark model.

关键词： Accuracy Image resolution Computational modeling Object detection Benchmark testing Big Data Feature extraction Data models Safety Artificial intelligence

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International Conference on Computer Supported Cooperative Work in Design

作者： Wei Ding Luyao Wang Wei Zhang Myung Jin Lee Ling Wang Kwang Woo Nam Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Industry-University Cooperstion Foundation Chungbuk National University Cheongju Korea School of Computer Science Northeast Electric Power University Jilin China School of Computer Science and Engineering Kunsan National University Gunsan Korea

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this paper, we propose a new UAV video similarity measurement algorithm to cluster and analyze UAV videos by measuring the similarity between UAV videos and train them in sub-groups, which in turn improves the efficiency of UAV video data mining. Our preliminary work introduces the maximum common view subsequence (LCVS) to compute the similarity between UAV videos, but LCVS needs to frequently compute the common visible region of video frames generating extremely high time cost. In order to solve the problem of high computational cost of LCVS, we propose a similarity algorithm based on minimum tilted outer rectangle clustering (MTORC) for UAV videos. The MTORC algorithm adopts minimum tilted outer rectangle to cluster the field of view of the marked points in each frame, which reduces the time complexity of calculating the common visible region. By comparing experiments with LCSS and LCVS, the experimental results prove that MTORC is better than the present previous research in terms of algorithm accuracy and time cost.

关键词： Training Visualization Costs Accuracy Clustering algorithms Autonomous aerial vehicles Time measurement

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The Initial Screening of Laryngeal Tumors via Voice Acoustic Analysis Based on Siamese network Under Small Samples

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Journal of Voice 2025年

作者： You, Zhenzhen Sun, Delong Shi, Zhenghao Du, Shuangli Hei, Xinhong Kong, Demin Du, Xiaoying Yan, Jing Ren, Xiaoyong Hou, Jin Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China Department of Otolaryngology Second Affiliated Hospital of Medical College Xi'an Jiaotong University Xi'an China

Objective: The initial screening of laryngeal tumors via voice acoustic analysis is based on the clinician's experience that is subjective. This article introduces a Siamese network with an auxiliary gender classifier for automated, accurate, and objective initial screening of laryngeal tumors based on voice signals. Methods: The study involved 71 tumor patients and 293 non-tumor subjects of Chinese Mandarin. This dataset was divided into a training set and a test set in a ratio of 4:1. We applied nine data augmentation techniques to enlarge the voice training set and extracted the corresponding mel-frequency cepstral coefficients (MFCC) maps. The MFCC maps were randomly paired and fed into the proposed Siamese network to achieve multitask classification for tumor and non-tumor, woman and man. The performance of the proposed model was compared with one machine learning method and six classical deep learning models with and without the auxiliary gender classifier. Results: Experiments demonstrate the superiority of the proposed network compared with the reference models. The proposed model achieved an overall accuracy of 0.9437, an F score of 0.8462, a precision of 0.9167, a sensitivity of 0.7857, and a specificity of 0.9825. Conclusion: The proposed network can assist in the initial screening of laryngeal tumors through voice acoustic analysis. The initial screening solely through voice acoustic analysis can help individuals seek medical assistance outside the hospitals and reduce the burden on doctors as well. © 2025

关键词： Initial screening of laryngeal tumors—Voice acoustic analysis—Mel-frequency cepstral coefficients—Siamese network

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Sentiment Analysis of Web Text Based on Deep Learning with a Attention Mechanism 12

Sentiment Analysis of Web Text Based on Deep Learning with a...

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12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Yu, Qing Zhao, Hui Jiang, Kaiwen Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China

ISBN: (纸本)9781728148526

The sentiment analysis of online commentary is a hot research topic in the field of network information mining. Traditional text sentiment analysis methods are mainly based on emotional dictionaries or machine learning. However, it relies on the structure of the sentiment dictionary and the artificial design and extraction features, which limits the generalization ability. In contrast, the deep learning model has a stronger ability to express complex mapping functions from data to emotional semantics. This paper proposes a model combining Bidirectional Gated Recurrent Unit(BGRU) neural network and Convolutional Neural network(CNN), and introduces attention mechanism for text sentiment analysis. The experimental results show that compared with the traditional method, the method effectively improves the accuracy of text sentiment classification. © 2019 IEEE.

