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Artificial Intelligence Innovation (ICAII), International Conference on

作者： Zhuo Wang Hongli Li Yang Liu Kaimin Sun Xuhui Chen Yuntao Wu School of Computer Science Engineering Wuhan Institute of Technology Wuhan China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China ChangJiang Waterway Bureau Survey Center Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

Image matching is a fundamental technology for data processing and analysis in remote sensing robots. To address the limitations of deep learning models in image matching, particularly the poor matching performance and limited number of matching points in remote sensing images, this paper presents an enhanced SuperPoint model based on deep learning. It incorporates a sub-model called DC-SuperPoint for feature point extraction and description. Within the feature extraction network, the CBAM (Convolutional Block Attention Module) attention mechanism is introduced to enhance the salience of point features in the graph. Additionally, the network extraction branch for feature points and descriptors is replaced with deformable convolution DCNV2 (Deformable Convolutional Networks v2), which is specifically designed to learn different types of features in various scenarios. Comparative experiments are conducted on two different datasets to evaluate the algorithm. The experimental results demonstrate that the proposed improved algorithm achieves a matching accuracy of 49.2% for the images, surpassing the original algorithm by 30% in terms of the number of matching points. Moreover, it exhibits better adaptability to different orientations compared to the original model. The accuracy is significantly improved to a certain extent, and the overall performance surpasses that of the original algorithm. These findings indicate that the proposed method effectively enhances matching efficiency and accuracy in scenarios with varying levels of complexity.

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A Few-Shot Network Flow Attack Classification via Graph Contrastive Learning

A Few-Shot Network Flow Attack Classification via Graph Cont...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Binbin Gel Bo Li Xudong Mou Jun Zhao Xudong Liu School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China

ISBN: (数字)9798350376982

ISBN: (纸本)9798350376999

Accurately identifying network attacks is crucial for maintaining network security. However, these attacks are often hide within massive volumes of network traffic, posing significant challenges for traditional detection methods. Supervised learning approaches require substantial labeled data and struggle to adapt to unknown attack types, while unsupervised methods face difficulties in accurately pinpointing specific attack categories. To address these limitations, we propose a novel fewshot learning model for network flow attack classification based on graph contrastive learning. Our model leverages contrastive learning to enhance feature representation and generalization capabilities, enabling high-accuracy attack detection even with limited training data. Specifically, we first construct a multi- graph representation of network traffic and segment the data into snapshots. Then, we perform graph data augmentation within each snapshot to generate augmented sample pairs, which are used to pre-train the model via contrastive learning. Finally, we fine-tune the model parameters to achieve multi-class attack classification, leveraging the learned feature representations to identify various attack types, even those unseen during training. Experimental results demonstrate that our model exhibits excellent generalization ability and achieves high attack detection performance, even with limited training data.

关键词： Training Adaptation models Supervised learning Training data Contrastive learning Telecommunication traffic Data augmentation

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Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning

SSRN

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SSRN 2023年

作者： Miao, Qing Wu, Xiaohe Xu, Chao Ji, Yanli Zuo, Wangmeng Guo, Yiwen Meng, Zhaopeng College of Intelligence and Computing Tianjin University Tianjin300072 China School of Computer Science and Technology Harbin Institute of Technology Harbin150006 China Center for Future Media School of Computer Science and Engineering UESTC Chengdu611731 China China

Learning with noisy labels (LNL) has been extensively studied, with existing approaches typically following a framework that alternates between clean sample selection and semi-supervised learning (SSL). However, this approach has a limitation: the clean set selected by the Deep Neural Network (DNN) classifier, trained through self-training, inevitably contains noisy samples. This mixture of clean and noisy samples leads to misguidance in DNN training during SSL, resulting in impaired generalization performance due to confirmation bias caused by error accumulation in sample selection. To address this issue, we propose a method called Collaborative Sample Selection (CSS), which leverages the large-scale pre-trained model CLIP. CSS aims to remove the mixed noisy samples from the identified clean set. We achieve this by training a 2-Dimensional Gaussian Mixture Model (2D-GMM) that combines the probabilities from CLIP with the predictions from the DNN classifier. To further enhance the adaptation of CLIP to LNL, we introduce a co-training mechanism with a contrastive loss in semi-supervised learning. This allows us to jointly train the prompt of CLIP and the DNN classifier, resulting in improved feature representation, boosted classification performance of DNNs, and reciprocal benefits to our Collaborative Sample Selection. By incorporating auxiliary information from CLIP and utilizing prompt fine-tuning, we effectively eliminate noisy samples from the clean set and mitigate confirmation bias during training. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed method in comparison with the state-of-the-art approaches. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample

