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It is a challenge for Web service composition to perceive dynamic changes of external environment and adapt to the changes of business process rapidly. In this paper, a context-aware adaptive Web service composition framework is presented and the functions of main modules are also described in detail. In this framework, BPEL is used to describe Web service(More)
Though with progress, model learning and performing posterior inference still remains a common challenge for using deep generative models, especially for handling discrete hidden variables. This paper is mainly concerned with algorithms for learning Helmholz machines, which is characterized by pairing the genera-tive model with an auxiliary inference model.(More)
In this paper, we study the scalable discovery of audio repetitive patterns/motifs in long broadcast streams, where two segments are said to be repetitive if their audio fingerprints are close to each other. In this task, as we are confined to handle limited variability, we can adapt an audio hashing technique, originally proposed for searching a given(More)
Recently, distributed word embeddings trained by neural language models are commonly used for text classification with Convolutional Neural Networks (CNNs). In this paper, we propose a novel neural language model, Topic-based Skip-gram, to learn topic-based word embeddings for biomedical literature indexing with CNNs. Topic-based Skip-gram leverages textual(More)
Personal Imagine being plunged perpetually into a silence where the ubiquity of sound is irrelevant. That is the world which many students in my high school experience. My inspiration for this project really came from the students in my high school's Deaf and Hard of Hearing (DHH) program. My school has a department which offers a high school education to(More)
Multi-label classification refers to the learning problem that a single training sample possibly has multiple labels at the same time. Many real world applications consist of high-dimensional feature vectors, which generally involve some irrelevant and redundant features. This possibly reduces classification performance and increases computational costs.(More)
The Allan variance (AV) is a widely used quantity in areas focusing on error measurement as well as in the general analysis of variance for autocorrelated processes in domains such as engineering and, more specifically, metrology. The form of this quantity is widely used to detect noise patterns and indications of stability within signals. However, the(More)
State-of-the-art i-vector based speaker verification relies on variants of Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis. We are mainly motivated by the recent work of the joint Bayesian (JB) method, which is originally proposed for discriminant analysis in face verification. We apply JB to speaker verification and make three(More)
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