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arXiv 2023年

作者： Leng, Yicheng Fang, Chaowei Li, Gen Fang, Yixiang Li, Guanbin School of Artificial Intelligence Xidian University Xi’an China School of Data Science The Chinese University of Hong Kong Shenzhen China Afirstsoft Shenzhen China School of Computer Science and Engineering Research Institute of Sun Yat-sen University in Shenzhen Sun Yat-sen University Guangzhou China GuangDong Province Key Laboratory of Information Security Technology China

Visible watermarks, while instrumental in protecting image copyrights, frequently distort the underlying content, complicating tasks like scene interpretation and image editing. Visible watermark removal aims to eliminate the interference of watermarks and restore the background content. However, existing methods often implement watermark component removal and background restoration tasks within a singular branch, leading to residual watermarks in the predictions and ignoring cases where watermarks heavily obscure the background. To address these limitations, this study introduces the Removing Interference and Recovering Content Imaginatively (RIRCI) framework. RIRCI embodies a two-stage approach: the initial phase centers on discerning and segregating the watermark component, while the subsequent phase focuses on background content restoration. To achieve meticulous background restoration, our proposed model employs a dual-path network capable of fully exploring the intrinsic background information beneath semi-transparent watermarks and peripheral contextual information from unaffected regions. Moreover, a Global and Local Context Interaction module is built upon multi-layer perceptrons and bidirectional feature transformation for comprehensive representation modeling in the background restoration phase. The efficacy of our approach is empirically validated across two large-scale datasets, and our findings reveal a marked enhancement over existing watermark removal techniques. Copyright © 2023, The Authors. All rights reserved.

关键词： Restoration

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Reconfigurable Intelligent Surface Assisted High-Speed Train Communications: Coverage Performance Analysis and Placement Optimization

arXiv

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arXiv 2023年

作者： Liu, Changzhu He, Ruisi Niu, Yong Han, Zhu Ai, Bo Gao, Meilin Ma, Zhangfeng Wang, Gongpu Zhong, Zhangdui The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The Tsinghua Space Center the Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100091 China The College of Information Engineering Shaoyang University Shaoyang422000 China The Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Reconfigurable intelligent surface (RIS) emerges as maximization problem is formulated to facilitate RIS discrete an efficient and promising technology for the next wireless phase design and RIS placement optimization, which is subject generation networks and has attracted a lot of attention owing to coverage probability constraint. Simulation results validate to the capability of extending wireless coverage by reflecting that better coverage performance and higher travel distance signals toward targeted receivers. In this paper, we consider can be achieved with deployment of RIS. The impacts of some a RIS-assisted high-speed train (HST) communication system key system parameters including transmission power, signal-to-to enhance wireless coverage and improve coverage probability. noise ratio threshold, number of RIS elements, number of RIS First, coverage performance of the downlink single-input-single- quantization bits, horizontal distance between base station and output system is investigated, and the closed-form expression RIS, and speed of HST on system performance are investigated. of coverage probability is derived. Moreover, travel distance In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Railroads

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Prediction of Estimated Time of Arrival for Multi-Airport Systems Via "Bubble" Mechanism

SSRN

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SSRN 2022年

作者： Wang, Lechen Mao, Jianfeng Li, Lishuai Li, Xuechun Tu, Yilei School of Data Science The Chinese University of Hong Kong Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Guangdong China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China Faculty of Aerospace Engineering Delft University of Technology Delft2600 AA Netherlands School of Data Science City University of Hong Kong Hong Kong

Predicting Estimated Time of Arrival (ETA) for a Multi-Airport System (MAS) is much more challenging than for a single airport system because of complex air route structure, dense air traffic volume and vagaries of traffic conditions in an MAS. In this work, we propose a novel Bubble mechanism to accurately and e ciently predict medium-term ETA for a Multi-Airport System (MAS), in which the prediction of travel time of an origin-destination (OD) pair is decomposed into two stages, termed as out-MAS and in-MAS stages. For the out-MAS stage, Auto-Regressive Integrated Moving Average (ARIMA) is used to predict the travel time of a ight to reach the MAS boundary. For the in-MAS stage, we construct new spatio-temporal features based on clustering analysis of trajectory patterns facilitated by a novel data-driven hybrid polar sampling method. A sequence-to-sequence prediction model, Multi-variate Stacked Fully connected Bidirectional Long-Short Term Memory, is further developed to achieve multi-step-ahead predictions of in-MAS travel time for each trajectory pattern using the spatio-temporal features as input. Finally, the medium-term ETA prediction for an MAS is achieved by integrating the out-MAS and in-MAS prediction with the help of trajectory pattern prediction via random forest. A case study of predicting medium-term ETA for a typical MAS in china, Guangdong-Hong Kong-Macao Greater Bay Area, is conducted to demonstrate the usage and promising performance of the proposed method in comparison to several commonly used end-to-end learning methods. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Tackling Noisy Labels with Network Parameter Additive Decomposition

arXiv

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arXiv 2024年

作者： Wang, Jingyi Xia, Xiaobo Lan, Long Wu, Xinghao Yu, Jun Yang, Wenjing Han, Bo Liu, Tongliang The Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Department of Automation University of Science and Technology of China Hefei230026 China The Department of Computer Science Hong Kong Baptist University Hong Kong

