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2021 2nd International Conference on Computing and Data Science (CDS)最新文献

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Research on path-planning of particle swarm optimization based on distance penalty 基于距离惩罚的粒子群优化路径规划研究
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00032
Dechen Yuan
{"title":"Research on path-planning of particle swarm optimization based on distance penalty","authors":"Dechen Yuan","doi":"10.1109/CDS52072.2021.00032","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00032","url":null,"abstract":"Nowadays, designing the optimal path in the complex urban environment is still a significant problem. This paper studies an application of path planning using Particle Swarm Optimization. The grid method is used to divide the environment to fit the implementation of particle swarm optimization. The objective function evaluates the distance of the path, and the penalty is involved to avoid obstacles along the path. Finally, the particle swarm optimization is used in two path planning problems regarding emergency and common vehicles to design the optimal path.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131215350","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
The development of face recognition in accuracy and speed: A Review 人脸识别在准确性和速度方面的发展综述
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00020
Weijie Wang
{"title":"The development of face recognition in accuracy and speed: A Review","authors":"Weijie Wang","doi":"10.1109/CDS52072.2021.00020","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00020","url":null,"abstract":"This paper reviews the latest state of face recognition technology and applications. Provides a brief background of face recognition and specific algorithms, including Region-CNN (R-CNN), Spatial Pyramid Pooling Network (SPP-Net), Fast Region-CNN (Fast R-CNN), Faster Region-CNN (Faster R-CNN), Multi-task convolutional neural network (MTCNN), FaceBoxes, HAMBox. We have compared in detail the technological breakthroughs made by the algorithms, especially in terms of computing speed and accuracy. At the same time, we further introduced the opportunities and challenges faced by face recognition under the impact of the new crown epidemic. We foresee that in the near future, when we face a severe situation here, face recognition will adapt to changes well and solve the pain points in our lives.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121303779","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
Survey on Unsupervised Techniques for Person Re-Identification 无监督人员再识别技术研究综述
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00034
Changshui Yang, Feng Qi, Huizhu Jia
{"title":"Survey on Unsupervised Techniques for Person Re-Identification","authors":"Changshui Yang, Feng Qi, Huizhu Jia","doi":"10.1109/CDS52072.2021.00034","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00034","url":null,"abstract":"Unsupervised person re-identification (ReID) might be difficult if lacking labeling information. The feature extraction scheme generally divides existing methods into handcrafted feature-based methods, unsupervised domain adaptation (UDA) based methods, and pseudo-labels estimation-based methods. Feature representations are extracted or learnt directly from unlabeled datasets to address the scalability issue by hand-crafted feature-based methods. The purpose of unsupervised domain adaptation is to relieve the domain bias as the learnt features are transferred to an unlabeled target from a labeled source. For pseudo-labels estimation-based methods, they take supervised pseudo-labels to learn feature representations and labels are estimated together for unlabeled datasets. In this paper, the state-of-the-art unsupervised techniques are reviewed to solve the task of person re-identification, a brief review of each method along with their evaluations on a set of widely used datasets in included. In addition, we give a detail comparison among these methods according to corresponding category.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115403612","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: 3
Study and Analysis of Collaborative Management System of Network Security in Universities (CMSNSU) Under the Background of 2.0 Criteria of Classified Protection of Network Security 网络安全分类保护2.0标准背景下高校网络安全协同管理系统研究与分析
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00074
Yunjia Li, Zhi Zhang, Zhixin Chen, Xinxiang Xiao
{"title":"Study and Analysis of Collaborative Management System of Network Security in Universities (CMSNSU) Under the Background of 2.0 Criteria of Classified Protection of Network Security","authors":"Yunjia Li, Zhi Zhang, Zhixin Chen, Xinxiang Xiao","doi":"10.1109/CDS52072.2021.00074","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00074","url":null,"abstract":"Currently, such new technologies as big data, cloud computing and mobile Internet have been in a rapid development in the field of university; accordingly, there is more and more serious network security threat and information security. Through the analysis of the difficulty and pain points of network security management in universities in the new situation of the implementation of 2.0 criteria classified protection of network security, the paper would analyze the specific triggers. Based on the actual situation of universities, it is suggested with collaborative management system of network security in universities (CMSNSU), so as to improve the collaboration ability of network security management team and to implement the network security responsibility system as well as effectively improving the network and information security in universities.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116388416","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
RF-based Drone Detection using Machine Learning 使用机器学习的基于射频的无人机检测
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00079
Yongxu Zhang
{"title":"RF-based Drone Detection using Machine Learning","authors":"Yongxu Zhang","doi":"10.1109/CDS52072.2021.00079","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00079","url":null,"abstract":"Drones or unmanned aerial vehicles have become a new option for multiple tasks including delivery, photograph, etc. However, the small size and flight ability of drones make it easier to break through any barriers and intrude important facilities. With an increasing safety concern of drone incursions, the research for an effective drone detection and identification approach has drawn a lot of attention in recent years. Among existing methods, passive radio frequency sensing is both reliable and cost-effective. However, previous studies are evaluating both machine learning and statistical methods on private datasets under different settings. To make a fair comparison, we evaluate six machine learning models on an open drone dataset for RF-based drone detection in this paper. The results demonstrate that XGBoost achieves the state-of-the-art results on this pioneering dataset.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116808879","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: 8
An Introduction of Prediction Models From the View of Integration Between Basic Models 从基本模型集成的角度介绍预测模型
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00031
Yu-Chung Peng
