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2. Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph

Author

Le Wang, Wenna Du, and Zehua Chen

Subjects
academic paper recommendation, knowledge graph, neural networks, attention mechanism, sequential recommendation, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

This paper addresses the challenges of data sparsity and personalization limitations inherent in current recommendation systems when processing extensive academic paper datasets. To overcome these issues, the present work introduces an innovative recommendation model that integrates the wealth of structured information from knowledge graphs and refines the amalgamation of temporal and relational data. By applying attention mechanisms and neural network technologies, the model thoroughly explores the text characteristics of papers and the evolving patterns of user behaviors. Additionally, the model elevates the accuracy and personalization of recommendations by meticulously examining citation patterns among papers and the networks of author collaboration. The experimental findings show that the present model surpasses baseline models on all evaluation metrics, thereby enhancing the precision and personalization of academic paper recommendations.

Published
2024
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4. Object Detection Model Based on Attention Mechanism

Author

Han, Mengxue, Tang, Xiangyan, Yang, Yue, Huang, Zhennan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tian, Yuan, editor, Ma, Tinghuai, editor, and Khan, Muhammad Khurram, editor

Published
2024
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5. The Bilinear-MAC Network for Visual Reasoning

Author

Zeng, Jiaxing, Zheng, Wangli, Lin, Yunhan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pan, Linqiang, editor, Wang, Yong, editor, and Lin, Jianqing, editor

Published
2024
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6. Lithology Identification Method Based on CNN-LSTM-Attention: A Case Study of Huizhou Block in South China Sea

Author

Liu, Zhikun, Yan, Xuedong, She, Yanhong, Zhang, Fan, Shi, Chongdong, Wang, Liupeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fang, Lu, editor, Pei, Jian, editor, Zhai, Guangtao, editor, and Wang, Ruiping, editor

Published
2024
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13. Artificial Intelligence Empowered Language Models: A Review

Author

Negi, Aradhana, Verma, Charu Vaibhav, Tayyebi, Yakuta, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello Coello, Carlos A., editor, and Bansal, Jagdish C., editor

Published
2024
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14. Fashion Label Relation Networks for Attribute Recognition

Author

Wang, Tongyang, Huang, Yan, Qian, Jianjun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fang, Lu, editor, Pei, Jian, editor, Zhai, Guangtao, editor, and Wang, Ruiping, editor

Published
2024
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15. Airport Boarding Bridge Pedestrian Detection Based on Spatial Attention and Joint Crowd Density Estimation

Author

Han, Xu, Wan, Hao, Tang, Wenxiao, Kang, Wenxiong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fang, Lu, editor, Pei, Jian, editor, Zhai, Guangtao, editor, and Wang, Ruiping, editor

Published
2024
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16. Knowledge Graph Embedding Based on Triple Multilayer Perceptron

Author

Hou, Zhihou, Zhou, Junxiang, Xu, Tianwei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gan, Jianhou, editor, Pan, Yi, editor, Zhou, Juxiang, editor, Liu, Dong, editor, Song, Xianhua, editor, and Lu, Zeguang, editor

Published
2024
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17. Explicit Coordination Based Multi-agent Reinforcement Learning for Intelligent Traffic Signal Control

Author

Li, Yixuan, Che, Qian, Zhou, Yifeng, Wang, Wanyuan, Jiang, Yichuan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Wang, Tong, editor, Fan, Hongfei, editor, Liu, Dongning, editor, and Du, Bowen, editor

Published
2024
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20. Object Detection with Depth Information in Road Scenes

Author

Liu, Ruowang, Chen, Xinbo, Tao, Bo, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Fuchun, editor, Meng, Qinghu, editor, Fu, Zhumu, editor, and Fang, Bin, editor

Published
2024
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22. Iris Recognition Network Based on Full Coordinate Attention

Author

Liu, Xina, Xu, Xinying, Zhang, Jiayan, Li, Pengyue, Tang, Jijun, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Fuchun, editor, Meng, Qinghu, editor, Fu, Zhumu, editor, and Fang, Bin, editor

