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24th COTA International Conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Zhang, Yue Dong, Chunjiao Li, Penghui Dong, Qianru Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong Univ. Beijing China State Key Laboratory of Intelligent Vehicle Safety Technology China Automotive Engineering Research Institute Co. Ltd. Chongqing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

The intermodal travel characteristics and combination selection behaviors of passengers are key issues to providing reasonable, efficient, and safe solutions, ensuring the traffic order, and improving quality. With the Beijing Winter Olympics as a background, by combining the Stated Preference and Revealed Preference surveys, the research develops a decision-making behavior model based on nested logit to investigate the passenger travel choices. The impacts of individual attributes, epidemic prevention policies, and preferential measures on the choice of intermodal travel, as well as the combination of departure time and travel mode, are studied. The results show that cultural and creative products have a significant impact on the combined travel choices. The cultural and creative products and consumer vouchers have a significant impact on the choice of intermodal travel. The research results can provide insights into traffic organization and management policies during large-scale events. © ASCE.

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CLOE: Novelty Detection via Contrastive Learning with Outlier Exposure 1

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16th International Conference on intelligent Robotics and Applications, ICIRA 2023

作者： Liu, Tianyang Liang, Quan Yang, Hua Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

Novelty detection (ND) methods seek to identify anomalies within a specific dataset. Although self-supervised representation learning is commonly used in such applications, inadequate training data may reduce the effectiveness of these methods. It is thus reasonable to use external data to improve these performances. Here, we propose a simple and effective network, CLOE, for image-based novelty detection. Our method includes a pretrained ViT model as a feature extractor and employs the contrastive learning technique to train the dataset with external data. We compare the performance of two types of extra training settings: (1) The augmented data of the original dataset. (2) The fake images obtained from generative models. The demonstrated approach achieves a new state-of-the-art performance in novelty detection, as evidenced by achieving an ROC-AUC of 99.72% on the CIFAR-10 dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Statistics

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KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Lin, Shiying Hu, Rong Li, Zuoyong Lin, Qinghua Zeng, Kun Wu, Xiang Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Fuzhou University Affiliated Provincial Hospital Provincial Clinical Medical College Fujian Medical University Department of Urology Fuzhou350001 China

In the field of deep learning-based medical image segmentation, convolutional neural networks (CNNs) extract image features by combining linear convolutional layers with nonlinear activation functions. However, excessive stacking of linear layers in the network limits the model’s ability to capture fine-grained details. In addition, the feature distribution imbalance caused by the traditional fixed grouping strategy (FGS) can affect the deep model’s capacity to perceive the overall structure of the image. To address these challenges, we propose a medical image segmentation framework, called Kolmogorov-Arnold Network with the adaptive group strategy and contextual Transformer based on Unet (KAC-Unet). First, we propose the adaptive group strategy (AGS) to balance the grouping of different input channels, alleviating the performance degradation caused by differences in group information. Then, we propose the Shift Tokenized Kolmogorov-Arnold Network (KAN) Block to capture complex features in medical images through flexible nonlinear transformations and shift operations. Extensive experiments are conducted on three medical image segmentation datasets. The results demonstrate the effectiveness and superiority of our proposed method compared with state-of-the-art algorithms. © 1963-2012 IEEE.

关键词： Convolutional neural networks

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big-Moe: Bypassing Isolated Gating For Generalized Multimodal Face Anti-Spoofing

Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...

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

作者： Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators; (2) Gated networks’ susceptibility to input noise affecting decision-making; (3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (big-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues; (2) An isolation gating mechanism to counteract input noise; (3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/big-MoE.

关键词： Convolutional codes Sensitivity Convolution Face recognition Noise Logic gates Feature extraction Security Speech processing Overfitting

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Research on WNN Greenhouse Temperature Prediction Method Based on GA

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Phyton-International Journal of Experimental Botany 2022年第10期91卷 2283-2296页

作者： Wenbin Dai Lina Wang Binrui Wang Xiaohong Cui Xue Li College of Mechanical and Electronic Engineering China Jiliang UniversityHangzhou310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou310018China

Temperature in agricultural production has a direct impact on the growth of *** emergence of greenhouses has improved the impact of the original unpredictable changes in temperature,but the temperature modeling of greenhouses is still the main direction at *** network modeling relies on sufficient actual data to model greenhouses,but there is a widening gap in the application of different neural *** paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm(GA-WNN).With the simple network structure and the nonlinear adaptability of the wavelet basis function,wavelet neural network(WNN)improved model training speed and accuracy of prediction results compared with back propagation neural networks(BPNN),which was conducive to the prediction and control of short-term greenhouse temperature *** the same time,the genetic algorithm(GA)was introduced to globally optimize the initial weights of the original model,which improved the insensitivity of the model to the initial weights and thresholds,and improved the training speed and stability of the ***,simulation results for the greenhouse showed that the model training speed,prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.

