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1. DrivCapsNet: A Driving Style Detection Algorithm Based on Capsule Network | |||
Yueyue Wang,Chenxing Wang,Haiyong Luo | |||
Computer Science and Technology 26 May 2023 | |||
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Abstract:Driver behavior plays a fundamental role in the driver-vehicle-environment system, where the driving style can significantly impact vehicle emissions, fuel consumption, insurance expenses, road safety, and advanced driver assistance systems (ADAS). Nonetheless, detecting driver behavior is a complex and challenging task, traditional methods require a lot of data pre-processing and there is still no research on discriminative driving behavior with capsule networks which can capture the spatial relationships of data. However, it has not been fully studied and applied for driver behavior detection. To tackle these challenges, we propose an methodology for detecting driving style using a capsule network, named DrivCapsNet, which is capable of detecting various driving styles using either inertial measurement unit (IMU) data or camera data. A crucial advantage of this method is that its dynamic routing mechanism can extract the relationships between the parts and the entity, thereby improving detection accuracy. We performed comprehensive experiments on two realistic driving datasets to substantiate the efficacy of our proposed DrivCapsNet approach. The outcomes validate that our approach performs well and achieves accurate driving style detection, highlighting its potential to contribute significantly to the field of driver behavior analysis. | |||
TO cite this article:Yueyue Wang,Chenxing Wang,Haiyong Luo. DrivCapsNet: A Driving Style Detection Algorithm Based on Capsule Network[OL].[26 May 2023] http://en.paper.edu.cn/en_releasepaper/content/4760841 |
2. Light field Stitching via 4D Homography | |||
DAI Yi-chen,CAI Min-jie | |||
Computer Science and Technology 14 May 2023 | |||
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Abstract:The problem of the light field (LF) stitching aims to align two 4D LFs seamlessly. However, the prior works use conventional $3\times3$ homography to draw the 2D relation and ignore the depth information, leading to two main disadvantages, namely, significant stitching artifacts in the general scene and failure to produce stitched depth map. This paper tackles these challenges by proposing a $4\times4$ homography that analytically and globally describes the relationship between two LFs under pure rotation. Besides, we also present a novel linear solver called 4ry, which can estimate the 4D homography by giving four 4D LF feature correspondences. Extensive synthetic and real data experiments demonstrate that the proposed method outperforms state-of-the-art approaches in LF stitching qualitatively and quantitatively. More importantly, the output of our method is still an LF that retains the nature of LF, such as refocusing, viewpoint shifting, and depth estimation. | |||
TO cite this article:DAI Yi-chen,CAI Min-jie. Light field Stitching via 4D Homography[OL].[14 May 2023] http://en.paper.edu.cn/en_releasepaper/content/4760779 |
3. Learning Fair and Efficient Policies in Sequential Public Goods Dilemmas | |||
CHEN Yi-Tian,LIU Xuan,CHEN Xin-Ning,ZHANG Shi-Geng | |||
Computer Science and Technology 12 May 2023 | |||
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Abstract:Rational individuals can obtain higher rewards in the short term by defecting in social dilemmas, which, however, leads to low collective utility or even task failure. Many recent works have induced cooperative behaviors in social dilemmas though, they work only in stateless matrix games but fail in sequential social dilemmas. In tasks of sequential social dilemmas involving large number of players and complex states, cooperation is no longer simply one-step action and is hard to learn. Some works take payoffs equality into agents’ reward signals in decentralized multi-agent reinforcement learning to prevent some agents from taking up too much resources and starving others. However, this payoffs equality cannot lead to effective cooperative strategy, because it will force well-learned agents to sacrifice their high efficiency for equality if some agents have extremely low performance. In this work, we consider sequential public goods dilemmas in which group members can donate voluntarily for public welfare. We take fairness into account for training, well-learned agents obtain adequate rewards without being constrained by the policies of others, and meanwhile, the laggards have more access to learning owing to sufficient public goods. We empirically show that our method has excellent performance both in terms of collective efficiency and fairness. Compared to baselines, our agents acquire more universal and sustainable policies in sequential public goods dilemmas. | |||
TO cite this article:CHEN Yi-Tian,LIU Xuan,CHEN Xin-Ning, et al. Learning Fair and Efficient Policies in Sequential Public Goods Dilemmas[OL].[12 May 2023] http://en.paper.edu.cn/en_releasepaper/content/4760736 |
4. Data Augmentation Based Biaffine Attention Coreference Resolution Algorithm | |||
LI Yuanxin | |||
Computer Science and Technology 11 May 2023 | |||
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Abstract:Event text generally has a variety of descriptions. Event coreference resolution aims to find the similarity matching between two kinds of event text descriptions, which can help professionals find the same event from multi-source information. We propose a data augmentation based biaffine attention Coreference resolution algorithm, DEBACR. Roberta was selected as the pre-training model for feature extraction. Various data enhancement methods such as back translation and FGM were used to enhance the generalization ability of the model. We propose a fusion model of global and local information, which combines the coreference relation judgment of trigger word consistency, event elements consistency and event overall consistency. By using the biaffine attention mechanism, the important features are screened out for resolution and classification. Experimental results show that the accuracy of the algorithm is effectively improved in multiple Chinese event coreference resolution datasets than semantic similarity model based on Siamese network. | |||
