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	1. Federated learning based on hybrid blockchain
	FAN Linxuan,LI Lixiang
	Computer Science and Technology 16 March 2023
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	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


	2. Online Chinese Polyphone Disambiguation with Progressive Neural Networks
	ZHANG Yi-Fei
	Computer Science and Technology 16 March 2023
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	Abstract:There has been plenty of research on Chinese polyphonic disambiguation (CPD) problems. However, badcases are always found in real-life products. To fix such bad cases without affecting system performance on known cases is a rigid demand. In this paper, continual learning is introduced to CPD problems, and Progressive Neural Networks (PNN) is used to learn new knowledge from bad cases without sacrificing system performance on old datasets. The experimental results show that the proposed method can repair the badcase without forgetting the feature of the original dataset. Compared with the traditional finetune method, the accuracy of the model on the old dataset decreases by nearly 20\%. Our method can ensure that the accuracy of the original dataset just decreases by about 0.3\% after learning the new feature data, and the time consumption is acceptable. Potential improvements like weight pruning are also discussed.
	TO cite this article:ZHANG Yi-Fei. Online Chinese Polyphone Disambiguation with Progressive Neural Networks[OL].[16 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759451


	3. A Semantic Segmentation Model for Top-Down View Image Based on Images from Multiple Vehicle On-board Cameras
	GE Mengcheng,SHI Yan
	Computer Science and Technology 10 March 2023
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	Abstract:Comprehensive environmental perception is crucial for autonomous driving. However, due to the issue of occlusion, current intelligent vehicle perception algorithms only recognize targets within the intelligent vehicle\'s perception area as much as possible, without predicting or annotating areas obscured by foreground objects. This limits the intelligent driving system\'s comprehensive perception and understanding of the driving environment.This paper proposes a semantic segmentation model that uses image data collected by cameras surrounding the intelligent vehicle as input. The model uses spatial transformation networks for perspective transformation and DeepLabv3p architecture as the backbone of the semantic segmentation network, which outputs the semantic segmentation perception results of the intelligent vehicle\'s driving environment from a bird\'s-eye view, including the obscured areas. In addition, this paper does not rely on manually labeled data but collects data sets through the Carla simulator and uses a designed ray-localization method for subsequent data annotation. By training on the collected data set, the proposed method achieved an MIoU score of 71.49%, which is better than traditional methods based on inverse perspective transformation and fully connected network models.
	TO cite this article:GE Mengcheng,SHI Yan. A Semantic Segmentation Model for Top-Down View Image Based on Images from Multiple Vehicle On-board Cameras[OL].[10 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759540


	4. Multi-grained Location Matching with Universal Structural Coordinate Encoder for Referring Expression Grounding
	Yihong Zhao,Xiaojie Wang
	Computer Science and Technology 07 March 2023
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