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Direction-Guided Sparse Representation Method for Finger Vein Recognition 18th

Direction-Guided Sparse Representation Method for Finger Vei...

引用

18th Chinese conference on Biometric Recognition, CCBR 2024

作者： Zhou, Lizhen Yang, Lu Meng, Qinggang Yang, Gongping Department of Information ZiBo Normal College Zibo255130 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China School of Software Shandong University Jinan250101 China

ISBN: (纸本)9789819610679

the extended sparse representation method is robust to image deformation and has shown promising performance in finger vein recognition. However, its large number of dictionary atoms results in high memory usage and long computation time. To address these issues, we propose a direction-guided sparse representation (DGSR) method for finger vein recognition. Our method computes the guidance direction based on rough matching results between the testing and training images, and then selects the corresponding dictionary atoms, reducing the dictionary size and computation time. Additionally, this method uses vein backbones, which are robust to noise, as image features in sparse representation. Experimental results on the open finger vein database from Hong Kong Polytechnic University demonstrate the effectiveness of the proposed method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Palmprint recognition

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Vehicle Information Monitoring and Identification on Highway Bridges Using an Array of Fiber Bragg Grating Sensors and Deep Learning Algorithms 4

Vehicle Information Monitoring and Identification on Highway...

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4th International Computational Imaging conference, CITA 2024

作者： Duan, Zhenhui Zhang, Faxiang Jiang, Shaodong Sun, Zhihui Liu, Zhaoying Shen, Yongxu Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510688834

Traditional traffic management methods are increasingly inadequate in coping with the growing pressure on traffic systems. Consequently, there is a need for a more intelligent and efficient approach to monitoring and identifying vehicle information. Current vehicle monitoring methods often suffer from environmental interference and limited coverage.. To address these challenges, we propose a novel method for monitoring and identifying vehicle information on bridges using an array of Fiber Bragg Grating (FBG) strain sensors, combined with a hybrid deep learning algorithm based on Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM). Strain data were collected from a continuous hollow slab beam on a section of a highway bridge. Vehicles were classified and labeled using on-site video data to create a dataset, which was subsequently used to train and test the proposed CNN-LSTM model. the results demonstrate that the proposed method can accurately identify and classify the lane position, vehicle type, and vehicle speed category, achieving detection accuracies of 94.0%, 93.11%, and 90.67%, respectively. the DWT-CNN-LSTM method, utilizing the FBG strain sensor array, leverages existing strain monitoring platforms, supplements current traffic monitoring systems, and offers high efficiency and cost-effectiveness. this approach provides valuable data references for management and statistical analysis. © 2025 SPIE.

关键词： Discrete wavelet transforms

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TSR-Jack: An In-Browser Crypto-Jacking Detection Method Based on Time Series Representation Learning 26th

TSR-Jack: An In-Browser Crypto-Jacking Detection Method Base...

引用

26th International conference on Information and Communications Security, ICICS 2024

作者： Cui, Bo Liu, Shuai Engineering Research Center of Ecological Big Data Ministry of Education Beijing China College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9789819788002

As the economic value of cryptocurrencies continues to ascend, an increasing number of cybercriminals exploit malicious browser scripts to commandeer the system and network resources of victims for unauthorized cryptocurrency mining. this form of browser-based mining, termed crypto-jacking, imparts substantial impact and harm to society, enterprises, and users. However, existing research methods lead to poor transferability because they require model training using labeling data from detection devices. To solve this problem, we introduce a crypto-jacking detection method based on time series representation learning, called TSR-jack. Specifically, we first collect hardware runtime system data for a period of time and generate datasets. After slicing the time series datasets, they are used to train a time series representation model, which introduces the loss of temporal information to extract temporal features. the temporal features can capture similarities between different devices and therefore address model transfer difficulties. Finally, the time vectors obtained from representation learning are fed into SVM for classification. Experimental results show that our TSR-jack method outperforms similar dynamic feature detection methods, especially when considering transferability, with a precision improvement of more than 5%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Review Rating Prediction Using BiLSTM 14th

Review Rating Prediction Using BiLSTM

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14th International conference on Frontier Computing, FC 2024

作者： Kim, Yongjun Lee, Sang-Joon Byun, Yung-Cheol Department of Computer Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

