Search Results

Robust PCA is a popular anomaly detection technique and has been widely used in many applications. Although Robust PCA is promising, it is usually designed in a two-order matrix form, which is inferior to the tensor that can capture multilinearity ...

In network distance measurement, how to estimate the whole network distance data from partially observed samples has attracted lots of attention because of its significance for network performance evaluation. Matrix completion becomes the most effective ...

Traffic prediction is a spatiotemporal predictive task that plays an essential role in intelligent transportation systems. Today, graph convolutional neural networks (GCNNs) have become the prevailing models in the traffic prediction literature since they ...

The attacks, faults, and severe communication/system conditions in Mobile Crowd Sensing (MCS) make false data detection a critical problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data ...

We exploit the embedding ability of a de-noising autoencoder for an implicit 3D rotation representation learning at the category level. Contrast to the exact 3D reconstruction model of each instance-level physical object, we leverage the inexact CAD/...

A prime question for plasma physicists is how a fraction of charged particles is accelerated to very high energy.To answer this question, physicists simulate trillions of particles with detailed dynamics and analyze their trajectories. This process ...

A major factor that prevents the large scale deployment of Mobile Crowd Sensing (MCS) is its sensing and communication cost. Given the spatio-temporal correlation among the environment monitoring data, matrix completion (MC) can be exploited to only ...

With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general ...

Traffic anomaly detection is critical for advanced Internet management. Existing detection algorithms usually work off-line and cannot timely detect anomalies. They also suffer from high cost for storage and computation. Although online and accurate ...

Traffic anomaly detection is critical for advanced Internet management. Existing detection algorithms generally convert the high-dimensional data to a long vector, which compromises the detection accuracy due to the loss of spatial information of data. ...

Detecting anomalous traffic is a crucial task of managing networks. Many anomaly detection algorithms have been proposed recently. However, constrained by their matrix-based traffic data model, existing algorithms often suffer from low detection accuracy. ...

Detecting anomalous traffic is a critical task for advanced Internet management. Many anomaly detection algorithms have been proposed recently. However, constrained by their matrix-based traffic data model, existing algorithms often suffer from low ...