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ITU Kaleidoscope: Machine Learning for a 5G Future

召开年：2018
召开地：Santa Fe(AR)
出版时间：-

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1.MULTIFRACTAL MODELING OF THE RADIO ELECTRIC SPECTRUM APPLIED IN COGNITIVE RADIO NETWORKS

机译：认知无线电网络中无线电频谱的多分形建模
- 作者：Luis Tuberquia-David;Cesar Hernandez
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Fractals;
- Time series analysis;
- Wireless fidelity;
- Fluctuations;
- Tools;
- Mathematical model;
- Urban areas;
2.ETHICAL FRAMEWORK FOR MACHINE LEARNING

机译：机器学习的道德框架
- 作者：Charru Malhotra;Vinod Kotwal;Surabhi Dalal
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Ethics;
- Machine learning;
- Robots;
- Big Data;
- Machine learning algorithms;
- Prediction algorithms;
3.MESSAGE COLLISION IDENTIFICATION APPROACH USING MACHINE LEARNING

机译：使用机器学习的消息冲突识别方法
- 作者：Juan Pablo Martín;Bruno Marengo;Juan Pablo Prina;Martín Gabriel Riolfo
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
4.ITU K 2018 Keynote Summary

机译：IT K 2018主题总结
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
5.DOUBLE SARSA BASED MACHINE LEARNING TO IMPROVE QUALITY OF VIDEO STREAMING OVER HTTP THROUGH WIRELESS NETWORKS

机译：基于双SARSA的机器学习，可通过无线网络提高HTTP上视频流的质量
- 作者：Dhananjay Kumar;Narmathaa Logganathan;Ved P. Kafle
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Streaming media;
- Servers;
- Quality of experience;
- Quality assessment;
- Video recording;
- Media;
- IP networks;
6.TOWARDS COGNITIVE AUTONOMOUS NETWORKS IN 5G

机译：建立5G认知自治网络
- 作者：Stephen S. Mwanje;Christian Mannweiler
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Automation;
- Cognition;
- Cats;
- 5G mobile communication;
- Organizations;
- Artificial intelligence;
7.A Deep Reinforcement Learning Approach For Data Migration in Multi-Access Edge Computing

机译：在多访问边缘计算中进行数据迁移的深度强化学习方法
- 作者：Fabrizio De Vita;Dario Bruneo;Antonio Puliafito;Giovanni Nardini;Antonio Virdis;Giovanni Stea
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Servers;
- 5G mobile communication;
- Long Term Evolution;
- Reinforcement learning;
- Edge computing;
- Quality of service;
8.UNDECLARED CONSTRUCTIONS: A GOVERNMENT’S SUPPORT DEEP LEARNING SOLUTION FOR AUTOMATIC CHANGE DETECTION

机译：未声明的结构：用于自动更改检测的政府支持的深度学习解决方案
- 作者：Pamela Ferrari Lezaun;Gustavo Olivieri
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Buildings;
- Satellites;
- Machine learning algorithms;
- Meters;
- Deep learning;
- Computer architecture;
- Training;
9.ITU K 2018 Chairman’s Message

机译：ITU K 2018主席致辞
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
10.Unsupervised Learning for Detection of Leakage from the HFC Network

机译：用于检测HFC网络泄漏的无监督学习
- 作者：Emilia Gibellini;Claudio E. Righetti
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Hybrid fiber coaxial cables;
- Modems;
- Power cables;
- Optical fiber cables;
- Communication cables;
- Clustering algorithms;
- Cable TV;
11.SELF-HEALING AND RESILIENCE IN FUTURE 5G COGNITIVE AUTONOMOUS NETWORKS

机译：未来5G认知自治网络中的自我修复和弹性
- 作者：Janne Ali-Tolppa;Szilard Kocsis;Benedek Schultz;Levente Bodrog;Marton Kajo
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Resilience;
- Anomaly detection;
- 5G mobile communication;
- Reliability;
- Machine learning;
- Gaussian distribution;
- Optimization;
12.AI AS A MICROSERVICE (AIMS) OVER 5G NETWORKS

机译：通过5G网络将AI作为微服务（AIMS）
- 作者：Gyu Myoung Lee;Tai-Won Um;Jun Kyun Choi
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- 5G mobile communication;
- cloud computing;
- decision making;
- Internet of Things;
- learning (artificial intelligence);
13.Machine Learning Opportunities In Cloud Computing Data Center Management for 5G Services

机译：针对5G服务的云计算数据中心管理中的机器学习机会
- 作者：Fabio López-Pires;Benjamín Barán
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Cloud computing;
- Optimization;
- Data centers;
- 5G mobile communication;
- Machine learning;
- Linear programming;
- Resource management;
14.OPTICAL FLOW BASED LEARNING APPROACH FOR ABNORMAL CROWD ACTIVITY DETECTION WITH MOTION DESCRIPTOR MAP

机译：运动描述符映射的基于光学流的异常活动检测方法
- 作者：Dhananjay Kumar;Govinda Raj Sampath Sarala
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Optical flow;
- Hidden Markov models;
- Computational modeling;
- Euclidean distance;
- Visualization;
- Context modeling;
- Training;
15.A GENDERED PERSPECTIVE ON ARTIFICIAL INTELLIGENCE

机译：人工智慧的一般观点
- 作者：Smriti Parsheera
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- artificial intelligence;
- ethical aspects;
- gender issues;
- learning (artificial intelligence);
16.SMART USAGE OF MULTIPLE RAT IN IOT-ORIENTED 5G NETWORKS: A REINFORCEMENT LEARNING APPROACH

机译：物联网5G网络中多大鼠的智能用法：一种强化学习方法
- 作者：Ruben M. Sandoval;Sebastian Canovas-Carrasco;Antonio-Javier Garcia-Sanchez;Joan Garcia-Haro
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
17.ITU K 2018 Committees

机译：ITU K 2018委员会
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
18.ITU K 2018 Foreword

机译：ITU K 2018前言
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
19.A Machine Learning Management Model for QoE Enhancement in Next-Generation Wireless Ecosystems

机译：下一代无线生态系统中增强QoE的机器学习管理模型
- 作者：Eva Ibarrola;Mark Davis;Camille Voisin;Ciara Close;Leire Cristobo
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
20.Consideration On Automation of 5G Network Slicing with Machine Learning

机译：基于机器学习的5G网络切片自动化的思考
- 作者：Ved P. Kafle;Yusuke Fukushima;Pedro Martinez-Julia;Takaya Miyazawa
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Machine learning;
- Automation;
- 5G mobile communication;
- Network slicing;
- Monitoring;
- Security;
- Resource management;
21.Predicting Activities in Business Processes with LSTM Recurrent Neural Networks

机译：使用LSTM递归神经网络预测业务流程中的活动
- 作者：Edgar Tello-Leal;Jorge Roa;Mariano Rubiolo;Ulises M. Ramirez-Alcocer
- 会议名称：《ITU Kaleidoscope: Machine Learning for a 5G Future》 | 2018年
- Business;
- Predictive models;
- Recurrent neural networks;
- Data mining;
- Unified modeling language;
- Information systems;
- Training;