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| 1 | 大数据法律监督建模的定位、流程与方法显示文摘大数据分析与建模技术为数字检察改革提供了坚实的技术支撑,提高了检察工作的质效。然而,当前大数据法律监督建模存在理论与实践的双重困境。大数据法律监督建模是创建供检察人员履行法律监督职权所使用的具有重复利用、实时性、可视化操作平台的技术活动。业务理解、数据理解与特征提取、检察数据挖掘、评估与验证、部署与优化构成了大数据法律监督建模的基本流程,其中检察数据挖掘是大数据法律监督建模的关键技术环节。未来在厘清大数据法律监督建模概念的基础上,可从拓展检察数据的来源并加强质量监管,推进对大数据法律监督建模软件的引入、使用和研发工作,制定完善的建模操作指南,加强复合型检察人才队伍建设等四个方面优化。 | 胡铭 陈竟 | 2024 | 北方法学2024，18，1： | 1 |
| 2 | Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors显示文摘Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road accidents.As a result,reckless driving behaviour can cause congestion and delays.Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem.Previous research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s face.On the other hand,driving a car is a complicated action that requires a wide range of body move-ments.In this work,we proposed a ResNet-SE model,an efficient deep learning classifier for driving activity clas-sification based on signal data obtained in real-world traffic conditions using smart glasses.End-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning model.Sensor data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this study.We performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its performance.According to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%. | Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul | 2023 | Intelligent Automation & Soft Computing2023，38，11： | 0 |
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