关键词： Sentiment analysis

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Dual Difficulty-aware Adaptive Pseudo Labeling for Semi-supervised CNV Segmentation

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Guo, Jie Sun, Liangyun Qiao, Lishan Nie, Xiushan Yang, Jixin Li, Weicui Guo, Ying Xi, Xiaoming Chen, Xinjian Yin, Yilong Shandong Jianzhu University School of Computer Science and Technology Jinan250101 China Kailin Environmental Protection Equipment Heze China Shandong Institute of Scientific and Technical Information Jinan250101 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Soochow University School of Electronics and Information Engineering Jiangsu215006 China Shandong University School of Sofware Jinan250101 China

In clinical practice, obtaining a large amount of labeled CNV data is very difficult. Semi-supervised learning can effectively utilize a large amount of unlabeled CNV data. Since CNV has complex features such as blurred and unevenly distributed pixels on the edges, there are differences in the segmentation difficulty between pixels in the same image. Existing semi-supervised segmentation methods do not consider the segmentation difficulty of pixels, which will reduce the segmentation accuracy. To address this problem, we propose a dual difficulty-aware adaptive pseudo-label learning (D2APL) method for semi-supervised CNV segmentation. The proposed dual difficulty awareness includes segmentation difficulty perception of pixels in labeled and unlabeled data. For labeled data, we propose a classification confidence-guided difficulty perception method. For unlabeled data, we propose a model stability-guided difficulty perception method. Finally, we propose a difficulty-aware self-training method to dynamically adjust the threshold of pseudolabels according to the difficulty, thereby improving the utilization of difficult-to-segment pixels in unlabeled data. Experimental results show that our method outperforms the state-of-the-art method in CNV segmentation. © 1991-2012 IEEE.

关键词： Semi-supervised learning

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Outsourced Secure Cross-Modal Retrieval based on Secret Sharing for Lightweight Clients

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IEEE Internet of Things Journal 2025年

作者： Niu, Ziyu Wang, Hao Li, Zhi Su, Ye Xu, Lijuan Zhang, Yudi Susilo, Willy Shandong Normal University School of Information Science and Engineering Jinan China University of Wollongong School of Computing and Information Technology Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education National Supercomputer Center in Jinan Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

Cross-modal retrieval is a technique that uses one modality to query another modality in multimedia data (e.g., retrieving images based on text, or retrieving text based on images). It can break down the barriers between different modalities and achieve seamless information connection. Secure cross-modal retrieval focuses on privacy issues in cross-modal retrieval, including private data of data owners and private query requests of users. Current work on secure cross-modal retrieval protects private information through homomorphic encryption, which makes the efficiency of the retrieval phase not ideal. Therefore, the conflict between retrieval efficiency and security has become an important issue that needs to be resolved in secure cross-modal retrieval. We propose a scheme to achieve secure cross-modal retrieval in the form of secret sharing in the IoT environment. In the scheme, the data owner (DO) can secretly divide all the original data into two parts and upload them to two non-collusive cloud servers respectively. The servers store the data and provide cross-modal retrieval for users. The security of the scheme is proved under semi-honest model, and the experiments show that our scheme is more efficient than previous work in the search phase. When the query dimension is 512 and the number of latent factors is 500, the search time is reduced by more than half compared with previous work. © 2014 IEEE.

关键词： Sensitive data

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A Multichannel CNN-GRU Hybrid Architecture for sEMG Gesture Recognition

A Multichannel CNN-GRU Hybrid Architecture for sEMG Gesture ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shouliang Song Anming Dong Jiguo Yu Yubing Han You Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Haikan New Media Research Institute Co. Ltd.

