Thereafter the PTMS-IP4 calculates the subnet permutations that display the hosts in each subnet . Not only is the system useful as a teaching model , it could also help the network or internet engineers to easily subnet complex networks . subsubsection : Efficiency comparison The complexity of methods in tab : set4 is determined by network and input image sizes . SRCNN and VSRnet upsample LR images before attempting to super - resolve them , which considerably increases the required number of operations . Table I and II show the overall performance on Criteo and iPinYou datasets , respectively . In FM , we employ 10 - order factorization and correspondingly , we employ 10 - order embedding in network models . CCPM has 1 embedding layer , 2 convolution layers ( with max pooling ) and 1 hidden layer ( 5 layers in total ) . They adopt this choice since only GPS coordinates are available and not camera orientations . Downside of this approach is that the chosen matching examples already have low distance , thus not forcing network to learn much out of the positive samples . paragraph : Positive images : maximum inliers . The former includes surface normal constraints and information propagation along edges ; the latter includes perceptual features extracted from images as a guidance . We carefully design our network structure and propose a very deep cascaded graph convolutional neural network with “ shortcut ” connections . Meshes are progressively refined by our network trained end - to - end with the chamfer loss and normal loss . Three nonlinear features such as Higuchi fractal dimension , Petrosian fractal dimension and Detrended Fluctuation Analysis are calculated to prepare three feature vectors respective to each frequency sub-bands . Three machine learning techniques are used for vigilance level detection such as Support Vector Machine , Least Square-Support Vector Machine and Artificial Neural Network . The approaches described are evaluated on the 25 most frequently requested city names in terms of the first-choice accuracy and the robustness of the decision making ( the separation between first and second word candidates ) . An artificial neural network achieved 92.9 % first-choice accuracy , a multistate Gaussian classifier obtained 86.6 % , and 91.7 % was achieved by the k-nearest neighbor classifier . The discrimination ratio between the first and second word candidates was significantly higher for the artificial neural net , yielding values of 3.5 vs. 1.4 and 1.2 for the Gaussian and k-nearest neighbor classifiers , respectively.<<ETX > > The paper introduces several classical algorithms and their application in basic traffic data fusion . These algorithms include Kalman filtering , artificial neural network , statistic analyses(mean weight , exponential smoothing , reasoning estimate of mean value),and prediction methods of traffic volume and road network travel time . Their validity and reliability were validated by practice . A Review . VEHICULAR ad hoc networks ( VANETs ) are the self-configuring networks where the nodes are vehicles ( equipped with on-board computers ) , elements of roadside infrastructure , sensors , and pedestrian personal devices . In the current state of the art in this field there is a need of studies on real outdoor experiments to validate the new VANETs optimizing protocols and applications . Examples of static sensor network applications include environmental monitoring systems , monitoring of industrial control systems , monitoring of degradation in slagging gasifiers , distributed object detection and tracking . Example of mobile applications include vehicular ad-hoc networks and networks of personal radios used in emergency dispatch and battlefields . For data aggregation in static networks with stable links , structured approaches such as spanning trees are generally preferred . Vehicular ad-hoc networking can provide efficient traffic information , emergency warning , alert messages and infotainment dissemination . It takes a vital role in safety fields such as defense system , traffic system , etc . This paper discusses the autonomous technology of driver-less cars , and the security problems with this technology . Exploring the current communication technologies of vehicular ad hoc networking , and how artificial neural networks and software defined networking can help to protect driver-less cars from malicious attacks . KUMP/D is a flexible parallel processor , based on fine grain parallel processing , which is which indispensable in complex and irregular computation , and is also equipped with a specialized I/O network . This I/O network has enough throughput for real time video I/O , providing a mechanism , which supports the synchronization of process executions and real time video frames . A multi-voltage power supply means includes a first power source voltage operation input / output ( I / O ) network , coupled to the I / O network and a core network to a second power supply voltage operation , and coupled to the I / O network and the core network energization control ( POC ) network . The POC POC network configured to transmit signals to the I / O network , and includes a state detector configured to power the core network adjustable current power on / off detector . The network further comprises a POC coupled to the adjustable current power on / off detector and configured to process the power state for the POC signal processing circuit , and one or more feedback circuit . An algorithm , which iteratively adjusts the non-work activity location , corresponding route and departure time choices to reach an extreme point of the minimization problem , is proposed . A numerical example with a capacity constrained network is used to illustrate the performance of the proposed model and solution algorithm . This paper presents a new probabilistic graphical model used to model and recognize words representing the names of Tunisian cities . In fact , this work is based on a dynamic hierarchical Bayesian network . The aim is to find the best model of Arabic handwriting to reduce the complexity of the recognition process by permitting the partial recognition . While , to represent the biological interactions , we use a Hierarchical Bayesian Network where we associate a level for each scale ( Tissue , cell-cluster , cell scale ) . Thus , the HMM induces a Dynamic Hierarchical Bayesian Network that encodes the tumor growth aspects and factors . A network operating system running on this control plane , is anticipated to provide necessary measures for scalability and reliability in order to stand against the gigantic traffic pumped by the network . In this paper , we propose a distributed OpenFlow network operating system built with necessary scalability and reliability qualifications without requiring any changes to the existing OpenFlow protocol and networking equipment . In order to promote progress on fusion and analysis methodologies for multi-source