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| There are 322 papers published in subject: > since this site started. | |||
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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 | |||
| Show/Hide Abstract | Cite this paper︱Full-text: PDF (0 B) | |||
| 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. 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 | |||
| Show/Hide Abstract | Cite this paper︱Full-text: PDF (0 B) | |||
| 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 | |||
| 3. Data Augmentation Based Biaffine Attention Coreference Resolution Algorithm | |||
| LI Yuanxin | |||
| Computer Science and Technology 11 May 2023 | |||
| Show/Hide Abstract | Cite this paper︱Full-text: PDF (0 B) | |||
| 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 | |||
| 4. 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 | |||
| Show/Hide Abstract | Cite this paper︱Full-text: PDF (0 B) | |||
| 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 | |||
| 5. An attention-enhanced neural network with distillation training for barcode detection | |||
| Wang Zijian,Zhou Xiaoguang | |||
| Computer Science and Technology 03 April 2023 | |||
| Show/Hide Abstract | Cite this paper︱Full-text: PDF (0 B) | |||
| 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 | |||
| 6. Face Image Animation Based on Detail Feature Restoration | |||
| ZHAO Runyuan,WANG Chun | |||
| Computer Science and Technology 03 April 2023 | |||
| Show/Hide Abstract | Cite this paper︱Full-text: PDF (0 B) | |||
| 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 | |||
| 7. 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 | |||
| Show/Hide Abstract | Cite this paper︱Full-text: PDF (0 B) | |||
| 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 | |||
| 8. 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 | |||
| Show/Hide Abstract | Cite this paper︱Full-text: PDF (0 B) | |||
| 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 | |||
| 9. Stroke-encoding and Pinyin-learning Enhanced Chinese Pre-trained Language Representation Model | |||
| ZHOU Tong | |||
| Computer Science and Technology 22 March 2023 | |||
| Show/Hide Abstract | Cite this paper︱Full-text: PDF (0 B) | |||
| Abstract:Language models pre-trained on large unlabeled corpora have proven to be very effective in improving many downstream NLP tasks. However, existing language models are primarily designed for English, and less consideration has been given to the more abundant semantic information that Chinese characters imply. The unique semantically related stroke sequence pattern and polyphony enable the enhancement of a Chinese language representation model. Masked language models, such as BERT, are also plagued by inefficient training data utilization, requiring more iterations to complete training. In light of these shortcomings, we propose an improved, customized Chinese pre-trained language model based on the transformer, called SPCLM (Stroke-encoding and Pinyin-learning enhanced Chinese pre-trained Language representation Model). SPCLM contains stroke encoders and an additional pronunciation prediction task. Moreover, the autoregressive objection and mask prediction jointly assist in model formulation. Experimental results demonstrate that SPCLM outperforms other baseline methods, achieving comparable results on five Chinese NLP tasks, with insufficient pre-training, including natural language inference, semantic similarity, named entity recognition, sentiment analysis, and question answering. | |||
| TO cite this article:ZHOU Tong. Stroke-encoding and Pinyin-learning Enhanced Chinese Pre-trained Language Representation Model[OL].[22 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759900 | |||
| 10. Federated learning based on hybrid blockchain | |||
| FAN Linxuan,LI Lixiang | |||
| Computer Science and Technology 16 March 2023 | |||
| Show/Hide Abstract | Cite this paper︱Full-text: PDF (0 B) | |||
| Abstract:With the rapid development of technologies such as machine learning, 5G communication, edge computing, artificial intelligence and blockchain, the field of machine learning has produced some new training methods. Among them, federated learning is a typical representative of distributed machine learning. Compared with traditional machine learning, federated learning can collaborate on model training between organizations without exchanging original data sets, which ensures the security and privacy of organizational data. As a kind of distributed machine learning, federated learning is faced with severe technical challenges in the process of model training: low participation in the training of edge nodes, untrustworthy edge nodes and untraceable training data. To solve the above problems, based on the federated learning theory and the blockchain theory, this paper carries out relevant research on the federated learning algorithm based on hybrid blockchain and the blockchain incentive mechanism. The main research achievements and innovations of this paper are as follows: the existing federated learning algorithm hides training data, which gives the attacker an opportunity to exploit, and the attacker can use this defect to carry out backdoor attacks on model training. In addition, in the training process of federated learning algorithm, the identity of the participating nodes is not authenticated, so that the attacker can pretend the nodes to contribute dirty data, which reduces the accuracy of model training. Therefore, this paper proposes a federated learning algorithm based on hybrid blockchain, which mainly adopts consortium blockchain to authenticate and manage the identity of nodes participating in training. Meanwhile, public blockchain is used to store training parameters to achieve traceability of training data. In addition, the introduction of blockchain architecture enables federated learning to be further decentralized. The results of simulation experiments show that the proposed scheme has advantages in robustness and accuracy of model training under the same training task. | |||
| TO cite this article:FAN Linxuan,LI Lixiang. Federated learning based on hybrid blockchain[OL].[16 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759526 | |||
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