2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet)最新文献_第2页

Green Energy Sources: Issues and Challenges 绿色能源:问题与挑战

2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) Pub Date : 2019-10-01 DOI: 10.1109/ROEDUNET.2019.8909595

Mostafa Abdulghafoor Mohammed, Inas Mohammed, Raed Abdulkareem Hasan, N. Tapus, Ahmed Ali, Omar A. Hammood

引用次数: 22

Stress Level Prediction Using Data From Wearables 利用可穿戴设备数据预测压力水平

2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) Pub Date : 2019-10-01 DOI: 10.1109/ROEDUNET.2019.8909463

V. Stefanescu, I. Radoi

{"title":"Stress Level Prediction Using Data From Wearables","authors":"V. Stefanescu, I. Radoi","doi":"10.1109/ROEDUNET.2019.8909463","DOIUrl":"https://doi.org/10.1109/ROEDUNET.2019.8909463","url":null,"abstract":"Nowadays, stress is one of the main concerns when it comes to people's well-being and health. Knowing beforehand which events may cause stress, can be helpful in managing it. This paper aims to provide a solution for predicting stress levels caused by future events. We are proposing a system targeted at a representative test scenario for this problem, which uses calendar events and heart-rate variation data from wearables to offer predictions on stress levels for future events. This test scenario has a wide applicability as the majority of individuals in modern societies have desk jobs, and a significant part of their daily activities is recorded in their digital calendars. The system consists of a microservices data gathering infrastructure and a random forest algorithm for correlating heart-rate variations to calendar events. A dataset including data from 2 employees of a tech company over a period of one year, having attended a total of 1053 meetings, was used for evaluating the proposed system. The accuracy of the heart-rate sensors used in this study was validated against a professional electrocardiogram device. Our selected machine learning model for predicting heart rate variations for future events, trained on only 400 events, shows errors of less than 12 heart beats per minute 90% of the times. This represents a good performance considering the high heart rate variation when exposed to stress. Furthermore, we have performed our own validation experiment for the correlation of heart rate to stress, which revealed a strong positive correlation of 0.793. This offers us further confirmation that our system is capable of providing indicative estimations of stress levels for future events.","PeriodicalId":309683,"journal":{"name":"2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129016987","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: 4

Proactive Secure Coding for iOS Applications 主动安全编码的iOS应用程序

2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) Pub Date : 2019-10-01 DOI: 10.1109/ROEDUNET.2019.8909672

M. Marin, Costin Carabas, Răzvan Deaconescu, N. Tapus

引用次数: 0

Balancing between centralized vs. edge processing in IoT platforms with applicability in advanced people flow analysis 平衡物联网平台中的集中式和边缘处理，并适用于高级人员流动分析

2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) Pub Date : 2019-10-01 DOI: 10.1109/ROEDUNET.2019.8909424

Eduard Cojocea, Stefan Hornea, Traian Rebedea

{"title":"Balancing between centralized vs. edge processing in IoT platforms with applicability in advanced people flow analysis","authors":"Eduard Cojocea, Stefan Hornea, Traian Rebedea","doi":"10.1109/ROEDUNET.2019.8909424","DOIUrl":"https://doi.org/10.1109/ROEDUNET.2019.8909424","url":null,"abstract":"Understanding consumer behavior has always been a major concern for any business. Although in the last decades there has been a steep rise in online shopping and online marketing, a big chunk of the revenue for retailers still comes from traditional, in-store shopping. As such, analyzing and understanding the flow of customers inside supermarkets and stores can offer invaluable insights regarding their business and customer behavior to retailers. With the rise of smart IoT devices that allow live recording of video streams and even processing these streams on-edge, analyzing people flows in the hyperspace of crowd size, person categories (by sex and age), person location and time can prove to be an essential tool for decision makers. This paper presents a method for performing people flow analysis using deep learning for person recognition and profiling. The proposed method is encapsulated in a platform that can analyze the people flow on-edge or on a central server with powerful GPUs. The platform also encompasses several business intelligence graphical dashboards for presenting and analyzing the resulted time series data. Our preliminary findings show that on-edge processing, on embedded devices, is a plausible alternative to central processing with GPUs. Despite having lower performance, both in term of mean average precision and frames per second, embedded devices and the corresponding algorithms still manage to achieve reasonable results, while being cheaper and significantly more power efficient. Having this said, the most reasonable conclusion is to combine the two solutions mentioned above into a hybrid approach.","PeriodicalId":309683,"journal":{"name":"2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128485658","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

