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Recommender system has been widely used in e-commerce systems nowadays. Current methodologies focus on predicting users' preferences from their previous ratings. Although the prediction is largely helpful, it gives limited insight to managers of e-commerce systems on how to utilize the interactions between users and items for designing new business and marketing strategies. Besides, big data collected...
Forced migration is increasingly becoming a global issue of concern. In this paper, we present an effective model of targeted event detection, as an essential step towards the forced migration detection problem. To date, most of the the approaches deal with the event detection in a general setting with the main objective of detecting the presence or onset of an event. However, we focus on analyzing...
Time always exists in our lives and time data can easily be collected in a variety of applications. For example, when you purchase items online or click on an ad, the time at which you chose the item or clicked the ad is recorded. The analysis of time information can therefore be applied in various areas. It is important to note that user preferences change over time. For example, a person who watched...
In this paper, we propose a new network architecture called Convolutional Multi-directional Recurrent Network (CDRN) for offline handwritten text recognition. The conventional recurrent neural network model obtains the local context from limited directions, whereas we build up the multi-directional long short-term memory (MDirLSTM) module to abstract contextual information in various directions. Moreover,...
In this paper we will present our investigations related to contextual modeling for HMM-based handwritten Arabic text recognition. We will, first, discuss the justifications and the need for contextual modeling for handwritten Arabic text recognition. Next, we will discuss the issues related to contextual modeling for Arabic text recognition. Finally, we will present our novel class-based contextual...
The segmentation of touching characters is still a challenging problem in offline Chinese handwriting recognition. One feasible solution is through the over-segmentation strategy which maintains a high recall of correct cuts between adjacent characters and a moderate level of redundant cuts within a single character. Previous redundant cut filtering methods rely on either pure heuristics or learned...
In this paper we tackle the ℋ∞ filtering problem for a discrete-time Markov Jump Linear System (MJLS) with hidden parameters. We consider a hidden Markov model (θ(k); θ̂(k)) in which θ(k) is not accessible and corresponds to the mode of operation of the system, while θ̂(k) is a signal coming from a detector. The signal θ̂(k) acts as an estimation of θk) and is the observable part of the hidden Markov...
We present detailed analysis of phoneme recognition performance of a context dependent tied-state triphone Gaussian Mixture Model Hidden Markov Model (CD-GMM-HMM) acoustic model (state-of-the-art large acoustic model (AM)) and a four hidden layer context dependent Deep Neural Network (CD-DNN-HMM) AM on the WSJ speech corpus. Using a bigram phoneme language model, phoneme recognition experiments are...
This paper presents a load disaggregation method for the monitoring and supervision of the load profiles of individual equipment in an HVAC installation. The method takes advantage of the wealth of sensor and actuation information found in Building Energy Management Systems in order to find correlations between the state of operation of each machine and the power demand of the installation. This enables...
Human activity recognition, especially exceptional activity recognition has been regarded as an important aspect in intelligent service robotics. Several challenges in activity recognition — unexpected and untypical exceptional behaviors, a small but growing number of training examples — make it hard to solve this problem. Despite the variety of human behaviors, there are some normal patterns, especially...
This paper proposes a multi-class learning (MCL) algorithm for a deep neural network (DNN)-based statistical parametric speech synthesis (SPSS) system. Although the DNN-based SPSS system improves the modeling accuracy of statistical parameters, its synthesized speech is often muffled because the training process only considers the global characteristics of the entire set of training data, but does...
In recent years, with the continuous development and transmission of micro-blogs. people do more research on analyzing the emotional tendency of short texts. Ignoring the context information, traditional emotional analysis methods on micro-blog are limited in a tweet itself. To tackle this problem, this paper proposes a method of emotional tendency analysis which based on view of the context. Firstly,...
Sound event recognition without the context is challenging for both humans and robots due to the diversity of sound events. Contextual information allows them to disambiguate the sound events, for example, in a home environment. This paper proposes and implements a context-aware sound event recognition for a home service robot that monitors the elderly living alone at home. The location context of...
Dynamic texture segmentation is to partition a video (image sequence) comprising of different dynamic texture into disjointed regions with uniformity and consistency property. This paper introduces a Markov random field based dynamic texture segmentation algorithm using the inter-scale context. The method models the label field and the observed field with the Multi-level Logistic Model (MLL) and Gaussian...
The recent surge in electronic and social media has led to an explosion of sentiment data embedded in public and private documents, fueling interest in sentiment analysis, especially as individuals, brands and corporations look to manage their reputational risk which is directly correlated to company performance. In this paper, we describe two approaches to score sentiments from a large unstructured...
The article presents studies on the automatic whispery speech recognition. In the performed research a new corpus with whispery speech has been used. It has been checked whether the extended set of articulatory units (allophones have been used instead of phonemes) improves quality of whispery speech recognition. Experimental results show that the small changes in the allophone set may provide better...
In the recognition of osteosarcoma magnetic resonance images (MRI), the probability of a pixel belonging to a class is not only related to its own features, but also closely correlated with the information distribution of the surrounding pixels. However, it is currently unable to recognize the osteosarcoma lesions and surrounding issues simultaneously. In order to solve the problem, we propose a fully...
In this study, an algorithm having a nice dynamic-programming structure is proposed for unit selection. This algorithm considers the costs of pitch and duration transformations, and the costs of contextual and spectral discontinuities. Here, the voice unit, demi-syllable, is adopted. In the training phase, each demi-syllable unit is analyzed to obtain a sequence of discrete cepstral coefficient (DCC)...
We present a context-aware hybrid classification system for the problem of fine-grained product class recognition in computer vision. Recently, retail product recognition has become an interesting computer vision research topic. We focus on the classification of products on shelves in a store. This is a very challenging classification problem because many product classes are visually similar in terms...
Sign Language Recognition (SLR) aims at translating the sign language into text or speech, so as to realize the communication between deaf-mute people and ordinary people. This paper proposes a framework based on the Hidden Markov Models (HMMs) benefited from the utilization of the trajectories and hand-shape features of the original sign videos, respectively. First, we propose a new trajectory feature...
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