remote sensing data , The Image Analysis and Data Fusion Technical Committee organized the 2018 IEEE GRSS Data Fusion contest . In this contest , we proposed a two-branch convolution network for hyperspectral image classification with a data re-sampling strategy and semi-supervised learning to address three existing problems , i.e. multi-scale feature learning , data imbalance , and small size of the dataset . The contest showed that our proposal achieved the best performance on two metrics : the overall accuracy of 77.39 % and a kappa coefficient of 0.76 on the hyperspectral images provided by 2018 IEEE GRSS Data Fusion Contest . The method of reducing the response time for the kinds of information network center comprises : receiving an indication from an entry node into the network of the content object , the content associated with the new object is transmitted through said network flow ; for identifying the content size and egress node of the network of the content object in the object . The portion size of the object based on the content , for determining the amount to be completed and the new bandwidth of the transport stream . This paper describes an implementation of FDDI-I1 local area network ( LAN ) , especially its data transmission formats , the method of circuit switching service with branch connections in a ring network and the structure of node equipment . We show how the definitions of these measures can be corrected in such a way that they give the expected results . We illustrate the proposed corrected measures by applying them to the US Airports network using the program Pajek . This work introduces our Unified Network Information Service ( UNIS ) , designed to represent physical and virtual networks and services . We describe the UNIS network data model and its RESTful interface , which provide a common interface to topology , service , and measurement resources . In addition , we describe the security mechanisms built into the UNIS framework . Finally , simulation is carried out on ZS-IV vehicle . The control performance is compared with that of neural network controller with different structures such as normal adaptive neural network . The results show that the AUV motion controller based on parallel neural network has a great possibility to solve the problems in the AUV control system design . It uses curvelet transform based entropy features for the recognition , and the transform is applied only to the segmented hand region . Then , the features of each sign of English alphabets are modelled by a classier network called Hidden Markov Models ( HMM ) . The system gives an average accuracy of 82.95 % using 3rd level features which can help to reduce the communication gap between deaf-dumb and normal people in the world . In this paper , we shed light on how to combine the two models to learn more discriminative pedestrian descriptors . Specifically , we propose a siamese network that simultaneously computes the identification loss and verification loss . Given a pair of training images , the network predicts the identities of the two input images and whether they belong to the same identity . The key idea underlying our method is to train the model 's initial parameters such that the model has maximal performance on a new task after the parameters have been updated through one or more gradient steps computed with a small amount of data from that new task . Unlike prior meta - learning methods that learn an update function or learning rule [ reference ][ reference ][ reference ][ reference ] , our algorithm does not expand the number of learned parameters nor place constraints on the model architecture ( e.g. by requiring a recurrent model [ reference ] or a Siamese network [ reference ] ) , and it can be readily combined with fully connected , convolutional , or recurrent neural networks . It can also be used with a variety of loss functions , including differentiable supervised losses and nondifferentiable reinforcement learning objectives . In addition , Varior et al . [ reference ] merge long short - term memory ( LSTM ) model into a siamese network that can handle image parts sequentially so that the spatial information can be memorized to enhance the discriminative capability of the deep features . Another effective strategy is the classification model , which makes full use of the re - ID labels [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . Zheng et al . proposed denoising autoencoder ( DAE ) that learns the data distribution in a supervised learning fashion . Both DBN and DAE learn a low dimensional representation ( encoding ) for each data instance and generate it from a decoding network . Recently , variational autoencoder ( VAE ) that combines deep learning with statistical inference intended to represent a data instance in a latent hidden space , while still utilizing ( deep ) neural networks for non - linear mapping . Alternatively , small architecturally variant models are used for semantic image segmentation tasks . For example , a fully - connected convolutional neural network ( FCN ) also provides state - of - the - art results for image segmentation tasks in computer vision [ reference ] . Randomly cropped and flipped image patches of size are used for training . We adopt a NIN network with three stacked layers . We denote it as CIFAR100 - NIN which will be the HD - CNN building block . In SNIPER , this is achieved by randomly sampling a fixed number of chips ( maximum of in this paper ) from regions which are likely to cover false positives . To find such regions , we train a lightweight RPN network with a short schedule . The proposals of this network are used to generate chips for regions which are likely to contain false positives ( this could potentially be replaced with unsupervised proposals like EdgeBoxes as well ) . , there is a huge performance gap in all four measurement metrics , which means the failure of optimizing Chamfer distance . The main reason is the degradation problem observed in the very deep 2D convolutional neural network . Such problem leads to a higher training error ( and thus higher testing error ) when adding more layers to a suitably deep model . Mean error indicates the average 3D distance error . To fairly compare four combinations , we used the same network building blocks and design , which were introduced in Section 6 . The only difference is that the model for the per - voxel likelihood estimation is fully convolutional , whereas for the coordinate regression , we used fully connected layers at the end of the network . Besides , BW - - QIG ( 818 ) , BW - QIG ( 616 ) and TW - QIG ( 626 ) also have small balance index . Among all the combinations , the binarized network with scaling factor , i.e. BW - gets the closest error to the original network DU - Net ( 4 ) . For BW - - QIG ( 818 ) , the performance is not better than BW - QIG ( 818 ) . In this paper , we presented the first experiments using neural network