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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Qiao, Pingan Li, Yuan Shen, Ruixue Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Aiming at the problem that existing image description models cannot model high-order multimodal feature interaction, this paper introduces the X-Linear attention mechanism, which uses bilinear pooling and ELU activation function to model high-order feature interaction between multimodal features. At the same time, the X-Linear attention mechanism uses spatial and channel attention mechanisms to enhance the expression ability of the model and the ability to generate image description sentences. The experimental results on the MSCOCO data set show that this method is effective and has a great improvement in each evaluation metric. © 2022 ACM.

关键词： Image enhancement

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An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification

arXiv

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arXiv 2024年

作者： Chen, Zhuowei Wang, Lianxi Wu, Yuben Liao, Xinfeng Tian, Yujia Zhong, Junyang School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Guangzhou Key Laboratory of Multilingual Intelligent Processing Guangzhou China

Sentiment classification (SC) often suffers from low-resource challenges such as domain-specific contexts, imbalanced label distributions, and few-shot scenarios. The potential of the diffusion language model (LM) for textual data augmentation (DA) remains unexplored, moreover, textual DA methods struggle to balance the diversity and consistency of new samples. Most DA methods either perform logical modifications or rephrase less important tokens in the original sequence with the language model. In the context of SC, strong emotional tokens could act critically on the sentiment of the whole sequence. Therefore, contrary to rephrasing less important context, we propose DiffusionCLS to leverage a diffusion LM to capture in-domain knowledge and generate pseudo samples by reconstructing strong label-related tokens. This approach ensures a balance between consistency and diversity, avoiding the introduction of noise and augmenting crucial features of datasets. DiffusionCLS also comprises a Noise-Resistant Training objective to help the model generalize. Experiments demonstrate the effectiveness of our method in various low-resource scenarios including domain-specific and domain-general problems. Ablation studies confirm the effectiveness of our framework’s modules, and visualization studies highlight optimal deployment conditions, reinforcing our conclusions. Copyright © 2024, The Authors. All rights reserved.

关键词： Modeling languages

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Corrigendum to "Hyperledger sawtooth based supplychain traceability system for counterfeit drugs" [Comput. Ind. Eng. 190 (2024) 110021] (Computers & Industrial Engineering (2024) 190, (S0360835224001426), (10.1016/***.2024.110021))

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Computers and Industrial Engineering 2025年 204卷

作者： Nawaz, Anum Wang, Liguan Irfan, Muhammad Westerlund, Tomi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Finland

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Context-Aware Grammatical Error Correction Model

Context-Aware Grammatical Error Correction Model

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Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Jianxing Lin Guimin Huang JiaHao Wang School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

The pursuit of accurate and fluent English communication is the cornerstone of global academic exchange and expression. The accuracy of written English is crucial for effective discourse; however, despite advancements in grammatical error correction (GEC) technology, these systems often fall short when analyzing coherent text. This paper delves into the complexities of English writing and the challenges posed by current GEC systems, advocating for a shift toward understanding document-level context in error correction. We propose a dual-encoder architecture within an encoder-decoder model, referred to as the Context-Aware Grammatical Error Correction (CAGEC) model. The CAGEC model employs an innovative dual-encoder structure, combining the encoder of the Transformer model with a Bi-GRU (Bidirectional Gated Recurrent Unit) neural network, and integrates encoding into the decoder through attention and gating mechanisms to achieve a deep understanding of the source sentence and its context.

关键词： Accuracy Neural networks Writing Logic gates Transformers Encoding Error correction Context modeling Synthetic data Software engineering

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3D Hand Bones and Tissue Estimation from a Single 2D X-Ray Image Via a Two-Stream Deep Neural Network

3D Hand Bones and Tissue Estimation from a Single 2D X-Ray I...

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IEEE International Symposium on Biomedical Imaging

作者： Wanlin Huang Wenhui Wu Yuanhao Gong Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China Reexen Technology Ltd.

The hand bones and soft tissue are essential for many applications such as clinical diagnosis, hand modeling and meta-verse. However, their 3D reconstruction from CT or MRI data is time consuming and requires a lot of computational resources from modern hardware. To address this issue, we propose a novel two-stream deep neural network to estimate the 3D hand bones and soft tissue from a single X-ray image. The first stream of the network is for the bone estimation, which incorporates a module component from other modalities. The second stream is for the soft tissue modeling, which utilizes a sub-network from hand pose estimation. After combining these two streams, we can successfully construct a 3D virtual hand from a single 2D X-ray image. We conduct several numerical experiments to validate the proposed two-stream network. Our method can be used for hand bone and soft tissue modeling from X-ray images.