Published
2024
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28. Road Traffic Waterlogging Detection Based on YOLOv5

Author

Liu, Jianqiang, Shang, Yujie, Li, Xingyao, Hao, Huizhen, Geng, Peng, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Jin, Hai, editor, Pan, Yi, editor, and Lu, Jianfeng, editor

Published
2024
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30. Content Selection in Abstractive Summarization with Biased Encoder Mixtures

Author

Chernyshev, Daniil, Dobrov, Boris, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ignatov, Dmitry I., editor, Khachay, Michael, editor, Kutuzov, Andrey, editor, Madoyan, Habet, editor, Makarov, Ilya, editor, Nikishina, Irina, editor, Panchenko, Alexander, editor, Panov, Maxim, editor, Pardalos, Panos M., editor, Savchenko, Andrey V., editor, Tsymbalov, Evgenii, editor, Tutubalina, Elena, editor, and Zagoruyko, Sergey, editor

Published
2024
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31. Subject-Independent Brain-Computer Interfaces: A Comparative Study of Attention Mechanism-Driven Deep Learning Models

Author

Keutayeva, Aigerim, Abibullaev, Berdakh, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Choi, Bong Jun, editor, Singh, Dhananjay, editor, Tiwary, Uma Shanker, editor, and Chung, Wan-Young, editor

Published
2024
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32. A Deep Learning Framework for Assamese Toxic Comment Detection: Leveraging LSTM and BiLSTM Models with Attention Mechanism

Author

Neog, Mandira, Baruah, Nomi, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello Coello, Carlos A., editor, and Bansal, Jagdish C., editor

Published
2024
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33. Text-Oriented Modality Reinforcement Network for Multimodal Sentiment Analysis from Unaligned Multimodal Sequences

Author

Lei, Yuxuan, Yang, Dingkang, Li, Mingcheng, Wang, Shunli, Chen, Jiawei, Zhang, Lihua, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fang, Lu, editor, Pei, Jian, editor, Zhai, Guangtao, editor, and Wang, Ruiping, editor

Published
2024
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34. A Novel Neighborhood-Augmented Graph Attention Network for Sequential Recommendation

Author

Xu, Shuxiang, Xiang, Qibu, Fan, Yushun, Yan, Ruyu, Zhang, Jia, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fang, Lu, editor, Pei, Jian, editor, Zhai, Guangtao, editor, and Wang, Ruiping, editor

Published
2024
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35. TGPPN: A Transformer and Graph Neural Network Based Point Process Network Model

Author

Qi, Qing, Shen, Shitong, Cao, Jian, Guan, Wei, Gan, Shiyu, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Wang, Tong, editor, Fan, Hongfei, editor, Liu, Dongning, editor, and Du, Bowen, editor

Published
2024
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40. Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph.

Author

Wang, Le, Du, Wenna, and Chen, Zehua

Subjects
KNOWLEDGE graphs, RECOMMENDER systems
Abstract

This paper addresses the challenges of data sparsity and personalization limitations inherent in current recommendation systems when processing extensive academic paper datasets. To overcome these issues, the present work introduces an innovative recommendation model that integrates the wealth of structured information from knowledge graphs and refines the amalgamation of temporal and relational data. By applying attention mechanisms and neural network technologies, the model thoroughly explores the text characteristics of papers and the evolving patterns of user behaviors. Additionally, the model elevates the accuracy and personalization of recommendations by meticulously examining citation patterns among papers and the networks of author collaboration. The experimental findings show that the present model surpasses baseline models on all evaluation metrics, thereby enhancing the precision and personalization of academic paper recommendations. [ABSTRACT FROM AUTHOR]

Published
2024
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45. PEERRec: An AI-based approach to automatically generate recommendations and predict decisions in peer review.