TO cite this article:LI Yuanxin. Data Augmentation Based Biaffine Attention Coreference Resolution Algorithm[OL].[11 May 2023] http://en.paper.edu.cn/en_releasepaper/content/4760702 |
5. Automated Left Ventricular Myocardium Segmentation of Coronary Computed Tomography Angiography Based on Improved 2.5D U-Net | |||
CHU Dong-Heng, LI Shu-Fang | |||
Computer Science and Technology 24 April 2023 | |||
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Abstract:Cardiovascular disease has become one of the major health hazards and non-invasive diagnostic methods are of great clinical importance for early intervention and treatment of cardiovascular disease. The widespread use of computed tomography (CT) and the rapid development of deep learning technologies have made it possible to perform non-invasive cardiac flow functional assessment based on coronary CTA images. Among them, the accurate segmentation of the left ventricular myocardium (LVM) region in coronary CTA images is the basis for effective quantification of functional pathological differences, which is essential to assist in the treatment and management of coronary artery disease. In this paper, a 2.5D segmentation algorithm based on improved U-Net is proposed for the purpose of segmenting LVM based on coronary CTA images. Unlike other deep learning-based segmentation methods, firstly, this paper implements a 2.5D segmentation model by thickening the samples. Secondly, this study proposes Dilated Slice and introduces an information correction module and a multi-stage multi-scale pooling module into the network model, which enables it to better focus on the features of the LVM region, thus improving the segmentation accuracy. In this paper, the algorithm is trained and tested based on a coronary CTA image dataset. Experiments demonstrate that the use of the newly proposed sampling method and the improved segmentation model can achieve optimal results in the task of implementing LVM region segmentation based on coronary CTA images. | |||
TO cite this article:CHU Dong-Heng, LI Shu-Fang. Automated Left Ventricular Myocardium Segmentation of Coronary Computed Tomography Angiography Based on Improved 2.5D U-Net[OL].[24 April 2023] http://en.paper.edu.cn/en_releasepaper/content/4760523 |
6. Moving Object Detection Algorithm Based on Dynamic Vision Sensor | |||
SUN Xue,SUN Xue,LIU Dengfeng,LIU Dengfeng | |||
Computer Science and Technology 11 April 2023 | |||
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Abstract:Target detection and tracking mainly use RGB camera and deep learning algorithm. The information in the image is complex and redundant so the computing consumes a lot of resources. To solve the above problems, this paper proposes an improved spectral clustering algorithm to detect moving object. The algorithm is based on event data generated by dynamic vision sensors. In this paper, the cosine - Manhattan fusion distance is used to obtain a more accurate similarity matrix. The clustering results of some data are used to guide other data to speed up operation. The number of clusters is set adaptively to avoid the subjective influence of human beings. The results show that the accuracy of the improved algorithm on multiple data sets is more than 80%, and the time is significantly shortened. Spectral clustering algorithm based on dynamic vision sensor has great application potential in dealing with multi-target motion problems. | |||
TO cite this article:SUN Xue,SUN Xue,LIU Dengfeng, et al. Moving Object Detection Algorithm Based on Dynamic Vision Sensor[OL].[11 April 2023] http://en.paper.edu.cn/en_releasepaper/content/4760291 |
7. An attention-enhanced neural network with distillation training for barcode detection | |||
Wang Zijian,Zhou Xiaoguang | |||
Computer Science and Technology 03 April 2023 | |||
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Abstract:Barcodes have played an essential role in our daily life. Localizing, or detecting them in real scenes in a fast and robust way has many practical applications. Recently, some deep learning-based methods have shown great potential in object detection. However, because barcodes are placed at any angle, vertical bounding boxes cannot sufficiently capture accurate orientation and scale information. In this paper, we propose a barcode detector that performs dense prediction to accurately locate the position of pixels belonging to the barcode region. For better detection performance, we design a spatial attention module to integrate global information adaptively, which can be easily plugged into the prediction backbone. Meanwhile, we employ the knowledge distillation training strategy to train a small student network with the help of a heavy teacher network. Extensive experiment results demonstrate that our method can perform real-time speed on CPU environments and locate barcodes in images with complex scenes. | |||
TO cite this article:Wang Zijian,Zhou Xiaoguang. An attention-enhanced neural network with distillation training for barcode detection[OL].[ 3 April 2023] http://en.paper.edu.cn/en_releasepaper/content/4759920 |
8. Face Image Animation Based on Detail Feature Restoration | |||
ZHAO Runyuan,WANG Chun | |||
Computer Science and Technology 03 April 2023 | |||
