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Existing machine-learning-based vehicle detection algorithms for intelligent vehicles have an obvious disadvantage in that the detection effect decreases dramatically when the distribution of training samples and the scene target samples do not match. To address this issue, a scene-adaptive vehicle detection algorithm based on a composite deep structure is proposed in this paper. Inspired by the Bagging...
We present an advanced dialog state tracking system designed for the 5th Dialog State Tracking Challenge (DSTC5). The main task of DSTC5 is to track the dialog state in a human-human dialog. For each utterance, the tracker emits a frame of slot-value pairs considering the full history of the dialog up to the current turn. Our system includes an encoder-decoder architecture with an attention mechanism...
In the development of a damage tolerance plan for composite airframe structures, the way to characterize the impact damage of sandwich composites under different levels of impact events and material property is crucial. The aim of the present research is to investigate the influence of material configuration and impact parameters on damage resistance responses of composite sandwich structures comprised...
In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training...
In this paper, we present a new technique, called stream projected ouliter detector (SPOT), to deal with outlier detection problem in high-dimensional data streams. SPOT is unique in a number of aspects. First, SPOT employs a novel window-based time model and decaying cell summaries to capture statistics from the data stream. Second, sparse subspace template (SST), a set of top sparse subspaces obtained...
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