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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

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A Novel Shadow Variable Catcher for Addressing Selection Bias in Recommendation Systems

A Novel Shadow Variable Catcher for Addressing Selection Bia...

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IEEE International Conference on Data Mining (ICDM)

作者： Qingfeng Chen Boquan Wei Debo Cheng Jiuyong Li Lin Liu Shichao Zhang School of Computer Electronics and Information Guangxi University Nanning China UniSA STEM University of South Australia Adelaide Australia Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Recommender systems rely on observational data to predict user ratings for unseen items. Since the observational data is typically missing not at random (MNAR), they contain biases, predominantly selection bias, thus models trained on such data are inherently biased. If a shadow variable, which is a variable used instead of user's latent variables that influence both the treatment and the outcome, can be identified, it is possible to build unbiased models for recommender systems. To overcome the challenge of manually identifying valid shadow variables in the data, we propose a Shadow Variable Catcher (SVC), a model designed to learn the latent representation of shadow variables from observational data. By using the learned representation of shadow variables by SVC, we develop the Shadow Debiased Recommender (SDR) method to build an unbiased collaborative filtering model for addressing selection bias in recommender systems. Comprehensive experiments on both synthetic and real-world datasets, have verified the performance of SDR and demonstrated its effectiveness and robustness, and offer new insights into the mitigation of bias in recommender systems.

关键词： Training Accuracy Collaborative filtering Prevention and mitigation Static VAr compensators Data models Robustness Real-time systems Data mining Recommender systems

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Privacy Protection Scheme of POI Query Based on Semantic and Temporal Association

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International Journal of network security 2022年第5期24卷 922-932页

作者： Zuo, Kaizhong Zhao, Jun Hu, Peng Shen, Zhangyi Chu, Xixi School of Computer and Information Anhui Normal University Wuhu241002 China Anhui Provincial Key Laboratory of Network and Information Security Wuhu241002 China

In a digital society, location-based service (LBS) is a necessary function for mobile devices. However, while enjoying the convenience, users also face the risk of privacy disclosure. A novel location privacy protection scheme based on semantic and temporal association is proposed in this paper to solve this problem. Specifically, this scheme establishes a temporal association model according to users’ historical query requests to measure the temporal association relationship. Then, the scheme constructs an optimal association table at the current moment based on association entropy. Finally, based on users’ personalized privacy requirements and the optimal association table, the scheme builds a dummy location set distributed on different road segments. In addition, we have applied this scheme to the Android platform to implement a novel location privacy protection app called QueryWithMe. Theoretical analysis and experimental results show that the scheme can resist inference and temporal association attacks © 2022, International Journal of network *** Rights Reserved.

关键词： Location based services

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SCPS:A Secure and Copyright-Preserving System for IIoT Based on Hyperledger Fabric 9

SCPS:A Secure and Copyright-Preserving System for IIoT Based...

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9th International Conference on Advanced Cloud and Big Data, CBD 2021

作者： Peng, Jiahui Li, Chunpei Yuan, Bo Meng, Hongwei Yu, Feng Li, Xianxian Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining Security GuiLin China Beihang University Development Environment School of Computer Science and Engineering State Key Laboratory of Software Beijing100191 China Xiong'An Intelligent City Innovation Federation Blockchain Lab Beijing China

ISBN: (纸本)9781665407458

Data, a key asset in Industrial Internet of Things(IIoT), has fueled the emergence of a new data trading in-dustry, which can be collected and traded for better development of IIoT. However, copyright protection is not considered in traditional transactions, resulting in the loss of copyright control of the data after the data was sold. A large number of piracy activities will reduce the enthusiasm of data owners to collect data. The solution we proposed was to use blockchain and smart contract to achieve a decentralized and secure copyright protection scheme. The Trusted Execution Environment(TEE) helped our system realized the piracy tracking. To be specific, our solution effectively achieves a secure and copyright protection system of IIoT data, we realize the real simulation on Hyperledger Fabric shows the good performance of our system. © 2022 IEEE.