models for the task of error detection in learner writing . Six alternative compositional network architectures for modeling context were evaluated . Based on the findings , we propose a novel error detection framework using token - level embeddings , bidirectional LSTMs for context representation , and a multi - layer architecture for learning more complex features . In this study , we propose a style - controllable Multi - source Abstractive Summarization model for QUEstion answering , called Masque . The model is an end - to - end deep neural network that can generate answers conditioned on a given style . Experiments with MS MARCO 2.1 show that our model achieved state - of - the - art performance on two tasks with different answer styles . Recently , PCI-Express , which is the third generation high-performance I/O bus used to interconnect peripheral devices , has been released . The third generation of InfiniBand adapters allow applications to take advantage of PCI-Express . PCI-Express offers very low latency access of the host memory by network interface cards ( NICs ) . It supports Docker and Podman as containerization software and OpenMPI , MVAPICH2 and Intel MPI as MPI implementations . It can work with Slurm and can use devices that popular in HPC like GPUs or InfiniBand adapters . The latency of inter-node GPU-GPU communication of PEACH3 was about 2.8 μ sec which is one third of that of CUDA API with MPI/Infiniband . The bandwidth was about 1.21 times of that of the previous version PEACH2 , and 1.54 times of that with MPI/Infiniband for 512 KB data transfer . Two application programs : BFS ( breadth first search ) and CG ( conjugate gradient ) were implemented with TCA IP and CUDA IP with MPI/Infiniband . The performance of BFS with PEACH3 was 1.16 times better than that with PEACH2 , and 1.3 times better than that with MPI/Infiniband for a graph with scale = 15 . In CG , for the small matrix ( CLASS = S ) , the PEACH3 achieved 12 % better performance than that with PEACH2 and 25 % with MPI/Infiniband . However , since the bandwidth of PEACH3 with PCI gen3x8 is smaller than Infiniband with PCI gen3x16 , the performance benefit was disappeared for CLASS = A matrix . A kind of quick processing system of satellite remote sensing date , calculates node , InfiniBand switch and storage array including multiple stage；Described calculating node includes a Master node and multiple slave node , each calculates , and node hardware configuration is identical and slave nodes can be with spread , every calculates node and is loaded with multi-core CPU and multiple GPU board , and more than 2 times that guarantee CPU core number is GPU board quantity；Described storage array is deposited parameter configuration files , pending strip data in advance；Calculating node and storage array are joined together to form a High speed network by described InfiniBand switch , constitute the underlying hardware facility quickly processed . The efficiency of the proposed low-latency Software Defined Networking is analyzed and evaluated for high performance applications . The results of the experiments show that the latest Mellanox FDR InfiniBand interconnect and Mellanox OpenStack plugin gives the best performance for implementing VM-based high performance clouds with large message sizes . The efficiency of the proposed low-latency SDN is analyzed and evaluated for high performance applications . The results of the experiments show that the latest Mellanox FDR InfiniBand interconnect and Mellanox OpenStack plugin gives the best performance for implementing virtual machine based high performance clouds with large message sizes . We have designed and implemented both ACM algorithms in MVAPICH to study their benefits . Two mechanisms have been exploited for the establishment of new RC connections : one using InfiniBand unreliable datagram and the other using InfiniBand connection management . For both mechanisms , MPI communication issues , such as progress rules , reliability and race conditions are handled to ensure efficient and lightweight connection management . InfiniBand has emerged as a compelling interconnect technology , and provides more scalability and significantly better cost- performance than any other known protocols . This paper presents a reliability , availability , and serviceability ( RAS ) modeling and analysis of the Sun Datacenter Switch 3456 system , the world 's largest standards-based InfiniBand switch , with direct capacity to host up to 3,456 server nodes , against hardware faults . The results show that the system reliability , in terms of connectivity between the server nodes physically connected to the switch , is high for configurations with redundant ports . This paper describes the improvement of Hybrid OpenCL by using high speed networks and its results from experimentation . The experimental results show that high speed networks reduce the overhead introduced by Hybrid OpenCL , and InfiniBand SDP shows the best performance among the results . The InfiniBand standard describes CC functionality for detecting and resolving congestion . The InfiniBand CC concept is rich in the way that it specifies a set of parameters that can be tuned in order to achieve effective CC . There is , however , limited experience with the InfiniBand CC mechanism . Sun HPC ClusterTools includes packages of binaries built from the Open MPI source code by the Sun ™ Studio compilers and install scripts for installing those packages across a cluster of nodes . The Sun HPC ClusterTools team contributed a Sun Grid Engine plug-in and developed the uDAPL Byte Transfer Layer module as its Infiniband solution on Solaris ™ operating system . Additionally , Sun HPC ClusterTools includes examples of using DTrace to analyze performance and debug MPI applications . These challenges usually increase congestion on some links while other links are almost free . In this paper , we propose a novel mechanism to enhance QoS in multipath routing protocols in MANETs based on the InfiniBand ( IB ) QoS architecture . The basic idea of our approach is to enhance the load balancing and thus reduce congestion on overloaded links . Une communication multicast est une communication dans laquelle un meme paquet de donnees peut etre envoye a un groupe de recepteurs , quelque soit leur localisation . Dans le modele Internet IPv6 , une station peut potentiellement emettre un paquet multicast vers n'importe quel groupe . Compare aux communications point a point ( unicast ) , le multicast evite la duplication des paquets de donnees au niveau de la source , et minimise l'utilisation de la bande passante au niveau du reseau . However , the current solutions do not provide any means for the end systems to perform optimized packet routing during the operation of a mobile device . In this paper , following a performance analysis of Mobile IPv6 routing mechanisms , we propose an tunneling-based route optimization - an extension to Mobile IPv6 for