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A Norm Regularization Training Strategy for Robust Image Quality Assessment Models

International Journal of computer Vision 2025年 1-15页

作者： Liu, Yujia Yang, Chenxi Li, Dingquan Jiang, Tingting Huang, Tiejun School of Computer Science Peking University Beijing China National Key Laboratory for Multimedia Information Processing Peking University Beijing China National Engineering Research Center of Visual Technology Peking University Beijing China School of Mathematical Sciences Peking University Beijing China Department of Networked Intelligence Pengcheng Laboratory Shenzhen China

Image Quality Assessment (IQA) models predict the quality score of input images. They can be categorized into Full-Reference (FR-) and No-Reference (NR-) IQA models based on the availability of reference images. These models are essential for performance evaluation and optimization guidance in the media industry. However, researchers have observed that introducing imperceptible perturbations to input images can notably influence the predicted scores of both FR- and NR-IQA models, resulting in inaccurate assessments of image quality. This phenomenon is known as adversarial attacks. In this paper, we initially define attacks targeted at both FR-IQA and NR-IQA models. Subsequently, we introduce a defense approach applicable to both types of models, aimed at enhancing the stability of predicted scores and boosting the adversarial robustness of IQA models. To be specific, we present theoretical evidence showing that the magnitude of score changes is related to the $$\ell _1$$ norm of the model’s gradient with respect to the input image. Building upon this theoretical foundation, we propose a norm regularization training strategy aimed at reducing the $$\ell _1$$ norm of the gradient, thereby boosting the robustness of IQA models. Experiments conducted on three FR-IQA and four NR-IQA models demonstrate the effectiveness of our strategy in reducing score changes in the presence of adversarial attacks. To the best of our knowledge, this work marks the first attempt to defend against adversarial attacks on both FR- and NR-IQA models. Our study offers valuable insights into the adversarial robustness of IQA models and provides a foundation for future research in this area.

关键词： Damage detection

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SenseFuzz: Black-box Fuzzing of ICS Protocols via Runtime Statistics Sensing 9

SenseFuzz: Black-box Fuzzing of ICS Protocols via Runtime St...

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9th IEEE Smart World Congress, SWC 2023

作者： Li, Yin Fu, Anmin Zhang, Yuqing University of Chinese Academy of Sciences National Computer Network Intrusion Protection Center Beijing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350319804

The ICS protocols undertake critical functions in industrial control systems, by enabling real-time data exchange, data acquisition, parameter configuration, status monitoring, fault diagnosis, command issuance and execution. It serves as the foundation for the collaborative operations of all components in industrial control systems. With the development of internet technology, industrial control systems, which were originally physically isolated, have become new targets for hackers. However, due to the high level of privatization of ICS protocols and their complex workflow, existing methods are insufficient in detecting vulnerabilities in these protocols. This paper proposes a black-box fuzzing test method based on sensing the program runtime feedback. By collecting run-time statistics, including CPU utilization, memory utilization, disk I/O, and message interaction, our method is able to determine whether the program has triggered a new execution path, thus improving the efficiency of the fuzzing procedure. We evaluated our prototype on five open-source ICS protocol programs and discovered six reported vulnerabilities, among which five could cause the industrial control programs to become unresponsive or crash. © 2023 IEEE.

关键词： Fuzzing ICS Protocols

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Quorum sensing-based interactions among drugs,microbes,and diseases

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science China(Life sciences) 2023年第1期66卷 137-151页

作者： Shengbo Wu Shujuan Yang Manman Wang Nan Song Jie Feng Hao Wu Aidong Yang Chunjiang Liu Yanni Li Fei Guo Jianjun Qiao School of Chemical Engineering and Technology Tianjin UniversityTianjin 300072China School of Computer Science and Engineering Central South UniversityChangsha 410083China State Key Laboratory of Chemical Engineering Tianjin UniversityTianjin 300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) Tianjin 300072China Key Laboratory of Systems Bioengineering Ministry of Education(Tianjin University)Tianjin 300072China School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin 300350China Department of Engineering Science University of OxfordOxford OX13PJUK Institute of Shaoxing Tianjin UniversityShaoxing 312300China

