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学者姓名：王丹
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Abstract ：
Cross-lingual image description, the task of generating image captions in a target language from images and descriptions in a source language, is addressed in this study through a novel approach that combines neural network models and semantic matching techniques. Experiments conducted on the Flickr8k and AraImg2k benchmark datasets, featuring images and descriptions in English and Arabic, showcase remarkable performance improvements over state-of-the-art methods. Our model, equipped with the Image & Cross-Language Semantic Matching module and the Target Language Domain Evaluation module, significantly enhances the semantic relevance of generated image descriptions. For English-to-Arabic and Arabic-to-English cross-language image descriptions, our approach achieves a CIDEr score for English and Arabic of 87.9% and 81.7%, respectively, emphasizing the substantial contributions of our methodology. Comparative analyses with previous works further affirm the superior performance of our approach, and visual results underscore that our model generates image captions that are both semantically accurate and stylistically consistent with the target language. In summary, this study advances the field of cross-lingual image description, offering an effective solution for generating image captions across languages, with the potential to impact multilingual communication and accessibility. Future research directions include expanding to more languages and incorporating diverse visual and textual data sources.
Keyword ：
reward mechanisms reward mechanisms Cross -language image description Cross -language image description multimodal deep learning multimodal deep learning semantic matching semantic matching
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| GB/T 7714 | Al-Buraihy, Emran , Wang, Dan . Enhancing Cross-Lingual Image Description: A Multimodal Approach for Semantic Relevance and Stylistic Alignment [J]. | CMC-COMPUTERS MATERIALS & CONTINUA , 2024 , 79 (3) : 3913-3938 . |
| MLA | Al-Buraihy, Emran 等. "Enhancing Cross-Lingual Image Description: A Multimodal Approach for Semantic Relevance and Stylistic Alignment" . | CMC-COMPUTERS MATERIALS & CONTINUA 79 . 3 (2024) : 3913-3938 . |
| APA | Al-Buraihy, Emran , Wang, Dan . Enhancing Cross-Lingual Image Description: A Multimodal Approach for Semantic Relevance and Stylistic Alignment . | CMC-COMPUTERS MATERIALS & CONTINUA , 2024 , 79 (3) , 3913-3938 . |
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Abstract ：
Research on electroencephalogram-based motor imagery (MI-EEG) can identify the limbs of subjects that generate motor imagination by decoding EEG signals, which is an important issue in the field of brain-computer interface (BCI). Existing deep-learning-based classification methods have not been able to entirely employ the topological information among brain regions, and thus, the classification performance needs further improving. In this paper, we propose a multi-view graph convolutional attention network (MGCANet) with residual learning structure for multi-class MI decoding. Specifically, we design a multi-view graph convolution spatial feature extraction method based on the topological relationship of brain regions to achieve more comprehensive information aggregation. During the modeling, we build an adaptive weight fusion (Awf) module to adaptively merge feature from different brain views to improve classification accuracy. In addition, the self-attention mechanism is introduced for feature selection to expand the receptive field of EEG signals to global dependence and enhance the expression of important features. The proposed model is experimentally evaluated on two public MI datasets and achieved a mean accuracy of 78.26% (BCIC IV 2a dataset) and 73.68% (OpenBMI dataset), which significantly outperforms representative comparative methods in classification accuracy. Comprehensive experiment results verify the effectiveness of our proposed method, which can provide novel perspectives for MI decoding.