routing data packets . It reduces per-packet forwarding cost for the communications of mobile devices . Through the analysis of hierarchical mobile IPv6 protocol , a kind of managing strategy of mobile IPv6 routing on the basis of hierarchical mechanisms was proposed , the routing optimization was realized , fast handoff was realized in intra-domain mobility and inter-domain mobility and to reduce the delay and improve the resource utilization ratio of the network . This document defines binary formats for IPv4 and IPv6 traffic selectors to be used in conjunction with flow bindings for Mobile IPv6 . Measurements done in a real time environment shows negligible difference in QoS parameters for VoIP traffic in native and tunnel mode IPv6 networks . This is important for future transition from IPv4 to IPv6 protocol , where tunnel mode IPv6 is one of the temporary solutions . Because of the ubiquity of BLE-enabled smartphones nowadays , BLE has gained much attention in the IoT industry recently . In this research , we aim at implementing an IPv6 over BLE experimental system using Raspberry Pi 3 and nRF51-DK development boards , and then run the Message Queuing Telemetry Transport for Sensor Networks ( MQTT-SN ) protocol and the Constrained Application Protocol ( CoAP ) over the protocol stack of IPv6/BLE . Specifically , in our experimental system every BLE node is IPv6-addressable and accessible through the MQTT/CoAP protocols from anywhere over the Internet . Next generation internet IPv6 is gradually being spread , the associated security issues also attracted attention , network intrusion detection system is to achieve active defense of the key technologies . First , based on the IPv6 feature analysis and formal description , describe IPv6-based network intrusion detection system-level framework . Then , research system 's work processes and the main technical characteristics , established using hierarchical design idea , combining the characteristics of IPv6 various modules from the system architecture analysis and study . The invention provides a cloud service-based IPv6 ( internet protocol version 6)/IPv4 ( internet protocol version 4 ) translation method with communication initiated by an IPv6 party , and relates to the technical field of a computer network . The method is used for realizing the service that an IPv6 user or a dual-stack user can seamlessly initiate the information resource for accessing the IPv4 ; a cloud service data center is provided with an information resource data base , a domain name analysis server , a stateless IPv4/IPv6 translator and a stateful IPv6/IPv6 translator , wherein the stateless IPv4/IPv6 translator and the stateful IPv6/IPv6 are connected with each other in series ; a translation algorithm of the stateless IPv4/IPv6 translator is used for translating an IPv4 address which is reserved for accessing a pure IPv4 server from an IPv6 network into a special IPv6 address which is embedded with the IPv4 address and uses an operator prefix ; a translation algorithm of the stateful IPv6/IPv6 is used for dynamically mapping any IPv6 addresses of the user into the special IPv6 address which is embedded with the IPv4 address ; and the domain name analysis server is used for answering a domain name analysis request of the user , and returning an A record or an AAAA record for the user according to the content of an information resource data base , therefore , the pure IPv4 user of the dual-stack user can initiate the information resource for accessing the pure IPv4 by the IPv6 . This paper aims to optimize the current IPv6 packet structure by reducing the size of the headers , increasing the size of the data , reducing packet processing time and speed up the data transmission . A method of initiating a PPP ( Point-to-Point Protocol ) on an RG to obtain address information has very wide application demands in practice , and as the existing IPv6 ( Internet Protocol version 6 ) address consultation collocation method can not allocate complete IPv6 addresses through allocation option consultation , the invention provides a simple and vigorous IPv6 address consultation allocation scheme . According to the scheme , the consultation allocation of IPv6 address information is conducted by expanding allocation options of a IPv6 CP ( content provider ) , thereby obtaining the complete IPv6 addresses and corresponding information or prefixed information on a DNS ( Domain Name Server ) and the like , so that the implementation method is simple and consistent with a related implementation method of an original IPv4 , thereby reducing the cost of equipment and leading the running of the equipment to be more stable . A 13-Mbps unidirectional broadcast satellite covers Asia region enabling lecture sharing among student sites . IPv6 multicast overlay network is established by UDLR and IPv6 tunnel technologies to create a virtual network connecting lecturer-site and student-sites on top of network heterogeneity . Bandwidth optimization and scalability are achieved by IPv6 multicast .
Thereafter the PTMS-IP4 calculates the subnet permutations that display the hosts in each subnet . Not only is the system useful as a teaching model , it could also help the network or internet engineers to easily subnet complex networks .
subsubsection : Efficiency comparison The complexity of methods in tab : set4 is determined by network and input image sizes . SRCNN and VSRnet upsample LR images before attempting to super - resolve them , which considerably increases the required number of operations .
Table I and II show the overall performance on Criteo and iPinYou datasets , respectively . In FM , we employ 10 - order factorization and correspondingly , we employ 10 - order embedding in network models . CCPM has 1 embedding layer , 2 convolution layers ( with max pooling ) and 1 hidden layer ( 5 layers in total ) .
They adopt this choice since only GPS coordinates are available and not camera orientations . Downside of this approach is that the chosen matching examples already have low distance , thus not forcing network to learn much out of the positive samples . paragraph : Positive images : maximum inliers .
The former includes surface normal constraints and information propagation along edges ; the latter includes perceptual features extracted from images as a guidance . We carefully design our network structure and propose a very deep cascaded graph convolutional neural network with “ shortcut ” connections . Meshes are progressively refined by our network trained end - to - end with the chamfer loss and normal loss .
Three nonlinear features such as Higuchi fractal dimension , Petrosian fractal dimension and Detrended Fluctuation Analysis are calculated to prepare three feature vectors respective to each frequency sub-bands . Three machine learning techniques are used for vigilance level detection such as Support Vector Machine , Least Square-Support Vector Machine and Artificial Neural Network .
