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A Text Image Watermarking Algorithm Based on Image Enhancement

computers, Materials & Continua 2023年第10期77卷 1183-1207页

作者： Baowei Wang Luyao Shen Junhao Zhang Zenghui Xu Neng Wang School of Computer Nanjing University of Information Science and TechnologyCollaborative Innovation Center of Jiangsu Atmospheric Environment and Equipment TechnologyDigital Forensics Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

Digital watermarking technology is adequate for copyright protection and content *** needs to be more research on the watermarking algorithm after printing and *** at the problem that existing anti-print scanning text image watermarking algorithms cannot take into account the invisibility and robustness of the watermark,an anti-print scanning watermarking algorithm suitable for text images is *** algorithm first performs a series of image enhancement preprocessing operations on the printed scanned image to eliminate the interference of incorrect bit information on watermark embedding and then uses a combination of Discrete Wavelet Transform(DWT)-Singular Value Decomposition(SVD)to embed the *** show that the average Normalized Correlation(NC)of the watermark extracted by this algorithm against attacks such as Joint Photographic Experts Group(JPEG)compression,JPEG2000 compression,and print scanning is above ***,the average NC of the watermark extracted after print scanning attacks is greater than 0.964,and the average Bit Error Ratio(BER)is 5.15%.This indicates that this algorithm has strong resistance to various attacks and print scanning attacks and can better take into account the invisibility of the watermark.

关键词： Print-resistant scanning image enhancement DWT SVD embedding intensity

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GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching 38

GoMatching: A Simple Baseline for Video Text Spotting via Lo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Haibin Ye, Maoyuan Zhang, Jing Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science Nanyang Technological University Singapore

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets. © 2024 Neural information processing systems foundation. All rights reserved.

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A Peer Review Approach to Grading Projects in computer Courses 53

A Peer Review Approach to Grading Projects in Computer Cours...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Zhang, Liang Zong, Yue Chen, Tianyi Ma, Yafeng An, Shuo Zhang, Lijun School of Computer Science and Engineering Beihang University Beijing China School of Software Beihang University Beijing China School of Aeronautic Science and Engineering Beihang University Beijing China School of Electronic and Information Engineering Beihang University Beijing China

ISBN: (纸本)9798350336429

This research to practice full paper presented a peer review approach to grading projects in computer courses. Educators commonly adopt project-based learning activities in computer courses to achieve the goal. Implementations of these projects typically involve hardly quantifiable dimensions, such as novelty, functionality, user-friendly, coding style, and document. Automated tools are not competent for project assessment. However, assessing projects by instructors can be labor intensive and generally involves a high degree of subjectivity. Peer review is widely used in high education as effective active learning in computer courses, especially in the past decade. Despite its many advantages, peer review still faces some challenges. Students need to be more expert in assessment and might make mistakes when grading, and instructors still need to make great efforts to supervise the process and ensure that students give fair scores to their peers. In addition, the social relationship between students could lead to subjective assessments and affect the fairness of grading. We present the peer review approach to grading projects in computer courses. First, we design a grading specification to improve the accuracy of scoring. Second, we propose a score calculation method based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to calculate the assignment's final score, which is a multi-objective decision-making problem. Third, we give an anomaly detection algorithm based on Dixon's Q test to filter peer reviewers' unreasonable scores. Based on the approach, we designed a peer review workflow and developed an online peer review tool called Peer Review Studio (PRS). We carry out the method in the undergraduate computer course of 2022. We collect and analyze the learning data between 2020 and 2022. When measured by Krippendorff's alpha, the inter-rater reliability between instructor and peer grading is in good agreement. When measured by Kruskal-W

关键词： computer courses critical thinking peer learning peer review

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Back in time:digital restoration techniques for the millennium Dunhuang murals

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The Journal of China Universities of Posts and Telecommunications 2022年第2期29卷 13-23页

作者： Tao Yifeng Song Yucheng Xu Mengqiu Zhang Chuang Wu Ming Bai Sule School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

In the long history of more than 1 500 years,Dunhuang murals suffered from various deteriorations causing irreversible damage such as falling off,fading,and so ***,the existing Dunhuang mural restoration methods are time-consuming and not feasible to facilitate cultural dissemination and permanent *** by cultural computing using artificial intelligence,gated-convolution-based dehaze net(GD-Net) was proposed for Dunhuang mural refurbishment and comprehensive ***,a neural network with gated convolution was applied to restore the falling off areas of the mural to ensure the integrity of the mural ***,a dehaze network was applied to enhance image quality to cope with the fading of the ***,a Dunhuang mural dataset was presented to meet the needs of deep learning approach,containing 1 180 images from the Cave 290 and Cave 112 of the Mogao *** experimental results demonstrate the effectiveness and superiority of GD-Net.