Keyword ：
motor imagery motor imagery brain-computer interface brain-computer interface deep learning deep learning self-attention self-attention graph convolutional networks graph convolutional networks
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| GB/T 7714 | Tan, Xiyue , Wang, Dan , Xu, Meng et al. Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding [J]. | BIOENGINEERING-BASEL , 2024 , 11 (9) . |
| MLA | Tan, Xiyue et al. "Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding" . | BIOENGINEERING-BASEL 11 . 9 (2024) . |
| APA | Tan, Xiyue , Wang, Dan , Xu, Meng , Chen, Jiaming , Wu, Shuhan . Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding . | BIOENGINEERING-BASEL , 2024 , 11 (9) . |
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Exploring the effective signal features of electroencephalogram (EEG) signals is an important issue in the research of brain-computer interface (BCI), and the results can reveal the motor intentions that trigger electrical changes in the brain, which has broad research prospects for feature extraction from EEG data. In contrast to previous EEG decoding methods that are based solely on a convolutional neural network, the traditional convolutional classification algorithm is optimized by combining a transformer mechanism with a constructed end-to-end EEG signal decoding algorithm based on swarm intelligence theory and virtual adversarial training. The use of a self-attention mechanism is studied to expand the receptive field of EEG signals to global dependence and train the neural network by optimizing the global parameters in the model. The proposed model is evaluated on a real-world public dataset and achieves the highest average accuracy of 63.56% in cross-subject experiments, which is significantly higher than that found for recently published algorithms. Additionally, good performance is achieved in decoding motor intentions. The experimental results show that the proposed classification framework promotes the global connection and optimization of EEG signals, which can be further applied to other BCI tasks.
Keyword ：
motor imagery (MI) motor imagery (MI) transformer transformer self-attention self-attention EEG signal EEG signal brain computer interface (BCI) brain computer interface (BCI)
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| GB/T 7714 | Tan, Xiyue , Wang, Dan , Chen, Jiaming et al. Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification [J]. | BIOENGINEERING-BASEL , 2023 , 10 (5) . |
| MLA | Tan, Xiyue et al. "Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification" . | BIOENGINEERING-BASEL 10 . 5 (2023) . |
| APA | Tan, Xiyue , Wang, Dan , Chen, Jiaming , Xu, Meng . Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification . | BIOENGINEERING-BASEL , 2023 , 10 (5) . |
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Abstract ：
Objective. Motor Imagery Brain-Computer Interface (MI-BCI) is an active Brain-Computer Interface (BCI) paradigm focusing on the identification of motor intention, which is one of the most important non-invasive BCI paradigms. In MI-BCI studies, deep learning-based methods (especially lightweight networks) have attracted more attention in recent years, but the decoding performance still needs further improving. Approach. To solve this problem, we designed a filter bank structure with sinc-convolutional layers for spatio-temporal feature extraction of MI-electroencephalography in four motor rhythms. The Channel Self-Attention method was introduced for feature selection based on both global and local information, so as to build a model called Filter Bank Sinc-convolutional Network with Channel Self-Attention for high performance MI-decoding. Also, we proposed a data augmentation method based on multivariate empirical mode decomposition to improve the generalization capability of the model. Main results. We performed an intra-subject evaluation experiment on unseen data of three open MI datasets. The proposed method achieved mean accuracy of 78.20% (4-class scenario) on BCI Competition IV IIa, 87.34% (2-class scenario) on BCI Competition IV IIb, and 72.03% (2-class scenario) on Open Brain Machine Interface (OpenBMI) dataset, which are significantly higher than those of compared deep learning-based methods by at least 3.05% (p = 0.0469), 3.18% (p = 0.0371), and 2.27% (p = 0.0024) respectively. Significance. This work provides a new option for deep learning-based MI decoding, which can be employed for building BCI systems for motor rehabilitation.