Author

Bharti, Prabhat Kumar, Ghosal, Tirthankar, Agarwal, Mayank, and Ekbal, Asif

Subjects
ARTIFICIAL intelligence, ARTIFICIAL neural networks
Abstract

One key frontier of artificial intelligence (AI) is the ability to comprehend research articles and validate their findings, posing a magnanimous problem for AI systems to compete with human intelligence and intuition. As a benchmark of research validation, the existing peer-review system still stands strong despite being criticized at times by many. However, the paper vetting system has been severely strained due to an influx of research paper submissions and increased conferences/journals. As a result, problems, including having insufficient reviewers, finding the right experts, and maintaining review quality, are steadily and strongly surfacing. To ease the workload of the stakeholders associated with the peer-review process, we probed into what an AI-powered review system would look like. In this work, we leverage the interaction between the paper's full text and the corresponding peer-review text to predict the overall recommendation score and final decision. We do not envisage AI reviewing papers in the near future. Still, we intend to explore the possibility of a human–AI collaboration in the decision-making process to make the current system FAIR. The idea is to have an assistive decision-making tool for the chairs/editors to help them with an additional layer of confidence, especially with borderline and contrastive reviews. We use a deep attention network between the review text and paper to learn the interactions and predict the overall recommendation score and final decision. We also use sentiment information encoded within peer-review texts to guide the outcome further. Our proposed model outperforms the recent state-of-the-art competitive baselines. We release the code of our implementation here: https://github.com/PrabhatkrBharti/PEERRec.git. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Multi-Beam Sonar Target Segmentation Algorithm Based on BS-Unet.

Author

Zhang, Wennuo, Zhang, Xuewu, Zhang, Yu, Zeng, Pengyuan, Wei, Ruikai, Xu, Junsong, and Chen, Yang

Subjects
SONAR imaging, IMAGE segmentation, DAM safety, INSPECTION & review, SONAR
Abstract

Multi-beam sonar imaging detection technology is increasingly becoming the mainstream technology in fields such as hydraulic safety inspection and underwater target detection due to its ability to generate clearer images under low-visibility conditions. However, during the multi-beam sonar detection process, issues such as low image resolution and blurred imaging edges lead to decreased target segmentation accuracy. Traditional filtering methods for echo signals cannot effectively solve these problems. To address these challenges, this paper introduces, for the first time, a multi-beam sonar dataset against the background of simulated crack detection for dam safety. This dataset included simulated cracks detected by multi-beam sonar from various angles. The width of the cracks ranged from 3 cm to 9 cm, and the length ranged from 0.2 m to 1.5 m. In addition, this paper proposes a BS-UNet semantic segmentation algorithm. The Swin-UNet model incorporates a dual-layer routing attention mechanism to enhance the accuracy of sonar image detail segmentation. Furthermore, an online convolutional reparameterization structure was added to the output end of the model to improve the model's capability to represent image features. Comparisons of the BS-UNet model with commonly used semantic segmentation models on the multi-beam sonar dataset consistently demonstrated the BS-UNet model's superior performance, as it improved semantic segmentation evaluation metrics such as Precision and IoU by around 0.03 compared to the Swin-UNet model. In conclusion, BS-UNet can effectively be applied in multi-beam sonar image segmentation tasks. [ABSTRACT FROM AUTHOR]

Published
2024
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47. A Microvascular Segmentation Network Based on Pyramidal Attention Mechanism.

Author

Zhang, Hong, Fang, Wei, and Li, Jiayun

Subjects
RETINAL blood vessels, DIABETIC retinopathy, COMPLEX variables, EYE diseases, PIXELS, MEDICAL screening, BLOOD vessels, FREE flaps
Abstract

The precise segmentation of retinal vasculature is crucial for the early screening of various eye diseases, such as diabetic retinopathy and hypertensive retinopathy. Given the complex and variable overall structure of retinal vessels and their delicate, minute local features, the accurate extraction of fine vessels and edge pixels remains a technical challenge in the current research. To enhance the ability to extract thin vessels, this paper incorporates a pyramid channel attention module into a U-shaped network. This allows for more effective capture of information at different levels and increased attention to vessel-related channels, thereby improving model performance. Simultaneously, to prevent overfitting, this paper optimizes the standard convolutional block in the U-Net with the pre-activated residual discard convolution block, thus improving the model's generalization ability. The model is evaluated on three benchmark retinal datasets: DRIVE, CHASE_DB1, and STARE. Experimental results demonstrate that, compared to the baseline model, the proposed model achieves improvements in sensitivity (Sen) scores of 7.12%, 9.65%, and 5.36% on these three datasets, respectively, proving its strong ability to extract fine vessels. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Research on Radar Target Detection Based on the Electromagnetic Scattering Imaging Algorithm and the YOLO Network.

