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arXiv 2023年

作者： Shen, Shuanghong Chen, Enhong Xu, Bihan Liu, Qi Huang, Zhenya Zhu, Linbo Su, Yu Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science School of Computer Science and Techonology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Anhui Hefei230026 China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science School of Computer Science and Techonology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230026 China School of Computer Science and Technology Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230026 China

Knowledge tracing (KT) aims to assess individuals’ evolving knowledge states according to their learning interactions with different exercises in online learning systems (OIS), which is critical in supporting decision-making for subsequent intelligent services, such as personalized learning source recommendation. Existing researchers have broadly studied KT and developed many effective methods. However, most of them assume that students’ historical interactions are uniformly distributed in a continuous sequence, ignoring the fact that actual interaction sequences are organized based on a series of quizzes with clear boundaries, where interactions within a quiz are consecutively completed, but interactions across different quizzes are discrete and may be spaced over days. In this paper, we present the Quiz-based Knowledge Tracing (QKT) model to monitor students’ knowledge states according to their quiz-based learning interactions. Specifically, as students’ interactions within a quiz are continuous and have the same or similar knowledge concepts, we design the adjacent gate followed by a global average pooling layer to capture the intra-quiz short-term knowledge influence. Then, as various quizzes tend to focus on different knowledge concepts, we respectively measure the inter-quiz knowledge substitution by the gated recurrent unit and the inter-quiz knowledge complementarity by the self-attentive encoder with a novel recency-aware attention mechanism. Finally, we integrate the inter-quiz long-term knowledge substitution and complementarity across different quizzes to output students’ evolving knowledge states. Extensive experimental results on three public real-world datasets demonstrate that QKT achieves state-of-the-art performance compared to existing methods. Further analyses confirm that QKT is promising in designing more effective quizzes. Copyright © 2023, The Authors. All rights reserved.

关键词： Students

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A mutual bootstrapping model for automated skin lesion segmentation and classification

arXiv

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arXiv 2019年

作者： Xie, Yutong Zhang, Jianpeng Xia, Yong Shen, Chunhua National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'an710072 China School of Computer Science University of Adelaide SA Australia

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhancedSN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training

arXiv

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arXiv 2023年

作者： Bai, Xiaoyu Ma, Benteng Li, Changyang Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Poly-technical University Xi’an710072 China Sydney Polytechnic Institute NSW2000 Australia

Universal lesion detection (ULD) has great value for daily clinical practice as it aims to detect various types of lesions in multiple organs on medical images. Deep learning methods have shown promising results, but demanding substantial volumes of annotated data for effective training. Nevertheless, annotating medical images is costly and requires specialized knowledge. Additionally, the diverse forms and contrasts of objects in medical images make fully annotation even more challenging, resulting in incomplete annotations. Directly training ULD detectors on such datasets can yield suboptimal results. Pseudo-label-based methods examine the data in the training set and mine unlabelled objects for retraining, which have shown to be effective to tackle this issue. Presently, top-performing methods rely on a dynamic label-mining mechanism, operating at the mini-batch level. However, the model’s performance varies at different iterations, leading to inconsistencies in the quality of the mined labels during training. This inconsistency limits their potential for performance enhancement. Inspired by the observation that deep models learn concepts with increasing complexity, we introduce an innovative exploratory training strategy to assess the reliability of mined lesions over time. Specifically, we introduce a teacher-student detection model as the basis, where the teacher model’s predictions are combined with incomplete annotations to train the student model. On top of that, we design a prediction bank to record all high-confidence predictions. Each sample is trained several times, allowing us to get a sequence of records for each sample. If a prediction consistently appears in the record sequence, it is likely to be a true object, otherwise it may just a noise. This serves as a crucial criterion for selecting reliable mined lesions for subsequent retraining. Our experimental results substantiate that the proposed framework surpasses state-of-the-art methods on two med

关键词： Forecasting

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Unique in the Metro System: The Likelihood to Re-Identify a Metro User with Limited Trajectory Points

SSRN

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SSRN 2023年

作者： Yang, Hongtai Ping, An Wei, Hongmin Zhai, Guocong School of Transportation and Logistics Institute of System Science and Engineering National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu611756 China Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities Shenzhen518060 China School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu611756 China Department of Civil & Environmental Engineering Old Dominion University 129C Kaufman Hall NorfolkVA23529 United States

