A Hybrid Machine Learning Approach for Improvised QoE in Video Services over 5G Wireless Networks

Video streaming applications have grown considerably in recent years.As a result,this becomes one of the most significant contributors to global internet traffic.According to recent studies,the telecommunications industry loses millions of dollars due to poor video Quality of Experience(QoE)for users.Among the standard proposals for standardizing the quality of video streaming over internet service providers(ISPs)is the Mean Opinion Score(MOS).However,the accurate finding of QoE by MOS is subjective and laborious,and it varies depending on the user.A fully automated data analytics framework is required to reduce the inter-operator variability characteristic in QoE assessment.This work addresses this concern by suggesting a novel hybrid XGBStackQoE analytical model using a two-level layering technique.Level one combines multiple Machine Learning(ML)models via a layer one Hybrid XGBStackQoE-model.Individual ML models at level one are trained using the entire training data set.The level two Hybrid XGBStackQoE-Model is fitted using the outputs(meta-features)of the layer one ML models.The proposed model outperformed the conventional models,with an accuracy improvement of 4 to 5 percent,which is still higher than the current traditional models.The proposed framework could significantly improve video QoE accuracy.

A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

The Internet service provider(ISP)is the heart of any country’s Internet infrastructure and plays an important role in connecting to theWorld WideWeb.Internet exchange point(IXP)allows the interconnection of two or more separate network infrastructures.All Internet traffic entering a country should pass through its IXP.Thus,it is an ideal location for performing malicious traffic analysis.Distributed denial of service(DDoS)attacks are becoming a more serious daily threat.Malicious actors in DDoS attacks control numerous infected machines known as botnets.Botnets are used to send numerous fake requests to overwhelm the resources of victims and make them unavailable for some periods.To date,such attacks present a major devastating security threat on the Internet.This paper proposes an effective and efficient machine learning(ML)-based DDoS detection approach for the early warning and protection of the Saudi Arabia Internet exchange point(SAIXP)platform.The effectiveness and efficiency of the proposed approach are verified by selecting an accurate ML method with a small number of input features.A chi-square method is used for feature selection because it is easier to compute than other methods,and it does not require any assumption about feature distribution values.Several ML methods are assessed using holdout and 10-fold tests on a public large-size dataset.The experiments showed that the performance of the decision tree(DT)classifier achieved a high accuracy result(99.98%)with a small number of features(10 features).The experimental results confirmthe applicability of using DT and chi-square for DDoS detection and early warning in SAIXP.

Implementation of Machine-to-Machine Solutions to Improve Quality of Service for Electrical Energy Distribution Networks in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of a GENEKO modem that exploits its digital inputs together with a full coverage of certain required auxiliary services so as to generate proper detection signals whenever failure currents occur;which allows incorporating the latest failure detection technology into the system.

Machine learning-based zero-touch network and service management:a survey

The exponential growth of mobile applications and services during the last years has challenged the existing network infrastructures.Consequently,the arrival of multiple management solutions to cope with this explosion along the end-to-end network chain has increased the complexity in the coordinated orchestration of different segments composing the whole infrastructure.The Zero-touch Network and Service Management(ZSM)concept has recently emerged to automatically orchestrate and manage network resources while assuring the Quality of Experience(QoE)demanded by users.Machine Learning(ML)is one of the key enabling technologies that many ZSM frameworks are adopting to bring intelligent decision making to the network management system.This paper presents a comprehensive survey of the state-of-the-art application of ML-based techniques to improve ZSM performance.To this end,the main related standardization activities and the aligned international projects and research efforts are deeply examined.From this dissection,the skyrocketing growth of the ZSM paradigm can be observed.Concretely,different standardization bodies have already designed reference architectures to set the foundations of novel automatic network management functions and resource orchestration.Aligned with these advances,diverse ML techniques are being currently exploited to build further ZSM developments in different aspects,including multi-tenancy management,traffic monitoring,and architecture coordination,among others.However,different challenges,such as the complexity,scalability,and security of ML mechanisms,are also identified,and future research guidelines are provided to accomplish a firm development of the ZSM ecosystem.

