2022 IEEE Symposium on Computers and Communications (ISCC)最新文献_第7页

MobVis: A Framework for Analysis and Visualization of Mobility Traces MobVis:移动轨迹分析和可视化的框架

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912988

Lucas N. Silva, Paulo H. L. Rettore, Vinícius F. S. Mota, B. P. Santos

引用次数: 1

Incident Handling for Healthcare Organizations and Supply-Chains 针对医疗保健组织和供应链的事件处理

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912965

Eftychia Lakka, George Hatzivasilis, Stylianos Karagiannis, Andreas D. Alexopoulos, M. Athanatos, S. Ioannidis, Manolis Chatzimpyrros, Grigoris Kalogiannis, G. Spanoudakis

引用次数: 1

A Blockchain-Based Architecture for Access Control Management of IoT Applications 基于区块链的物联网应用访问控制管理架构

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912781

I. Moursy, S. Ghanem, Mohamed Nazih ElDerini

引用次数: 1

DyPro: Dynamic Probing Planning for In-Band Network Telemetry 带内网络遥测的动态探测规划

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912881

Leandro M. Dallanora, A. G. Castro, R. I. T. D. C. Filho, F. Rossi, A. Lorenzon, M. C. Luizelli

引用次数: 3

Opinion Leaders and Twitter: Metric Proposal and Psycholinguistic Analysis 意见领袖和推特:度量建议和心理语言学分析

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912909

M. Furini, E. Flisi

{"title":"Opinion Leaders and Twitter: Metric Proposal and Psycholinguistic Analysis","authors":"M. Furini, E. Flisi","doi":"10.1109/ISCC55528.2022.9912909","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912909","url":null,"abstract":"Social media and personal health might be a dan-gerous combination: people are influenced by what they read online and don't pay attention to who wrote what they read. What happened during the COVID-19 pandemic? Who were the opinion leaders on social media? What were the conversations about? How did the health institutions communicate? To under-stand this, we focus on Twitter, and we analyze more than three million of Italian-written tweets posted from January 2020 to December 2021. We propose a method to identify opinion leaders and to analyze the content of the conversations. Results show that: (i) opinion leaders are linked to what they say and when they say it; (ii) politicians, newscast, and ordinary people accounts were able to become opinion leaders during the pandemic; (iii) conversations moved from a medical focus (at the beginning of the pandemic) to a social focus (in the last months of 2021); (iv) absence of health care institutions among opinion leaders. These results show that our approach might be useful for those who want to monitor the social scenario in terms of health (e.g., to identify as soon as possible accounts against or critical to medicine or to health authorities).","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127939413","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

Dynamic Graph Convolutional Network for Long Short-term Traffic Flow Prediction 基于动态图卷积网络的长短期交通流预测

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912866

Yan Wang, Q. Ren

{"title":"Dynamic Graph Convolutional Network for Long Short-term Traffic Flow Prediction","authors":"Yan Wang, Q. Ren","doi":"10.1109/ISCC55528.2022.9912866","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9912866","url":null,"abstract":"Traffic prediction is a critical component of intel-ligent transportation systems. However, highly non-linear and dynamical spatial-temporal correlations propose challenges for traffic prediction, especially long-term prediction. We propose a spatial-temporal channel-attention based graph convolutional network (STCAGCN) to improve the accuracy of both long-term and short-term traffic flow prediction. Firstly we design an attention mechanism to learn complex temporal and spatial correlations. Then we develop the stacked spatial-temporal convo-lution layer to model complex temporal and spatial correlations. Each spatial-temporal convolution layer is composed of a gated time convolution network and a graph convolution network. We develop a gated time convolution network to model non-linear temporal correlations, which process long sequences through stacked dilated convolution. Moreover, the graph convolution network exploits the hidden spatial correlations via learning self-adaptive adjacency matrix. Experiment results on real-world datasets demonstrate that the proposed STCAGCN model obtains improvements over the state-of-the-art, especially for long-term traffic flow prediction.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128036145","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

Implicit Continuous Authentication Model Based on Mobile Terminal Touch Behavior 基于移动终端触摸行为的隐式连续认证模型

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9913017

Rui Mao, Heming Ji, Di Cheng, Xiaoyu Wang, Yan Wang, Degang Sun

{"title":"Implicit Continuous Authentication Model Based on Mobile Terminal Touch Behavior","authors":"Rui Mao, Heming Ji, Di Cheng, Xiaoyu Wang, Yan Wang, Degang Sun","doi":"10.1109/ISCC55528.2022.9913017","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9913017","url":null,"abstract":"Most existing identity authentication technologies rely on some ways for the first login authentication, such as personal identification number (PIN), track, or biological characteristics. However, these ways exist plenty of security risks, which make people face password guessing attacks, trace attacks, and shoulder surfing attacks for a long time. Once the illegal users forge identity to complete authentication or bypass first login authentication, their subsequent behavior will become out of control. To solve the above problems, we propose an implicit continuous authentication model based on the touch behavior of the mobile terminal. The model uses the data collected by the accelerometer, gyroscope, and magnetometer to generate feature vectors and extracts the feature vectors containing macroscopic features, microscopic features, and joint features. And we design a convolutional bidirectional recurrent neural network model to distinguish the sensor feature vectors. On this basis, we perform various experiments on a large dataset Hand Movement, Orientation, and Grasp (HMOG) with different sensor characteristics. Compared with the most advanced models proposed recently, the results show that our model achieves an equal error rate (EER) of 0.53%, which significantly improves authentication accuracy.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131506952","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

Sensing Social Media to Forecast COVID-19 Cases 利用社交媒体预测COVID-19病例

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9913033

C. Comito

{"title":"Sensing Social Media to Forecast COVID-19 Cases","authors":"C. Comito","doi":"10.1109/ISCC55528.2022.9913033","DOIUrl":"https://doi.org/10.1109/ISCC55528.2022.9913033","url":null,"abstract":"Social media has become a key tool for spreading the news, discussing ideas and comments on world events, playing a relevant role also in public health management, especially in epidemics surveillance like seasonal flu. Online social media actually can provide an important help in monitoring disease spreading as users self-report their health-related issues. Since the very first days of COVID-19 outbreak, people exchanged news, updates, sentiment and opinion about the pandemics. The paper describes a study aiming at evaluating the correlation of tweets with official COVID-19 data. Based on the outcomes of the correlation study, the paper proposes a forecasting model to predict the number of new daily COVID-19 cases. The approach is formulated as an autoregressive model that combines tweets and official COVID-19 data. A real-word dataset of tweets is used for the correlation study and to evaluate the performance of the forecasting model. Results shown the feasibility of the approach, highlighting the improvement obtained when tweets are integrated in the forecasting model, allowing to predict new COVID-19 cases in advance, on average 4–6 days before they were confirmed.","PeriodicalId":309606,"journal":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131842437","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

Fast-Converging Congestion Control in Datacenter Networks 数据中心网络中的快速收敛拥塞控制

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912977

Yukun Zhou, Dezun Dong, Zhengbin Pang, Junhong Ye, Feng Jin

引用次数: 0

Spatial-based Bayesian Hidden Markov Models with Dirichlet Mixtures for Video Anomaly Detection 基于空间的Dirichlet混合贝叶斯隐马尔可夫模型用于视频异常检测

2022 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2022-06-30 DOI: 10.1109/ISCC55528.2022.9912983

Guojian Luo, J. Qu, Lina Zhang, Xiaoyu Fang, Yi Zhang, Tong Zhou

引用次数: 0