Keyword ：
brain-computer interface brain-computer interface data augmentation data augmentation motor imagery motor imagery deep learning deep learning self-attention self-attention
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| GB/T 7714 | Chen, Jiaming , Wang, Dan , Yi, Weibo et al. Filter bank sinc-convolutional network with channel self-attention for high performance motor imagery decoding [J]. | JOURNAL OF NEURAL ENGINEERING , 2023 , 20 (2) . |
| MLA | Chen, Jiaming et al. "Filter bank sinc-convolutional network with channel self-attention for high performance motor imagery decoding" . | JOURNAL OF NEURAL ENGINEERING 20 . 2 (2023) . |
| APA | Chen, Jiaming , Wang, Dan , Yi, Weibo , Xu, Meng , Tan, Xiyue . Filter bank sinc-convolutional network with channel self-attention for high performance motor imagery decoding . | JOURNAL OF NEURAL ENGINEERING , 2023 , 20 (2) . |
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Abstract ：
Network security has emerged as a crucial universal issue that affects enterprises, governments, and individuals. The strategies utilized by the attackers are continuing to evolve, and therefore the rate of attacks targeting the network system has expanded dramatically. An Intrusion Detection System (IDS) is one of the significant defense solutions against sophisticated cyberattacks. However, the challenge of improving the accuracy, detection rate, and minimal false alarms of the IDS continues. This paper proposes a robust and effective intrusion detection framework based on the ensemble learning technique using eXtreme Gradient Boosting (XGBoost) and an embedded feature selection method. Further, the best uniform feature subset is extracted using the up-to-date real-world intrusion dataset Canadian Institute for Cybersecurity Intrusion Detection (CICIDS2017) for all attacks. The proposed IDS framework has successfully exceeded several evaluations on a big test dataset over both multi and binary classification. The achieved results are promising on various measurements with an accuracy overall, precision, detection rate, specificity, F-score, false-negative rate, false-positive rate, error rate, and The Area Under the Curve (AUC) scores of 99.86%, 99.69%, 99.75%, 99.69%, 99.72%, 0.17%, 0.2%, 0.14%, and 99.72 respectively for abnormal class. Moreover, the achieved results of multi-classification are also remarkable and impressively great on all performance metrics.
Keyword ：
xgboost algorithm xgboost algorithm ensemble learning ensemble learning intrusion detection intrusion detection Network security Network security features selection features selection
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| GB/T 7714 | Mokbal, Fawaz , Dan, Wang , Osman, Musa et al. An Efficient Intrusion Detection Framework Based on Embedding Feature Selection and Ensemble Learning Technique [J]. | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY , 2022 , 19 (2) : 237-248 . |
| MLA | Mokbal, Fawaz et al. "An Efficient Intrusion Detection Framework Based on Embedding Feature Selection and Ensemble Learning Technique" . | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY 19 . 2 (2022) : 237-248 . |
| APA | Mokbal, Fawaz , Dan, Wang , Osman, Musa , Ping, Yang , Alsamhi, Saeed . An Efficient Intrusion Detection Framework Based on Embedding Feature Selection and Ensemble Learning Technique . | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY , 2022 , 19 (2) , 237-248 . |
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Abstract ：
In the rapid serial visual presentation (RSVP) classification task, the data from the target and non-target classes are incredibly imbalanced. These class imbalance problems (CIPs) can hinder the classifier from achieving better performance, especially in deep learning. This paper proposed a novel data augmentation method called balanced Wasserstein generative adversarial network with gradient penalty (BWGAN-GP) to generate RSVP minority class data. The model learned useful features from majority classes and used them to generate minority-class artificial EEG data. It combines generative adversarial network (GAN) with autoencoder initialization strategy enables this method to learn an accurate class-conditioning in the latent space to drive the generation process towards the minority class. We used RSVP datasets from nine subjects to evaluate the classification performance of our proposed generated model and compare them with those of other methods. The average AUC obtained with BWGAN-GP on EEGNet was 94.43%, an increase of 3.7% over the original data. We also used different amounts of original data to investigate the effect of the generated EEG data on the calibration phase. Only 60% of original data were needed to achieve acceptable classification performance. These results show that the BWGAN-GP could effectively alleviate CIPs in the RSVP task and obtain the best performance when the two classes of data are balanced. The findings suggest that data augmentation techniques could generate artificial EEG to reduce calibration time in other brain-computer interfaces (BCI) paradigms similar to RSVP.