arXiv

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arXiv 2024年

作者： Shao, Zhiwen Zhu, Hancheng Zhou, Yong Xiang, Xiang Liu, Bing Yao, Rui Ma, Lizhuang School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Ministry of Education Wuhan430074 China Mine Digitization Engineering Research Center of the Ministry of Education Xuzhou221116 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Facial action unit (AU) detection remains a challenging task, due to the subtlety, dynamics, and diversity of AUs. Recently, the prevailing techniques of self-attention and causal inference have been introduced to AU detection. However, most existing methods directly learn self-attention guided by AU detection, or employ common patterns for all AUs during causal intervention. The former often captures irrelevant information in a global range, and the latter ignores the specific causal characteristic of each AU. In this paper, we propose a novel AU detection framework called AC2D by adaptively constraining self-attention weight distribution and causally deconfounding the sample confounder. Specifically, we explore the mechanism of self-attention weight distribution, in which the self-attention weight distribution of each AU is regarded as spatial distribution and is adaptively learned under the constraint of location-predefined attention and the guidance of AU detection. Moreover, we propose a causal intervention module for each AU, in which the bias caused by training samples and the interference from irrelevant AUs are both suppressed. Extensive experiments show that our method achieves competitive performance compared to state-of-the-art AU detection approaches on challenging benchmarks, including BP4D, DISFA, GFT, and BP4D+ in constrained scenarios and Aff-Wild2 in unconstrained scenarios. The code is available at https://***/ZhiwenShao/AC2D. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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A lightweight method for Android malware classification based on teacher assistant distillation

A lightweight method for Android malware classification base...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Junwei Tang Qiaosen Pi Ruhan He Jin Huang Tao Peng Xinrong Hu Wenlong Tian Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion Wuhan Textile University School of Computer Science and Artificial Intelligence Wuhan Textile University School of Computer Science University of South China School of Physical and Mathematical Sciences Nanyang Technological University Singapore

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

In recent years, the growing concern over mobile security and the associated risks posed by mobile malware have prompted an increased focus on utilizing deep learning models for analyzing Android application security. However, the expansion of deep learning model sizes results in an exponential growth of model parameters, demanding significant computing resources for execution. To address this challenge, we propose a lightweight Android malware detection method based on teacher-assistant-student knowledge distillation. Our method enables predicting on local clients, eliminating the need for cloud-base service interactions, and protecting user privacy. We visualize the binary file of the target Android application as an RGB three-channel color image, using ResNeSt50 as the teacher model, and compress it based on knowledge distillation. An assistant model is incorporated to address the issue of insufficient distillation resulting from the significant gap between the teacher and student models. Additionally, we integrate a split-attention mechanism to enhance the ability of the professor model to acquire deep features of malware images. We conduct experiments on Drebin and CICMalDroid 2020 datasets and the results show that the proposed method can ensure that the detection results of student model are more similar to those of the teacher model while reducing model complexity. Our method reduces the number of model parameters by 95% compare to the teacher model while maintaining accuracy. And the accuracy is improved by 0.63% compare to the traditional distillation method.

关键词： Deep learning Visualization Privacy Accuracy Computational modeling Transforms Malware

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MSDAC: A multi-source domain adversarial framework for motion prediction in intracortical brain-computer interfaces

MSDAC: A multi-source domain adversarial framework for motio...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Haozhou Liu Banghua Yang Shouliang Guan Fenqi Rong Mengao Guo Ying Fang Bingyu Liu Yan Gao Yong Gu School of Mechatronic Engineering and Automation Research Center of Brain-Computer Engineering Shanghai University Shanghai China Lingang Laboratory Shanghai China National Center for Nanoscience and Technology Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Neurosciences Shanghai China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Intracortical brain-computer interfaces (iBCIs) restore motor function in patients with paralysis by converting neural activity into control signals for external devices. However, the frequent recalibration required by current decoding methods due to turnover and loss of recording neurons poses a challenge for achieving stable online decoding. To address these issues, we propose a multi-source domain adversarial classification (MSDAC) framework for cross-day decoding that utilizes an out-of-distribution (OOD) generalization approach. This framework divides the historical data into source domains by date and employs adversarial networks to minimize the distribution discrepancies among multiple source domains, thereby achieving robust domain-invariant characteristics and superior performance on unseen test data. The MSDAC framework was evaluated using five months of monkey center-out neural activity data and demonstrated exceptional performance. Without relying on test day data for model calibration or parameter updating, the MSDAC achieved an average decoding accuracy of 84.38% (day-5 to day-150, 27968 trials). These results underscore that the MSDAC-based decoding framework can be an ideal choice for establishing stable iBCI systems.

