Quality Assessment by Evaluation of Network-based Teaching Environments

This paper introduced the objectives, instruments and methods for the evaluation firstly, Then the paper exemplified the criteria to evaluate the learntng management system and online courses. At the end, the author came the conclusion that the results of evaluation should be implemented continuously to implore the systems services and submitted to the scientific community to check their rigidity and validity.

AN ADAPTIVE OPPORTUNISTIC RETRANSMISSION CONTROL SCHEME ENVIRONMENT-AWARE-BASED FOR WIRELESS MULTIMEDIA MESH NETWORKS

In this paper, we propose an aware-based adaptive opportunistic retransmission control scheme for wireless multimedia Mesh networks. The proposed scheme provides maximum retransmis-sion count optimization based on environment-aware to improve packet relay probability. The scheme discriminates the types of packet loss in wireless link by means of environment information and selects the retransmission count by taking the IEEE 802.11 wireless channel characteristics into consideration. Furthermore, the maximum retransmission count of MAC is adjusted adaptively. Extensive simulations demonstrate that the proposed scheme significantly reduces packet collision probability and packet loss rate, and thus improves network throughput.

Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

Theoretical Framework for a Polymorphic Network Environment

The question of whether an ideal network exists with global scalability in its full life cycle has always been a first-principles problem in the research of network systems and architectures.Thus far,it has not been possible to scientifically practice the design criteria of an ideal network in a unimorphic network system,making it difficult to adapt to known services with clear application scenarios while supporting the ever-growing future services with unexpected characteristics.Here,we theoretically prove that no unimorphic network system can simultaneously meet the scalability requirement in a full cycle in three dimensions—the service-level agreement(S),multiplexity(M),and variousness(V)—which we name as the“impossible SMV triangle”dilemma.It is only by transforming the current network development paradigm that the contradiction between global scalability and a unified network infrastructure can be resolved from the perspectives of thinking,methodology,and practice norms.In this paper,we propose a theoretical framework called the polymorphic network environment(PNE),the first principle of which is to separate or decouple application network systems from the infrastructure environment and,under the given resource conditions,use core technologies such as the elementization of network baselines,the dynamic aggregation of resources,and collaborative software and hardware arrangements to generate the capability of the“network of networks.”This makes it possible to construct an ideal network system that is designed for change and capable of symbiosis and coexistence with the generative network morpha in the spatiotemporal dimensions.An environment test for principle verification shows that the generated representative application network modalities can not only coexist without mutual influence but also independently match well-defined multimedia services or custom services under the constraints of technical and economic indicators.

DPGL:The Direct3D9-based Parallel Graphics Library for Multi-display Environment

The emergence of high performance 3D graphics cards has opened the way to PC clusters for high performance multi- display environment. In order to exploit the rendering ability of PC clusters, we should design appropriate parallel rendering algorithms and parallel graphics library interfaces. Due to the rapid development of Direct3D, we bring forward DPGL, the Direct3D9-based parallel graphics library in D3DPR parallel rendering system, which implements Direct3D9 interfaces to support existing Direct3D9 application parallelization with no modification. Based on the parallelism analysis of Direct3D9 rendering pipeline, we briefly introduce D3DPR parallel rendering system. DPGL is the fundamental component of D3DPR. After presenting DPGL three layers architecture, we discuss the rendering resource interception and management. Finally, we describe the design and implementation of DPGL in detail, including rendering command interception layer, rendering command interpretation layer and rendering resource parallelization layer.

Multi-agent-based Approach for Determination of Time-quota in Integrated Environment

Time-quota is one of important factors in producti on system. It is affected by various factors. time-quota is studied in CAPP and p roduction schedule integration environment in this paper. An agent-based time- quota method is put forward and the structure model is established by means of i ntelligent agent in integrated environment. The method can map the influencing t ime-quota factors into part agent related to process state and machine method a gent, resorting to the function of agent rule-based reasoning, the agents can t ransform these factors into data mode that artificial neural network (ANN) can a ccept and recognize. As a tool, ANN agent can calculate time-quota quickly. A b lackboard method is used as the means of communication and collaborative control between agents. The experiments show that precise process time-quota can be obtained rapidly with proper samples selected, continuous self-study and self -organization in system, and multi-agent approach is an effective method for d etermination of time-quota.

