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A Multi-Scale Infrared and Visible Image Fusion Network Based on Context Perception

A Multi-Scale Infrared and Visible Image Fusion Network Base...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Huixuan Zhao Jinyong Cheng Rundong Du Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Existing image fusion methods often fail to fully utilize the contextual information of the images, resulting in blurry and distorted fusion results. In addition, although the existing multi-scale feature extraction methods can consider the information of the source image from multiple perspectives, they tend to ignore the local information. These limitations degrade the performance and effectiveness of image fusion techniques in practical applications. To address above issues, we propose a fusion network architecture called MIA-Nest, consisting of encoder network, fusion network, and decoder network. Specifically, we introduce the Squeeze-and-Excitation Blocks(SEBs) into the encoder network, so that it can take local information into account while extracting multi-scale features. At the same time, in order to allow the fusion network to integrate the context information of the extracted features, we introduce Global Context Blocks (GCBs), so as to enrich the details in the fusion result and enable it to contain more information. Finally, the decoder network reconstructs the fusion image from the fused features obtained by the fusion network. Experimental results demonstrate that our proposed method significantly improves the quality and detail preservation capability of the fused images.

关键词： Federated learning computer architecture Network architecture Feature extraction Decoding Data mining Image fusion

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Data-Free Backdoor Model Inspection: Masking and Reverse Engineering Loops for Feature Counting

Data-Free Backdoor Model Inspection: Masking and Reverse Eng...

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International Joint Conference on Neural networks (IJCNN)

作者： Qi Zhou Wenjian Luo Zipeng Ye Yubo Tang Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Deep Neural networks (DNNs) are widely used for the outstanding performance in many fields. However, the training of DNN models has high requirements for the users’ data and computation resources, so many users with limited resources tend to download pre-trained models from some platforms and then finetune the pre-trained models to match their own tasks. However, the pre-trained models are under the threat of the backdoor attack. The backdoor attackers inject backdoors in the models, leading the backdoor models to predict target predictions designed by the attackers in advance. However, most existing backdoor model inspection methods rely on the clean data samples from the dataset of the model, which are difficult to get for users who just download the pre-trained models from the platforms. There are also a few defense methods not dependent on the data, but they also have their limits in practice. We propose Data-Free Masking and Reverse Engineering Loops (DF-MREL), a simple yet efficient data-free method for backdoor model inspection, which is widely applicable when resources are limited. Our experiments show its excellent performance in detecting backdoor models. Source code will be published after accepted.

关键词： Training Computational modeling Source coding Reverse engineering Artificial neural networks Inspection Predictive models

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GNN-ASG: A Double Feature Selection-based Adversarial Sample Generation Method in Industrial Control System

GNN-ASG: A Double Feature Selection-based Adversarial Sample...

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International Conference on computer Supported Cooperative Work in Design

作者： Lijuan Xu Zhiang Yao Dawei Zhao Xin Li Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Due to the unique constraints of industrial control data, industrial control adversarial sample attacks are particularly challenging. Existing methods strive to conduct adversarial sample attacks under the conditions of satisfying industrial control data constraints, however, the results are not ideal. Therefore, this study proposes a new adversarial sample generation method GNN-ASG based on double feature selection. GNN-ASG uses data constraints to ensure the rationality of generated data, and uses Graph Deviation Network (GDN) and Autoencoder to improve the quality and versatility of adversarial samples. A new adversarial sample evaluation metrics Adversarial Sample Attack Impact Rate (ASAIR) is proposed to address the problem that existing evaluation metrics are difficult to accurately judge the effectiveness of adversarial sample attacks. This method considers the principle and application environment of adversarial samples, and fully demonstrates the practical effect of adversarial samples. In a comprehensive experiment conducted on three public datasets, GNN-ASG achieves an impressive ASAIR of 21.83%, higher than existing methods of 13.94%. This paper demonstrates the versatility and effectiveness of GNN-ASG by comparing its performance with three state-of-the-art adversarial sample generation methods on four anomaly detection models. GNN-ASG can maximally reduce the F1-Score of the detection model by 0.7605.

关键词： Measurement Federated learning Industrial control Feature extraction Anomaly detection Glass box

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Efficient CNN-Transformer Aggregation Network for Remote Sensing Image Change Detection

Efficient CNN-Transformer Aggregation Network for Remote Sen...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Bingjie Kong Qinglong Meng Fengqi Hao Jinqiang Bai Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Change detection is a crucial task in the field of remote sensing, focusing on identifying areas where significant changes occur between two remote sensing images captured at different time points. However, existing change detection methods based on CNNs or Transformers face two main challenges. Firstly, CNNs often lack the ability to effectively model long-range dependencies, resulting in diminished recognition performance for targets sharing the same semantics but having different features. Secondly, Transformers leverage self-attention mechanisms to capture long-range dependencies adeptly but struggle to capture local details and multi-scale features effectively. In response to these challenges, we propose a CNN and Transformer Aggregation Network (CTA-Net) for change detection in remote sensing images. Specifically, we devise two encoders based on Transformers and CNNs, respectively, facilitating the generation of complementary global and local features during the encoding phase. In the decoding phase, we design a pyramid-structured complementary decoder to aggregate multi-level complementary features from the CNN and Transformer branches. Furthermore, we propose a novel skip connection strategy to exploit the relationship between decoded features and multi-level complementary features, thereby enhancing the model's multi-scale invariance. Extensive experiments conducted on multiple benchmark datasets validate the effectiveness of our proposed CTA-Net model.

