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A comprehensive survey of oracle character recognition: Challenges, datasets, methodology, and beyond

Pattern Recognition 2025年

作者： Jing Li Xueke Chi Qiufeng Wang Kaizhu Huang Da-Han Wang Yongge Liu Cheng-Lin Liu College of Science & Technology Ningbo University Ningbo China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Duke Kunshan University Suzhou China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen China Anyang Normal University Anyang China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Oracle character recognition—an analysis of ancient Chinese inscriptions found on oracle bones—has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.

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The vulnerability of cyber-physical system under stealthy attacks

arXiv

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arXiv 2020年

作者： Sui, Tianju Mo, Yilin Marelli, Damián Sun, Ximing Fu, Minyue School of Control Science and Engineering Dalian University of Technology Dalian China Department of Automation Tsinghua University Beijing China School of Automation Guangdong University of Technology Guangzhou China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Electrical Engineering and Computer Science University of Newcastle CallaghanNSW2308 Australia School of Automation Guandong University of Technology China

In this paper, we study the impact of stealthy attacks on the Cyber-Physical System (CPS) modeled as a stochastic linear system. An attack is characterised by a malicious injection into the system through input, output or both, and it is called stealthy (resp. strictly stealthy) if it produces bounded changes (resp. no changes) in the detection residue. Correspondingly, a CPS is called vulnerable (resp. strictly vulnerable) if it can be destabilized by a stealthy attack (resp. strictly stealthy attack). We provide necessary and sufficient conditions for the vulnerability and strictly vulnerability. For the invulnerable case, we also provide a performance bound for the difference between healthy and attacked system. Numerical examples are provided to illustrate the theoretical results. Copyright © 2020, The Authors. All rights reserved.

关键词： Embedded systems

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Uncertainty-aware Score Distribution Learning for Action Quality Assessment

arXiv

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arXiv 2020年

作者： Tang, Yansong Ni, Zanlin Zhou, Jiahuan Zhang, Danyang Lu, Jiwen Wu, Ying Zhou, Jie Department of Automation Tsinghua University China State Key Lab of Intelligent Technologies and Systems China Beijing National Research Center for Information Science and Technology China Tsinghua Shenzhen International Graduate School Tsinghua University China Electrical and Computer Engineering Department Northwestern University

Assessing action quality from videos has attracted growing attention in recent years. Most existing approaches usually tackle this problem based on regression algorithms, which ignore the intrinsic ambiguity in the score labels caused by multiple judges or their subjective appraisals. To address this issue, we propose an uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA). Specifically, we regard an action as an instance associated with a score distribution, which describes the probability of different evaluated scores. Moreover, under the circumstance where fine-grained score labels are available (e.g., difficulty degree of an action or multiple scores from different judges), we further devise a multi-path uncertainty-aware score distributions learning (MUSDL) method to explore the disentangled components of a score. We conduct experiments on three AQA datasets containing various Olympic actions and surgical activities, where our approaches set new state-of-the-arts under the Spearman’s Rank Correlation.1 Copyright © 2020, The Authors. All rights reserved.

关键词： Probability distributions

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Uncertainty-Aware Score Distribution Learning for Action Quality Assessment

Uncertainty-Aware Score Distribution Learning for Action Qua...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yansong Tang Zanlin Ni Jiahuan Zhou Danyang Zhang Jiwen Lu Ying Wu Jie Zhou Department of Automation Tsinghua University China State Key Lab of Intelligent Technologies and Systems China Beijing National Research Center for Information Science and Technology China Electrical and Computer Engineering Department Northwestern University Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Assessing action quality from videos has attracted growing attention in recent years. Most existing approaches usually tackle this problem based on regression algorithms, which ignore the intrinsic ambiguity in the score labels caused by multiple judges or their subjective appraisals. To address this issue, we propose an uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA). Specifically, we regard an action as an instance associated with a score distribution, which describes the probability of different evaluated scores. Moreover, under the circumstance where finer-grained score labels are available (e.g., difficulty degree of an action or multiple scores from different judges), we further devise a multi-path uncertainty-aware score distribution learning (MUSDL) method to explore the disentangled components of a score. In order to demonstrate the effectiveness of our proposed methods, We conduct experiments on two AQA datasets containing various Olympic actions. Our approaches set new state-of-the-arts under the Spearman's Rank Correlation (i.e., 0.8102 on AQA-7 and 0.9273 on MTL-AQA).