Author

Guo, Guangbin, Wang, Rui, and Guo, Lixin

Abstract

In this paper, the radar imaging technology based on the time-domain (TD) electromagnetic scattering algorithm is used to generate image datasets quickly and apply them to target detection research. Considering that radar images are different from optical images, this paper proposes an improved strategy for the traditional You Only Look Once (YOLO)v3 network to improve target detection accuracy on radar images. The speckle noise in radar images can cover the real information of a target image and increase the difficulty of target detection. The attention mechanisms are added to the traditional YOLOv3 network to strengthen the weight of the target region. By comparing the target detection accuracy under different attention mechanisms, an attention module with higher detection accuracy is obtained. The validity of the proposed detection network is verified on a simulation dataset, a measured real dataset, and a mixed dataset. This paper is about an interdisciplinary study of computational electromagnetics, remote sensing, and artificial intelligence. Experiments verify that the proposed composite network has better detection performance. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Target Recognition Based on Infrared and Visible Image Fusion and Improved YOLOv8 Algorithm.

Author

Guo, Wei, Li, Yongtao, Li, Hanyan, Chen, Ziyou, Xu, Enyong, Wang, Shanchao, and Gu, Chengdong

Subjects
IMAGE fusion, INFRARED imaging, CONVOLUTIONAL neural networks, FEATURE extraction, IMAGE reconstruction
Abstract

In response to the issue that the fusion process of infrared and visible images is easily affected by lighting factors, in this paper, we propose an adaptive illumination perception fusion mechanism, which was integrated into an infrared and visible image fusion network. Spatial attention mechanisms were applied to both infrared images and visible images for feature extraction. Deep convolutional neural networks were utilized for further feature information extraction. The adaptive illumination perception fusion mechanism is then integrated into the image reconstruction process to reduce the impact of lighting variations in the fused images. A Median Strengthening Channel and Spatial Attention Module (MSCS) was designed to be integrated into the backbone of YOLOv8. In this paper, we used the fusion network to create a dataset named ivifdata for training the target recognition network. The experimental results indicated that the improved YOLOv8 network saw further enhancements of 2.3%, 1.4%, and 8.2% in the Recall, mAP50, and mAP50-95 metrics, respectively. The experiments revealed that the improved YOLOv8 network has advantages in terms of recognition rate and completeness, while also reducing the rates of false negatives and false positives. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Research on low-light image enhancement based on MER-Retinex algorithm.

Author

Zhou, Rongfeng, Wang, Rugang, Wang, Yuanyuan, Zhou, Feng, and Guo, Naihong

Abstract

To solve blurring and poor visual effects after enhancement of low-light images by conventional low-light algorithms, this paper proposes a MER-Retinex (multi-scale expansion reconstruction retinex) algorithm that integrates attention mechanism and multi-scale expansion pyramid reconstruction. It includes two parts: decomposition and enhancement module. In the decomposition module, two U-shaped networks are used to decompose the image into reflectance and illumination, then, use multi-layer convolution to expand the field of perception and improve the ability to decompose the image to obtain reflectance and illumination. In the enhancement module, a U-shaped network is used to fuse multi-scale expansion pyramids with a multi-attention mechanism to enrich image information, increase image brightness and fuse the processed global information with local information to enhance the recovered image details. In the enhanced reconstruction section, super-resolution techniques are used to enhance and denoise image feature details. Experimental analysis of the MER-Retinex algorithm was carried out on the LOL dataset. The PSNR of the algorithm in this paper was 25.26 and the NIQE was 3.43. The algorithm in this paper can effectively solve the problems of blurred images and poor visual effects, and has improved in both subjective perception and objective evaluation indexes. [ABSTRACT FROM AUTHOR]

Published
2024
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