Voting-based edge detection 基于投票的边缘检测

2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) Pub Date : 2019-10-01 DOI: 10.1109/ROEDUNET.2019.8909664

Giorgiana Violeta Vlasceanu, R. Paraschiv, Cristina Artene, C. Boiangiu

引用次数: 0

Double Standard Method for Designing Adaptive Backup Systems 设计自适应备份系统的双重标准方法

2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) Pub Date : 2019-10-01 DOI: 10.1109/ROEDUNET.2019.8909670

F. B. Manolache, O. Rusu

引用次数: 0

RoEduNet 2019 Committees

2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) Pub Date : 2019-10-01 DOI: 10.1109/roedunet.2019.8909394

引用次数: 0

Security News Aggregator 安全新闻聚合器

2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) Pub Date : 2019-10-01 DOI: 10.1109/ROEDUNET.2019.8909609

R. Radu, Octavian Grigorescu, R. Rughinis

引用次数: 0

Redundancy for an all-in-one ISA100.11a Gateway: Protocol Design and Hardware Integration ISA100.11a一体化网关的冗余:协议设计和硬件集成

2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) Pub Date : 2019-10-01 DOI: 10.1109/ROEDUNET.2019.8909476

Eusebiu Jecan, C. Pop, Z. Padrah, Mircea Vlasin, Ovidiu Ratiu, E. Puschita

引用次数: 0

On Parallelizing Geometrical PCA Approximation 并行化几何PCA逼近

2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) Pub Date : 2019-10-01 DOI: 10.1109/ROEDUNET.2019.8909644

Alina L. Machidon, C. Ciobanu, O. Machidon, P. Ogrutan

{"title":"On Parallelizing Geometrical PCA Approximation","authors":"Alina L. Machidon, C. Ciobanu, O. Machidon, P. Ogrutan","doi":"10.1109/ROEDUNET.2019.8909644","DOIUrl":"https://doi.org/10.1109/ROEDUNET.2019.8909644","url":null,"abstract":"Remote sensing data has known an explosive growth in the past decade. This has led to the need for efficient dimensionality reduction techniques, mathematical procedures that transform the high-dimensional data into a meaningful, reduced representation. Principal Component Analysis (PCA) is a well-known dimensionality reduction technique used in the field of hyperspectral satellite images. However, PCA suffers from high computational costs and increased complexity, an issue that led to elaborating PCA adaptations capable of running on multi-core computing architectures. This paper proposes a parallel implementation of the geometrical PCA approximation (gaPCA) algorithm. Three parallel implementations are studied: two on multi-core CPUs and a NVIDIA Graphics Processing Units (GPU) CUDA accelerated implementation. Our results show significant speedups of the parallel implementations when applied on hyperspectral image datasets. Our results show that on the Intel Core i5 CPU, Python multi-core implementation is up to 2.01$times$ faster than its Matlab equivalent. Our GPU PyCUDA implementation is considerably faster than both our Python multi-core CPU implementations: up to 1.76$times$ faster than Intel Core i5-6200U and up to 5.72$times$ faster than the NVIDIA Jetson Nano quad-core ARM A53 CPU. We performed data analysis on the output data for the three methods and the maxim relative error was less than 0.001%.","PeriodicalId":309683,"journal":{"name":"2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128358564","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: 2