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	1. Research on Deep Learning Stock Selection Method Based on Trend Decomposition Algorithm
	Wu Cheng-Hui,Zhang Hong-Jian
	Computer Science and Technology 20 June 2023
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	Abstract:Some studies using machine learning and deep learning models did not fully explore other indicator features that affect stock price trends, resulting in significant prediction errors and difficulty in directly applying the prediction results to obtain excess returns. Therefore, it is necessary to establish an efficient deep learning model based on stock characteristics and fully explore the factors that affect stock price trends to improve prediction accuracy.In view of the shortcomings of traditional stock trend prediction, this paper constructs LSTM-BP neural network based on Tianniuxu optimization for stock price prediction, and constructs PCA-BP neural network model based on particle swarm optimization based on multi angle correlation analysis for trend prediction. Taking into account both price prediction and trend prediction dimensions, construct a deep quantitative stock selection method. In this process, a stock trend decomposition judgment algorithm is proposed to identify and classify the stock data, improve the data utilization, build a new data set, and use the particle swarm optimization to adjust and optimize the parameters.The results of strategy backtesting show that compared to other deep learning strategies and benchmark strategies, the stock selection method and strategy proposed in this article have significantly improved in terms of victory and return. This indicates that the model has good predictive ability and application potential, and can provide effective decision support for stock investment.
	TO cite this article:Wu Cheng-Hui,Zhang Hong-Jian. Research on Deep Learning Stock Selection Method Based on Trend Decomposition Algorithm[OL].[20 June 2023] http://en.paper.edu.cn/en_releasepaper/content/4760906


	2. 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


	3. 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


	4. 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,

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