2020 IEEE 8th International Conference on Smart City and Informatization (iSCI)最新文献

Intelligent Surveillance in Smart City Using 3D Road Monitoring 基于3D道路监控的智慧城市智能监控

2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) Pub Date : 2020-12-01 DOI: 10.1109/iSCI50694.2020.00013

Aroma Tariq, Saqib Ali, X. Xing, Guojun Wang

{"title":"Intelligent Surveillance in Smart City Using 3D Road Monitoring","authors":"Aroma Tariq, Saqib Ali, X. Xing, Guojun Wang","doi":"10.1109/iSCI50694.2020.00013","DOIUrl":"https://doi.org/10.1109/iSCI50694.2020.00013","url":null,"abstract":"Traffic accidents and lack of road monitoring are never-ending cause of tribulation in metropolitan areas. A solution is required to tackle with these problems. The human eye is not capable of capturing each and everything happening on the roads. To build a secure environment in smart cities an intelligent surveillance system is required to keep an eye on what is happening on roads. The current security measures for surveillance are not enough to capture everything. For instance, single view and fixed CCTV cameras are installed on roads. They are only capable of providing information on a fixed angle. The current solution lacks at recognizing the type, model, and license plate of the vehicle. Therefore, the objective of this paper is to develop a 3D road monitoring model to provide intelligent surveillance in smart cities. The proposed solution is capable of performing efficient vehicle detection along with sensing unusual activities on the road. For this, pre-trained models trained using deep neural networks for vehicle detection, license plate recognition, and unusual activity detection are combined through stacking. It will reduce the man work, time, and complexity of the traffic controls.","PeriodicalId":433521,"journal":{"name":"2020 IEEE 8th International Conference on Smart City and Informatization (iSCI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127158685","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

Machine Learning-based Update-time Prediction for Battery-friendly Passenger Information Displays 基于机器学习的乘客信息显示更新时间预测

2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) Pub Date : 2020-12-01 DOI: 10.1109/iSCI50694.2020.00016

P. Herrmann, Ergys Puka, T. Skoglund

{"title":"Machine Learning-based Update-time Prediction for Battery-friendly Passenger Information Displays","authors":"P. Herrmann, Ergys Puka, T. Skoglund","doi":"10.1109/iSCI50694.2020.00016","DOIUrl":"https://doi.org/10.1109/iSCI50694.2020.00016","url":null,"abstract":"Personal Information Displays (PID) at bus stops help making the usage of public transport more attractive. If no electric grid is nearby, however, the installation of PIDs is very expensive due to the high wiring costs. To resolve this issue, the partners of the R&D project IoT-STOP develop a novel PID system that will be independent from the access to power lines. The system uses e-papers as displays that can be accessed using a cellular network. To prevent long, energy-intensive idle listening, the network receiver operates only when the passenger information, in particular, the Expected Times of Arrival (ETA) of the buses, is updated. Between two updates, the receiver is switched off such that adjustments after sudden events are not possible. Therefore, the update periods have to be carefully selected. In this paper, we introduce a predictor that estimates time intervals between updates. Our method is based on linear regression using samples of previous bus rides to forecast arrival times. Its predictions are applied by an algorithm to detect areas during the journey of a bus at which its ETA at a later stop changes with a certain probability. The forecasted times for passing such areas are then selected to update the PID at this stop. In addition, we present a number of tests of the predictor carried out at some bus stops in Bergen, Norway. The results show that the proposed method indeed predicts sensible update times of the PID systems.","PeriodicalId":433521,"journal":{"name":"2020 IEEE 8th International Conference on Smart City and Informatization (iSCI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115191156","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

Design of an E-Attendance Checker through Facial Recognition using Histogram of Oriented Gradients with Support Vector Machine 基于支持向量机定向梯度直方图的人脸识别电子考勤系统设计

2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) Pub Date : 2020-12-01 DOI: 10.1109/iSCI50694.2020.00008

Allan Jason C. Arceo, Renee Ylka N. Borejon, Mia Chantal R. Hortinela, A. Ballado, A. Paglinawan