Construction of overlay network based on P2P technology in Grid environment

Based on the advantages of both Grid and peer-to-peer （P2P） networks, an overlay network in the Grid environment is constructed by P2P technologies by a modified version of the Chord protocol. In this mechanism, different nodes＇ accesses to different resources are determined by their contribution. Therefore, the heterogeneous resources of virtual organizations in large-scale Grid can be effectively integrated, and the key node failure as well as system bottleneck in the traditional Grid environment is eliminated. The experimental results indicate that this management mechanism can achieve better average performance in the Grid environment and maintain the P2P characteristics as well.

Risk Assessment of Marine Environments Along the South China Sea and North Indian Ocean on the Basis of a Weighted Bayesian Network

Marine environments have a considerable influence on the construction of the Chinese 21st Century Maritime Silk Road.Thus,an objective and quantitative risk assessment of marine environments has become a key problem that must be solved urgently.To deal with the uncertainty in marine environmental risks caused by complex factors and fuzzy mechanisms,a new assessment technique based on a weighted Bayesian network(BN)is proposed.Through risk factor analysis,node selection,structure construc-tion,and parameter learning,we apply the proposed weighted BN-based assessment model for the risk assessment and zonation of marine environments along the Maritime Silk Road.Results show that the model effectively fuses multisource and uncertain envi-ronmental information and provides reasonable risk assessment results,thereby offering technical support for risk prevention and disaster mitigation along the Maritime Silk Road.

Study on Robust H_∞ Filtering in Networked Environments

This paper is concerned with the robust H ∞ filter problem for networked environments, which are subject to both transmission delay and packet dropouts randomly. By employing random series which have Bernoulli distributions taking value of 0 or 1, the data transmission model is obtained. Based on state augmentation and stochastic theory, the sufficient condition for robust stability with H ∞ constraints is derived for the filtering error system. The robust filter is designed in terms of feasibility of one certain linear matrix inequality （LMI）, which is formed by adopting matrix congruence transformations. A numerical example is given to show the effectiveness of the proposed filtering method.

Semi-supervised learning based hybrid beamforming under time-varying propagation environments

Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

A Predictive 6G Network with Environment Sensing Enhancement:From Radio Wave Propagation Perspective

In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get the electromagnetic wave propagation model of typical scenarios firstly and then do the network design by simulation offline,which obviously leads to a 6G network lacking of adaptation to dynamic environments.Recently,with the aid of sensing enhancement,more environment information can be obtained.Based on this,from radio wave propagation perspective,we propose a predictive 6G network with environment sensing enhancement,the electromagnetic wave propagation characteristics prediction enabled network(EWave Net),to further release the potential of 6G.To this end,a prediction plane is created to sense,predict and utilize the physical environment information in EWave Net to realize the electromagnetic wave propagation characteristics prediction timely.A two-level closed feedback workflow is also designed to enhance the sensing and prediction ability for EWave Net.Several promising application cases of EWave Net are analyzed and the open issues to achieve this goal are addressed finally.

Developing Environment of Hierarchical and Distributed Real-Time Fire-Control Network

This paper introduces a hierarchical real-time environment for developing ship-bornefire-control system. Advanced computer networks are used to simulate the system with the requiredengagement scenario, including own-ship and parameters, and data processing and transmission,mission calculation, graphical supervision and gunnery ballistics outputting. The simulation systemis able to receive instruction from, or send information to the command-control center. Furthermore,the system can also be used to compare various designed schemes and analyze the accuracy andeffectiveness of the system.

Dynamic Social Network Analysis with Heterogeneous Sensors in Ambient Environment

This paper presents our vision of large-scale, dynamic social network analysis in real environments, which we expect to be enabled by the introduction of large-scale heterogeneous sensors in the ambient environment. We address challenges in realizing large-scale dynamic social network analysis in real environments, and discuss several promising applications. Moreover, we present our design and implementation of a prototype system for quasi-realtime social network construction. We finally present preliminary experimental results of dynamic social network analysis for six-person social gatherings in a real environment, and discuss the feasibility of dynamic social network analysis and its effectiveness.

SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment

With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.

Mobile Ubiquitous Network Environment

The Mobile Ubiquitous Service Environment (MUSE), established through the coordination and integration of mobile telecommunications and ubiquitous network in the pursuit of Always Best Experience (ABE), represents the major development trend for the next generation mobile wireless network. Research on MUSE will involve the integration of computing model system, service platform system, operating system and terminal structure system, all of which involve exploration and innovation of new networking structure, its control and management as well as way of measuring. The change in network resources triggers the change in network computing models. To make readers have a basic understanding of mobile ubiquitous network environment, this lecture will introduce MUSE in two sections. The first section describes the background of MUSE and its future development prospect. The second section focuses on the design principles and key technologies in MUSE model, which make services realized, as well as the networking structure of MUSE model, and the key technologies and design principles of networking.

