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Video-based coaching systems have seen increasing adoption in various applications including dance, sports, and surgery training. Most existing systems are either passive (for data capture only) or barely active (with limited automated feedback to a trainee). In this paper, we present a video-based skill coaching system for simulation-based surgical training by exploring a newly proposed problem of...
Classification is a technique in data mining for categorizing objects. Text Classification is re-challenged for classifying very short documents or text as shown in social media collection. This paper proposes a method to improve the performance of classification on short documents. In this work, we expand words in every document before the documents are classified We use TFIDF model, Hidden Markov...
We introduce a new method for representing the dynamics of human-object interactions in videos. Previous algorithms tend to focus on modeling the spatial relationships between objects and actors, but ignore the evolving nature of this relationship through time. Our algorithm captures the dynamic nature of human-object interactions by modeling how these patterns evolve with respect to time. Our experiments...
The question classification system is one of the important subsystems in the Question Answering Systems (QAS). In such systems through retrieval methods and information extraction the texts are retrieved in order to get to a correct answer. The current study is designed to present the architecture of question classification (QC) in Persian based on the Conditional Random Fields (CRF) machine learning...
In this paper we propose a method for singing voice detection in popular music recordings. The method is based on statistical learning of spectral features extracted from the audio tracks. In our method we use Mel Frequency Cepstrum Coefficients (MFCC) to train two Gaussian Mixture Models (GMM). Special attention is brought to our novel approach for smoothing the errors produced by the automatic classification...
This paper proposes a strategy of semantic processing implemented in an Indonesian text understanding evaluation system. It uses component that already developed in Institut Teknologi Bandung consists of POS Tagger and Syntactic Parser.
We propose an error learning model for image classification. Motivated by the observation that classifiers trained using local grid regions of the images are often biased, i.e., contain many classification error, we present a two-level combined model to learn useful classification information from these errors, based on Bayes rule. We give theoretical analysis and explanation to show that this error...
The key element of a spoken dialogue system is Spoken Language Understanding (SLU) part. HVS and EHVS are two most popular statistical methods employed to implement the SLU part which need lightly annotated data. Since annotation is a time consuming, we present a novel semi-supervised learning for EHVS to reduce the human labeling effort using two different statistical classifiers, SVM and KNN. Experiments...
Bibliographical information of scientific papers is of great value since the Science Citation Index is introduced to measure research impact. Most scientific documents available on the web are unstructured or semi-structured, and the automatic reference metadata extraction process becomes an important task. This paper describes a framework for automatic reference metadata extraction from scientific...
We present a probabilistic graphical model that finds a sequence of optimal categories for a sequence of input symbols. Based on this mode, three algorithms are developed for identifying semantic patterns in texts. They are the algorithm for extracting semantic arguments of a verb, the algorithm for classifying the sense of an ambiguous word, and the algorithm for identifying noun phrases from a sentence...
We propose a new statistical model, named Hierarchical Topic Trajectory Model (HTTM), for acquiring a dynamically changing topic model that represents the relationship between video frames and associated text labels. Model parameter estimation, annotation and retrieval can be executed within a unified framework with a few computation. It is also easy to add new modals such as audio signal and geotags...
This paper addresses the problem of language modeling for LVCSR of Cantonese-English code-mixing utterances spoken in daily communications. In the absence of sufficient amount of code-mixing text data, translation-based and semantics-based mapping are applied on n-grams to better estimate the probability of low-frequency and unseen mixed-language n-grams events. In translation-based mapping scheme,...
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