This paper introduces a method to apply the topic models in an audio scene understanding framework. Assuming that an audio signal consists of latent topics that generate acoustic words describing an audio scene, we propose to use a vector quantization method to build an acoustic word dictionary. The classification experiments with semantic labels yield promising results of using the topic model...

In the discussions of trading, unemployment and income distribution inequality, this question is raised by economists that in economic development of developing and developed countries, which policy is necessary in the first stage. Is trading liberalization the pre-requirement of transfer from a close economy to a relative open economy and is it necessary for the developed economy compared to o...

The aim of this study was to investigate the asymmetric effects of exchange rate fluctuations on Stock index of Tehran Stock Exchange. For this purpose, we first calculated the exchange rate fluctuations using model General Autoregressive Conditional Heteroskedastic (GARCH), and then the effect of these fluctuations on the Stock index of Tehran Stock Exchange was estimated using the Generalized...

OBJECTIVE The limitation of small sample size of functional genomics experiments has made it necessary to integrate DNA microarray experimental data from different sources. However, experimentation noises and biases of different microarray platforms have made integrated data analysis challenging. In this work, we propose an integrative computational framework to identify candidate biomarker gen...

Following the very recent line of work on the “generalized min-max” (GMM) kernel [7], this study proposes the “generalized intersection” (GInt) kernel and the related “normalized generalized min-max” (NGMM) kernel. In computer vision, the (histogram) intersection kernel has been popular, and the GInt kernel generalizes it to data which can have both negative and positive entries. Through an ext...

Gaussian mixture models (GMM) have been widely and successfully used in speaker recognition during the last decade. However, they are generally trained using the generative criterion of maximum likelihood estimation. In this paper, we propose a simple and efficient discriminative approach to learn GMM with a large margin criterion to solve the classification problem. Our approach is based on a ...

Echoed parts of Quranic accent (Qiraat) signals are exposed to reverberation of signals especially if they are listened to in a conference room or the Quranic recordings found in different media such as the web. Quranic verse rules identification/Tajweed are prone to additive noise and may reduce classification results. This research work aims to present our work towards Quranic accents (Qiraat...

Our research aims to determine the effect of electricity distribution and energy consumption on industrial development dynamics that occur between regions in Indonesia by adding investment inflation as control variables. The analysis tools we use are static (Fixed Effect) dynamic (GMM) panel data model with a dataset 34 provinces for 2012-2019 period. results state has significant positive indu...

This paper considers two-step effi cient GMM estimation and inference where the weighting matrix and asymptotic variance matrix are based on the series long run variance estimator. We propose a simple and easy-to-implement modification to the trinity of test statistics in the two-step effi cient GMM setting and show that the modified test statistics are all asymptotically F distributed under th...

With a dramatic increase in the number and variety of applications running over the internet, it is very important to be capable of dynamically identifying and classifying flows/traffic according to their network applications. Meanwhile, internet application classification is fundamental to numerous network activities. In this paper, we present a novel methodology for identifying different inte...