Many diseases and health conditions are closely related to various microbes,which participate in complex interactions with diverse drugs;nonetheless,the detailed targets of such drugs remain to be *** existing studies have reported causal associations among drugs,gut microbes,or diseases,calling for a workflow to reveal their intricate *** this study,we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease(QSDMD)database(http://***/),which includes diverse interactions for more than 8,000 drugs,163 microbes,and 42 common *** interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental ***,we have constructed a QS-based drug-receptor interaction network,proposed a systematic framework including various drug-receptor-microbe-disease connections,and mapped a paradigmatic circular interaction network based on the QS-DMD,which can provide the underlying QS-based mechanisms for the reported causal *** QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse *** work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

关键词： quorum sensing drugs microbe-disease association microbe-host interaction gut microbiota personalized medicine

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Comprehensive asset surveillance and prediction modeling using real time data analytics 2

Comprehensive asset surveillance and prediction modeling usi...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Rawat, Aayushi Gupta, Sumita Raghuvanshi, Gauri Aggarwal, Tanya Majhi, Vinayak Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Amity Innovation and Design Center Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798331530389

The rapid evolution in the technology industry highlights the importance of developing a more advanced asset tracking system. The need for efficient and accurate asset management has become crucial and predominant across various industries. This paper displays the utilization of low-cost, and energy efficient technologies like Seeed Studio XIAO nRF52840 and Arduino Uno in designing and implementation of an asset tracking system. The design of the system primarily focuses on providing real time location of an asset, improving its tracking accuracy and operational efficiency. This study practically demonstrates the use of inexpensive microcontroller platforms along with BLE technology so as to develop an asset tracking system, the scalable solution that can be implemented across various industries such as logistics, healthcare, and manufacturing. © 2025 IEEE.

关键词： Costs

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Happy: A Debiased Learning Framework for Continual Generalized Category Discovery 38

Happy: A Debiased Learning Framework for Continual Generaliz...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ma, Shijie Zhu, Fei Zhong, Zhun Liu, Wenzhuo Zhang, Xu-Yao Liu, Cheng-Lin MAIS Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS China School of Computer Science and Information Engineering Hefei University of Technology China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aims to incrementally discover new classes from unlabeled data while maintaining the ability to recognize previously learned classes. Although several settings are proposed to study the C-GCD task, they have limitations that do not reflect real-world scenarios. We thus study a more practical C-GCD setting, which includes more new classes to be discovered over a longer period, without storing samples of past classes. In C-GCD, the model is initially trained on labeled data of known classes, followed by multiple incremental stages where the model is fed with unlabeled data containing both old and new classes. The core challenge involves two conflicting objectives: discover new classes and prevent forgetting old ones. We delve into the conflicts and identify that models are susceptible to prediction bias and hardness bias. To address these issues, we introduce a debiased learning framework, namely Happy, characterized by Hardness-aware prototype sampling and soft entropy regularization. For the prediction bias, we first introduce clustering-guided initialization to provide robust features. In addition, we propose soft entropy regularization to assign appropriate probabilities to new classes, which can significantly enhance the clustering performance of new classes. For the harness bias, we present the hardness-aware prototype sampling, which can effectively reduce the forgetting issue for previously seen classes, especially for difficult classes. Experimental results demonstrate our method proficiently manages the conflicts of C-GCD and achieves remarkable performance across various datasets, e.g., 7.5% overall gains on ImageNet-100. Our code is publicly available at https://***/mashijie1028/Happy-CGCD. © 2024 Neural information processing systems foundation. All rights reserved.

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover 40

Efficient Relaxed Functional Dependency Discovery with Minim...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ding, Xiaoou Liu, Yida Wang, Hongzhi Wang, Chen Song, Yichen Yang, Donghua Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China National Engineering Research Center for Big Data Software EIRI Tsinghua University China

ISBN: (纸本)9798350317152

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks. © 2024 IEEE.