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually memorize mislabeled training data. A simple and effective method that exploits the memorization effect to combat noisy labels is early stopping. However, early stopping cannot distinguish the memorization of clean data and mislabeled data, resulting in the network still inevitably overfitting mislabeled data in the early training stage. In this paper, to decouple the memorization of clean data and mislabeled data, and further reduce the side effect of mislabeled data, we perform additive decomposition on network parameters. Namely, all parameters are additively decomposed into two groups, i.e. , parameters w are decomposed as w = σ + γ. Afterward, the parameters σ are considered to memorize clean data, while the parameters γ are considered to memorize mislabeled data. Benefiting from the memorization effect, the updates of the parameters σ are encouraged to fully memorize clean data in early training, and then discouraged with the increase of training epochs to reduce interference of mislabeled data. The updates of the parameters γ are the opposite. In testing, only the parameters σ are employed to enhance generalization. Extensive experiments on both simulated and real-world benchmarks confirm the superior performance of our method. The implementation is available at: https://***/wangjingyi9924/TNLPAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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A Distinguisher for RNGs with LFSR Post-processing 16th

A Distinguisher for RNGs with LFSR Post-processing

引用

16th International Conference on Information Security and Cryptology, Inscrypt 2020

作者： Wu, Xinying Ma, Yuan Chen, Tianyu Lv, Na State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Data Assurance and Communications Security Research Center Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030718510

Random number generator (RNG) is a fundamental element in modern cryptography. If the quality of the outputs generated by RNGs is not as well as expected, the cryptographic applications which use the random number service are vulnerable to security threats. In reality, the entropy source of RNGs could be impressible by the changes of environmental factors, resulting in defects in the generated data, such as poor statistical properties. Thus, RNG is generally designed with a preset post-processing module to improve the quality of the output sequences. Linear feedback shift register (LFSR) is one of the frequently used methods for post-processing thanks to the characteristic of simplicity and no reduction in output throughput. However, we point out that even if the statistical properties of the outputs of the entropy source are extremely poor, the sequences processed by LFSR can still pass the statistical test. This undoubtedly increases the security risks in the usage of RNGs. In this work, we propose a distinguisher for the RNGs with LFSR post-processing for the first time. The distinguisher can be used to detect the RNGs with LFSR post processing, and we theoretically prove the sequences before processing can be recovered. On this basis, we design a new statistical test via combining the distinguisher with the Frequency Test in the NIST test suite. The experimental results show that if the sequence is biased before being processed by LFSR, our proposed method can detect it, but the NIST SP 800-22 Test Suite cannot. © 2021, Springer Nature Switzerland AG.

关键词： Statistical tests

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Highly Sensitive, Ultra-Reliable Flexible Piezoelectret Sensor for Non-Contact Sitting Motion Tacking and Physiological Signal Monitoring

SSRN

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SSRN 2023年

作者： Ma, Xingchen Qi, Yan Niu, Yangyang Zhang, Qi Xiang, Xinhao Zhang, Ke He, Pengfei Dai, Ying Niu, Wenxin Zhang, Xiaoqing Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China School of Medicine Tongji University Shanghai201619 China Department of Rehabilitation Sciences Tongji University School of Medicine Shanghai200092 China Shenzhen Data Thinking Technology Co. Ltd. Shenzhen518055 China School of Aerospace Engineering and Applied Mechanics Tongji University Shanghai200092 China

Non-contact, cost-effective, multifunctional and robust monitoring platform without mechanical wearing burden is highly desired to enhance the immersive experience for users during the long-term health management. However, most reported intelligent electronics in human life need to be adhered conformably to key human body parts for accurate signal detection, giving rising to activity interference and wearing burden in the long-term usage. Herein, a highly sensitive flexible film pressure sensor with a folded structure configuration assembled from irradiation-cross linked polypropylene (IXPP) piezoelectret film featured with piezoelectric-like dynamic electrical output attributes are reported. Such folded structure incorporated specially into the device design endows the piezoelectric film sensor with merits such as enhanced sensitivity, high signal-to-noise ratio and excellent anti-interference capacity. Consequently, the sensor can achieve a linear pressure sensitivity of 105 pC/kPa over a wide pressure range of 0.6 to 62.4 kPa, a pressure resolution of 0.98%, and fast response/recovery time of 40 ms. The flexible piezoelectric film sensors are embedded into cushions and attached to the back of chairs for application demonstrations of non-contact sensing. Remarkably, low-frequency human motion and weak physiological signal detection are achieved in a non-contact mode by a highly sensitive piezoelectric sensing principle in an all-in-one configuration, simplifying the signal processing and monitoring system design. A vital sign monitoring system based on the intelligent cushion is further developed to unlock the health status of sedentary crowd. Moving forward, such piezoelectret sensors show the capability to be connected to the commercial "C-life" smart home ecosystem, presenting its strong practicability and application prospects in smart households and remote healthcare beyond the laboratory. © 2023, The Authors. All rights reserved.