The approaches described are evaluated on the 25 most frequently requested city names in terms of the first-choice accuracy and the robustness of the decision making ( the separation between first and second word candidates ) . An artificial neural network achieved 92.9 % first-choice accuracy , a multistate Gaussian classifier obtained 86.6 % , and 91.7 % was achieved by the k-nearest neighbor classifier . The discrimination ratio between the first and second word candidates was significantly higher for the artificial neural net , yielding values of 3.5 vs. 1.4 and 1.2 for the Gaussian and k-nearest neighbor classifiers , respectively.<<ETX > >
The paper introduces several classical algorithms and their application in basic traffic data fusion . These algorithms include Kalman filtering , artificial neural network , statistic analyses(mean weight , exponential smoothing , reasoning estimate of mean value),and prediction methods of traffic volume and road network travel time . Their validity and reliability were validated by practice .
A Review . VEHICULAR ad hoc networks ( VANETs ) are the self-configuring networks where the nodes are vehicles ( equipped with on-board computers ) , elements of roadside infrastructure , sensors , and pedestrian personal devices . In the current state of the art in this field there is a need of studies on real outdoor experiments to validate the new VANETs optimizing protocols and applications .
Examples of static sensor network applications include environmental monitoring systems , monitoring of industrial control systems , monitoring of degradation in slagging gasifiers , distributed object detection and tracking . Example of mobile applications include vehicular ad-hoc networks and networks of personal radios used in emergency dispatch and battlefields . For data aggregation in static networks with stable links , structured approaches such as spanning trees are generally preferred .
Vehicular ad-hoc networking can provide efficient traffic information , emergency warning , alert messages and infotainment dissemination . It takes a vital role in safety fields such as defense system , traffic system , etc .
This paper discusses the autonomous technology of driver-less cars , and the security problems with this technology . Exploring the current communication technologies of vehicular ad hoc networking , and how artificial neural networks and software defined networking can help to protect driver-less cars from malicious attacks .
KUMP/D is a flexible parallel processor , based on fine grain parallel processing , which is which indispensable in complex and irregular computation , and is also equipped with a specialized I/O network . This I/O network has enough throughput for real time video I/O , providing a mechanism , which supports the synchronization of process executions and real time video frames .
A multi-voltage power supply means includes a first power source voltage operation input / output ( I / O ) network , coupled to the I / O network and a core network to a second power supply voltage operation , and coupled to the I / O network and the core network energization control ( POC ) network . The POC POC network configured to transmit signals to the I / O network , and includes a state detector configured to power the core network adjustable current power on / off detector . The network further comprises a POC coupled to the adjustable current power on / off detector and configured to process the power state for the POC signal processing circuit , and one or more feedback circuit .
An algorithm , which iteratively adjusts the non-work activity location , corresponding route and departure time choices to reach an extreme point of the minimization problem , is proposed . A numerical example with a capacity constrained network is used to illustrate the performance of the proposed model and solution algorithm .
This paper presents a new probabilistic graphical model used to model and recognize words representing the names of Tunisian cities . In fact , this work is based on a dynamic hierarchical Bayesian network . The aim is to find the best model of Arabic handwriting to reduce the complexity of the recognition process by permitting the partial recognition .
While , to represent the biological interactions , we use a Hierarchical Bayesian Network where we associate a level for each scale ( Tissue , cell-cluster , cell scale ) . Thus , the HMM induces a Dynamic Hierarchical Bayesian Network that encodes the tumor growth aspects and factors .
A network operating system running on this control plane , is anticipated to provide necessary measures for scalability and reliability in order to stand against the gigantic traffic pumped by the network . In this paper , we propose a distributed OpenFlow network operating system built with necessary scalability and reliability qualifications without requiring any changes to the existing OpenFlow protocol and networking equipment .
In order to promote progress on fusion and analysis methodologies for multi-source remote sensing data , The Image Analysis and Data Fusion Technical Committee organized the 2018 IEEE GRSS Data Fusion contest . In this contest , we proposed a two-branch convolution network for hyperspectral image classification with a data re-sampling strategy and semi-supervised learning to address three existing problems , i.e. multi-scale feature learning , data imbalance , and small size of the dataset . The contest showed that our proposal achieved the best performance on two metrics : the overall accuracy of 77.39 % and a kappa coefficient of 0.76 on the hyperspectral images provided by 2018 IEEE GRSS Data Fusion Contest .
The method of reducing the response time for the kinds of information network center comprises : receiving an indication from an entry node into the network of the content object , the content associated with the new object is transmitted through said network flow ; for identifying the content size and egress node of the network of the content object in the object . The portion size of the object based on the content , for determining the amount to be completed and the new bandwidth of the transport stream .
This paper describes an implementation of FDDI-I1 local area network ( LAN ) , especially its data transmission formats , the method of circuit switching service with branch connections in a ring network and the structure of node equipment .
We show how the definitions of these measures can be corrected in such a way that they give the expected results . We illustrate the proposed corrected measures by applying them to the US Airports network using the program Pajek .
This work introduces our Unified Network Information Service ( UNIS ) , designed to represent physical and virtual networks and services . We describe the UNIS network data model and its RESTful interface , which provide a common interface to topology , service , and measurement resources . In addition , we describe the security mechanisms built into the UNIS framework .
Finally , simulation is carried out on ZS-IV vehicle . The control performance is compared with that of neural network controller with different structures such as normal adaptive neural network . The results show that the AUV motion controller based on parallel neural network has a great possibility to solve the problems in the AUV control system design .
It uses curvelet transform based entropy features for the recognition , and the transform is applied only to the segmented hand region . Then , the features of each sign of English alphabets are modelled by a classier network called Hidden Markov Models ( HMM ) . The system gives an average accuracy of 82.95 % using 3rd level features which can help to reduce the communication gap between deaf-dumb and normal people in the world .