关键词： image inpainting image enhancement mural restoration

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Theoretical Analyses of Integral-Type Zhang Neurodynamics with Disturbances Inhibited Handling Temporally-Varying Nonlinear System of Equations

Theoretical Analyses of Integral-Type Zhang Neurodynamics wi...

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2023 China Automation Congress, CAC 2023

作者： Mao, Mingzhi Lu, Ji Shi, Yang Zhang, Yunong School of Software Engineering Sun Yat-sen University Zhuhai519082 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

ISBN: (纸本)9798350303759

We design and present an integral-type Zhang neurodynamics (ITZN) model for handling temporally-varying nonlinear system of equations (TVNSE). To investigate the disturbances inhibition of the model, the general form of the disturbed ITZN model is presented, and it is instantiated by introducing the constant disturbance, bounded random disturbance, and linear-form temporally-varying disturbance, resulting in three corresponding disturbed ITZN variants. Finally, by means of Lyapunov theory and Laplace transformation, we theoretically prove the globally exponential convergence of the ITZN model and the effective disturbances inhibition of the three disturbed ITZN variants. © 2023 IEEE.

关键词： Nonlinear equations

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Multi-view Hypergraph-based Self-supervised Learning Model for Web API Recommendation 22

Multi-view Hypergraph-based Self-supervised Learning Model f...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Shen, Jiexun Li, Dongfan Chen, Wanjun Xiao, Yong Kang, Guosheng Liu, Jianxun Peng, Zhenlian School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (纸本)9798331509712

With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collaborative filtering are limited by data sparsity. With the support of multi-dimensional relational feature modeling, graph neural network-based methods are proposed to mitigate data sparsity, but their convolution nature may amplify the noise effect. Therefore, how to simultaneously reduce the impact of sparsity and noisy data has been an open question in the field of API recommendation. To address the problem, this paper proposes a self-supervised learning method based on multi-view hypergraph for Web API recommendation. First, the Mashup-API interaction graph is transformed into a hypergraph, and the hyperedges are used as intermediate hubs to transfer messages between nodes, maintaining the global collaboration effect between Mashup and API nodes. Then, a multi-view strategy is adopted to generate embeddings of Mashups and APIs through information fusion, by which recommendation probability is derived by dot product between the embeddings of Mashups and Web APIs. To train the model parameters effectively, a self-supervised learning method is used to reduce the effect of noisy data to improve the embeddings. Extensive experiments are conducted on a real-world dataset, and the experimental results show that the proposed model outperforms the baselines. © 2024 IEEE.

关键词： Self-supervised learning

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EOSAD: An Event-Oriented Physiological and Behavioral Social Anxiety Annotation Dataset in Virtual Reality

EOSAD: An Event-Oriented Physiological and Behavioral Social...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hou, Qinyao Ding, Ding Yang, Jiaju Tu, Jiahang School of Computer Science and Engineering Southeast University Nanjing China Chien-Shiung Wu College Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China

ISBN: (纸本)9781665410205

Social phobia is a prevalent mental health con-dition characterized by overwhelming fear on apprehension of social situations, often leading individuals to avoid such encounters. It is a very common mental disorder among adolescents and can have a significant bad effect on their social skills and well-beings if not intervened early. Existing methods to assess social anxiety level rely on self-report scales or clinical diagnosis, both of which may face challenges like subjective judgments and patient resistance. To address this, we applied Virtual Reality technology (VR) and introduced event-oriented social anxiety dataset (EOSAD) to serve for fine-grained social anxiety level detected model. A case study (N = 44) was conducted where participants wore a Vive Pro Eye HMD to experience two types of scenes and labeled their social anxiety level accordingly. During the experiment, relevant signals and social anxiety scores were collected, including (1) behavioral signals (head and eye movements) (2) physiological signals (heart rate (HR), electrodermal activity (EDA), etc.) (3) discrete social anxiety self-reported labels (4) evaluation questionnaires. We first verified the labels, and furthermore ran baseline classification experiments, where GRU model showed best accuracy: 92.84 %for binary classification. It was also found that either behavioral data or physiological signals alone could achieve satisfactory accuracies (85.64% and 91.02%) while the combined achieved slightly higher(92.84 %) © 2024 IEEE.