关键词： Target recognition Face recognition Semantics Focusing Transformers Feature extraction Encoding Decoding Remote sensing Cybernetics

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MMFI-Net: Multilevel Multimodal Feature Injection Network for Infrared and Visible Image Fusion

MMFI-Net: Multilevel Multimodal Feature Injection Network fo...

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International Joint Conference on Neural networks (IJCNN)

作者： Rundong Du Jinyong Cheng Huixuan Zhao Ying Guo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Infrared and visible image fusion aims at a better transfer of feature information from both modalities into the fused image. However, almost all existing methods currently focus on the performance of only one part of a single-modal feature extraction or image reconstruction, and do not link multimodal and multilevel features well. This leads to the problem of insufficient feature information in the image reconstruction process, and the resulting fused image lacks texture details and edge information. In this paper, we propose a multilevel multimodal feature injection network (MMFI-Net). First, the DMII module is used to complement the information of the difference modals. Then, the complemented feature information is decomposed more specifically into basic and detail information by fuzzy filtering and Laplace filtering. Next, in the final image reconstruction section, we concatenate the multi-level features from the feature extraction section with the low-level feature information from the image reconstruction section. In this way, the network is better able to capture information in the image and mitigate the problem of feature information loss. Our method demonstrates significant superiority in terms of contrast, edge information, and texture details, as evidenced by experimental results on publicly available datasets.

关键词： Visualization Filtering Image edge detection Neural networks Feature extraction Image reconstruction Image fusion

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Patent Recommendation Methods Based on Transformer Encoders and Regularization Strategies

Patent Recommendation Methods Based on Transformer Encoders ...

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Advanced Information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

作者： Wan Zheng Qingyan Ding Na Li Yu Pan Xuecheng Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Patent recommendation service has become a major demand of all walks of life in the society. Patent data collection and analysis can not only help enterprises grasp the development status of the industry, but also provide decision support for them in the research and development process of science and technology services. In view of the problem of inaccurate patent recommendation caused by the inconsistency between the search text and the patent text length and the low accuracy of traditional patent recommendation, a word and sentence dual processing model based on Transformer encoder and regularization strategy is proposed to complete the patent recommendation task from the level of sentence granularity and word granularity. The sentence granularity feature extraction module uses the transformer encoder superimposed multi-head attention method to obtain the feature information between the sentence vector groups, so that the sentence vector fuses the feature information of each sentence, and solves the problem of low recommendation accuracy caused by the inconsistent length of the patent text and the search text; The word granularity feature extraction module uses the twin network model to extract the feature information of the sentence granularity vector using the Transformer encoder using the double splicing operation, improve the model performance and model accuracy; Introduce a strategy based on Dropout and symmetric JS divergence regularization, improve the model generalization ability and accuracy. Experiments have proved that it outperforms SBERT, BERT, MGMSN and SVM models in terms of performance.

关键词： Patents Accuracy Fuses Splicing Feature extraction Transformers Search problems Vectors Data models Data mining

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Decoding Respiratory Signals from LFP in Olfactory Bulb based on ConvLSTM Framework 7

Decoding Respiratory Signals from LFP in Olfactory Bulb base...

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7th International Conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Xu, Haoze Yu, Bowen Yu, Chaonan Xu, Kedi Qiushi Academy for Advanced Studies Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory for Biomedical Engineering of Ministry of Education School of Biomedical Engineering and Instrument Science Zhejiang University China Nanhu Brain-computer interface institute Hangzhou311100 China State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9783800762910

Respiratory signals are crucial for decoding olfactory neural circuits and understanding respiration-entrained local field potential (LFP) rhythms. Acquiring high-precision respiratory signals often requires additional equipment, increasing the experimental burden, particularly in free-moving paradigms. However, within the current neuroscience paradigm, we have not yet found a non-invasive method for obtaining the respiratory signals of laboratory animals. To minimize surgical trauma to experimental animals, this paper introduces a ConvLSTM-based decoding method, which uses the LFP from the olfactory bulb (OB) region of animals to accurately decode respiratory signals. After validating the effectiveness of the ConvLSTM method through comparative experiments, we further investigated the factors influencing the model's decoding performance. The Gamma frequency band exhibited the best decoding results, with a mean absolute error (MAE) of 0.16±0.09, which may be related to the causal direction from respiratory signals to Gamma rhythms. Additionally, the ConvLSTM model demonstrated strong trend prediction capabilities in multi-sequence point predictions and forward predictions, despite a slight decline in single-point prediction accuracy. Extensive validation on publicly available datasets indicates that the ConvLSTM-based respiratory decoding approach is highly applicable, offering a non-invasive alternative for respiratory signal acquisition in animal studies. © VDE VERLAG GMBH.