Optimal online algorithms for scheduling on two identical machines under a grade of service

This work is aimed at investigating the online scheduling problem on two parallel and identical machines with a new feature that service requests from various customers are entitled to many different grade of service (GoS) levels, so each job and machine are labelled with the GoS levels, and each job can be processed by a particular machine only when its GoS level is no less than that of the machine. The goal is to minimize the makespan. For non-preemptive version, we propose an optimal online al-gorithm with competitive ratio 5/3. For preemptive version, we propose an optimal online algorithm with competitive ratio 3/2.

Toward Secure Software-Defined Networks Using Machine Learning: A Review, Research Challenges, and Future Directions

Over the past few years,rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems.As a result,greater intelligence is necessary to effectively manage,optimize,and maintain these systems.Due to their distributed nature,machine learning models are challenging to deploy in traditional networks.However,Software-Defined Networking(SDN)presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control planes.SDN provides a centralized network view and allows for dynamic updates of flow rules and softwarebased traffic analysis.While the programmable nature of SDN makes it easier to deploy machine learning techniques,the centralized control logic also makes it vulnerable to cyberattacks.To address these issues,recent research has focused on developing powerful machine-learning methods for detecting and mitigating attacks in SDN environments.This paper highlighted the countermeasures for cyberattacks on SDN and how current machine learningbased solutions can overcome these emerging issues.We also discuss the pros and cons of using machine learning algorithms for detecting and mitigating these attacks.Finally,we highlighted research issues,gaps,and challenges in developing machine learning-based solutions to secure the SDN controller,to help the research and network community to develop more robust and reliable solutions.

会话类E-Service的接口兼容和服务组合分析

针对会话类E-Service的会话完整性对环境提出的更多限制以及由此导致的行为差异,给出了对接口环境的严格定义和接口兼容检查的精确算法．在应用上,由于接口兼容的组合算法COMP可以得到组合接口自动机,通过计算COMP(COMP(A1,…,COMP(AN-1,AN)))可以保证最终的组合结果是协议兼容的．

会话类E-Service的接口兼容分析

为保证最终获得的组合服务能够正确运行,首先对涉及会话类E-Service服务组合过程中的E-Service动态行为接口进行了形式化描述.然后采用“乐观”方式进行接口兼容检查;同时针对会话类E-Service的完整性要求,提出了验证会话类E-Service接口兼容的算法,以此对业务组合过程进行设计时的检查,确保组合的正确性.

一种可扩展的深度神经网络机器翻译Service架构

提出了一种可扩展的基于深度神经网络方法的在线翻译系统架构方法,采用GPU和CPU混合解码的后端部署方法来提高系统的并发能力,降低系统延迟.实验结果表明,所提出的系统架构方法相比于只使用GPU或CPU架构,系统并发能力更强,而响应延迟相对较低.同时系统的架构方法可以方便地扩展到多服务器架构中,整体上提高系统的性能.

基于Web Services的产生式专家系统设计

结合WebServices的异构平台互通的能力,设计一个跨平台的、具有良好扩展性的基于Web的分布式决策专家系统架构,系统通过组件封装推理机、知识库管理和维护等业务逻辑处理模块,从而具有良好的扩展性、伸缩性、兼容性和协作性,并且实现多专家系统的分布式协同合作。

基于Web Services的并联机床制造服务系统(英文)

针对并联机床加工曲面零件的优势与目前并联机床未得到广泛应用之间的矛盾,开发了并联机床的制造服务系统。该系统将并联机床原有制造系统中的后置处理功能封装成Web services,包括编译、工作空间计算、干涉校验和运动学变换,应用面向服务的架构使并联机床的制造服务与异地制造企业协同工作,并给出了此架构下该系统的工作流程和功能模块。实例说明,应用Web services和面向服务架构的制造服务系统与传统的分布式系统相比,具有易集成性、松耦合性及易操作性,可以为企业减少成本、提高效率。

基于WebService的自动贴标机数据交互系统的设计与实现

主要介绍了一种基于Web Service技术的PCB自动贴标机数据交互系统,通过对系统分析,完成了以下设计:系统执行远程Web Service提供的各个服务端口,获取相关信息,控制条码标签打印机打印对应条码,并完成贴标、识别等任务,并将相应数据通过服务端口上传,从而完成生产设备和企业数据库的数据交互。

Web Service技术在数控机床远程故障诊断中的应用研究

Web Service技术是当前广泛应用的一种新型数据共享技术,研究探讨了Web Service技术在数控机床远程故障诊断系统中的应用。通过visual basic.net开发了一套用于在线采集数控机床现场状态的系统,实现了系统通过网络进行实时数据更新的需求,并传输至远程服务器端进行机床的状态监控及故障诊断。

Event-driven process execution model for process virtual machine

Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.