关键词： Videos Uncertainty Games Gaussian distribution Task analysis Quality assessment Training

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Adjacency Constraint for Efficient Hierarchical Reinforcement Learning

arXiv

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arXiv 2021年

作者： Zhang, Tianren Guo, Shangqi Tan, Tian Hu, Xiaolin Chen, Feng The Department of Automation Tsinghua University Beijing100086 China The Beijing Innovation Center for Future Chip Beijing100086 China The LSBDPA Beijing Key Laboratory Beijing100084 China The Department of Civil and Environmental Engineering Stanford University Stanford CA94305 United States The Department of Computer Science and Technology Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing100084 China

Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is large. Searching in a large goal space poses difficulty for both high-level subgoal generation and low-level policy learning. In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a k-step adjacent region of the current state using an adjacency constraint. We theoretically prove that in a deterministic Markov Decision Process (MDP), the proposed adjacency constraint preserves the optimal hierarchical policy, while in a stochastic MDP the adjacency constraint induces a bounded state-value suboptimality determined by the MDP’s transition structure. We further show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks including challenging simulated robot locomotion and manipulation tasks show that incorporating the adjacency constraint significantly boosts the performance of state-of-the-art goal-conditioned HRL approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Blockchain and Artificial Intelligence Applications

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Journal of Artificial Intelligence and Technology 2021年第2期1卷 83-83页

作者： Guanqiu Qi Zhiqin Zhu Computer Information Systems Department State University of New York at Buffalo StateBuffaloNY 14222USA College of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China

The emergence and popularity of blockchain,distributed ledger technology distributed computing,and network security and trust techniques are significantly changing the operation and management of computing and communication systems,as these techniques have the potential to disrupt any domain involving coordination among autonomous resources without trusted third *** techniques and their applications include finance and payments(e.g.,Facebook Libra),but also networks(e.g.,power grids or telecom networks),computing(e.g.,brokering of edge resources),Internet of Things(e.g.,supply chain or industry 4.0),and service platforms(e.g.,identity management).The market capitalization,investor appetite,and institutional coverage for cryptocurrency(as well as bitcoin and blockchain)have all jumped *** total market capitalization of the cryptocurrency market has significantly increased in the past three *** applications of blockchain exhibit a variety of complicated problems and new requirements,which brings more open issues and challenges for artificial intelligence(AI)and related research.

关键词： artificial finance autonomous

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Semi-autonomous greenhouse environment control by combining expert knowledge and machine learning

Semi-autonomous greenhouse environment control by combining ...

引用

Chinese automation Congress (CAC)

作者： Mengzhen Kang Yuchen Weng Haitong Pang Liangde Li Xing-Rong Fan Jing Hua Fangle Chang Xiujuan Wang Longhua Ma The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Ningbo Institute of Technology Zhejiang University Ningbo China The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao AgriTech Co. Ltd Qingdao Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The concept of "autonomous greenhouse" hints the automatic setting of control target, and giving orders on facilities to reach these targets (set point). information and communication technologies are widely used, but how to integrate common sense or knowledge in decision-making is still challenging. Semi-autonomous greenhouse control is to set rules with joint advantages of experienced grower and powerful machinery, with the support of knowledge graph and semantic analysis. The contribution of each side is dependent on the intelligence level of computers, the availability of data, etc. In this work, the hybrid system between human and machinery is shown with the temperature control in greenhouse. The target is set mainly by expert knowledge, while the temperature controlled by vent opening is decided by an algorithm. Result shows that the target is better reached compared to that of the experience-based approach. This example shows the feasibility of human-machine hybrid intelligence system for greenhouse environment control. Although this approach is tested for temperature control, it can be extended to control multiple factors simultaneously (temperature, light intensity, humidity, etc.) by driving multiple facilities (heating, lightening, venting, etc.).

