• TrueTime

  • Referenced in 42 articles [sw13636]
  • TrueTime: Simulation of Networked and Embedded Control Systems. TrueTime is a Matlab/Simulink-based simulator for real ... controller task execution in real-time kernels, network transmissions, and continuous plant dynamics...
  • Graphs

  • Referenced in 109 articles [sw12277]
  • labeling schemes in networking and distributed computing and for metric embeddings in geometry as well ... significance such as planar graphs and complex networks...
  • FaceNet

  • Referenced in 30 articles [sw21626]
  • uses a deep convolutional network trained to directly optimize the embedding itself, rather than ... describe different versions of face embeddings (produced by different networks) that are compatible to each...
  • SONET

  • Referenced in 22 articles [sw10644]
  • Toolkit reads in data about the network, its embedded capacity, the available equipment, the customer...
  • metapath2vec

  • Referenced in 11 articles [sw37749]
  • limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning ... heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous ... structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec ... state-of-the-art embedding models in various heterogeneous network mining tasks, such as node...
  • TinyOS

  • Referenced in 7 articles [sw02023]
  • Visualizing the runtime behavior of embedded network systems: A toolkit for TinyOS. TinyOS ... effective platform for developing lightweight embedded network applications. But the platform’s lean programming model ... issue exacerbated by the fact that embedded network systems are inherently distributed and reactive...
  • PTE

  • Referenced in 7 articles [sw37756]
  • through Large-scale Heterogeneous Text Networks. Unsupervised text embedding methods, such as Skip-gram ... deep learning architectures such as convolutional neural networks, these methods usually yield inferior results when ... large-scale heterogeneous text network, which is then embedded into a low dimensional space through ... approaches based on convolutional neural networks, predictive text embedding is comparable or more effective, much...
  • DiffEqFlux

  • Referenced in 7 articles [sw27559]
  • defining neural ordinary differential equations (neural networks embedded into the differential equation) and describe...
  • vnep-approx

  • Referenced in 5 articles [sw38921]
  • approximation algorithms for the Virtual Network Embedding Problem...
  • MobileNets

  • Referenced in 28 articles [sw39590]
  • MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. We present a class of efficient ... models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined ... convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters...
  • AutoNE

  • Referenced in 2 articles [sw38085]
  • AutoNE: Hyperparameter Optimization for Massive Network Embedding. Network embedding (NE) aims to embed the nodes...
  • JCSP

  • Referenced in 3 articles [sw25045]
  • including e-commerce, agent technology, home networks, embedded systems, high-performance clusters and The Grid...
  • Sancus

  • Referenced in 3 articles [sw24321]
  • propose Sancus, a security architecture for networked embedded devices. Sancus supports extensibility in the form...
  • hydra+

  • Referenced in 2 articles [sw40553]
  • method for strain-minimizing hyperbolic embedding of network- and distance-based data. We introduce hydra ... approximation), a new method for embedding network- or distance-based data into hyperbolic space ... minimizes the `hyperbolic strain’ between original and embedded data points. Moreover, it is able ... space. Testing on real network data we show that the embedding quality of hydra...
  • SINE

  • Referenced in 1 article [sw32344]
  • SINE: Sclable Incomplete Network Embedding. Attributed network embedding aims to learn low-dimensional vector representations ... correlation, incorporating node attribute proximity into network embedding is beneficial for learning good vector representations ... content or linkages, yet existing attributed network embedding algorithms all operate under the assumption that ... paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from...
  • Flask

  • Referenced in 5 articles [sw09691]
  • Flask: staged functional programming for sensor networks. Severely resource-constrained devices present a confounding challenge ... language embedded in Haskell that brings the power of functional programming to sensor networks, collections ... code fragments; syntactic support for embedding standard sensor network code; a restricted subset of Haskell...
  • NetSMF

  • Referenced in 1 article [sw37752]
  • NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. We study the problem of large ... scale network embedding, which aims to learn latent representations for network mining applications. Previous research ... shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing ... present the algorithm of large-scale network embedding as sparse matrix factorization (NetSMF). NetSMF leverages...
  • SBERT

  • Referenced in 6 articles [sw36204]
  • SBERT, Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. BERT (Devlin ... siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared...
  • ProNE

  • Referenced in 1 article [sw37757]
  • Network Representation Learning. Recent advances in network embedding has revolutionized the field of graph ... network mining. However, (pre-)training embeddings for very large-scale networks is computationally challenging ... faster than efficient network embedding benchmarks with 20 threads, including LINE, DeepWalk, node2vec, GraRep ... achieve this, ProNE first initializes network embeddings efficiently by formulating the task as sparse matrix...
  • FSCNMF

  • Referenced in 1 article [sw32347]
  • negative Matrix Factorization for Embedding Information Networks. Analysis and visualization of an information network ... appropriate embedding of the network. Network embedding learns a compact low-dimensional vector representation ... network is considered by a majority of the current embedding algorithms. However, some content ... current state-of-the-art network embedding methods. In this paper, we propose a nonnegative...