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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Du, Lu Liu, Weidong Xiao, Kaiyang Gao, Shixuan Han, Yong College of Computer Science Inner Mongolia University Hohhot China Institute of Scientific and Technical Information of China Beijing China Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China Inner Mongolia Intellectual Property Service Center Hohhot China

ISBN: (纸本)9781665405270

As the number of patent applications increases yearly, the negation relation between patents has become intertwined, which makes it difficult for constructing negation relation in patent examination manually. Therefore, how to automatically construct negation relation between patents has become the hot research. Constructing negation relation is the process of comparing technical features between patents, which depends on the technical features in the patent claims and the technical function-effect. Although there are many researches on patent one-to-one negation, few researches on patent multi-to-one negation. The research on negation relation of multi-to-one patents, has the following challenges: (1) the definition of patent features is ambiguous in comparison of negation relation;(2) the role of patent function-effect is often ignored in judgement of negation relation. To solve the above issues, we propose a technical function-effect based patent multi-to-one negation game model. In the model, we decompose patent technical features into technical utility and technical function-effect. Patent technical utility is mainly realized by using the valence theory in linguistics. After obtaining technical utility, we choose to have the same technical effect, express the technical features of different technical functions, and obtain the patent technical function-effect. Finally, we combine the technical utility and function-effect to construct a technical utility/function-effect game tree to judge the patent negation relation. According to the two-tailed test in the A/B-test, our model outperforms the baseline model in the evaluation measurement. This model can subsequently be used to calculate the portfolio value and the strategic value of the patents. © 2022 IEEE.

关键词： Patents and inventions

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A C0-continuous nonconforming virtual element method for linear strain gradient elasticity

arXiv

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arXiv 2024年

作者： Huang, Jianguo Yu, Yue School of Mathematical Sciences MOE-LSC Shanghai Jiao Tong University Shanghai200240 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

A robust C0-continuous nonconforming virtual element method (VEM) is developed for a boundary value problem arising from strain gradient elasticity in two dimensions, with the family of polygonal meshes satisfying a very general geometric assumption given in Brezzi et al. (2009) and Chen and Huang (2018). The stability condition of the VEMs is derived by establishing Korn-type inequalities and inverse inequalities. Some crucial commutative relations for locking-free analysis as in elastic problems are derived. The sharp and uniform error estimates with respect to both the microscopic parameter and the Lamé coefficient are achieved in the lowest-order case, which is also verified by numerical *** Codes 65N12, 65N15, 65N22, 65N30 © 2024, CC BY-NC-ND.

关键词： Boundary value problems

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PACE: Reputation-Driven Hierarchical Incentive Mechanism for Trusted Healthcare Transactions in Model Marketplace

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IEEE Transactions on Consumer Electronics 2024年

作者： Lu, Jianfeng Dou, Xiaochi Rathore, Bharati Jhaveri, Rutvij H. Seid, Abegaz Mohammed Erbad, Aiman Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China United Kingdom Pandit Deendayal Energy University Department of Computer Science and Engineering School of Technology India Hamad Bin Khalifa University College of Science and Engineering Doha Qatar Qatar University College of Engineering Doha Qatar

Federated learning-based model marketplaces have the potential to securely leverage healthcare data for efficient healthcare transactions. However, the willingness to participate in this marketplace is severely hindered by self-interested model sellers, who maximize their personal gains via distorting the quality of their models. To this end, we propose a rePutation-driven hierArchical inCentive mEchanism, called PACE, which combines contract cooperation and game pricing to motivate model sellers to be honest, thereby improving the sustainability of the model marketplace via two-layer cooperation. Specifically, at the training layer, we design a reward payment rule based on multidimensional contracts, while at the trading layer, we use a competitive market pricing game to develop the accuracy pricing rule. In particular, the above rules cleverly combine the fine-grained model sellers' reputations with the willingness of healthcare devices to indirectly affect the model sellers' utilities. Theoretically, we prove that PACE can effectively incentivize model sellers to truthfully report model quality while satisfying both individual rationality and incentive compatibility with a fairness guarantee. Experimental results conducted on four real-world datasets show that our PACE improves the global performance by up to 12.85% and reduces the global loss by at least 19.23% compared with the state-of-the-art baselines. © 1975-2011 IEEE.

关键词： Marketplaces

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Lesion-Aware Open Set Medical Image Recognition with Domain Shift

Lesion-Aware Open Set Medical Image Recognition with Domain ...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yiqian Xu Rui-Wei Zhao Rui Feng School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China National Children’s Medical Center Children’s Hospital of Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Shanghai China

Medical image diagnosis in real clinical scenario faces the challenging open set recognition problem with domain shift. Based on the observation that it is often merely the local lesion areas in the whole image decide or cause the true seen or potential unseen disease labels, we propose a unified deep learning framework to address it by establishing a lesion-aware mask generator to guide the model towards focusing on the lesion areas for both seen and unseen diseases, followed by a multi-task feature learning module to extract the lesion image features; and an open set recognizer with improved capability to reject unseen classes. Last but not least, we propose tailored training strategies with mixed up data and mask augmentation for improved OSR and multi-clue cross-domain feature alignment for domain generalization. Through comparative experiments and ablation studies on the representative skin lesion benchmarks, we successfully validate the effectiveness of our proposed framework.