Keyword ：
Brain modeling Brain modeling Electroencephalography Electroencephalography Mathematical models Mathematical models Wasserstein generative adversarial network (WGAN) Wasserstein generative adversarial network (WGAN) Visualization Visualization data augmentation data augmentation Generative adversarial networks Generative adversarial networks Training Training class imbalance problem class imbalance problem Task analysis Task analysis auto-encoder auto-encoder Rapid serial visual presentation (RSVP) Rapid serial visual presentation (RSVP)
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| GB/T 7714 | Xu, Meng , Chen, Yuanfang , Wang, Yijun et al. BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks [J]. | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , 2022 , 30 : 251-263 . |
| MLA | Xu, Meng et al. "BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks" . | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 30 (2022) : 251-263 . |
| APA | Xu, Meng , Chen, Yuanfang , Wang, Yijun , Wang, Dan , Liu, Zehua , Zhang, Lijian . BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks . | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , 2022 , 30 , 251-263 . |
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Abstract ：
The social network allows individuals to create public and semi-public web-based profiles to communicate with other users in the network and online interaction sources. Social media sites such as Facebook, Twitter, etc., are prime examples of the social network, which enable people to express their ideas, suggestions, views, and opinions about a particular product, service, political entity, and affairs. This research introduces a Machine Learning-based (ML-based) classification scheme for analyzing the social network reviews of Yemeni people using data mining techniques. A constructed dataset consisting of 2000 MSA and Yemeni dialects records used for training and testing purposes along with a test dataset consisting of 300 Modern Standard Arabic (MSA) and Yemeni dialects records used to demonstrate the capacity of our scheme. Four supervised machine learning algorithms were applied and a comparison was made of performance algorithms based on Accuracy, Recall, Precision and F-measure. The results show that the Support Vector Machine algorithm outperformed the others in terms of Accuracy on both training and testing datasets with 90.65% and 90.00, respectively. It is further noted that the accuracy of the selected algorithms was influenced by noisy and sarcastic opinions.
Keyword ：
sentiment analysis sentiment analysis Social network Social network MSA MSA data mining data mining supervised machine learning supervised machine learning Arabic sentiment analysis Arabic sentiment analysis
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| GB/T 7714 | Al-Buraihy, Emran , Khan, Rafi Ullah , Dan, Wang et al. An ML-Based Classification Scheme for Analyzing the Social Network Reviews of Yemeni People [J]. | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY , 2022 , 19 (6) : 904-914 . |
| MLA | Al-Buraihy, Emran et al. "An ML-Based Classification Scheme for Analyzing the Social Network Reviews of Yemeni People" . | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY 19 . 6 (2022) : 904-914 . |
| APA | Al-Buraihy, Emran , Khan, Rafi Ullah , Dan, Wang , Ullah, Mohib . An ML-Based Classification Scheme for Analyzing the Social Network Reviews of Yemeni People . | INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY , 2022 , 19 (6) , 904-914 . |
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Abstract ：
Objective. Motor imagery-based brain-computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding. Approach. A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method channel group attention (CGA) to build a lightweight neural network Filter Bank CGA Network (FB-CGANet). Accompanied with FB-CGANet, the band exchange data augmentation method was proposed to generate training data for networks with filter bank structure. Main results. The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment. Significance. This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.