{"title":"An Introduction of Prediction Models From the View of Integration Between Basic Models","authors":"Yu-Chung Peng","doi":"10.1109/CDS52072.2021.00031","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00031","url":null,"abstract":"The combination model is a widely used method way in machine learning to design the more sophisticated and well-performed model, which can be applied for the field of the recommendation system and so on. This paper is aims to explore the prediction model The normal prediction models have to deal with neither dense numerical features or sparse categorical features. Decision tree is a popular way to deal with dense data, doing both regression and classification. It has various famous expanding algorithms, such as GBDT, XGBoost, and so on, which is designed by different ways of combing the basic decision tree. GBDT and XGBoost are both good at Large-scale machine learning and perform well with dense numerical features. As for sparse categorical features. FM is no doubt the best basic algorithm designed for it. By combing FM with other models, Wide&Deep and DeepFM are better for the recommended system because they perform well and can deal with high-order feature intersection. Furthermore, we can not only just add models together, Deep&Cross and DeepGBM are examples which use NN (natural network) to approximate other models, combing the different parts to an integration. And NN can not only substitute the model is approximated, but also is suited for the online training. This paper is aims to explore the development of the prediction models and the future trend for its improvement.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130392982","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
Exponential Smoothing Forecast of African Americans' COVID-19 Fatalities 非裔美国人COVID-19死亡人数的指数平滑预测
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00086
T. Oladunni, M. Denis, E. Ososanya, Abdoulaye Barry
{"title":"Exponential Smoothing Forecast of African Americans' COVID-19 Fatalities","authors":"T. Oladunni, M. Denis, E. Ososanya, Abdoulaye Barry","doi":"10.1109/CDS52072.2021.00086","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00086","url":null,"abstract":"This work focuses on the spread and impact of COVID-19 in the black community. A detailed analysis will improve the universality of a comprehensive mitigation strategy in reducing and combatting the spread of coronavirus. Our analysis of COVID-19 spans March 2020 to November 2020. Forecasting computation was based on exponential smoothing. The quality of our models was evaluated using the Mean Absolute Percentage Error. Predominantly black states in the US were considered for the experiment. All things being equal, a forecast to February 2021 suggests a disturbing forecast for the African American communities in the states investigated.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130695443","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: 3
Student Performance Prediction Using XGBoost Method from A Macro Perspective 基于XGBoost方法的宏观学生成绩预测
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00084
Kuan Yan
{"title":"Student Performance Prediction Using XGBoost Method from A Macro Perspective","authors":"Kuan Yan","doi":"10.1109/CDS52072.2021.00084","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00084","url":null,"abstract":"Student performance prediction has attracted more and more attention in the educational data mining field in recent years. An accurate and useful forecast on student performance can play a huge role in many aspects, such as solving student dropout, allocating teaching resources reasonably, and improving teaching methods. In this paper, we employed an XGBoost-based method to forecast student performance. Instead of using individual students as samples, we used a novel educational dataset structured from a macro perspective, which rarely appeared in existing research. We used data cleaning, feature selection, and feature creation to increase the model's generalizability and the accuracy of the predictions. The XGBoost model achieved the best results than five other classic machine learning models (i.e., Random Forest, Lasso, Elastic Net, Support Vector Machine, and Decision Tree). It achieved a significant improvement in the R2 score by 6.3% to 12.1% on different sub-datasets. Furthermore, through feature importance analysis, we have drawn some forward-looking and meaningful conclusions.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128594616","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: 5
The Application of Machine Learning in Predicting Absenteeism at Work 机器学习在预测旷工中的应用
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00054
Bingqing Hu
{"title":"The Application of Machine Learning in Predicting Absenteeism at Work","authors":"Bingqing Hu","doi":"10.1109/CDS52072.2021.00054","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00054","url":null,"abstract":"The employee attendance is an important indicator to judge employees' work attitude and measure their workload, which directly impact the development of the corporation. Meanwhile, the absenteeism, referring to the employees' intentional or habitual absence from work, is also significant for the whole company. Having a good knowledge of the reasons and the predictions of employees' absenteeism can help the leaders to adjust the ways of working and be prepared to avoid the major effect on company finances, morale and other factors, which made by decreased productivity. While the leaders may judge the absenteeism subjectively, which is inaccurate and time-consuming., by means of machine learning, the prediction of employees' absenteeism can be more objective and efficient. In this paper, we used the data provided by UCI machine learning database, which was created with records of absenteeism at work of a courier company in Brazil, to build absenteeism prediction model. We first conduct descriptive statistical analysis, and then employ four classical machine learning models to solve the problem. The integrated learning algorithm has the highest accuracy, which reaches 52% on the test set.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129673388","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
Design of a game control system based on Brain-computer interface: link to a game 基于脑机接口的游戏控制系统设计:链接游戏
2021 2nd International Conference on Computing and Data Science (CDS) Pub Date : 2021-01-01 DOI: 10.1109/CDS52072.2021.00051
Yuliang Chen
{"title":"Design of a game control system based on Brain-computer interface: link to a game","authors":"Yuliang Chen","doi":"10.1109/CDS52072.2021.00051","DOIUrl":"https://doi.org/10.1109/CDS52072.2021.00051","url":null,"abstract":"Recent years have witnessed people's increasing interests in the application of brain-computer interface (BCI) technology in the field of games. In the human-computer interaction of games, BCI is considered as one of the many possible input methods (together with keyboard, voice, gesture, etc.), which can be used to control games. However, so far, we have only seen simple BCI practice in games, almost all of which only use a single electroencephalography (EEG) signal, and only simple judgment is used as the form of human-computer interaction in the game. The reason may be that these games are still designed for traditional purposes, and BCI is also designed for medical purposes. Based on the background mentioned above, this paper proposes a specific BCI system designed for games, which realizes the brain-computer interaction between people and games through BCI technology. The system enables BCI technology to be applied to more extensive public designs besides the medical field.","PeriodicalId":380426,"journal":{"name":"2021 2nd International Conference on Computing and Data Science (CDS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129763452","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
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