2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K)最新文献

Beyond 5G/6G telecommunications ensuring continuity in business, research and education 超越5G/6G电信，确保业务、研究和教育的连续性

2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K) Pub Date : 2020-12-07 DOI: 10.23919/ituk50268.2020.9303228

A. Nakao

引用次数: 2

An accelerated digital transformation, courtesy of the recent pandemic 由于最近的大流行，加速了数字化转型

2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K) Pub Date : 2020-12-07 DOI: 10.23919/ituk50268.2020.9303181

R. Saracco

引用次数: 0

ITU K 2020 Index ITU K 2020指数

2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K) Pub Date : 2020-12-07 DOI: 10.23919/ituk50268.2020.9303215

引用次数: 0

ITU K 2020 Preface ITU K 2020前言

2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K) Pub Date : 2020-12-07 DOI: 10.23919/ITUK50268.2020.9303212

Yihua Ma, Zhifeng Yuan, Weimin Li

引用次数: 0

Technical Programme Committee Chair's message 技术计划委员会主席的致辞

2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K) Pub Date : 2020-12-07 DOI: 10.23919/ituk50268.2020.9303227

M. Sherif

引用次数: 0

An AI-Based Optimization of Handover Strategy in Non-Terrestrial Networks 基于人工智能的非地面网络切换策略优化

2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K) Pub Date : 2020-12-07 DOI: 10.23919/ITUK50268.2020.9303210

Chenchen Zhang, Nan Zhang, Wei Cao, Kaibo Tian, Zhen Yang

引用次数: 11

Visual Action Recognition Using Deep Learning in Video Surveillance Systems 视频监控系统中使用深度学习的视觉动作识别

2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K) Pub Date : 2020-12-07 DOI: 10.23919/ITUK50268.2020.9303222

Dhananjay Kumar, Priyanka T, Aishwarya Murugesh, Ved P. Kafle

{"title":"Visual Action Recognition Using Deep Learning in Video Surveillance Systems","authors":"Dhananjay Kumar, Priyanka T, Aishwarya Murugesh, Ved P. Kafle","doi":"10.23919/ITUK50268.2020.9303222","DOIUrl":"https://doi.org/10.23919/ITUK50268.2020.9303222","url":null,"abstract":"The skeleton tracking technique allows the usage of the skeleton information of human-like objects for action recognition. The major challenge in action recognition in a video surveillance system is the large variability across and within subjects. In this paper, we propose a deep-learning-based novel framework to recognize human actions using skeleton estimation. The main component of the framework consists of pose estimation using a stacked hourglass network (HGN). The pose estimation module provides the skeleton joint points of humans. Since the position of skeleton varies according to the point of view, we apply transformations on the skeleton points to make it invariable to rotation and position. The skeleton joint positions are identified using HGN-based deep neural networks (HGN-DNN), and the feature extraction and classification is carried out to obtain the action class. The skeleton action sequence is encoded using Fisher Vector before classification. The proposed system complies with Recommendation ITU-T H.626.5 \"Architecture for intelligent visual surveillance systems\", and has been evaluated over benchmarked human action recognition data sets. The evaluation results show that the system performance achieves a precision of 85% and the accuracy of 95.6% in recognizing actions like wave, punch, kick, etc. The HGN-DNN model meets the requirements and service description specified in Recommendation ITU-T F.743.","PeriodicalId":353075,"journal":{"name":"2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128715441","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

Toward a Typology of "Going Digital" 走向“走向数字化”的类型学

2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K) Pub Date : 2020-12-07 DOI: 10.23919/ITUK50268.2020.9303188

Cheng Gong, V. Ribière

引用次数: 0

PERT: Payload Encoding Representation from Transformer for Encrypted Traffic Classification PERT:用于加密流量分类的变压器的有效负载编码表示

2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K) Pub Date : 2020-12-07 DOI: 10.23919/ITUK50268.2020.9303204

H. He, Zhi Guo Yang, Xiang Chen

{"title":"PERT: Payload Encoding Representation from Transformer for Encrypted Traffic Classification","authors":"H. He, Zhi Guo Yang, Xiang Chen","doi":"10.23919/ITUK50268.2020.9303204","DOIUrl":"https://doi.org/10.23919/ITUK50268.2020.9303204","url":null,"abstract":"Traffic identification becomes more important yet more challenging as related encryption techniques are rapidly developing nowadays. In difference to recent deep learning methods that apply image processing to solve such encrypted traffic problems, in this paper, we propose a method named Payload Encoding Representation from Transformer (PERT) to perform automatic traffic feature extraction using a state-of-the-art dynamic word embedding technique. Based on this, we further provide a traffic classification framework in which unlabeled traffic is utilized to pre-train an encoding network that learns the contextual distribution of traffic payload bytes. Then, the downward classification reuses the pre-trained network to obtain an enhanced classification result. By implementing experiments on a public encrypted traffic data set and our captured Android HTTPS traffic, we prove the proposed method can achieve an obvious better effectiveness than other compared baselines. To the best of our knowledge, this is the first time the encrypted traffic classification with the dynamic word embedding alone with its pre-training strategy has been addressed.","PeriodicalId":353075,"journal":{"name":"2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128012236","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: 24

A Case Study For Modeling Machine Tool Systems Using Standard Representations 使用标准表示对机床系统建模的案例研究

2020 ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K) Pub Date : 2020-12-07 DOI: 10.23919/ITUK50268.2020.9303218

M. Bärring, Guodong Shao, M. Helu, B. Johansson

引用次数: 3