关键词： Green products Temperature measurement Monitoring Temperature sensors Vents Agriculture Temperature distribution

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DUSA-UNet: Dual Sparse Attentive U-Net for Multiscale Road Network Extraction

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Song, Jie Sun, Yue Cai, Ziyun Xiao, Liang Huang, Yawen Zheng, Yefeng Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210023 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Nanjing University of Science and Technology Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense Nanjing210094 China Tencent YouTu Lab Jarvis Research Center Shenzhen518057 China

The challenges of road network segmentation demand an algorithm capable of adapting to the sparse and irregular shapes, as well as the diverse context, which often leads traditional encoding-decoding methods and simple Transformer embeddings to failure. We introduce a computationally efficient and powerful framework for elegant road-aware segmentation. Our method, called DUSA-UNet, effectively encodes fine-grained local road connectivity and holistic global topological semantics while decoding multiscale road network information. DUSA-UNet offers a novel alternative to the U-Net architecture by integrating connectivity attention, which can exploit intra-road interactions across multi-level sampling features with reduced computational complexity. This local interaction serves as valuable prior information for learning global interactions between road networks and the background through another integrality attention mechanism. The two forms of sparse attention are arranged alternatively and complementarily, and trained jointly, resulting in performance improvements without significant increases in computational complexity. Extensive experiments on various datasets with different resolutions, including Massachusetts, DeepGlobe, SpaceNet, and Large-Scale remote sensing images, demonstrate that DUSA-UNet outperforms state-of-the-art techniques. Our approach represents a significant advancement in the field of road network extraction, providing a computationally feasible solution that achieves high-quality segmentation results. © 1980-2012 IEEE.

关键词： NP-hard

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Resonant beam communications with echo interference elimination

arXiv

引用

arXiv 2021年

作者： Xiong, Mingliang Liu, Qingwen Wang, Gang Giannakis, Georgios B. Zhang, Sihai Zhu, Jinkang Huang, Chuan College of Electronics and Information Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Key Lab of Wireless-Optical Communications University of Science and Technology of China Hefei Anhui230026 China School of Science and Engineering Chinese University of Hong Kong Shenzhen Guangdong518172 Hong Kong

Resonant beam communications (RBCom) is capable of providing wide bandwidth when using light as the carrier. Besides, the RBCom system possesses the characteristics of mobility, high signal-to-noise ratio (SNR), and multiplexing. Nevertheless, the channel of the RBCom system is distinct from other light communication technologies due to the echo interference issue. In this paper, we reveal the mechanism of the echo interference and propose the method to eliminate the interference. Moreover, we present an exemplary design based on frequency shifting and optical filtering, along with its mathematic model and performance analysis. The numerical evaluation shows that the channel capacity is greater than 15 bit/s/Hz. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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The Influence of Knowledge Management Capabilities on Hospital Services Performance 3

The Influence of Knowledge Management Capabilities on Hospit...

引用

3rd International Conference on information Management and Technology, ICIMTech 2018

作者： Johan Kurniawan, Yohannes Sudiana Cabezas Tapia, Diego Fernando Information Systems Department Systems Bina Nusantara University School of Information Jakarta11480 Indonesia Peter the Great St. Petersburg Polytechnic University Interdepartmental Center of Embedded Systems of Automation and Computer Sciences Russia

ISBN: (纸本)9781538658215

Currently healthcare is one of the main problems in Indonesia, particularly with the primary care provided by hospitals. Problems that often occur at hospitals in Indonesia was shown from the high malpractice rate in Indonesia, this problem made public confidence to the hospital was decline, this is indicated by the increasing number of Indonesian citizens who choose to seek the treatment at the hospital in neighboring countries. The suspected problems related to the knowledge management capability of the hospital. This study aims to reveal the role of knowledge management capabilities on the performance of hospital services and provide a prototype system that can improve the knowledge management capabilities of the hospital. The sample for this study are hospitals located in Jakarta. Where the respondent is the head of the hospital. The research method that will be used in this research is qualitative and quantitative method. This research generated a framework strategy to improve knowledge management capability at hospital by looking at factors influencing knowledge management capability of hospital in Indonesia, especially Jakarta. © 2018 IEEE.

关键词： Hospitals

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