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Abstract:With the development of mobile Internet and deep learning technology, short videos have become one of the most common software in people's mobile phones, and the secondary creation of videos based on deep learning algorithms has become a typical application scene. For the problem of video-to-image motion transfer in the face scene, there are many problems in the existing unsupervised algorithms, such as face and image over-distortion. In order to deal with these problems, this paper designs a face motion transfer model that combines face reenactment and optimized motion modeling. It mainly includes three parts: the face module reconstructs the target face based on 3DMM and FLAME algorithms and outputs the face reenactment and face motion field , the motion module outputs the predicted image optical flow and multi-scale occlusion map through the 2D interpolation algorithm, and the generator extracts the features of the original image and generates the face image of the target pose under the guidance of the front output. After the video reconstruction test on the VoxCeleb1 dataset, the algorithm in this paper can better restore the detailed features of the face, and has better performance in related indicators. | |||
TO cite this article:ZHAO Runyuan,WANG Chun. Face Image Animation Based on Detail Feature Restoration[OL].[ 3 April 2023] http://en.paper.edu.cn/en_releasepaper/content/4759987 |
9. A Graph Learning based Approach for Similar Text Recommendation in Enforcement Document of Urban Management | |||
Liu Huiyong,Xiong Songlin | |||
Computer Science and Technology 31 March 2023 | |||
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Abstract:The research on text classification is the most basic part of the natural language processing sector in the field of machine learning. Since the topic was proposed, quite a few methods have been produced to solve this problem. However, most of the current text classification methods are for text classification of short texts or long texts containing a certain specific content. There are relatively few studies on text classification methods that contain multiple texts and specific structural structures. In the professional field of urban management and law enforcement documents, the composition of documents has the characteristics of multiple texts and specific structural structures. In order to complete the text classification work of this type of task, it is necessary to adopt some targeted methods. In the past decade, deep learning has achieved great success in various fields. In order to improve the accuracy and efficiency of such text classification, methods of introducing graph structure and deep learning are considered. Combining the characteristics of such texts, this paper mainly studies the graph convolutional neural network algorithm for such text classification. In order to extract more information from the text, construct a graph on the entire corpus, use the vocabulary in the text and the text itself as nodes in the graph, use the vocabulary co-occurrence matrix information to construct the edge between texts, and use the vocabulary The edge between words and words is constructed by using the position context relationship between them, and the edge between words and text is constructed by using the frequency of words appearing in the text, and then the multi-text classification problem is regarded as a graph node classification problem. After iteration and optimization of the algorithm, the final conclusion is that the method of using the graph convolutional neural network can greatly improve the accuracy of this type of multi-text classification within the acceptable efficiency loss range. And further improve the accuracy of text recommendation and tailoring suggestions in the follow-up work. | |||
TO cite this article:Liu Huiyong,Xiong Songlin. A Graph Learning based Approach for Similar Text Recommendation in Enforcement Document of Urban Management[OL].[31 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759931 |
10. Using Pre-Trained Language Models for Aspect Category Sentiment Analysis in a Simple Causal Inference way | |||
XIAO Zhiyi | |||
Computer Science and Technology 22 March 2023 | |||
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Abstract:Aspect category sentiment analysis (ACSA) is a fine-grained sentiment analysis task that aims to identify the sentiment polarity to wards an explicit or implicit aspect (term) in a sentence. While pre trained language models like BERT have shown promising results in this area, they can also have inaccuracies caused by spurious correlations that do not necessarily reflect the sentiment. The bias in pre-trained language models and the unbalanced distribution of the finetuning data always leads to high accuracy in test data, but it could perform poorly on the prediction in reality. Inspired by the stability of causality, we propose counterfactual ACSA (CF-ACSA), a model-agnostic framework that reduces language bias by separating the direct language effect from the total effect on sentiment. We first identify the spurious correlations be tween term and sentiment polarity through a semi-supervised approach. Next, we use the spurious term we find to construct counterfactual sam ples for the original data. Finally, we make causal inferences and get an unbiased result between counterfactual samples and the original data. Extensive experiments demonstrate the effctiveness of our model, and results show that it outperforms the state-of-the-art methods on ASAP datasets. | |||
TO cite this article:XIAO Zhiyi. Using Pre-Trained Language Models for Aspect Category Sentiment Analysis in a Simple Causal Inference way[OL].[22 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759903 |
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