关键词： Blockchain

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Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition 24

Generating Action-conditioned Prompts for Open-vocabulary Vi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Jia, Chengyou Luo, Minnan Chang, Xiaojun Dang, Zhuohang Han, Mingfei Wang, Mengmeng Dai, Guang Dang, Sizhe Wang, Jingdong School of Computer Science and Technology MOEKLINNS Lab Xi'an Jiaotong University Shaanxi Xi'an China University of Science and Technology of China Anhui Hefei China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China ReLER Lab AAII University of Technology Sydney SydneyNSW Australia Zhejiang University of Technology College of Computer Science and Technology China SGIT AI Lab State Grid Corporation of China Beijing China Baidu Inc Beijing China United Arab Emirates Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China SGIT AI Lab State Grid Corporation of China China School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798400706868

Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing methods typically adapt pretrained image-text models to the video domain, capitalizing on their inherent strengths in generalization. A common thread among such methods is the augmentation of visual embeddings with temporal information to improve the recognition of seen actions. Yet, they compromise with standard less-informative action descriptions, thus faltering when confronted with novel actions. Drawing inspiration from human cognitive processes, we argue that augmenting text embeddings with human prior knowledge is pivotal for open-vocabulary video action recognition. To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts. Specifically, we harness the knowledge in LLMs to produce a set of descriptive sentences that contain distinctive features for identifying given actions. Building upon this foundation, we further introduce a multi-modal action knowledge alignment mechanism to align concepts in video and textual knowledge encapsulated within the prompts. Extensive experiments on various video benchmarks, including zero-shot, few-shot, and base-to-novel generalization settings, demonstrate that our method not only sets new SOTA performance but also possesses excellent interpretability. © 2024 ACM.

关键词： Embeddings

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Leveraging Blockchain and Coded Computing for Secure Edge Collaborate Learning in Industrial IoT

Leveraging Blockchain and Coded Computing for Secure Edge Co...

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International Conference on computer Communications and networks (ICCCN)

作者： Yuliang Chen Xi Lin Hansong Xu Siyi Liao Jin Guo Chunming Zou Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China China United Network Communication Co. Ltd The Department of Computer Science Morgan State University USA The Third Research Institute of Ministry of Public Security

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

In recent years, the rapid development of the Industrial Internet of Things (IIoT) has enabled real-time communication and data sharing among devices, significantly enhancing industrial production efficiency and security. Furthermore, the introduction of edge learning allows models to be trained on edge industrial devices. However, with the continuous growth of industrial data, challenges such as resource optimization in edge environments, edge node motivation, and threats from malicious nodes are increasingly posing obstacles to the advancement of edge learning. In this paper, we propose Blockchain and Coded Computing based Secure Edge Learning (BCC-SEL). First, we introduce a coded edge learning framework with Lagrange Coded Computing (LCC) for resource-efficient use of idle nodes during training. Based on the blockchain, we further propose an incentive mechanism to reward and punish the participating training clients. Finally, we guarantee the robustness of the framework using the detection method based on cosine-similarity. We provide theoretical proof that our approach effectively reduces the computational consumption of training nodes. In terms of experiments, our method effectively rewards honest nodes that participate in training and penalizes malicious nodes while guaranteeing accuracy.

关键词： Training Accuracy Image edge detection Computational modeling Production Robustness Real-time systems

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Towards Hierarchically Extracting network Topologies

Towards Hierarchically Extracting Network Topologies

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International Conference on networking and network Applications (NaNA)

作者： Xiaoli Ma Guangsen Chen Haoxuan Zhang Tao Qin MOE Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Collaborative Innovation Center of Information Sensing and Understanding Xidian University Xi¶an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

network management has become increasingly difficult due to the growing complexity of these networks. It is important for network administrators, engineers, and analysts to understand network topology in order to design, troubleshoot, and optimize networks for performance, reliability, and security. By analyzing the topology of a network, professionals can identify areas that are causing delays, optimize data flow, and improve the overall efficiency of network communication. In this paper, we empirically study hierarchically extracting network topologies. The hierarchical perspective in network topology analysis provides a systematic and organized approach to studying complex networks, enabling researchers to uncover hidden patterns, optimize network performance, and make informed decisions based on the network’s structure and behavior.

关键词： Systematics network topology Scalability Redundancy Reliability engineering Load management Topology

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Automatic Classification and Recognition of Seismic Waveforms Based on Convolutional Neural networks

Automatic Classification and Recognition of Seismic Waveform...