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Event-Triggered Adaptive Finite-Time Control for Switched Cyberphysical Systems With Uncertain Deception Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 136-146页

作者： Wang, Xin Zhou, Yuhao Luo, Biao Jiang, Yuming Huang, Tingwen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Automation Central South University Changsha410083 China Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7491 Norway T&M University at Qatar Doha23874 Qatar

This article is concerned with the event-triggered adaptive finite-time control problem for a class of switched cyber-physical systems (CPSs) subject to uncertain deception attacks. We deploy the finite time command filter to alleviate the possible explosion of complexity rendered by the virtual control signal’s duplicative differentiation. To stabilize the target system with a guaranteed disturbance rejection performance index, a disturbance observer is constructed by using fuzzy logic systems (FLS) with the hope of estimating the unknown external disturbance. Furthermore, the Nussbaum gain is utilized to resist the unknown control efficiency generated by uncertain deception attacks. Based on the common Lyapunov function (CLF) method, sufficient conditions are derived to ensure that all signals in the closed-loop system are uniformly bounded at a finite time. Finally, the simulation example shows the validity and efficacy of the developed control method. © 2023 Institute of Electrical and Electronics Engineers. All rights reserved.

关键词： Closed loop systems

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Dynamic Student Classification with Forgetting Mechanisms on Memory Networks for Knowledge Tracing

Dynamic Student Classification with Forgetting Mechanisms on...

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intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Guimin Huang Xiangqian Qin Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Knowledge Tracing (KT), a technique for modeling students' knowledge levels and predicting their future question-answering performance based on their historical answer data, is one of the key research areas to strengthen the ability of personalized education. In recent years, memory networks have received more and more attention and application in the field of KT, however, the current KT model based on memory networks ignores the effect of students' learning sequence on the level of forgetting, and fails to model the forgetting behavior of students in the process of learning by using the characteristics of time intervals in the interaction data. Therefore, in this paper, we propose a dynamic student classification with forgetting mechanisms on memory networks model (DSCFMN), which enhance the existing model by dynamically classifying students similar to their learning abilities at specified time intervals, incorporating forgetting factors and introducing a weight decay strategy. Experiments show that our model performs well on online education datasets, and the proposed model achieves better prediction results than existing knowledge tracking methods.

关键词： Knowledge engineering Human computer interaction Computational modeling Education Predictive models Data models

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Cross-Modality Perturbation Synergy Attack for Person Re-identification 38

Cross-Modality Perturbation Synergy Attack for Person Re-ide...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

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Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification

Knowledge-Data Fusion Based Source-Free Semi-Supervised Doma...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ruimin Peng Jiayu An Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Electroencephalogram (EEG)-based seizure sub-type classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutuaISHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Diver-gence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification.

关键词： Training Adaptation models Data privacy Fuses Brain modeling Transformers Data models Electroencephalography Decision trees Cybernetics

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BeatKAN: An Efficient and Drum-Attuned Beat Tracking Method Using Kolmogorov-Arnold Networks

BeatKAN: An Efficient and Drum-Attuned Beat Tracking Method ...

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

作者： Zhicheng Zhang Ganghui Ru Wei Li School of Computer Science and Technology Fudan University Shanghai China School of Computer Science and Technology Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we propose an efficient and drum-attuned beat tracking method based on Kolmogorov-Arnold networks (KAN). Traditional MLP-based frameworks struggle with complex musical signals due to limited capacity in modeling intricate patterns. Inspired by KAN’s efficient ability to capture complex time-frequency relationships, we leverage it to enhance our model by employing learnable nonlinear activation functions on convolutional kernels. Additionally, we utilize music source separation techniques to extract drum tracks from the original audio, thereby expanding the existing beat datasets and simulating the human perception of beats through drum sounds. Experimental results demonstrate that our approach significantly reduces the parameter count while maintaining high accuracy in beat tracking.

关键词： Time-frequency analysis Accuracy Source separation Convolution Information retrieval Acoustics Kernel Speech processing

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