Keyword ：
channel attention channel attention motor imagery motor imagery data augmentation data augmentation deep learning deep learning brain-computer interface brain-computer interface
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| GB/T 7714 | Chen, Jiaming , Yi, Weibo , Wang, Dan et al. FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification [J]. | JOURNAL OF NEURAL ENGINEERING , 2022 , 19 (1) . |
| MLA | Chen, Jiaming et al. "FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification" . | JOURNAL OF NEURAL ENGINEERING 19 . 1 (2022) . |
| APA | Chen, Jiaming , Yi, Weibo , Wang, Dan , Du, Jinlian , Fu, Lihua , Li, Tong . FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification . | JOURNAL OF NEURAL ENGINEERING , 2022 , 19 (1) . |
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Abstract ：
Image super-resolution reconstruction is a research hotspot in the field of computer vision. Traditional image super-resolution reconstruction methods based on deep learning mostly up-sample low-resolution images ignoring categories and instances, which will cause some problems such as unrealistic texture in the reconstructed images or sawtooth phenomenon on the edge of instance. In this manuscript, we propose an image super-resolution reconstruction method based on instance spatial feature modulation and feedback mechanism. First, the prior knowledge of instance spatial features is introduced in the reconstruction process. Instance spatial features of low-resolution images are extracted to modulate super-resolution reconstruction features. Then, based on the feedback mechanism, the modulated low-resolution image features are iteratively optimized for the reconstruction results, so that the model can finally learn instance-level reconstruction ability. Experiments on COCO-2017 show that, compared with traditional deep learning-based image super-resolution reconstruction methods, the proposed method can obtain better image reconstruction results, and the reconstructed images have more realistic instance textures.
Keyword ：
Feedback network Feedback network Back projection Back projection Instance spatial feature Instance spatial feature Modulator Modulator Super-resolution Super-resolution
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| GB/T 7714 | Fu, Lihua , Jiang, Hanxu , Wu, Huixian et al. Image super-resolution reconstruction based on instance spatial feature modulation and feedback mechanism [J]. | APPLIED INTELLIGENCE , 2022 , 53 (1) : 601-615 . |
| MLA | Fu, Lihua et al. "Image super-resolution reconstruction based on instance spatial feature modulation and feedback mechanism" . | APPLIED INTELLIGENCE 53 . 1 (2022) : 601-615 . |
| APA | Fu, Lihua , Jiang, Hanxu , Wu, Huixian , Yan, Shaoxing , Wang, Junxiang , Wang, Dan . Image super-resolution reconstruction based on instance spatial feature modulation and feedback mechanism . | APPLIED INTELLIGENCE , 2022 , 53 (1) , 601-615 . |
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Abstract ：
Motor imagery-based brain computer interface (MI-BCI) is a representative active BCI paradigm which is widely employed in the rehabilitation field. In MI-BCI, a classification model is built to identify the target limb from MI-based EEG signals, but the performance of models cannot meet the demand for practical use. Lightweight neural networks in deep learning methods are used to build high performance models in MI-BCI. Small sample sizes and the lack of multi-scale information extraction in frequency domain limit the performance improvement of lightweight neural networks. To solve these problems, the Filter Bank Sinc-ShallowNet (FB-Sinc-ShallowNet) algorithm combined with the mixed noise adding method based on empirical mode decomposition (EMD) was proposed. The FB-Sinc-ShallowNet algorithm improves a lightweight neural network Sinc-ShallowNet with a filter bank structure corresponding to four sensory motor rhythms. The mixed noise adding method employs the EMD method to improve the quality of generated data. The proposed method was evaluated on the BCI competition IV IIa dataset and can achieve highest average accuracy of 77.2%, about 634% higher than state-of-the-art method Sinc-ShallowNet. This work implies the effectiveness of filter bank structure in lightweight neural networks and provides a novel option for data augmentation and classification of MI-based EEG signals, which can be applied in the rehabilitation field for decoding MI-EEG with few samples.
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| GB/T 7714 | Chen, Jiaming , Yi, Weibo , Wang, Dan . Filter Bank Sinc-ShallowNet with EMD-based Mixed Noise Adding Data Augmentation for Motor Imagery Classification [J]. | 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) , 2021 : 5837-5841 . |
| MLA | Chen, Jiaming et al. "Filter Bank Sinc-ShallowNet with EMD-based Mixed Noise Adding Data Augmentation for Motor Imagery Classification" . | 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) (2021) : 5837-5841 . |
| APA | Chen, Jiaming , Yi, Weibo , Wang, Dan . Filter Bank Sinc-ShallowNet with EMD-based Mixed Noise Adding Data Augmentation for Motor Imagery Classification . | 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) , 2021 , 5837-5841 . |
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