In this paper , we shed light on how to combine the two models to learn more discriminative pedestrian descriptors . Specifically , we propose a siamese network that simultaneously computes the identification loss and verification loss . Given a pair of training images , the network predicts the identities of the two input images and whether they belong to the same identity .
The key idea underlying our method is to train the model 's initial parameters such that the model has maximal performance on a new task after the parameters have been updated through one or more gradient steps computed with a small amount of data from that new task . Unlike prior meta - learning methods that learn an update function or learning rule [ reference ][ reference ][ reference ][ reference ] , our algorithm does not expand the number of learned parameters nor place constraints on the model architecture ( e.g. by requiring a recurrent model [ reference ] or a Siamese network [ reference ] ) , and it can be readily combined with fully connected , convolutional , or recurrent neural networks . It can also be used with a variety of loss functions , including differentiable supervised losses and nondifferentiable reinforcement learning objectives .
In addition , Varior et al . [ reference ] merge long short - term memory ( LSTM ) model into a siamese network that can handle image parts sequentially so that the spatial information can be memorized to enhance the discriminative capability of the deep features . Another effective strategy is the classification model , which makes full use of the re - ID labels [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . Zheng et al .
proposed denoising autoencoder ( DAE ) that learns the data distribution in a supervised learning fashion . Both DBN and DAE learn a low dimensional representation ( encoding ) for each data instance and generate it from a decoding network . Recently , variational autoencoder ( VAE ) that combines deep learning with statistical inference intended to represent a data instance in a latent hidden space , while still utilizing ( deep ) neural networks for non - linear mapping .
Alternatively , small architecturally variant models are used for semantic image segmentation tasks . For example , a fully - connected convolutional neural network ( FCN ) also provides state - of - the - art results for image segmentation tasks in computer vision [ reference ] .
Randomly cropped and flipped image patches of size are used for training . We adopt a NIN network with three stacked layers . We denote it as CIFAR100 - NIN which will be the HD - CNN building block .
In SNIPER , this is achieved by randomly sampling a fixed number of chips ( maximum of in this paper ) from regions which are likely to cover false positives . To find such regions , we train a lightweight RPN network with a short schedule . The proposals of this network are used to generate chips for regions which are likely to contain false positives ( this could potentially be replaced with unsupervised proposals like EdgeBoxes as well ) .
, there is a huge performance gap in all four measurement metrics , which means the failure of optimizing Chamfer distance . The main reason is the degradation problem observed in the very deep 2D convolutional neural network . Such problem leads to a higher training error ( and thus higher testing error ) when adding more layers to a suitably deep model .
Mean error indicates the average 3D distance error . To fairly compare four combinations , we used the same network building blocks and design , which were introduced in Section 6 . The only difference is that the model for the per - voxel likelihood estimation is fully convolutional , whereas for the coordinate regression , we used fully connected layers at the end of the network .
Besides , BW - - QIG ( 818 ) , BW - QIG ( 616 ) and TW - QIG ( 626 ) also have small balance index . Among all the combinations , the binarized network with scaling factor , i.e. BW - gets the closest error to the original network DU - Net ( 4 ) . For BW - - QIG ( 818 ) , the performance is not better than BW - QIG ( 818 ) .
In this paper , we presented the first experiments using neural network models for the task of error detection in learner writing . Six alternative compositional network architectures for modeling context were evaluated . Based on the findings , we propose a novel error detection framework using token - level embeddings , bidirectional LSTMs for context representation , and a multi - layer architecture for learning more complex features .
In this study , we propose a style - controllable Multi - source Abstractive Summarization model for QUEstion answering , called Masque . The model is an end - to - end deep neural network that can generate answers conditioned on a given style . Experiments with MS MARCO 2.1 show that our model achieved state - of - the - art performance on two tasks with different answer styles .
Recently , PCI-Express , which is the third generation high-performance I/O bus used to interconnect peripheral devices , has been released . The third generation of InfiniBand adapters allow applications to take advantage of PCI-Express . PCI-Express offers very low latency access of the host memory by network interface cards ( NICs ) .
It supports Docker and Podman as containerization software and OpenMPI , MVAPICH2 and Intel MPI as MPI implementations . It can work with Slurm and can use devices that popular in HPC like GPUs or InfiniBand adapters .
The latency of inter-node GPU-GPU communication of PEACH3 was about 2.8 μ sec which is one third of that of CUDA API with MPI/Infiniband . The bandwidth was about 1.21 times of that of the previous version PEACH2 , and 1.54 times of that with MPI/Infiniband for 512 KB data transfer . Two application programs : BFS ( breadth first search ) and CG ( conjugate gradient ) were implemented with TCA IP and CUDA IP with MPI/Infiniband . The performance of BFS with PEACH3 was 1.16 times better than that with PEACH2 , and 1.3 times better than that with MPI/Infiniband for a graph with scale = 15 . In CG , for the small matrix ( CLASS = S ) , the PEACH3 achieved 12 % better performance than that with PEACH2 and 25 % with MPI/Infiniband . However , since the bandwidth of PEACH3 with PCI gen3x8 is smaller than Infiniband with PCI gen3x16 , the performance benefit was disappeared for CLASS = A matrix .
A kind of quick processing system of satellite remote sensing date , calculates node , InfiniBand switch and storage array including multiple stage；Described calculating node includes a Master node and multiple slave node , each calculates , and node hardware configuration is identical and slave nodes can be with spread , every calculates node and is loaded with multi-core CPU and multiple GPU board , and more than 2 times that guarantee CPU core number is GPU board quantity；Described storage array is deposited parameter configuration files , pending strip data in advance；Calculating node and storage array are joined together to form a High speed network by described InfiniBand switch , constitute the underlying hardware facility quickly processed .