Though the collection of metro smart card data could help improve the operations of the metro system, the release of such data might lead to privacy issues. Few studies have quantified the probability to re-identify a user from the smart card data using very limited trajectory points. Thus, this study investigates this topic by analyzing eight-day metro smart card data of Chengdu, China. Results reveal that, on the macro level, three random trajectory points with a temporal resolution of one minute and one hour are enough to identify over 90% and 67% of the users. Even when the resolution is reduced to one day, 20% of the users could be still be identified by three points. On the individual level, three carefully selected points with a temporal resolution of one minute, one hour, and one day could lead to a re-identification risk no less than 0.5 for 99%, 89%, and 52% of the users. The effects of number of points, number of users, and other temporal resolutions are also thoroughly evaluated. These findings emphasize the great privacy issues involved in the release of metro smart card data and remind metro operators to take proactive measures to enhance privacy protection. © 2023, The Authors. All rights reserved.

关键词： Smart cards

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Framework for Fuzzy Temporal Association Rule Mining Based on Density Clustering Optimization

Framework for Fuzzy Temporal Association Rule Mining Based o...

引用

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

作者： Yang Cao Pengzhi Ma School of Computer Science and Engineering Shenyang Jianzhu University(SJZU) Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This paper proposes a framework for fuzzy temporal association rule mining based on density clustering optimization, aimed at improving the fuzzification of continuous attributes and enhancing the efficiency of mining temporal association rules. The first part of the framework effectively handles continuous attribute data and reduces data redundancy by adopting a density peak clustering algorithm and designing an optimized membership function. The second part introduces a fuzzy list ranking structure combined with item lifespan to mine fuzzy temporal association rules. Experimental results demonstrate that the framework exhibits superior performance in mining fuzzy temporal association rules and outperforms advanced algorithms in handling continuous attributes.

关键词： Fuzzy sets Systematics Redundancy Memory management Signal processing algorithms Clustering algorithms Signal processing Association rule learning Optimization Sorting

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An adversarial human pose estimation network injected with graph structure

arXiv

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arXiv 2021年

作者： Tian, Lei Wang, Peng Liang, Guoqiang Shen, Chunhua School of Computer Science Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China School of Computer Science The University of Adelaide Australia

Because of the invisible human keypoints in images caused by illumination, occlusion and overlap, it is likely to produce unreasonable human pose prediction for most of the current human pose estimation methods. In this paper, we design a novel generative adversarial network (GAN) to improve the localization accuracy of visible joints when some joints are invisible. The network consists of two simple but efficient modules, i.e., Cascade Feature Network (CFN) and Graph Structure Network (GSN). First, the CFN utilizes the prediction maps from the previous stages to guide the prediction maps in the next stage to produce accurate human pose. Second, the GSN is designed to contribute to the localization of invisible joints by passing message among different joints. According to GAN, if the prediction pose produced by the generator G cannot be distinguished by the discriminator D, the generator network G has successfully obtained the underlying dependence of human joints. We conduct experiments on three widely used human pose estimation benchmark datasets, i.e., LSP, MPII and COCO, whose results show the effectiveness of our proposed framework. © 2021, CC BY.

关键词： Generative adversarial networks

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Renin-angiotensin system inhibitors prescriptions in Chinese hospitalized chronic kidney disease patients

引用

World Journal of Clinical Cases 2024年第17期12卷 3061-3075页

作者： Chun Zhang Zhi-Yu Duan Sa-Sa Nie Zhou Zhang Xin-Ru Guo Chao-Yang Zhang Jing Dong Guang-Yan Cai Department of Nephrology The First Medical CentreChinese PLA General HospitalBeijing 100853China School of Medicine Nankai UniversityTianjin 300071China National Engineering Laboratory for Medical Big Data Application Technology Chinese PLA General HospitalBeijing 100853China

BACKGROUND Many guidelines have recommended renin-angiotensin system inhibitors(RASI)as the first-line treatment for patients with chronic kidney disease(CKD).We studied RASI prescription trends from 2010 to 2019,and analyzed the characteristics associated with RASI prescription in Chinese hospitalized CKD *** To study the prescription of renin angiotensin system inhibitors in hospitalized patients with CKD in *** It was retrospectively,cross-sectional reviewed RASI prescriptions in hospitalized CKD patients in China from 2010 to *** prescribing trends were analyzed from 2010 to 2019,and bivariate and multivariate logistic regression analyses were conducted to identify characteristics associated with RASI *** A total of 35090 CKD patients were included,with 10043(28.6%)RASI *** these patients,18919(53.9%)met the criteria for RASI treatments based on the 2012 kidney disease:Improving global outcomes *** these,7246(38.3%)patients received RASI *** prescriptions showed an initial rapid increase from 2011 to 2012,reached its peak around 2015 and 2016,and then exhibited a subsequent slight decreasing *** bivariate and multivariate analyses showed that several characteristics,including the male gender,age less than 60-year-old,nephrology department admission,lower CKD stage,history of hypertension or diabetes,proteinuria,glomerulonephritis as the CKD etiology,and non-acute kidney injury were associated with RASI *** The frequency of RASI prescriptions showed an initial increase but a slight decreasing trend in more recent *** patients with certain characteristics such as elderly age,advanced disease stage,surgery department admission,or acute kidney injury were less likely to receive RASI *** the application of RASI in hospitalized CKD patients is *** actual clinical practice needs to be *** development of related research