Analysis of the thinking method of English teaching under network environment

How to make a good job of foreign language teaching and improve the quality of teaching is a topic which the general teaching workers are facing, in this paper, the author express ideas from many aspects, multi-angle, multi-level, this brings a valuable theory reference on methods of English teaching which is under network environment. As for the problems of how to solve the bottle neck of English teaching, how to train the talent who are more adaptive to the world economy globalization, how to get rid of the traditional teaching method in teaching and how to improve the teaching level more efficiently, this paper gave some good methods and suggestions.

Earthquake Electromagnetic Precursor Anomalies Detected by a New Ground-based Observation Network

It is a debated topic if there are any observable precursor anomalies prior to the earthquake(EQ hereafter)and if the stronger EQ can be successfully predicted.During last few decades quite a lot of observable electromagnetic(EM)precursors were published by using techniques equipped in either satellites or on ground-based stations.But there are only a few cases that the shortterm precursor anomalies of EM field before earthquakes were observed by using alternate EM fields on ground.This paper will present a new EM observation network built in recent years and show a new finding of EM field with the variation of a one-year cycle observed using the network.As an example,the short-term precursor anomalies of apparent resistivity before the Yangbi EQ(Ms 5.1)occurred on March 27,2017 in Yunnan Province will be studied.The observed anomalous phenomena indicate that the anomaly before the EQ can be captured only if reasonable effective methods including sophisticated analytical techniques are used,and it is believed that continuously observed data on the fixed observation network for a long time is an effective means for studying anomalies that appeared before earthquakes.This network can also play an important role in studying the EM environment from space.

Research on the New Mode of college students’mental Health Education under the Network environment

With the rapid development of science andtechnology, internet technology has become matureincreasingly. It has become an important part ofpeople’s work, life and study. At the same time, thenetwork environment has also brought an impact onthe physical and mental health of college students.Nowadays, the quality and level of college students’mental health education has become society topics.College psychological health education work shouldkeep pace with the network environment developmentand the students’ physical and mental development.It is the effective innovation of psychological healtheducation work. The new model of college students’psychological health education development isconstructed. It is improve the level of students’ physicaland mental health development, and the psychologicalquality of college students is strengthen. Based onthis, this paper analyzes the existing problems on thecollege students’ mental health development. Under thenetwork environment, it proposes an effective method toconstruct a new mode of college students’ mental healtheducation .

Research on Application Technology Oriented Rapid Response Manufacturing in the Distributed Network Environment

A new chance of developing traditional manufacturing industry comes forth with the development of network technology. Application technology oriented rapid response manufacturing in the distributed network environments, that is, how to take advantage of the Intranet and Internet, combine the numerous manufacturing resources spread around the region, the country and even the globe is the key to the agile design, manufacturing and the buildup of comprehensively competitive power, at the same time, is also an important research direction in the field of advanced manufacturing technology. Rapid response manufacturing in the distributed network environment is a newly manufactory pattern that can be used to implement the conception of agile design and manufacturing, but there are some new problems coming with it, which will directly influence the enterprise’s ability of rapid response in the distributed network manufacturing pattern and lead to the failure of the league and the lost of the given orders. In this paper, we establish some approaches to solve these problems in product development process. The paper then presents the research on key application technologies and solutions includes: network safety strategy which guarantees data transferring among the leaguer members, production data management based on Web/DOT (Distributed Object Technology) and XML criteria which guarantees data exchange in structure-variance characteristic environments, the network platform which provides the conversion service of different types of CAD files each other. All of these solutions are aim for technology problems existing in the distributed network environments and among the league members. Finally, the paper takes one project, that is, the establishment of the online application service system for Shanghai Advance Manufacturing Technology Research Center as a good instance.

A survey of neural network accelerator with software development environments

Recent years,the deep learning algorithm has been widely deployed from cloud servers to terminal units.And researchers proposed various neural network accelerators and software development environments.In this article,we have reviewed the representative neural network accelerators.As an entirety,the corresponding software stack must consider the hardware architecture of the specific accelerator to enhance the end-to-end performance.And we summarize the programming environments of neural network accelerators and optimizations in software stack.Finally,we comment the future trend of neural network accelerator and programming environments.