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Recurrent neural networks (RNNs) have shown clear superiority in sequence modeling, particularly the ones with gated units, such as long short-term memory (LSTM) and gated recurrent unit (GRU). However, the dynamic properties behind the remarkable performance remain unclear in many applications, e.g., automatic speech recognition (ASR). This paper employs visualization techniques to study the behavior...
Human gait is an important biometric feature for person identification in surveillance videos because it can be collected at a distance without subject cooperation. Most existing gait recognition methods are based on Gait Energy Image (GEI). Although the spatial information in one gait sequence can be well represented by GEI, the temporal information is lost. To solve this problem, we propose a new...
In this paper, we focus on a study of the timing of different kinds of cars on the road. This information will enable us to infer the life style of the car owners. The results can further be used to guide marketing towards car owners and setting auto insurance policies. Conventionally, this kind of study is carried out by sending out questionnaires, which is limited in scale and diversity. To solve...
Learning distributed word representations (word embeddings) has gained much popularity recently. Current learning approaches usually treat all dimensions of the embeddings as homogeneous, which leads to non-structured representations where the dimensions are neither interpretable nor comparable. This paper proposes a method to generate ordered word embed-dings where the significance of the dimensions...
In the task of action recognition, object and scene can provide rich source of contextual information for analyzing human actions, as human actions often occur under particular scene settings with certain related objects. Therefore, we try to utilize the contextual object and scene for improving the performance of action recognition. Specifically, a latent structural SVM is introduced to build the...
Recognizing events in consumer videos is becoming increasingly important because of the enormous growth of consumer videos in recent years. Current researches mainly focus on learning from numerous labeled videos, which is time consuming and labor expensive due to labeling the consumer videos. To alleviate the labeling process, we utilize a large number of loosely labeled Web videos (e.g., from YouTube)...
In a modern software system, when a program fails, a crash report which contains an execution trace would be sent to the software vendor for diagnosis. A crash report which corresponds to a failure could be caused by multiple types of faults simultaneously. Many large companies such as Baidu organize a team to analyze these failures, and classify them into multiple labels (i.e., multiple types of...
The online monitoring for NOx emission of coal-fired boilers in power plants is more difficult to achieve. The soft-sensor technology of artificial neural network (ANN) method that was commonly used has not strong generalization ability, but support vector machine modeling-method can solve the problem better. In this paper, a soft-sensor modeling on NOx emission of power station boilers based on least...
In modern power system, it is more difficult to maintain stability than ever when system is disturbed by fast phenomena. Training simulator is a useful tool to improve operator's skill in controlling power system in stress conditions after serious contingency. This paper presents a distributed training simulator based on High Level Architecture. The distributed training simulator is consists of Dispatcher...
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