Surface electromyography (sEMG) signal is a physiological electrical signal produced by muscle contraction. Different gestures can be effectively recognized from the characteristics of the sEMG signal. Currently, convolutional neural networks (CNNs) have been widely used in sEMG gesture recognition systems due to their capabilities in acquiring spatial features of sEMG signals. However, these classical CNNs are inefficient in extracting temporal correlation that resides in the time serials of sEMG signals, which is definitely important for gesture recognition. To overcome such a drawback of traditional CNN-based gesture recognition methods, we propose a multichannel hybrid deep learning model for gesture recognition by combining the multichannel CNNs with a gated recurrent unit (GRU). Specifically, we use multiple CNNs to preprocess the original multichannel EMG signals in a one-by-one manner to obtain the spatial features in the current observing window. The outputs of the multiple CNNs are concatenated and fed to a temporal-feature extracting module, which is designed by cascading a GRU with an attention mechanism. Through the GRU, the temporal features of successive signal frames can be established, while the attention mechanism is introduced to further focus on the key information in recognizing the gestures, which is beneficial to improve the robustness and accuracy of the model. Experiments show that the recognition accuracy of the proposed method reaches 97.6% and 96.7% on the Ninapro DB2 and Ninapro DB5 datasets, respectively. Compared with the classical CNN method, the performance improvement is 2.9% and xx% higher than that of the traditona CNN model, respectively.

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Deepfake Detection: A Comprehensive Survey from the Reliability Perspective

arXiv

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arXiv 2022年

作者： Wang, Tianyi Liao, Xin Chow, Kam Pui Lin, Xiaodong Wang, Yinglong Department of Computer Science The University of Hong Kong Hong Kong College of Computer Science and Electronic Engineering Hunan University China School of Computer Science University of Guelph Canada Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. We identify three reliability-oriented research challenges in the current Deepfake detection domain: transferability, interpretability, and robustness. Moreover, while solutions have been frequently addressed regarding the three challenges, the general reliability of a detection model has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake-related cases in court. We, therefore, introduce a model reliability study metric using statistical random sampling knowledge and the publicly available benchmark datasets to review the reliability of the existing detection models on arbitrary Deepfake candidate suspects. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of the reliably qualified detection models as reviewed in this survey. Reviews and experiments on the existing approaches provide informative discussions and future research directions for Deepfake detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Data reliability

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Image Electronic Evidence Screening Based on Improved SSD

Image Electronic Evidence Screening Based on Improved SSD

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Yafei Liu Liehui Jiang Tieming Liu Youwei Zhang Zhongyuan Network Security Research institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

ISBN: (纸本)9781665447300

With the development of information technology, electronic evidence plays an increasingly important role in the judicial trial. In Judicial Forensics, it is difficult to extract effective electronic evidence accurately and quickly from a large number of electronic data. Traditional means generally take artificial identification to collect, which takes a long time and is not efficient. Using SSD target detection and recognition algorithm instead of traditional means can effectively reduce the time of screening evidence, but the basic SSD neural network is prone to miss detection for small targets. To solve the above problems, an improved SSD-based image electronic evidence screening method is proposed. This method optimizes the SSD neural network adaptively, introduces the attention mechanism module in the shallow convolution layer of the network to improve the representation ability of the feature map, and fuses the image features obtained from different convolution layers with multi-scale features to increase the shallow feature information. The experimental data are used to test the improved algorithm and analyze the experimental results. It is found that compared with the original SSD neural network algorithm, the detection mean average precision of the improved algorithm is increased by 4.7%, reaching 84.3%, which shows the feasibility and effectiveness of the improved algorithm.

关键词： Image recognition Convolution Target recognition Fuses Neural networks Stacking Signal processing algorithms

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An Early Detection Model of Prostate Cancer Based on MRI

An Early Detection Model of Prostate Cancer Based on MRI

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yuankun Liu Lumin Xing Di Wang Xin Nie Xueyan Zhang Di Wu Wenjian Liu Faculty of Data Science City University of Macau China Shandong Provincial Qianfoshan Hospital The First Affiliated Hospital of Shandong First Medical University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Chongqing Jianzhu College Chongqing China School of Information Work Office Shandong Youth University of Political Science Jinan China School of Mathematics Physics and Computing University of Southern Queensland Queensland Australia

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively.

关键词： Computer vision Visualization Accuracy Magnetic resonance imaging Mortality Transformers Feature extraction Reliability Prostate cancer Medical diagnostic imaging

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