User review ratings are crucial for assessing the quality of restaurants, movies, products, and services online. Traditionally, predicting these ratings involved analyzing the content of user reviews using natural language processing techniques. While this approach allows for the consideration of diverse data by aggregating reviews from all users, it's hindered by the fact that users have unique tendencies and expressions, resulting in varying ratings for the same review. To address this challenge, a novel method is proposed in this paper: a BiLSTM model that incorporates the user's average rating alongside their review content. By doing so, this method aims to enhance prediction accuracy by accounting for individual user characteristics. this approach considers the textual content of reviews and integrates the user's typical rating behavior, resulting in more personalized and precise rating predictions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Interaction-Guided Two-Branch Image Dehazing Network 17th

Interaction-Guided Two-Branch Image Dehazing Network

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17th Asian conference on computer Vision, ACCV 2024

作者： Liu, Huichun Li, Xiaosong Tan, Tianshu School of Physics and Optoelectronic Engineering Foshan University Foshan528225 China Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9789819609109

Image dehazing aims to restore clean images from hazy ones. Convolutional Neural Networks (CNNs) and Transformers have demonstrated exceptional performance in local and global feature extraction, respectively, and currently represent the two mainstream frameworks in image dehazing. In this paper, we propose a novel dual-branch image dehazing framework that guides CNN and Transformer components interactively. We reconsider the complementary characteristics of CNNs and Transformers by leveraging the differential relationships between global and local features for interactive guidance. this approach enables the capture of local feature positions through global attention maps, allowing the CNN to focus solely on feature information at effective positions. the single-branch Transformer design ensures the network’s global information recovery capability. Extensive experiments demonstrate that our proposed method yields competitive qualitative and quantitative evaluation performance on both synthetic and real public datasets. Codes are available at https://***/Feecuin/Two-Branch-Dehazing. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Evaluation of LLM-Generated Distractors of Multiple-Choice Questions for the Japanese National Nursing Examination 17

Evaluation of LLM-Generated Distractors of Multiple-Choice Q...

引用

17th International conference on computer Supported education, CSEDU 2025

作者： Kido, Yûsei Yamada, Hiroaki Tokunaga, Takenobu Kimura, Rika Miura, Yuriko Sakyo, Yumi Hayashi, Naoko School of Computing Institute of Science Tokyo Japan

ISBN: (纸本)9789897587467

this paper reports the evaluation results in the usefulness of distractors generated by large language models (LLMs) in creating multiple-choice questions for the Japanese National Nursing Examination. Our research questions are: "(RQ1) Do question writers adopt LLM-generated distractor candidates in question writing?" and "(RQ2) Does providing LLM-generated distractor candidates reduce the time for writing questions?". We selected ten questions from the proprietary mockup examinations of the National Nursing Examination administered by a prep school, considering the analysis of the last ten-year questions of the National Nursing Examination. Distractors are generated by seven different LLMs, given a stem and a key for each question of the above ten, and they are compiled into the distractor candidate sets. Given a stem and a key for each question, 15 domain experts completed questions by filling in three distractors. Eight experts are provided with the LLM-generated distractor candidates;the other seven are not. the results of comparing the two groups provided us with affirmative answers to both RQs. the current evaluation remains subjective from the viewpoint of the question writers;it is necessary to evaluate whether questions generated with the assistance of LLM work in a real examination setting. Our future plan includes administering a large-scale mockup examination using both human-made and LLM-assisted questions and analysing the differences in the responses to both types of questions. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Engineering education

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A Review of Human Mesh Reconstruction: Beyond 2D Video Object Segmentation 16th

A Review of Human Mesh Reconstruction: Beyond 2D Video Obje...

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16th International conference on Social Robotics, ICSR + InnoBiz 2024

作者： Wu, Peng Wang, Zhicheng Pan, Feiyu Li, Fangkai Hu, Hao Lu, Xiankai Guo, Yiyou School of Software Shandong University Shandong Jinan250000 China School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data-Intensive Computing Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou Normal University Quanzhou China

ISBN: (纸本)9789819611508

Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video object segmentation methods produce 2D masks, which are not suitable for 3D scenarios where depth information is essential, such as in robotic grasping, virtual reality, and autonomous driving, etc.. In this paper, we present a comprehensive review of 3D human mesh reconstruction (HMR) as an extension beyond 2D video object segmentation. We begin by reviewing the mainstream video object segmentation methods, then transition from 2D video object segmentation to 3D HMR. We further categorize recent HMR methods based on key characteristics that define this research field, including the type of model input and the use of statistical models. Finally, we provide detailed information on HMR datasets and evaluation metrics. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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SwinECA-Net: A Novel Dual-Scale Attention Network for Gastric Cancer Pathology Image Segmentation 24

SwinECA-Net: A Novel Dual-Scale Attention Network for Gastri...

