Proceedings of the 5th International Conference on Computer Science and Application Engineering最新文献_第5页

Attention Mechanism Driven YOLOv3 on FPGA Acceleration for Efficient Vision Based Defect Inspection 关注机制驱动的YOLOv3基于FPGA加速的高效视觉缺陷检测

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487165

Longzhen Yu, Qian Zhao, Zhixian Wang

引用次数: 0

Community-Based Routing in Vehicular Social Networks 基于社区的车辆社交网络路由

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487183

Zifeng Hao, Xiaolan Tang, Qun Wang, Wenlong Chen, Yongting Zhang

{"title":"Community-Based Routing in Vehicular Social Networks","authors":"Zifeng Hao, Xiaolan Tang, Qun Wang, Wenlong Chen, Yongting Zhang","doi":"10.1145/3487075.3487183","DOIUrl":"https://doi.org/10.1145/3487075.3487183","url":null,"abstract":"Considering the social features of drivers and passengers on vehicles, different communities may have different levels of demand for each packet. In order to represent this difference, we design a significance value for each packet. Furthermore, a community-based routing protocol in vehicular social networks is proposed, named CRP. When selecting relays, the forwarding priority is calculated by combing the direct and indirect forwarding contributions. The direct forwarding contribution is computed based on the number of neighbor nodes in each community as well as its significance value, while the indirect forwarding contribution indicates the delivery ability in future by using the contact probabilities between communities. Then according to the forwarding priorities and the number of replicas, the new relays are selected and the number of replicas are distributed among new relays. Finally, experiments using real road map and well-designed routes for three communities in Beijing show that CRP outperforms other protocols in terms of the community delivery ratio, while keeping a short delay.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124687835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Research on TextCNN-based Evaluation of Rationality of Narrative Text Structure 基于textcnn的叙事文本结构合理性评价研究

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487160

Jincheng Wang, Jie Liu

{"title":"Research on TextCNN-based Evaluation of Rationality of Narrative Text Structure","authors":"Jincheng Wang, Jie Liu","doi":"10.1145/3487075.3487160","DOIUrl":"https://doi.org/10.1145/3487075.3487160","url":null,"abstract":"Automatic Essay Scoring refers to the use of computers to score composition by some technologies. This process does not require human intervention. Rational text structure analysis is an important part of automatic essay scoring. However, the study of text structure is still in its infancy, ignoring its importance to the evaluation. Existing research lacks a corpus for the evaluation of text structure. The recognition of text components mostly uses artificial experience for feature selection, and evaluation model is established based on them. To figure out these problems, this paper refers to the curriculum standards, works with experts to build text structure standard and labeling method, and formulate corresponding labeling specifications. Finally build a corpus of a certain scale. TextCNN are used to build a model for the text structure. Model treats each article as a whole for training, and realizes the use of deep learning algorithms to make the model automatically evaluate. The results in test set show that in the constructed narrative composition corpus for grades 5-9, the accuracy of the model can reach 72.4%.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125171875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Overlapping Routing Tree Transmission Model Based on Segment Identification: OTSI Model 基于段识别的重叠路由树传输模型:OTSI模型

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487108

Sushu Guo, Wenlong Chen, Jiacheng Wang

引用次数: 0

Machine Learning-based Prediction of Postoperative 30-days Mortality 基于机器学习的术后30天死亡率预测

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487130

Linna Wang, Linji Li, T. Zhu, Congli Ma, Li Lu

{"title":"Machine Learning-based Prediction of Postoperative 30-days Mortality","authors":"Linna Wang, Linji Li, T. Zhu, Congli Ma, Li Lu","doi":"10.1145/3487075.3487130","DOIUrl":"https://doi.org/10.1145/3487075.3487130","url":null,"abstract":"Surgical patients aged 65 and over are facing a 2-10 times higher risk of death after surgery. Early prediction of postoperative mortality is essential, as timely and appropriate treatment can improve survival outcomes. With the development of medical and computer technology, numerous available health-related data can be recorded for research. Among various patient indicators which may affect the accuracy of prediction, it is necessary to find highly relevant and efficient features. The aims of this study were to use machine learning algorithms, specifically Bagging and Boosting Algorithms (e.g. Random Forest, eXtreme Gradient Boosting), to predict the postoperative 30-days mortality in surgical patients aged over 65, and to identify the optimal features using genetic algorithm(GA). This prospective study was developed and validated on the cohort from electronic health records (EHRs) of West China Hospital, Sichuan University, which contained 7467 surgical patients (0.924% mortality rate) who underwent surgery between July 1, 2019 and October 31, 2020. Compared with models like the traditional logistic regression model and the baseline ASA physical status, We found that XGBoost with hyper-parameters had best performance based solely on the automatically obtained features (area under the curve [AUC] of 0.9318, 95% confidence interval [CI] 0.9041 - 0.9594). The AUC of baseline ASA-PS was 0.6787 (95% CI 0.6471 - 0.7103) using XGBoost. When both ASA-PS and the selected features are included as inputs, XGboost achieved the AUC of 0.9345 (95% CI 0.9076 - 0.9613).","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121224348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Automatic Thyroid Ultrasound Image Detection and Classification with Priori Knowledge 基于先验知识的甲状腺超声图像自动检测与分类

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487166

Mengdie Shi, Jianrui Ding, Shili Zhao, Zichen Huang

引用次数: 0

Spatial-Temporal Multi-Head Attention Networks for Traffic Flow Forecasting 交通流预测的时空多头注意网络

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487102

Zhao Zhang, Ming Liu, Wenquan Xu

引用次数: 2

Human Activity Recognition from Accelerometer Data: Axis-Wise Versus Axes-Resultant Feature Extraction 基于加速度计数据的人类活动识别:轴向与轴向合成特征提取

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487152

Aiguo Wang, Shenghui Zhao, Guilin Chen

{"title":"Human Activity Recognition from Accelerometer Data: Axis-Wise Versus Axes-Resultant Feature Extraction","authors":"Aiguo Wang, Shenghui Zhao, Guilin Chen","doi":"10.1145/3487075.3487152","DOIUrl":"https://doi.org/10.1145/3487075.3487152","url":null,"abstract":"Benefitting from the development of pervasive computing, recent years have witnessed a variety of meaningful human-centric applications, where automating the recognition of human activities plays a central role in bridging the gap between sensing data and high-level services. Accelerometer-based activity recognizer often remains a priority due to its recognition performance, low costs, and portability, however, few studies systematically investigate how to extract and use features from the time-series sensor data and further compare their discriminant power. To this end, we herein propose two different ways of extracting features and exploring their combinations. Specifically, we take as a resultant axis or separate channels the accelerometer axes and then extract axes-resultant and axis-wise features. Afterwards, we evaluate the cases where the two feature sets are used separately or jointly. Finally, we conduct comparative experiments on two public activity recognition datasets with five different classification models in terms of four performance metrics. Results show that the use of axis-wise features outperforms its competitor in the majority across the datasets and that their joint use generally leads to enhanced accuracy.","PeriodicalId":354966,"journal":{"name":"Proceedings of the 5th International Conference on Computer Science and Application Engineering","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115564038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Faster R-CNN for Railway Fastening System Detection 用于铁路扣件系统检测的改进型快速 R-CNN

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487184

Xin-Yi Peng, Shuzhen Tong, Xiaobo Lu, Yun Wei

引用次数: 0

Research on Fuzzing Technology for JavaScript Engines JavaScript引擎模糊测试技术研究

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI: 10.1145/3487075.3487107

Y. Tian, Xiaojun Qin, Shuitao Gan

引用次数: 1