The efficiency of the proposed low-latency Software Defined Networking is analyzed and evaluated for high performance applications . The results of the experiments show that the latest Mellanox FDR InfiniBand interconnect and Mellanox OpenStack plugin gives the best performance for implementing VM-based high performance clouds with large message sizes .
The efficiency of the proposed low-latency SDN is analyzed and evaluated for high performance applications . The results of the experiments show that the latest Mellanox FDR InfiniBand interconnect and Mellanox OpenStack plugin gives the best performance for implementing virtual machine based high performance clouds with large message sizes .
We have designed and implemented both ACM algorithms in MVAPICH to study their benefits . Two mechanisms have been exploited for the establishment of new RC connections : one using InfiniBand unreliable datagram and the other using InfiniBand connection management . For both mechanisms , MPI communication issues , such as progress rules , reliability and race conditions are handled to ensure efficient and lightweight connection management .
InfiniBand has emerged as a compelling interconnect technology , and provides more scalability and significantly better cost- performance than any other known protocols . This paper presents a reliability , availability , and serviceability ( RAS ) modeling and analysis of the Sun Datacenter Switch 3456 system , the world 's largest standards-based InfiniBand switch , with direct capacity to host up to 3,456 server nodes , against hardware faults . The results show that the system reliability , in terms of connectivity between the server nodes physically connected to the switch , is high for configurations with redundant ports .
This paper describes the improvement of Hybrid OpenCL by using high speed networks and its results from experimentation . The experimental results show that high speed networks reduce the overhead introduced by Hybrid OpenCL , and InfiniBand SDP shows the best performance among the results .
The InfiniBand standard describes CC functionality for detecting and resolving congestion . The InfiniBand CC concept is rich in the way that it specifies a set of parameters that can be tuned in order to achieve effective CC . There is , however , limited experience with the InfiniBand CC mechanism .
Sun HPC ClusterTools includes packages of binaries built from the Open MPI source code by the Sun ™ Studio compilers and install scripts for installing those packages across a cluster of nodes . The Sun HPC ClusterTools team contributed a Sun Grid Engine plug-in and developed the uDAPL Byte Transfer Layer module as its Infiniband solution on Solaris ™ operating system . Additionally , Sun HPC ClusterTools includes examples of using DTrace to analyze performance and debug MPI applications .
These challenges usually increase congestion on some links while other links are almost free . In this paper , we propose a novel mechanism to enhance QoS in multipath routing protocols in MANETs based on the InfiniBand ( IB ) QoS architecture . The basic idea of our approach is to enhance the load balancing and thus reduce congestion on overloaded links .
Une communication multicast est une communication dans laquelle un meme paquet de donnees peut etre envoye a un groupe de recepteurs , quelque soit leur localisation . Dans le modele Internet IPv6 , une station peut potentiellement emettre un paquet multicast vers n'importe quel groupe . Compare aux communications point a point ( unicast ) , le multicast evite la duplication des paquets de donnees au niveau de la source , et minimise l'utilisation de la bande passante au niveau du reseau .
However , the current solutions do not provide any means for the end systems to perform optimized packet routing during the operation of a mobile device . In this paper , following a performance analysis of Mobile IPv6 routing mechanisms , we propose an tunneling-based route optimization - an extension to Mobile IPv6 for routing data packets . It reduces per-packet forwarding cost for the communications of mobile devices .
Through the analysis of hierarchical mobile IPv6 protocol , a kind of managing strategy of mobile IPv6 routing on the basis of hierarchical mechanisms was proposed , the routing optimization was realized , fast handoff was realized in intra-domain mobility and inter-domain mobility and to reduce the delay and improve the resource utilization ratio of the network .
This document defines binary formats for IPv4 and IPv6 traffic selectors to be used in conjunction with flow bindings for Mobile IPv6 .
Measurements done in a real time environment shows negligible difference in QoS parameters for VoIP traffic in native and tunnel mode IPv6 networks . This is important for future transition from IPv4 to IPv6 protocol , where tunnel mode IPv6 is one of the temporary solutions .
Because of the ubiquity of BLE-enabled smartphones nowadays , BLE has gained much attention in the IoT industry recently . In this research , we aim at implementing an IPv6 over BLE experimental system using Raspberry Pi 3 and nRF51-DK development boards , and then run the Message Queuing Telemetry Transport for Sensor Networks ( MQTT-SN ) protocol and the Constrained Application Protocol ( CoAP ) over the protocol stack of IPv6/BLE . Specifically , in our experimental system every BLE node is IPv6-addressable and accessible through the MQTT/CoAP protocols from anywhere over the Internet .
Next generation internet IPv6 is gradually being spread , the associated security issues also attracted attention , network intrusion detection system is to achieve active defense of the key technologies . First , based on the IPv6 feature analysis and formal description , describe IPv6-based network intrusion detection system-level framework . Then , research system 's work processes and the main technical characteristics , established using hierarchical design idea , combining the characteristics of IPv6 various modules from the system architecture analysis and study .