关键词： Accuracy Recurrent neural networks Neural activity Time series analysis Neurons Brain-computer interfaces Stability analysis Decoding Recording Paralysis

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Cross-Lingual Summarization: English - Bahasa Indonesia 6

Cross-Lingual Summarization: English - Bahasa Indonesia

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6th International Workshop on Big Data and Information Security, IWBIS 2021

作者： Abka, Achmad F. Pratama, Mahardhika Jatmiko, Wisnu National Research and Innovation Agency Research Center for Informatics Indonesia Nanyang Technological University School of Computer Science and Engineering Singapore Singapore Universitas Indonesia Faculty of Computer Science Indonesia Indonesia

ISBN: (数字)9781665424516

ISBN: (纸本)9781665424516

Progress of abstractive summarization has been accelerated since the introduction of sequence-to-sequence neural networks. Summarization is no longer limited to selecting words or sentences that exist in the source document as in the extractive approach, but can generate completely new words or sentences that have never appeared in the source document. Big push came from machine translation research with the introduction of attention mechanisms. Attention mechanism is the key to the information bottleneck problem in encoder-decoder model. Cross-Lingual Summarization (CLS) is the task of generating a summary in target language from source document in different language. Traditional methods split this task into two steps: summarization and translation. This paper describes a study on CLS without explicitly using translator, thereby reducing one step as in existing method. We incorporate multilingual embeddings in sequence-to-sequence neural networks with attention mechanisms to handle this task. Multilingual embeddings are used to represent words as if the source language and the target language are the same language. Experiments show comparable performance between monolingual summarization and cross-lingual summarization in Amazon Fine Food review data indicated by ROUGE scores which are only 1-2 points apart. © 2021 IEEE.

关键词： Embeddings

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Pedestrain Detection in Infrared Images with Improved YOLOv2 Network

Pedestrain Detection in Infrared Images with Improved YOLOv2...

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2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2020

作者： Du, Chenshi Song, Peng Ma, Xin Center for Robotics School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9781728152240

With the progress of unmanned driving technology and the attention of security problems, pedestrian detection is getting more and more researchers' participation. Since 2012, the application of deep convolutional network has greatly improved the accuracy and detection speed of pedestrian detection. However, it is difficult to detect pedestrians at night, the detection accuracy of small objects and obstructed pedestrians is poor, which are problems that researchers are solving. This paper presents infrared image detection of pedestrain based on improved YOLOv2 detection network. It improves the detection accuracy of small volume and blocked pedestrian in the night. © 2020 IEEE.

关键词： Image enhancement

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Iterative Shaping of Multi-Particle Aggregates based on Action Trees and VLM

arXiv

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arXiv 2025年

作者： Lee, Hoi-Yin Zhou, Peng Duan, Anqing Yang, Chenguang Navarro-Alarcon, David Department of Mechanical Engineering The Hong Kong Polytechnic University KLN Hong Kong School of Advanced Engineering The Great Bay University Dongguan China Department of Robotics Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates Department of Computer Science University of Liverpool Liverpool United Kingdom

In this paper, we address the problem of manipulating multi-particle aggregates using a bimanual robotic system. Our approach enables the autonomous transport of dispersed particles through a series of shaping and pushing actions using robotically-controlled tools. Achieving this advanced manipulation capability presents two key challenges: high-level task planning and trajectory execution. For task planning, we leverage Vision Language Models (VLMs) to enable primitive actions such as tool affordance grasping and non-prehensile particle pushing. For trajectory execution, we represent the evolving particle aggregate's contour using truncated Fourier series, providing efficient parametrization of its closed shape. We adaptively compute trajectory waypoints based on group cohesion and the geometric centroid of the aggregate, accounting for its spatial distribution and collective motion. Through realworld experiments, we demonstrate the effectiveness of our methodology in actively shaping and manipulating multi-particle aggregates while maintaining high system cohesion. © 2025, CC BY-NC-SA.

关键词： Fourier series

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A Multi-Resolution Mutual Learning Network for Multi-Label ECG Classification

A Multi-Resolution Mutual Learning Network for Multi-Label E...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Huang Ning Wang Panpan Feng Haiyan Wang Zongmin Wang Bing Zhou School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou China School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Zhengzhou University of Aeronautics Zhengzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Electrocardiograms (ECG), essential for diagnosing cardiovascular diseases. In recent years, the application of deep learning techniques has significantly improved the performance of ECG signal classification. Multi-resolution feature analysis, which captures and processes information at different time scales, can extract subtle changes and overall trends in ECG signals, showing unique advantages. However, common multi-resolution analysis methods based on simple feature addition or concatenation may lead to the neglect of low-resolution features, affecting model performance. To address this issue, this paper proposes the Multi-Resolution Mutual Learning Network (MRMNet). MRM-Net includes a dual-resolution attention architecture and a feature complementary mechanism. The dual-resolution attention architecture processes high-resolution and low-resolution features in parallel. Through the attention mechanism, the high-resolution and low-resolution branches can focus on subtle waveform changes and overall rhythm patterns, enhancing the ability to capture critical features in ECG signals. Meanwhile, the feature complementary mechanism introduces mutual feature learning after each layer of the feature extractor. This allows features at different resolutions to reinforce each other, thereby reducing information loss and improving model performance and robustness. Experiments on the PTB-XL and CPSC2018 datasets demonstrate that MRM-Net significantly outperforms existing methods in multi-label ECG classification performance. The code for our framework will be publicly available at https://***/wxhdf/MRM.

关键词： Representation learning Attention mechanisms Pattern classification Electrocardiography Feature extraction Rhythm Market research Robustness Data mining Signal resolution

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