关键词： Eye movements

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Sequential Kernel Goodness-of-fit Testing 41

Sequential Kernel Goodness-of-fit Testing

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Zhengyu Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Goodness-of-fit testing, a classical statistical tool, has been extensively explored in the batch setting, where the sample size is ***, practitioners often prefer methods that adapt to the complexity of a problem rather than fixing the sample size *** batch tests are generally unsuitable for streaming data, as valid inference after data peeking requires multiple testing corrections, resulting in reduced statistical *** address this issue, we delve into the design of consistent sequential goodness-of-fit *** the principle of testing by betting, we reframe this task as selecting a sequence of payoff functions that maximize the wealth of a fictitious bettor, betting against the null in a repeated *** conduct experiments to demonstrate the adaptability of our sequential test across varying difficulty levels of problems while maintaining control over type-I errors. Copyright 2024 by the author(s)

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Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation 24

Devil is in Details: Locality-Aware 3D Abdominal CT Volume G...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yuran Wan, Zhijing Qiu, Yansheng Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

In the realm of medical image analysis, self-supervised learning (SSL) techniques have emerged to alleviate labeling demands, while still facing the challenge of training data scarcity owing to escalating resource requirements and privacy constraints. Numerous efforts employ generative models to generate high-fidelity, unlabeled 3D volumes across diverse modalities and anatomical regions. However, the intricate and indistinguishable anatomical structures within the abdomen pose a unique challenge to abdominal CT volume generation compared to other anatomical regions. To address the overlooked challenge, we introduce the Locality-Aware Diffusion (Lad), a novel method tailored for exquisite 3D abdominal CT volume generation. We design a locality loss to refine crucial anatomical regions and devise a condition extractor to integrate abdominal priori into generation, thereby enabling the generation of large quantities of high-quality abdominal CT volumes essential for SSL tasks without the need for additional data such as labels or radiology reports. Volumes generated through our method demonstrate remarkable fidelity in reproducing abdominal structures, achieving a decrease in FID score from 0.0034 to 0.0002 on AbdomenCT-1K dataset, closely mirroring authentic data and surpassing current methods. Extensive experiments demonstrate the effectiveness of our method in self-supervised organ segmentation tasks, resulting in an improvement in mean Dice scores on two abdominal datasets effectively. These results underscore the potential of synthetic data to advance self-supervised learning in medical image analysis. © 2024 ACM.

关键词： Self-supervised learning

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GL-DAE: Global-Local Denoising Auto-Encoder for Fundus Images Quality Representation Learning

GL-DAE: Global-Local Denoising Auto-Encoder for Fundus Image...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Cai, Xiangwen Lei, Haijun Xie, Hai Zhang, Guoming Lei, Baiying Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Shenzhen University Shenzhen Eye Hospital Shenzhen China

ISBN: (纸本)9798350386226

Fundus image is vital for the diagnosis and monitoring of various eye diseases, where the accuracy of diagnostic results is largely determined by the quality of the obtained images. However, the performance of existing Fundus Image Quality Assessment(FIQA) methods is primarily constrained by the quantity and quality of retinal datasets, highlighting a significant challenge in the field due to the absence of a large annotated quality dataset. To alleviate this problem, we purpose the Global-Local Denoising Auto-Encoder(GL-DAE), a novel and efficient self-supervised learning pre-training approach in FIQA to overcome the scarcity of annotated fundus images. Specifically, in the pre-training phase, given a batch of unlabeled fundus images, we apply different distortion algorithms to degrade the original fundus images. The degraded fundus images are then subjected to global and local cropping, and subsequently, global and local features are extracted using a joint encoder. By reconstructing global and local fundus images, the model learns to extract features related to fundus quality. Afterwards, to complement the global reconstruction task, we further design a noise identification based contrative learning to guide the models to identify different types of degradation algorithm. Fine-tuning results across two FIQA datasets highlight the superiority of our method compared to other classical image classification methods, demonstrating its potential to advance the reliability of FIQA. © 2024 IEEE.

关键词： Self-supervised learning

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