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In this paper, three sparse models for the human auditory system are proposed. Biological studies shows that the haircells in the inner ear of the auditory system generate sparse codes from the output of cochlea filterbank. Here, we employ two mathematical sparse representation methods, which are Orthogonal Matching Pursuit (OMP) and K Singular Value Decomposition (K-SVD), in three different strategies for sparse representation of the output of cochlea filterbank that is modeled by a Gammatone filterbank.

Modeling a location-unaware sensor network as a simplicial complex, where simplices correspond to cliques in the communication graph, has proven useful for solving a number of coverage problems under certain conditions using algebraic topology. Several approaches to finding a sparse cover for a fenced sensor network are considered, including calculating homology changes locally, strong collapsing, and Euler characteristic collapsing.

We characterize third-dimension (3D) of channel measurements based on two dimension channel model with fully synchronize

This paper proposes a novel framework for activity recognition from 3D motion capture data using topological data analysis (TDA). We extract point clouds describing the oscillatory patterns of body joints from the principal components of their time series using Taken's delay embedding. Topological persistence from TDA is exploited to extract topological invariants of the constructed point clouds. We propose a feature extraction method from persistence diagrams in order to generate robust low dimensional features used for classification of different activities.

In this paper we propose a hierarchical activity clustering methodology which incorporates the use of topological persistence analysis. Our clustering methodology captures the hierarchies present in the data and is therefore able to show the dependencies that exist between these activities. We make use of an aggregate persistence diagram to select robust graphical structures present within the dataset. These models are stable over a bound and provide accurate classification results.

The absence of manually annotated training data presents an obstacle for the development of machine-learning based NLP tools in Indonesia. Existing annotation tools lack a mobile-friendly interface which is a problem in Indonesia where most users access the internet using their smartphone. In this paper we propose the first mobile collaborative data annotation tool and evaluate it in an experiment involving 15 Indonesian students who annotated 1500 data records using their smartphones. Users confirmed

Although uni-directional recurrent neural network language
model(RNNLM) has been very successful, it’s hard to train a
bi-directional RNNLM properly due to the generative nature of
language model. In this work, we propose to train bi-directional
RNNLM with noise contrastive estimation(NCE), since the
properities of NCE training will help the model to acheieve
sentence-level normalization. Experiments are conducted on
two hand-crafted tasks on the PTB data set: a rescore task and

In process of learning Chinese as a second language (CSL), Japanese natives have difficulties in tone perception. Among the four Chinese lexical tones, the tone pairs Tone 1-Tone 2 and Tone 1-Tone 4 are problematic for Japanese CSL beginners. In order to help them develop efficiently discriminating capability of the tone pairs, we designed a hybrid perceptual training scheme which combined adaptive training and high variability phonetic training.