关键词： Noninvasive medical procedures

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A Novel Collaborative Edge Caching via User Preference Awareness and Adaptive Clustering in MENs

A Novel Collaborative Edge Caching via User Preference Aware...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Pengfei Gu Huiling Shi Hao Hao Mingfa Li Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

In this paper, we propose a collaborative caching placement scheme leveraging user preference awareness and adaptive clustering (UPAAC), which jointly considers the heterogeneity of user preferences and user activity to reduce user access latency and improve the quality of service. Specifically, we first utilize Bidirectional Long Short-Term Memory (Bi-LSTM) to capture dynamic user behavior to predict user preferences, and then density-based spatial clustering of applications with noise (DBSCAN) is used to cluster users based on the predicted user preferences for more efficient cache placement. Furthermore, the cache placement optimal problem is defined as the latency reduction maximization problem under the constraint of the cache capacity and latency threshold, and a genetic algorithm (GA) is used to optimize the problem and design the cache placement strategy. The simulation outcomes indicate that the suggested approach outperforms the conventional caching methods by significantly decreasing content access latency and enhancing the cache hit ratio.

关键词： Noise Collaboration Quality of service Prediction algorithms Smart systems Probability distribution Timing History Long short term memory Genetic algorithms

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Collaborative Computation Model Based on Dependency Types and Constituent Trees for Aspect-Based Sentiment Analysis

Collaborative Computation Model Based on Dependency Types an...

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International Conference on computer Supported Cooperative Work in Design

作者： Jia Yi Xiaoming Wu Yunfeng Dong Bei Qi Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Aspect-based sentiment analysis(ABSA) is a fine-grained sentiment classification task that aims to identify the sentiment polarity of specific aspects in a sentence. Graph convolutional networks(GCN) have recently been widely applied for modeling the associations between aspects and opinion words. However, most studies treat the relationships between all words in the graph equally through dependency analysis, without considering their dependency types, which may lead to the inability to distinguish important relationships. On the other hand, dependency trees can only reveal relationships between words and cannot simulate complex sentence relationships, such as conditional, parallel, and contrast relationships, which are crucial for capturing more accurate sentiment relationships for different aspects. To address these challenges, this paper proposes a Collaborative computation model based on Dependency Types and Constituent trees(CDTC) for aspect-based sentiment analysis. Specifically, collaborative computation of dependency type graph convolutional networks and syntax encoders enhances task efficiency. Utilizing dependency types helps distinguish important relationships, while the phrase segmentation and hierarchical structure of constituent trees provide richer syntactic information. Extensive experiments conducted on three datasets demonstrate that our proposed CDTC model achieves state-of-the-art performance.

关键词： Analytical models Sentiment analysis Graph convolutional networks Federated learning Computational modeling Semantics Collaboration

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Cross Auto-Encoder for Inscription Character Inpainting

Cross Auto-Encoder for Inscription Character Inpainting

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International Joint Conference on Neural networks (IJCNN)

作者： Long Zhao Zonglong Yuan Yuhao Lou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Character inpainting, a challenging task, has seen slow development. Existing networks focus on either enhancing the encoding process, involving feature extraction or improving the decoding process, which revolves around character reconstruction. Some also work on refining the loss function for training the model. However, none of these models enhances both the encoding and decoding processes in character inpainting. In response to this gap, our paper leverages insights from the field of image inpainting, specifically the multi-stage inpainting network, to introduce the Cross Auto-Encoder. The Cross Auto-Encoder comprises parallel encoders and decoders that simultaneously enhance both the encoding and decoding processes in character inpainting. The two parallel encoders employ dilated convolution and branching convolution channel attention modules to extract character features concurrently. Meanwhile, the parallel decoder incorporates a self-attention mechanism, utilizing different loss functions for training, sharing parameters, and decoding the reconstructed character. Our extensive experiments on the expanded inscription dataset demonstrate that the Cross AutoEncoder surpasses other existing character inpainting models in the context of inscriptions. Furthermore, we conducted experiments on the handwritten Chinese character recognition dataset, yielding results superior to those achieved by current character inpainting models. In conclusion, the Cross Auto-Encoder stands out as a novel and effective approach, successfully addressing the shortcomings of existing character inpainting models by enhancing both encoding and decoding processes.

关键词： Training Convolution Refining Feature extraction Encoding Decoding Complexity theory

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