关键词： Greenhouse temperature greenhouse modeling wavelet neural network genetic algorithm

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EFIE: Efficient and Fine-Grained Image Encryption Algorithm With Permutation-Substitution Synchronization

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IEEE Transactions on Consumer Electronics 2025年

作者： Xie, Dong Zhang, Guiting Yang, Menglin Chen, Fulong Wang, Taochun Hu, Peng Anhui Normal University Anhui Provincial Key Laboratory of Industrial Intelligence Data Security Anhui Engineering Research Centers of Medical Big Data Intelligent System School of Computer and Information Wuhu241002 China

Currently, both pixel-level and bit-level image encryption algorithms demonstrate excellent visual performance. However, most high-security algorithms usually involve complex encryption steps, which increase the time complexity. In addition, some algorithms are challenging to adjust flexibly according to specific scenarios, making it difficult to achieve a balance between security and efficiency. In this paper, we propose an efficient and fine-grained image encryption algorithm, named EFIE. The main step of EFIE is the proposed permutation-substitution synchronization procedure, which is much more efficient compared to existing image encryption solutions. To our knowledge, we first introduce the concept of fine-grained encryption, which allows the encryptor to flexibly set the encryption unit (e.g., 1-bit, 2-bit, or 1-pixel). The smaller the length of the encryption unit, the longer the encryption time and the higher the level of security. The experimental results and various security analyses demonstrate that EFIE provides excellent encryption performance. When the encryption unit is 1 bit, encrypting a 512 × 512 color image requires only about 2.34 seconds. © 1975-2011 IEEE.

关键词： Encryption algorithms

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Targeted Pareto Optimization for Subset Selection With Monotone Objective Function and Cardinality Constraint

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Shang, Ke Wu, Guotong Pang, Lie Meng Ishibuchi, Hisao National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Subset selection, a fundamental problem in various domains, is to choose a subset of elements from a large candidate set under a given objective or multiple objectives. Pareto optimization for subset selection (POSS) has emerged as a powerful paradigm for addressing subset selection problems. Recently, some POSS variants have been proposed to further improve its performance. In this paper, we propose a new POSS variant, named Targeted Pareto Optimization for Subset Selection (TPOSS). TPOSS differs from POSS in four aspects: problem formulation, population initialization, mutation, and environmental selection. The main idea of TPOSS is to focus the search on the target region of subset selection with respect to the subset cardinality in order to improve the search efficiency. We conduct comprehensive experiments to compare TPOSS with six state-of-the-art algorithms on three subset selection tasks (i.e., sparse regression, unsupervised feature selection, and hypervolume subset selection) where the size of the candidate sets ranges from 20 to 400. Experimental results show that with respect to the objective value of the best feasible subset, TPOSS outperforms the other algorithms on all the three tasks, which suggests the potential of TPOSS to enhance subset selection in various domains. IEEE

关键词： Linear programming

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Learning with Open-world Noisy data via Class-independent Margin in Dual Representation Space 39

Learning with Open-world Noisy Data via Class-independent Ma...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Pan, Linchao Gao, Can Zhou, Jie Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (纸本)157735897X

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55% and an AUROC improvement of 6.17% on CIFAR80N. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Learning with Open-world Noisy data via Class-independent Margin in Dual Representation Space

arXiv

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arXiv 2025年

关键词： Contrastive Learning

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Three-Dimensional Collision Avoidance Method for Robot-Assisted Minimally Invasive Surgery

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Cyborg and Bionic Systems 2023年第1期4卷 121-135页

作者： Ling Li Xiaojian Li Bo Ouyang Hangjie Mo Hongliang Ren Shanlin Yang School of Management Hefei University of TechnologyHefeiChina Key Laboratory of Process Optimization and Intelligent Decision-Making(Ministry of Education) Hefei University of TechnologyHefeiChina Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance(Ministry of Education) Hefei University of TechnologyHefeiChina Department of Biomedical Engineering Faculty of EngineeringNational University of SingaporeSingaporeSingapore Department of Electronic Engineering Shun Hing Institute of Advanced EngineeringThe Chinese University of Hong KongHong KongChina National Engineering Laboratory for Big Data Distribution and Exchange Technologies ShanghaiChina

In the robot-assisted minimally invasive surgery,if a collision occurs,the robot system program could be damaged,and normal tissues could be *** avoid collisions during surgery,a 3-dimensional collision avoidance method is proposed in this *** proposed method is predicated on the design of 3 strategic vectors:the collision-with-instrument-avoidance(CI)vector,the collision-with-tissues-avoidance(CT)vector,and the constrained-control(CC)*** CI vector demarcates 3 specific directions to forestall collision among the surgical *** CT vector,on the other hand,comprises 2 components tailored to prevent inadvertent contact between the robot-controlled instrument and nontarget ***,the CC vector is introduced to guide the endpoint of the robot-controlled instrument toward the desired position,ensuring precision in its movements,in alignment with the surgical *** results verify the proposed collision avoidance method for robot-assisted minimally invasive *** code and data are available at https://***/cynerelee/collision-avoidance.

关键词： collision desired verify

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