The invention provides a cloud service-based IPv6 ( internet protocol version 6)/IPv4 ( internet protocol version 4 ) translation method with communication initiated by an IPv6 party , and relates to the technical field of a computer network . The method is used for realizing the service that an IPv6 user or a dual-stack user can seamlessly initiate the information resource for accessing the IPv4 ; a cloud service data center is provided with an information resource data base , a domain name analysis server , a stateless IPv4/IPv6 translator and a stateful IPv6/IPv6 translator , wherein the stateless IPv4/IPv6 translator and the stateful IPv6/IPv6 are connected with each other in series ; a translation algorithm of the stateless IPv4/IPv6 translator is used for translating an IPv4 address which is reserved for accessing a pure IPv4 server from an IPv6 network into a special IPv6 address which is embedded with the IPv4 address and uses an operator prefix ; a translation algorithm of the stateful IPv6/IPv6 is used for dynamically mapping any IPv6 addresses of the user into the special IPv6 address which is embedded with the IPv4 address ; and the domain name analysis server is used for answering a domain name analysis request of the user , and returning an A record or an AAAA record for the user according to the content of an information resource data base , therefore , the pure IPv4 user of the dual-stack user can initiate the information resource for accessing the pure IPv4 by the IPv6 .
This paper aims to optimize the current IPv6 packet structure by reducing the size of the headers , increasing the size of the data , reducing packet processing time and speed up the data transmission .
A method of initiating a PPP ( Point-to-Point Protocol ) on an RG to obtain address information has very wide application demands in practice , and as the existing IPv6 ( Internet Protocol version 6 ) address consultation collocation method can not allocate complete IPv6 addresses through allocation option consultation , the invention provides a simple and vigorous IPv6 address consultation allocation scheme . According to the scheme , the consultation allocation of IPv6 address information is conducted by expanding allocation options of a IPv6 CP ( content provider ) , thereby obtaining the complete IPv6 addresses and corresponding information or prefixed information on a DNS ( Domain Name Server ) and the like , so that the implementation method is simple and consistent with a related implementation method of an original IPv4 , thereby reducing the cost of equipment and leading the running of the equipment to be more stable .
A 13-Mbps unidirectional broadcast satellite covers Asia region enabling lecture sharing among student sites . IPv6 multicast overlay network is established by UDLR and IPv6 tunnel technologies to create a virtual network connecting lecturer-site and student-sites on top of network heterogeneity . Bandwidth optimization and scalability are achieved by IPv6 multicast .
Phase teaching model for subnetting IPv4 Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation Product-Based Neural Networks for User Response Prediction CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images Channel optimization and nonlinear feature extraction for Electroencephalogram signals classification Word recognition using neural nets, multi-state Gaussian and k-nearest neighbor classifiers Review of Basic Traffic Data Fusion Methods Performance Analysis of Vehicular Ad Hoc Networks Handovers with Metaheuristic Algorithms: A Review Analysis of Structured and Un-Structured Network Protocols for Data Aggregation Over Distributed Wireless Sensor Networks Certificate Revocation List Distribution Based on Road Map Distance Protocol (RMDP) in Vanet Security, SDN, and VANET technology of driver-less cars KUMP/D: the Kyushu University multi-media processor Multi-voltage power supply on / off detector A combined activity/travel choice model for congested road networks with queues Dynamic hierarchical Bayesian network for Arabic handwritten word recognition HMDHBN: Hidden Markov Inducing a Dynamic Hierarchical Bayesian Network for Tumor Growth Prediction Architecture for a distributed openflow controller A Two-Branch Network with Semi-Supervised Learning for Hyperspectral Classification Method and apparatus for reducing the response time for the network information center Multimedia local area network MELNET R100 based on FDDI-II Corrected overlap weight and clustering coefficient Design and Implementation of a Unified Network Information Service Motion Controller for Autonomous Underwater Vehicle Based on Parallel Neural Network CURVELET TRANSFORM AND HMM CLASSIFIER BASED SIGN LANGUAGE RECOGNITION SYSTEM A Discriminatively Learned CNN Embedding for Person Reidentification Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Camera Style Adaptation for Person Re-identification SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition SNIPER: Efficient Multi-Scale Training Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map Quantized Densely Connected U-Nets for Efficient Landmark Localization Compositional Sequence Labeling Models for Error Detection in Learner Writing Multi-style Generative Reading Comprehension Can memory-less network adapters benefit next-generation infiniband systems? Containerization in Scientific Calculations Performance Evaluation of PEACH3: Field-Programmable Gate Array Switch for Tightly Coupled Accelerators A kind of quick processing system of satellite remote sensing date Low-latency software defined network for high performance clouds A SOFTWARE DEFINED NETWORKING ARCHITECTURE FOR HIGH PERFORMANCE CLOUDS 1 Adaptive connection management for scalable MPI over InfiniBand RAS Modeling of an HPC Switch System Improving Hybrid OpenCL Performance by High Speed Networks First experiences with congestion control in InfiniBand hardware Sun HPC ClusterTools™ 7+: A Binary Distribution of Open MPI An InfiniBand-Based Mechanism to Enhance QoS in Multipath Routing Protocols in MANETs Chapitre "Multicast" de l'ouvrage "IPv6, théorie et pratique" Tunnelling-based route optimization for Mobile IPv6 The Analysis and Research of an Optimized Hierarchical Mobile IPv6 Routing Strategy Traffic Selectors for Flow Bindings Comparison of QoS influence to VoIP traffic into native and tunnel mode IPv6 networks An Experimental System for MQTT/CoAP-based IoT Applications in IPv6 over Bluetooth Low Energy Design of Network Intrusion Detection System on IPv6 Cloud service-based IPv6 (internet protocol version 6)/IPv4 (internet protocol version 4) translation method with communication initiated by IPv6 party OPTIMIZATION OF IPV6 PACKET'S HEADERS OVER ETHERNET FRAME Method and device for distributing IPv6 (Internet Protocol version 6) addresses Internet Technologies and Infrastructure for Asia-Wide Distance Education
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