关键词： Quality management

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A Real-Time Semantic Segmentation Network for Robotic Arm Grasp 1

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41st computer Graphics International Conference, CGI 2024

作者： Liu, Li Zhou, Xinlei He, Mingwei Yu, Feng Peng, Tao Hu, Xinrong Jiang, Minghua School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (数字)9783031820243

ISBN: (纸本)9783031820236

A robotic arm equipped with a vision system can significantly improve production efficiency. However, due to the complexity of the robotic arm’s working environment, the useful information about the objects recognized by the visual system is significantly reduced. To solve this problem, we propose a real-time semantic segmentation network to achieve high-precision and efficient robotic arm grasp. First, we propose an enhanced feature extraction module for the encoding stage, which can improve feature extraction capabilities without increasing or even reducing the amount of model calculations and parameters. Then, we propose an enhanced feature reconstruction module for the decoding stage, which can better preserve essential features in the feature map restoration stage. To validate the efficacy of our proposed approach, experiments are conducted on the rigid dataset Jacquard and the flexible dataset Electric Wires. Experimental results show that our method can quickly and accurately identify target objects, achieving the best trade-off between accuracy and inference speed. Our method achieves an mIOU of 94.66% and an inference speed of 99.39 FPS on the Jacquard dataset. On the Electric Wires dataset, our method achieves an mIOU of 96.70% with an inference speed of 98.44 FPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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TSDDISCOVER: Discovering Data Dependency for Time Series Data 40

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Yingze Wang, Hongzhi Wang, Chen Liu, Yida Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Research Center for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods. © 2024 IEEE.

关键词： Semantics

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Externally triggered drug delivery systems

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Smart Materials in Medicine 2024年第3期5卷 386-408页

作者： Hu, Huiyang Busa, Prabhakar Zhao, Yue Zhao, Chao Department of Chemical and Biological Engineering University of Alabama TuscaloosaAL35487 United States School of Computer Science College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou China Center for Convergent Biosciences and Medicine University of Alabama TuscaloosaAL35487 United States

Externally triggered drug delivery systems empower patients or healthcare providers to utilize external stimuli to initiate drug release from implanted systems. This approach holds significant potential for clinical disease management, offering appealing features like enhanced patient adherence through the elimination of needles and medication reminders. Additionally, it facilitates personalized medicine by granting patients control over the timing, dosage, and duration of drug release. Moreover, it enables precise drug delivery to targeted locations where external stimuli are applied. Advances in materials science, nanotechnology, chemistry, and biology have been pivotal in driving the development of these systems. This review presents an overview of the progress in research on drug release systems responsive to external stimuli, such as light, ultrasound, magnetic fields, and temperature. It discusses the construction strategies of externally triggered drug delivery systems, the mechanisms governing triggered drug release, and their applications in disease management. © 2024 The Authors

关键词： Nanoclay

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Highlight mask-guided adaptive residual network for single image highlight detection and removal

Highlight mask-guided adaptive residual network for single i...

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作者： Wang, Shuaibin Li, Li Wang, Juan Peng, Tao Li, Zhenwei School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China Engineering Research Center of Hubei Province for Clothing Information Wuhan China

Specular highlights detection and removal is a challenging task. Although various methods exist for removing specular highlights, they often fail to effectively preserve the color and texture details of objects after highlight removal due to the high brightness and nonuniform distribution characteristics of highlights. Furthermore, when processing scenes with complex highlight properties, existing methods frequently encounter performance bottlenecks, which restrict their applicability. Therefore, we introduce a highlight mask-guided adaptive residual network (HMGARN). HMGARN comprises three main components: detection-net, adaptive-removal network (AR-Net), and reconstruct-net. Specifically, detection-net can accurately predict highlight mask from a single RGB image. The predicted highlight mask is then inputted into the AR-Net, which adaptively guides the model to remove specular highlights and estimate an image without specular highlights. Subsequently, reconstruct-net is used to progressively refine this result, remove any residual specular highlights, and construct the final high-quality image without specular highlights. We evaluated our method on the public dataset (SHIQ) and confirmed its superiority through comparative experimental results. © 2024 John Wiley & Sons Ltd.

关键词： Textures

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