引用

2024 International conference on Virtual Reality, Image and Signal Processing, ICVISP 2024

作者： Hu, Tao Pan, Jingshan Li, Na Tian, Tangbin Liu, Shengyu Han, Yujiao Zhang, Lu Xu, Jiwei Shandong Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan China

ISBN: (纸本)9798400710926

As a prevalent malignant tumor worldwide, accurate segmentation of gastric cancer pathology images is crucial for early diagnosis and treatment. the complexity of gastric cancer lesion morphology and the ambiguity of its boundaries pose challenges for deep learning image segmentation techniques. To address this, we propose a novel and efficient segmentation network called SwinECA-Net. this network utilizes Swin Transformer blocks for feature extraction and employs an Enhanced Channel Attention (ECA) module as a bottleneck to enhance local feature extraction capabilities. Additionally, we introduce the Dual-Scale Attention Fusion Module (DSAFM) between the encoder and decoder, which re-aligns feature maps in both channel and spatial dimensions to effectively extract and utilize global contextual information. Experimental results show that our method achieves an 89.62% Dice-Similarity coefficient (DSC) and a 95th percentile Hausdorff Distance (HD95) of 40.24 mm on gastric gland pathology slide images, significantly improving the segmentation accuracy of gastric cancer. © 2024 Copyright held by the owner/author(s).

关键词： Diseases

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Probabilistic Offloading Algorithm for Opportunistic Networks Integrating Node Influence Prediction 18th

Probabilistic Offloading Algorithm for Opportunistic Networ...

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18th International conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Tang, Qi Xu, Xiaodong Li, Shuai Seah, Winston Xu, Gang College of Computer Science Inner Mongolia University Hohhot010021 China The Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia University Hohhot010021 China Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9783031714634

In Opportunistic Network based mobile data diversion algorithms, the need for multi-hop transmission makes the selection of the next hop node critical. the traditional Prophet algorithm calculates the encounter delivery probability of a node based on historical encounter information as a forwarding condition. However, algorithms for data diversion are more concerned with the issue of nodes’ propagation influence in the network. therefore, in this paper, we propose a metric to measure the dynamic influence of a node (sir) and use the CNN-BiLSTM-Attention model to predict the sir value of a node during the current time period in the route. After that, a data diversion algorithm based on deep learning prediction of node influence is proposed in combination with encounter delivery probability. the experimental results show that the NS-Prophet algorithm performs well in terms of performance metrics in opportunistic networks, and the triage efficiency in cellular networks is also improved. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data diversion deep learning prediction node influence opportunistic networks

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Oracle Bone Inscription Image Retrieval Based on Improved ResNet Network 27th

Oracle Bone Inscription Image Retrieval Based on Improved Re...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Ding, Jun Wang, Jiaoyan Aysa, Alimjan Xu, Xuebin Ubul, Kurban School of Computer Science and Technology Xinjiang University Ürümqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Ürümqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi830046 China

ISBN: (纸本)9783031783043

Research on oracle bone inscription image retrieval is important for applications in academic and cultural heritage areas. the current oracle bone dataset faces problems such as the low similarity between the same category, the high similarity between the different categories, and imbalanced sample distribution. In addition, due to the complex background of oracle bone images, existing network models have certain limitations in extracting image features. To address these challenges, this study first adopts a Siamese network-based image retrieval method to learn feature representations of similar and dissimilar images. Subsequently, the existing dataset was partitioned, providing a practical and usable retrieval dataset for the oracle bone image retrieval field. Finally, an improved network model based on ResNet is proposed and integrated into the Siamese network framework. the model achieves the highest retrieval MP and MAP values of 83.26% and 90.68%, respectively, which is better than the current research. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Content based retrieval

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