Machine learning aided design of perovskite oxide materials for photocatalytic water splitting

Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate(RH_(2)) still remains a challenge in the field of photocatalytic water splitting(PWS). Herein, we established structural-property models targeted to RH_(2) and the proper bandgap(Eg) via machine learning(ML) technology to accelerate the discovery of efficient perovskite photocatalysts for PWS. The Pearson correlation coefficients(R) of leave-one-out cross validation(LOOCV) were adopted to compare the performances of different algorithms including gradient boosting regression(GBR), support vector regression(SVR), backpropagation artificial neural network(BPANN), and random forest(RF). It was found that the BPANN model showed the highest R values from LOOCV and testing data of 0.9897 and 0.9740 for RH_(2),while the GBR model had the best values of 0.9290 and 0.9207 for Eg. Furtherly, 14 potential PWS perovskite candidates were screened out from 30,000 ABO3-type perovskite structures under the criteria of structural stability, Eg, conduction band energy, valence band energy and RH_(2). The average RH_(2) of these14 perovskites is 6.4% higher than the highest value in the training data set. Moreover, the online web servers were developed to share our prediction models, which could be accessible in http://materialsdata-mining.com/ocpmdm/material_api/ahfga3d9puqlknig(E_g prediction) and http://materials-datamining.com/ocpmdm/material_api/i0 ucuyn3 wsd14940(RH_(2) prediction).

Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.

An Effective QoS-Constrained Scheduling Scheme for Cloud Computing Services

Cloud computing is becoming a hot topic of the information industry in recent years. Many companies provide the cloud services, such as Google Apps and Apple multimedia services. In general, by applying the virtulization technologies, the data center is built for cloud computing to provide users with the eomputing and storage resources, as well as the software environment. Thus, the quality of service （QoS） must be considered to satisfy users＇ requirements. This paper proposes a high efficiency scheduling scheme for supporting cloud computing. The virtual machine migration technique has been applied to the proposed scheduling scheme for improving the resources utilization and satisfying the QoS requirement of users. The experimental results show that in addition to satisfying the QoS requirement of users, the proposed scheme can improve the resources utilization effectively.

DDoS Detection in SDN using Machine Learning Techniques

Software-defined network(SDN)becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure.The SDN controller is considered as the operating system of the SDN based network infrastructure,and it is responsible for executing the different network applications and maintaining the network services and functionalities.Despite all its tremendous capabilities,the SDN face many security issues due to the complexity of the SDN architecture.Distributed denial of services(DDoS)is a common attack on SDN due to its centralized architecture,especially at the control layer of the SDN that has a network-wide impact.Machine learning is now widely used for fast detection of these attacks.In this paper,some important feature selection methods for machine learning on DDoS detection are evaluated.The selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller.A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN attacks.The experimental results show that the Random forest(RF)classifier trains the more accurate model with 99.97%accuracy using features subset by the Recursive feature elimination(RFE)method.

Introduction to the Special Issue on Mobile Service Robotics and Associated Technologies

The popularity of mobile service robotics is steadily growing,and over the last two decades,there has been a significant increase in the application of these machines in distinct fields,indoors,and outdoors.To become truly effective in performing their assigned tasks,these machines involve several associated technologies,namely sensors,actuators,and control systems.With these considerations in mind,this special issue of the Journal of Artificial Intelligence and Technology presents six state-of-the-art peer-reviewed contributions,ranging from theory to prototyping,and down to the final product development.

Machine intelligence,rough sets and rough-fuzzy granular computing:uncertainty handling in bio-informatics and Web intelligence

Machine intelligence,is out of the system by the artificial intelligence shown.It is usually achieved by the average computer intelligence.Rough sets and Information Granules in uncertainty management and soft computing and granular computing is widely used in many fields,such as in protein sequence analysis and biobasis determination,TSM and Web service classification Etc.