关键词： Chronic kidney disease Renin-angiotensin system inhibitors Prescriptions Adherence

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Explore Unsupervised Exposure Correction Via Illumination Component Divided Guidance

SSRN

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SSRN 2023年

作者： Wei, Sun Linyang, Tian Ruijia, Cui Xiaobao, Yang Yanning, Zhang School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Capturing images with poor exposures remains a major source of errors in camera-based photography. Under- or over-exposure problems greatly reduce contrast and naturalness, and the disappearance of detailed content affects the understanding of an image. While recent works have built a great deal of learning frameworks to address the problem, they mainly focus on low-light or general image enhancement. Towards this end, we explore an unsupervised exposure correction framework. Our model disentangles the exposure correction into two easier sub-tasks. Different branches focus on different missions, making the network easy to solve coupling correction problems. Additionally, a novel attention-based pixel-wise brightness estimation makes it possible for coarse-to-fine mapping adjustment and correction. Experiments on various widely used benchmark datasets show that, relying on illumination perception and components divided guidance, the proposed network can outperform previous state-of-the-art methods, especially for recovering fine detailed contents. © 2023, The Authors. All rights reserved.

关键词： Luminance

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A Passenger Transport Network Construction Method Based on Geographical Location

SSRN

引用

SSRN 2023年

作者： Qian, Chen Hao Li, Te Yang, Shengwen Li, Zhiyuan Chang, Zhangjing Chen, Fuze Ye, Yanjun College of Mechanics and Transportation Southwest Forestry University Kunming650000 China School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China

Passenger transportation plays an important role in China's social and economic development in the post-epidemic era. However, little research has been done on the composition of passenger transport networks and the identification of their key nodes. In this paper, we study this topic by using passenger travel information and geographic information after the full liberalization of Yunnan (January), to provide reliable decision-making for public transport operators and controllers to deal with emergencies. A new method is used to compound the passenger transportation network, and the complex network theory is used to conduct empirical research on the comprehensive passenger transportation network. The results show that the integrated passenger transport network has higher accessibility and connectivity and the convenience of passenger travel. Due to the timeliness and dynamics of the passenger transport network, the k-shell method is more effective in identifying the key nodes of the integrated passenger transport network. © 2023, The Authors. All rights reserved.

关键词： Complex networks

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A Robust Attentional Framework for License Plate Recognition in the Wild

arXiv

引用

arXiv 2020年

作者： Zhang, Linjiang Wang, Peng Li, Hui Li, Zhen Shen, Chunhua Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China School of Computer Science University of Adelaide SA5005 Australia big data and AI technology department Minsheng Fintech Corp. LTD.

Recognizing car license plates in natural scene images is an important yet still challenging task in realistic applications. Many existing approaches perform well for license plates collected under constrained conditions, e.g., shooting in frontal and horizontal view-angles and under good lighting conditions. However, their performance drops significantly in an unconstrained environment that features rotation, distortion, occlusion, blurring, shading or extreme dark or bright conditions. In this work, we propose a robust framework for license plate recognition in the wild. It is composed of a tailored CycleGAN model for license plate image generation and an elaborate designed image-to-sequence network for plate recognition. On one hand, the CycleGAN based plate generation engine alleviates the exhausting human annotation work. Massive amount of training data can be obtained with a more balanced character distribution and various shooting conditions, which helps to boost the recognition accuracy to a large extent. On the other hand, the 2D attentional based license plate recognizer with an Xception-based CNN encoder is capable of recognizing license plates with different patterns under various scenarios accurately and robustly. Without using any heuristics rule or post-processing, our method achieves the state-of-the-art performance on four public datasets, which demonstrates the generality and robustness of our framework. Moreover, we released a new license plate dataset, named "CLPD", with 1200 images from all 31 provinces in mainland China. The dataset can be available from: https://***/wangpengnorman/CLPD_dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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