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Nowadays, life scientists grapple with a problem how to fast and easily access/obtain high quality data, especially for a specific research area, from a large amount of biological data deposited in public databases. In this work, we developed an effective system for managing biological data which are a class of functionally important membrane protein; they are hard to collected from the existing databases...
The goal of this paper is to build a system that automatically creates synthetic data to enable data science endeavors. To achieve this, we present the Synthetic Data Vault (SDV), a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence the name SDV. When implementing the SDV, we also developed an algorithm that computes...
In this work, we propose a Frequency-based Action Descriptor (FADE) to represent human actions. In robotics, with the development of Programming by Demonstration (PbD) methods, representing and recognizing large sets of actions has become crucial to build autonomous systems that learn from humans. The FADE descriptor leverages Fast Fourier Transform (FFT) for action representation and is combined...
Hadoop has become a popular platform for the management of big data. To provide a healthy Hadoop platform for big data application, an HMM-based approach for performance diagnosis in Hadoop clusters is proposed. We use metrics which are collected under the normal situation to train HMM (Hidden Markov Model), then use this model to detect anomaly based on the probability, which is more accurate than...
The variation of spontaneous speech is much larger when compared to the planned speech because of speech disruption and a lot of ambiguities in conversations. These events cannot be properly evaluated during search and decoding in speech recognition systems and various errors occur in the output hypotheses. One possible solution is to include filled pauses and disfluent events into the training data...
This project creates a speech semantic recognition system that can be applied in the assistive robotic application by using the meaning of speech for people who suffer a permanent disability that cannot move around normally. The user interface of this speech semantic recognition system of this project are capable to receive the speech input of the user and an application interface transfers the input...
The basic concept of machine translation is to translate one human language to another human language. Many translation systems have been built using different approaches which have different accuracy levels. In this paper, a work is being proposed for Syntax Based Machine Translation System from English to Hindi language. The Syntax Based Machine Translation System has the goal of incorporating an...
The widely adopted i-vector performances well in text-independent speaker verification with long speech duration. How to integrate the state-of-the-art i-vector framework into the text-prompted speaker verification is addressed in this paper. To take advantage of the lexical information and enhance the performance for speaker verification with random digit sequences, this paper proposes to extract...
Directions into Velocities of Articulators (DIVA) model is a kind of self-adaptive neural network model which controls movements of a simulated vocal tract to produce words, syllables or phonemes. However, DIVA model lacks of emotion functions. To implement the emotion function in DIVA model, we investigate the process of affective speech production based on the combination of fundamental frequency...
They say that facial expressions are the window of human mind states. By interpreting the mind states, we can achieve a smooth interaction between human and machines. In this paper, we try to handle human mind states. We use vector chain code method to indicate a change of the face. The vector chain code can encode the direction information and distance information altogether. Through experiments,...
In this paper, we provide two databases DB1 and DB2 of motion characters written in the air, and present an accelerometers and gyroscopes based air-writing characters recognition system. The DB1 of 10 characters was collected by 40 subjects without writing constraints, while DB2 of 36 characters was collected by 49 participants in constrained stroke orders. We preprocessed the raw data with Moving...
Handwriting recognition always has been a difficult problem, with image related problems on the one hand and language processing on the other hand. Significant improvements have been made in handwriting recognition thanks to new recurrent neural networks based on LSTM cells. The high character recognition performances of these networks are almost systematically combined with linguistic knowledge,...
This paper deals with offline handwritten word recognition of a major Indic script: Bengali. Due to the structure of this script, the characters (mostly ortho-syllables) are frequently overlapping and hard to segment, especially when the writing is cursive. Individual character recognition and the combination of outputs can increase the likelihood of errors. Instead, a better approach can be sending...
This paper presents our attempts to collect and analyze unconstrained Vietnamese online handwriting text patterns by pen-based computers. Totally, our database contains over 120,000 strokes from more than 140,000 characters, which is one of the largest Vietnamese online handwriting pattern databases currently. For building and analyzing our database, we made a collection tool, a line segmentation...
Recently Recurrent Neural Networks (RNNs) have shown impressive performance in sequence classification tasks. In this paper we apply Long Short-Term Memory (LSTM) network on Persian phoneme recognition. For years Hidden Markov Model (HMM) was the dominant technique in speech recognition system but after introducing LSTM, RNNs outperformed HHM-based methods. We apply LSTM and deep LSTM on FARSDAT speech...
A method based on hidden Markov models is described capable of dealing with severe part occlusions in different object recognition situations. Occlusion is dealt with separating shapes into parts through high curvature points, tangent angle signatures for each part and continuous wavelet transform for signatures. A hidden Markov model is created for each probable class in an ensemble trained with...
Demographic change in the next few years will lead to a pronounced disparity in generation distribution. Hence there is a need to develop intelligent systems to support and maintain the autonomy of the elderly at home. A high priority in this case assumes the preparation-free acquisition of vital signs and patient parameters in long-term monitoring systems to detect early changes or deterioration...
As emotion recognition from speech has matured to a degree where it becomes suitable for real-life applications, it is time for developing techniques for matching different types of emotional data with multi-dimensional and categories-based annotations. The categorical approach is usually applied for acted ‘full blown’ emotions and multi-dimensional annotation is often preferred for spontaneous real...
This paper attempts to recognize online Farsi handwriting using the freeman chain codes and hidden Markov model. Chain codes reduce the number of data with using the direction of breaks and keeping the direction of pen movement. Hence, it can be used as an effective way to recognition of online sub-words. After breaking the sub-word into component parts (main body and strokes), each part separately...
Emotion recognition systems aim at identifying emotions of human subjects from underlying data with acceptable accuracy. Audio and visual signals, being the primary modalities of human emotion perception, have attained the most attention in developing intelligent systems for natural interaction. The emotion recognition system must automatically identify the human emotional states from his or her voice...
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