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arXiv 2024年

作者： Chen, Jiankang Zhang, Tong Zheng, Wei-Shi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China

Out-of-distribution (OOD) detection is crucial in many real-world applications. However, intelligent models are often trained solely on in-distribution (ID) data, leading to overconfidence when misclassifying OOD data as ID classes. In this study, we propose a new learning framework which leverage simple Jigsaw-based fake OOD data and rich semantic embeddings (‘anchors’) from the ChatGPT description of ID knowledge to help guide the training of the image encoder. The learning framework can be flexibly combined with existing post-hoc approaches to OOD detection, and extensive empirical evaluations on multiple OOD detection benchmarks demonstrate that rich textual representation of ID knowledge and fake OOD knowledge can well help train a visual encoder for OOD detection. With the learning framework, new state-of-the-art performance was achieved on all the benchmarks. The code is available at https://***/Cverchen/TagFog. Copyright © 2024, The Authors. All rights reserved.

关键词： Jigs

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Adaptive Datum Points Location Optimization Method for Flexible Workpieces based on Centroid Characteristics

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Procedia CIRP 2024年 129卷 61-66页

作者： Jia Luo Yanlong Cao Fan Liu Junnan Zhi Jiangxin Yang Tukun Li Hongsheng Fu Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province College of Mechanical Engineering Zhejiang University Hangzhou 310027 China State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou 310027 China School of Computing and Engineering University of Huddersfield Huddersfield HD1 3DH U.K.

This paper proposes a method for adapting the layout of flexible workpieces of various shapes, in order to reduce the difficulty of location design for personalized shapes. Different from the previous location design that relied on the experiences of engineers, this method establishes an optimization model that determines the number of datum points and the optimal solution based on the centroid characteristics of the workpieces. The optimization index of this model is to control the minimum deformation of the overall flexible workpieces and the minimum variance of the deformation at the maximum deformation point. Taking the characteristics of the workpieces as a priori constraints for location determination, this model obtains the optimal solution combined with finite element analysis. Finally, the effectiveness of the method is verified through a numerical example.

关键词： adaptive location optimization flexible workpieces finite element analysis

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TexCIL: Text-Guided Continual Learning of Disease with Vision-Language Model

TexCIL: Text-Guided Continual Learning of Disease with Visio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wentao Zhang Defeng Zhao Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Current intelligent diagnostic systems often catastrophically forget old knowledge when learning new diseases only from the training dataset of the new diseases. Inspired by human learning of visual classes with the effective help of language, we propose a continual learning framework based on a pre-trained visual-language model (VLM) without storing any image of previously learned diseases. In this framework, textual prior knowledge of each new disease can be obtained by utilizing the frozen VLM’s text encoder, and then used to guide the visual learning of the new disease. This framework innovatively utilizes the textual prior knowledge of all previously learned diseases as out-of-distribution (OOD) information to help differentiate currently being-learned diseases from others. Extensive empirical evaluations on both medical and natural image datasets confirm the superiority of the proposed method over existing state-of-the-art methods in continual learning of new visual classes. The source code is available at https://***/OpenMedIA/TexCIL.

关键词： Continuing education Training Visualization Source coding Biological system modeling Benchmark testing Bioinformatics Medical diagnostic imaging Diseases

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A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction 42

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

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42nd Chinese Control Conference, CCC 2023

作者： Yan, Weidong Xu, Zhaoliang Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Advanced Institute of Information Technology Peking University Hangzhou310000 China

ISBN: (纸本)9789887581543

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Brain computer interface

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Malicious Traffic Detection Model Refactor Method Based on Adaptive Sample Generation

Malicious Traffic Detection Model Refactor Method Based on A...

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IEEE advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Liang Jiao Jinlong Fei Min Zhao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Tianjin University of Science and Technology School of Electronic Information and automation Tianjin China

ISBN: (纸本)9781665479691

As machine learning models are widely used in malicious traffic detection, model security has become an important research topic. Aiming at the problems that the model refactor attack methods need a large amount of prior knowledge of the target model, rely on a large amount of real data, and have a narrow scope of application, a model refactor method based on adaptive sample generation was proposed. By means of an adaptive sample generation algorithm based on graph retrieval, a small number of real samples are used to quickly generate generated samples uniformly distributed in the input domain. The boundary of the refactored model is corrected according to the decision of the target model, the refactored model is iteratively trained by combining the network traffic feature selection method and the model hyperparameter optimization method, and the model refactor attack is finally realized. The refactor attack experiment against five malicious traffic detection machine learning models shows that the refactor rate and accuracy of the refactored model increase gradually with the increase of iteration rounds, and the refactor rate is more than 90%, using only the target model output label.

关键词： Training Adaptation models Fuses Optimization methods Machine learning Telecommunication traffic Feature extraction

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Deconfound Semantic Shift and Incompleteness in Incremental Few-shot Semantic Segmentation 39

Deconfound Semantic Shift and Incompleteness in Incremental ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wu, Yirui Xia, Yuhang Li, Hao Yuan, Lixin Chen, Junyang Liu, Jun Lu, Tong Wan, Shaohua Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing and Communication Lancaster University Lancaster United Kingdom National Key Lab for Novel Software Technology Nanjing University Nanjing China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)157735897X

Incremental few-shot semantic segmentation (IFSS) expands segmentation capacity of the trained model to segment new-class images with few samples. However, semantic meanings may shift from background to object class or vice versa during incremental learning. Moreover, new-class samples often lack representative attribute features when the new class greatly differs from the pre-learned old class. In this paper, we propose a causal framework to discuss the cause of semantic shift and incompleteness in IFSS, and we deconfound the revealed causal effects from two aspects. First, we propose a Causal Intervention Module (CIM) to resist semantic shift. CIM progressively and adaptively updates prototypes of old class, and removes the confounder in an intervention manner. Second, a Prototype Refinement Module (PRM) is proposed to complete the missing semantics. In PRM, knowledge gained from the episode learning scheme assists in fusing features of new-class and old-class prototypes. Experiments on both PASCAL-VOC 2012 and ADE20k benchmarks demonstrate the outstanding performance of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Practical software diversification tool chain for dissimilar redundancy architecture 9

Practical software diversification tool chain for dissimilar...