关键词： Pressure sensors

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Low-access repair of Reed-Solomon codes in rack-aware storage

Low-access repair of Reed-Solomon codes in rack-aware storag...

引用

IEEE International Symposium on Information Theory

作者： Jiaojiao Wang Zitan Chen Data Science and Information Technology Research Center Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Shenzhen China School of Science and Engineering the Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen China

We study the problem of repairing Reed-Solomon codes with low-access complexity in the rack-aware storage model that allows collective information processing in the nodes that share the same rack. Building on recent work of the access complexity for the rack-aware storage model, we derive a lower bound on the amount of information accessed for repairing multiple failed nodes within a single rack from any number of helper racks. Further, we construct a family of Reed-Solomon codes that only require accessing a relatively small number of symbols to repair failed nodes in a single rack. In particular, for certain code parameters, our construction attains the bound on the access complexity with equality and thus has optimal access.

关键词：

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Rack-aware minimum-storage regenerating codes with optimal access

arXiv

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arXiv 2023年

作者： Wang, Jiaojiao Chen, Zitan The Data Science and Information Technology Research Center Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School Shenzhen China The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen China

We derive a lower bound on the amount of information accessed to repair failed nodes within a single rack from any number of helper racks in the rack-aware storage model that allows collective information processing in the nodes that share the same rack. Furthermore, we construct a family of rack-aware minimum-storage regenerating (MSR) codes with the property that the number of symbols accessed for repairing a single failed node attains the bound with equality for all admissible parameters. Constructions of rack-aware optimal-access MSR codes were only known for limited parameters. We also present a family of Reed-Solomon (RS) codes that only require accessing a relatively small number of symbols to repair multiple failed nodes in a single rack. In particular, for certain code parameters, the RS construction attains the bound on the access complexity with equality and thus has optimal access. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimal systems

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COVID-19 knowledge Graph for Drug and Vaccine Development

COVID-19 Knowledge Graph for Drug and Vaccine Development

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lan Huang Hongrui Guan Yanchun Liang Renchu Guan Xiaoyue Feng The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China Zhuhai Sub Laboratory Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9781665429825

The worldwide spread of COVID-19 has made a severe impact on human health and life. It has shown rapid propagation, long in vitro survival, and a long incubation period. More seriously, COVID-19 is more susceptible to variation, as it is an RNA virus. Mutations of COVID-19 have been reported in multiple countries worldwide, which makes drug and vaccine development a significant challenge. To search for potential drugs and vaccines and reveal the atlas of COVID-19 evolution, we extract information from massive unstructured data and construct a COVID-19 knowledge graph using the COVID-19 data. Based on machine learning approaches, we infer and predict novel coronavirus pneumonia-related diseases, drug action targets, etc. to speculate on new and more effective treatment methods. In addition, to study transcriptome of SARS-CoV-2, new ideas can be provided to biomedical experts with flexible responses to viral variation. An in-depth analysis of the COVID-19 pathomechanism at the pharmaceutical, genetic, and protein levels provides effective means and tools for novel coronavirus pneumonia vaccines, drug development, and therapeutic program design.

关键词： COVID-19 Drugs Proteins Protein engineering Pulmonary diseases RNA Machine learning

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Threshold Image Segmentation for Non-uniform Illumination Using Otsu Optimization Approach 6th

Threshold Image Segmentation for Non-uniform Illumination Us...

引用

6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Quan, Liang Zicheng, Zhang Jinjing, Hu Chang, Kuo-Chi School of Computer Science and Mathematics Fujian University of Engineering Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China School of Information Science and Engineering Fujian University of Technology No3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China

ISBN: (纸本)9783030697167

With an improved algorithm based on the Otsu method, this paper proposes solving the shadow region problem caused by non-uniform illumination. The recent image processing algorithms for handling the shaded area’s issue for the massive difference in the image grey value will not be practical. It is not easy to directly set the shaded area’s content to "black" based on this problem. This paper proposes improved threshold image segmentation for Non-uniform illumination based on the Otsu optimization Approach. It uses the local area threshold and window image pixel by sliding to enhance the image recognition rate of a shaded area. Experimental results verify that the proposed segmentation method has more robust adaptability to images with shadows. Compared with the classical global OTSU algorithm and genetic algorithm optimization, the image recognition rate is 96%. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image recognition

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