{"title":"Design of an E-Attendance Checker through Facial Recognition using Histogram of Oriented Gradients with Support Vector Machine","authors":"Allan Jason C. Arceo, Renee Ylka N. Borejon, Mia Chantal R. Hortinela, A. Ballado, A. Paglinawan","doi":"10.1109/iSCI50694.2020.00008","DOIUrl":"https://doi.org/10.1109/iSCI50694.2020.00008","url":null,"abstract":"The usual way of checking the attendance in a class has its own drawbacks. To be able to resolve it, automated attendance systems were introduced. In this paper, the design and development of an e-attendance checker using a facial recognition system were implemented. It can scan the faces of multiple students in a standard classroom setup. A commonly used approach for face detection called Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM) was applied to examine the effect of luminance of the surrounding, the facial orientation of the student and so as their distance from the camera in the facial detection and recognition. The obtained attendance will then be uploaded to a database with authentication. It was found that the system has an accuracy of 95.65% and can detect and recognize up to 37 students. It is suggested that the classroom should have a luminance level of about 217.39 lux or higher to achieve a better accuracy performance of the system. As for the analysis of the effect of distance in the system, it is claimed that the distance of the student does not affect the accuracy of the system. Lastly, it is suggested that the face angles of the subject should be directly facing the camera to achieve a more accurate recognition result.","PeriodicalId":433521,"journal":{"name":"2020 IEEE 8th International Conference on Smart City and Informatization (iSCI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121345825","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

Challenges and Trends of Android Malware Detection in the Era of Deep Learning

2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) Pub Date : 2020-12-01 DOI: 10.1109/iSCI50694.2020.00014

Sancheng Peng, Lihong Cao, Yongmei Zhou, Jianguo Xie, Pengfei Yin, Jianli Mo

{"title":"Challenges and Trends of Android Malware Detection in the Era of Deep Learning","authors":"Sancheng Peng, Lihong Cao, Yongmei Zhou, Jianguo Xie, Pengfei Yin, Jianli Mo","doi":"10.1109/iSCI50694.2020.00014","DOIUrl":"https://doi.org/10.1109/iSCI50694.2020.00014","url":null,"abstract":"Android, the most popular open source mobile platform, attracts a lot of developers who have produced numerous widespread applications (apps). It also draws attackers who have delivered a large amount of malwares to unsuspecting users, due to its open nature. This is not only a threat to national security, but also affect our daily lives. Deep learning has become one of the most popular technologies, and has gained an appreciation to academic and industrial researchers, so it will inevitably become an essential tool to perform complex analysis in a broad application fields. It is appealing to an increasing amount of research ranging from popular topics extraction to Android malware. In this paper, we provide a comprehensive investigation of Android malware detection, and discuss the characteristics of malware and its analysis methods based on deep learning. The secure ecology of Android smartphone based on deep learning is also presented. In addition, research challenges relevant to realworld issues by applying deep learning in smartphone security are discussed, focusing on research issues such as the obtain of optimal parameters, processing of adversarial sample, collection of large scale sample dataset, defence against attack, possession of interpretability and traceability. Our goal is to provide a widespread research guideline to the existing and ongoing efforts via deep learning for smartphone malware, to help researchers better understand the existing work, and to design more and more effective mechanisms to detect smartphone malware.","PeriodicalId":433521,"journal":{"name":"2020 IEEE 8th International Conference on Smart City and Informatization (iSCI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126258050","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

E-government Deep Recommendation System Based on User Churn 基于用户流失的电子政务深度推荐系统

2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) Pub Date : 2020-12-01 DOI: 10.1109/iSCI50694.2020.00011

Yanan Wang, Airong Quan, Xiaonan Ma, Junqing Qu

引用次数: 1

An Autonomous Parking Space Planning System Based on Pattern Searching Algorithm 基于模式搜索算法的自主车位规划系统

2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) Pub Date : 2020-12-01 DOI: 10.1109/iSCI50694.2020.00009

Xinxin Huang, Yingguo Gao, Xiaohui Duan

引用次数: 2

IEEE iSCI 2020 Organizing and Program Committees IEEE iSCI 2020组织和项目委员会

2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) Pub Date : 2020-12-01 DOI: 10.1109/isci50694.2020.00007

引用次数: 0

Prediction of Parkinson's Disease using Principal Component Analysis and the Markov Chains 利用主成分分析和马尔可夫链预测帕金森病

2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) Pub Date : 2020-12-01 DOI: 10.1109/iSCI50694.2020.00015

Syed Qasim Afser Rizvi, Pin Liu, Guojun Wang, Muhammad Arif

引用次数: 0

A Survey of Emotion Analysis in Text Based on Deep Learning 基于深度学习的文本情感分析研究综述

2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) Pub Date : 2020-12-01 DOI: 10.1109/iSCI50694.2020.00020

Lihong Cao, Sancheng Peng, Pengfei Yin, Yongmei Zhou, Aimin Yang, Xinguang Li

引用次数: 8

Towards Blockchain-Driven Network Log Management System 面向区块链驱动的网络日志管理系统

2020 IEEE 8th International Conference on Smart City and Informatization (iSCI) Pub Date : 2020-12-01 DOI: 10.1109/iSCI50694.2020.00019

M. Rakib, S. Hossain, Mosarrat Jahan, U. Kabir

引用次数: 2