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Zhang, Gaofei Zhang, Zheng Wang, Jiang Ji, Xinsheng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Information Engineering University Zhengzhou China

With the rapid development of the information society, there are corresponding tool solutions for various daily problems. Aiming at the complex generation process and inconvenient operation of heterogeneous components in a security defense system constructed with dissimilar redundancy, in this paper, we propose a practical basic software application isomerization tool chain, which integrates a series of key technologies of heterogeneous structures. At the same time, corresponding automated tests are provided for users to conduct comprehensive evaluation. Experimental evaluation shows that the tool chain will be able to provide heterogeneous component support for security defense technologies based on dissimilarity redundancy construction and improve the security of software systems. © 2020 IEEE.

关键词： Application programs

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A Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition 42

A Multi-Head Convolution Network with Attention Consistency ...

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42nd Chinese Control Conference, CCC 2023

作者： Liu, Wenkang Sun, Mingyi Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Peking University Advanced Institute of Information Technology Hangzhou310000 China

ISBN: (纸本)9789887581543

In recent years, the demand for facial expression recognition applications has increased rapidly, and its research has received extensive attention from researchers. However, the current recognition methods based on deep learning ignore the idea of multiple head attention and semantic consistency, resulting in the model only paying attention to the local area of the feature map. In addition, the model's inconsistent attention to the images before and after the flip, resulting the poor robustness, poor interpretation, and other shortcomings in the model. To address the above problems, we propose an Affinity Separation Loss (ASLoss), which improves the separability of samples through clustering. Moreover, a Separate Multi-head Attention block (SMA), and a Zonal Loss (ZLoss) are also designed to decentralize the model's attention. Experimental results demonstrate that our proposed MACNet method achieves competitive recognition performance on two public datasets RAF-DB and FERPlus. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Face recognition

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MXenes@Te as a composite material for high-performance aluminum batteries

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Science China Materials 2022年第1期65卷 85-94页

作者： Xiaogeng Huo Xiaoxu Wang Jianling Li Bao Zhang Yu Zhang Te Qin Feiyu Kang State Key Laboratory of Advanced Metallurgy School of Metallurgical and Ecological EngineeringUniversity of Science and Technology BeijingBeijing 100083China Beijing computing center Beijing Academy of Science and TechnologyBeijing 100094China Laboratory of Advanced Materials School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China

The emerging two-dimensional(2D)materials,MXenes,play an important role in various fields of energy storage and exhibit excellent electrochemical ***,we prepared few-layered MXenes(F-Ti_(3)C_(2)T_(x))and loaded Te on the surface of F-Ti_(3)C_(2)T_(x) by using a simple hightemperature evaporation *** addition,the electrochemical performance of the aluminum battery with F-Ti_(3)C_(2)T_(x) as support material was *** initial charge/discharge specific capacities are 987/1096mAh g^(-1)at 0.2Ag^(-1).An obvious discharge voltage plateau of about 1.3V appears at various current *** specific capacity is about 258mAh g^(-1)with MXenes@Te as the active material in the aluminum battery,which benefits from the excellent electronic conductivity of the MXenes and their 2D layered *** functional theory calculations were carried out to explore the ***_(3)C_(2)O_(2)@Te is more inclined to adsorb[AlCl_(4)]^(-) than Ti_(3)C_(2)O_(2).Furthermore,the valence change behavior of element Te was studied by using thermodynamic calculation(FactSage 7.1).X-ray photoelectron spectroscopy results show that when the battery is fully charged to 2.4V element Te and Ti ions(Ti^(3+),Ti^(2+))are oxidized to Te^(4+)and Ti^(4+).In contrast to the charging process,the high-valence Te^(4+)and Ti^(4+)are reduced again during *** Te is reduced to lower-valence Te^(2-)when the discharge voltage is lower than 0.6 V,and a higher charge voltage(2.56 V)is required for Te to be oxidized to Te^(6+).

关键词： MXenes@Te aluminum batteries specific capacity density functional theory

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A riemannian exponential augmented lagrangian method for computing the projection robust Wasserstein distance 23

A riemannian exponential augmented lagrangian method for com...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Bo Jiang Ya-Feng Liu Ministry of Education Key Laboratory of NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

Projection robust Wasserstein (PRW) distance is recently proposed to efficiently mitigate the curse of dimensionality in the classical Wasserstein distance. In this paper, by equivalently reformulating the computation of the PRW distance as an optimization problem over the Cartesian product of the Stiefel manifold and the Euclidean space with additional nonlinear inequality constraints, we propose a Riemannian exponential augmented Lagrangian method (REALM) for solving this problem. Compared with the existing Riemannian exponential penalty-based approaches, REALM can potentially avoid too small penalty parameters and exhibit more stable numerical performance. To solve the subproblems in REALM efficiently, we design an inexact Riemannian Barzilai-Borwein method with Sinkhorn iteration (iRBBS), which selects the stepsizes adaptively rather than tuning the step-sizes in efforts as done in the existing methods. We show that iRBBS can return an ε-stationary point of the original PRW distance problem within O(ε-3) iterations, which matches the best known iteration complexity result. Extensive numerical results demonstrate that our proposed methods outperform the state-of-the-art solvers for computing the PRW distance.

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