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A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation

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intelligent Automation & Soft computing 2024年第2期39卷 189-211页

作者： Fangzhen Ge Yating Wu Debao Chen Longfeng Shen School of Computer Science and Technology Huaibei Normal UniversityHuaibei340604China School of Physic and Electronic Information Huaibei Normal UniversityHuaibei340604China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior(ICACB) Huaibei Normal UniversityHuaibei340604China Anhui Province Key Laboratory of Intelligent Computing and Applications Huaibei Normal UniversityHuaibei340604China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei230000China

It is still a huge challenge for traditional Pareto-dominatedmany-objective optimization algorithms to solve manyobjective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front,resulting in poor performance of those *** this reason,we propose a reference vector-assisted algorithmwith an adaptive niche dominance relation,for short *** new dominance relation forms a niche based on the angle between candidate *** comparing these solutions,the solutionwith the best convergence is found to be the non-dominated solution to improve the selection *** reproduction,a mutation strategy of k-bit crossover and hybrid mutation is used to generate high-quality *** 23 test problems with up to 15-objective,we compared the proposed algorithm with five state-of-the-art *** experimental results verified that the proposed algorithm is competitive.

关键词： Many-objective optimization evolutionary algorithm Pareto dominance reference vector adaptive niche

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Hand Hygiene Assessment via Joint Step Segmentation and key Action Scorer

arXiv

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arXiv 2022年

作者： Li, Chenglong Zhu, Qiwen Liu, Tubiao Tang, Jin Su, Yu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230061 China

Hand hygiene is a standard six-step hand-washing action proposed by the World Health Organization (WHO). However, there is no good way to supervise medical staff to do hand hygiene, which brings the potential risk of disease spread. Existing action assessment works usually make an overall quality prediction on an entire video. However, the internal structures of hand hygiene action are important in hand hygiene assessment. Therefore, we propose a novel fine-grained learning framework to perform step segmentation and key action scorer in a joint manner for accurate hand hygiene assessment. Existing temporal segmentation methods usually employ multi-stage convolutional network to improve the segmentation robustness, but easily lead to over-segmentation due to the lack of the long-range dependence. To address this issue, we design a multi-stage convolution-transformer network for step segmentation. Based on the observation that each hand-washing step involves several key actions which determine the hand-washing quality, we design a set of key action scorers to evaluate the quality of key actions in each step. In addition, there lacks a unified dataset in hand hygiene assessment. Therefore, under the supervision of medical staff, we contribute a video dataset that contains 300 video sequences with fine-grained annotations. Extensive experiments on the dataset suggest that our method well assesses hand hygiene videos and achieves outstanding performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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ESNet: evolution and succession network for high-resolution salient object detection 24

ESNet: evolution and succession network for high-resolution ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongyu Liu Runmin Cong Hua Li Qianqian Xu Qingming Huang Wei Zhang Institute of Information Science Beijing Jiaotong University & Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University & Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature; the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24.

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The Chebyshev Collocation Least Squares Approximation Method for the Audio Data 2021

The Chebyshev Collocation Least Squares Approximation Method...

引用

2nd International Conference on computing and Data Science, CONF-CDS 2021

作者： Zhang, Qian Qin, Yonghui Chen, Ziqiang Wen, Nengxiang Guilin University of Electronic Technology Guilin541004 China College of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Cognitive Radio and Information Processing Key Laboratory of Ministry of Education Guilin University of Electronic Technology Guilin541004 China

The Chebyshev collocation least squares configuration fitting method is investigated for the fitting of audio data. This method couples the least squares method with the Chebyshev-Gauss points. Numerical examples are ... 详细信息

ISBN: (纸本)9781450389570

关键词： Least squares approximations

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Facial StO2: A New Promising Biometric Identity

Facial StO2: A New Promising Biometric Identity

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Dairong Peng Sirui Sun Xinyu Liu Ju Zhou Tong Chen Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing Chongqing China School of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In this paper, we introduce a new biometric identity, facial tissue oxygen saturation (StO2). StO2 is an index of blood oxygen content in tissues and is related to blood vessel distribution pattern and metabolic rate. Experimental results show that classification accuracy can reach 83.33% in 42 participants with different stress states by using StO2 as the only input to the ResNet-50 model. We also proposed a module called StO2Net to eliminate the effects of stress on classification. The highest accuracy can reach up to 90.48% when the module is used. This pilot study shows that facial StO2 can be a promising biometric feature for identity recognition.

关键词： Databases Face recognition Biological system modeling Aging Security Bioinformatics Stress

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A Passive Mixed Loaded Over-Hundred-Octave Miniaturized Super-Wideband Antenna

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Xia, Yinfeng Li, Yingsong Xue, Wei Elsherbeni, Atef Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei230601 China Colorado School of Mines GoldenCO United States

An over-hundred-octave miniaturized super- wideband antenna based on passive elements mixed loading is proposed and analyzed in this paper. The proposed antenna is composed of inversed E-radiation patch, feeding line, several passive mixed loaded elements. The entire size of the proposed antenna is 0.012λ×0.01λ×0.00005λ (where λ is the lowest frequency of realized bandwidth). Derived from the analysis of impedance and equivalent circuit, by properly adjusting the inversed E-radiation patch and loading 5 passive elements into the construction of the antenna in position, the bandwidth is greatly improved. The simulated and measured results show that a -10dB fractional bandwidth of 196.2% is realized. Moreover, good omni-directional radiation patterns within the operating frequency band have been achieved from 10MHz to 1051MHz for HF, VHF and UHF applications. © 2002-2011 IEEE.

关键词： Antenna feeders

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