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network, Multimedia and information Technology (NMITCON) International Conference on

作者： Juhao Shan Hanming Huang Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

The development of efficient, high-precision, and universally applicable automatic waveform picking algorithms is becoming increasingly important in the era of seismic big data. The main challenge of waveform automatic picking algorithm lies in how to adapt to the classification and screening of different types of seismic events in different regions. Traditional seismic phase identification relies on the theoretical knowledge and experience of relevant researchers, which has problems such as large workload, long interpretation time, strong subjectivity, and low efficiency. Deep learning technology can learn the hidden features in data. This article studies how to use deep convolutional neural networks to learn the nonlinear mapping relationship between seismic data and seismic phases, providing technical support for achieving automated seismic phase recognition. This article mainly focuses on the automatic classification and recognition of seismic waveforms using convolutional neural networks. Firstly, relevant research is summarized, relevant application methods are proposed, and the results are discussed to provide assistance for the development of related fields.

关键词： Deep learning Training Landslides Earthquakes Noise Neural networks Explosions Classification algorithms Convolutional neural networks Character recognition

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Improving User QoE via Joint Trajectory and Resource Optimization in Multi-UAV Assisted MEC

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IEEE Transactions on Services Computing 2025年第03期18卷 1472-1486页

作者： Gao, Yang Tao, Jun Xu, Yifan Wang, Zuyan Gao, Yu Wang, Meiling Hainan University School of Cyberspace Security Haikou570228 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Southeast University School of Cyber Science and Engineering Nanjing211189 China ZTE Communications Co. Ltd Nanjing210012 China

As a promising network architecture, Mobile Edge Computing (MEC), has been proven that can effectively reduce the end-to-end latency and the energy consumption. The Unmanned Aerial Vehicle (UAV) assisted MEC network, where the UAV can provide the computation offloading services for the mobile users, can further alleviate the huge deployment cost of static edge servers. However, it remains unsolved how multiple cooperative flying UAVs serve the ground users, especially considering that these UAVs may share the same wireless channel and can communicate with the users while flying. In this paper, we first propose the Age of Task (AoT) metric to measure the quality of experience, and then formulate the joint optimization problem to minimize the worst AoT among all the users. Based on the block coordinate descent (BCD) method, this problem is transformed into three non-convex programming sub-problems (i.e., the UAV-user association sub-problem, the UAV trajectory planning sub-problem and the transmit power optimization sub-problem). Specifically, the successive convex approximation (SCA) technique is exploited iteratively to deal with the non-convexity in the UAV trajectory and transmit power optimization. Numerical results show that the proposed scheme outperforms the benchmark offloading schemes in terms of AoT. © 2008-2012 IEEE.

关键词： Drones

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Privacy-Preserving Computation Tookit on Floating-Point Numbers 14th

Privacy-Preserving Computation Tookit on Floating-Point Numb...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Chen, Zekai Zheng, Zhiwei Liu, Ximeng Guo, Wenzhong College of of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Security of Network Systems Fuzhou University Fuzhou350108 China

ISBN: (纸本)9783030898137

Computation outsourcing using virtual environment is getting more and more prevalent in cloud computing, which several parties want to run a joint application and preserves the privacy of input data in secure computation protocols. However, it is still a challenging task to improve the efficiency and speed of secure floating point calculations in computation outsourcing, which has efficient secure integer calculations. Therefore, in this paper, we propose a framework built-up with a privacy-preserving computation toolkit with floating-point numbers (FPN), called PCTF. To achieve the above goal, we provide efficient toolkit to ensure their own data that FPN operations can be securely handled by homomorphic encryption algorithm. Moreover, we provide simulation results to experimentally evaluate the performance of the accuracy and the efficiency of PCTF, which will slowdown with 10x time consumption per in the secure floating-point addition and secure floating-point multiplication. Existing FPN division is constantly approaching result of division, or obtaining the quotient and remainder of division, in terms of precision, it is impossible to guarantee the precise range stably. However, our PCTF has higher precision in secure floating-point division, and the precision can be guaranteed at least 10- 17. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Outsourcing

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