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In this paper, a prediction-based learning framework is proposed for a continuous prediction task of emotion recognition from speech, which is one of the key components of affective computing in multimedia. The main goal of this framework is to utmost exploit the individual advantages of different regression models cooperatively. To this end, we take two widely used regression models for example,...
Machine-learning algorithms have shown outstanding image recognition performance for computer vision applications. While these algorithms are modeled to mimic brain-like cognitive abilities, they lack the remarkable energy-efficient processing capability of the brain. Recent studies in neuroscience reveal that the brain resolves the competition among multiple visual stimuli presented simultaneously...
Group activity recognition from videos is a very challenging problem that has barely been addressed. We propose an activity recognition method using group context. In order to encode both single-person description and two-person interactions, we learn mappings from highdimensional feature spaces to low-dimensional dictionaries. In particular the proposed two-person descriptor takes into account geometric...
As microblogging services like Twitter are becoming more and more influential in today's globalized world, its facets like sentiment analysis are being extensively studied. We are no longer constrained by our own opinion. Others' opinions and sentiments play a huge role in shaping our perspective. In this paper, we build on previous works on Twitter sentiment analysis using Distant Supervision. The...
Efficiently allocating resources and predicting cell handovers is essential in modern wireless networks; however, this is only possible if there is an efficient way to estimate the future state of the network. In order to accomplish this, we investigate two learning techniques to predict the long-term channel gains in a wireless network. Previous works in the literature found efficient methods to...
Object detection is a challenging task in the field of pattern recognition. The objective of object detection is to locate the target objects in the testing images. In this paper, we use SVM trained active basis model as a sparse coding model for representing objects. The sparse coding model represents each image as the linear superposition of a small number of Gabor wavelets selected from an over-complete...
Failure of a task running on a Hadoop cluster is highly expensive in terms of computational time. A failure occurring even at the end phase of the task may cause the need to redo the entire task. Thus is really important to deploy fault tolerant techniques. Hadoop deploys a technique of checkpointing to prevent data loss. However, computational time-loss still pose a grim threat to critical applications...
Hadoop architecture provides one level of fault tolerance, in a way of rescheduling the job on the faulty nodes to other nodes in the network. But, this approach is inefficient when a fault occurs after most of the job is executed. Thus, it's necessary to predict the fault at the node at quite an early stage so that the rescheduling of the job is not costly in terms of time and efficiency. Prediction...
This article presents our recent study of a lightweight Deep Convolutional Neural Network (DCNN) architecture for document image classification. Here, we concentrated on training of a committee of generalized, compact and powerful base DCNNs. A support vector machine (SVM) is used to combine the outputs of individual DCNNs. The main novelty of the present study is introduction of supervised layerwise...
Background subtraction (BS) is one of the key steps for detecting moving objects in video surveillance applications. In the last few years, many BS methods have been developed to handle the different challenges met in video surveillance but the role and the relevance of the visual features used has been less investigated. In this paper, we present an Online Weighted Ensemble of One-Class SVMs (Support...
Multi-label classification (MLC), allowing instances to have multiple labels, has been received a surge of interests in recent years due to its wide range of applications such as image annotation and document tagging. One of simplest ways to solve MLC problems is label-power set method (LP) that regards all possible label subsets as classes. LP validates traditional multi-classification classifiers...
Extractive text or speech summarization endeavors to select representative sentences from a source document and assemble them into a concise summary, so as to help people to browse and assimilate the main theme of the document efficiently. The recent past has seen a surge of interest in developing deep learning- or deep neural network-based supervised methods for extractive text summarization. This...
Word2vec is a neural network language model which can convert words and phrases into a high-quality distributed vector (called word embedding) with semantic word relationships, so it offers a unique perspective to the text classification and other natural language processing (NLP) tasks. In this paper, we propose to combine improved tfidf algorithm and word embedding as a way to represent documents...
In this paper, we present Bengali word embeddings and it's application in the classification of news documents. Word embeddings are multi-dimensional vectors that can be created by exploiting the linguistic context of the words in large corpus. To generate the embeddings, we collected Bengali news document of last five years from the major daily newspapers. Word embeddings are generated using the...
To retrain an existing multilayer perceptron (MLP) on-line using newly observed data, it is necessary to incorporate the new information while preserving the performance of the network. This is known as the “plasticitystability” problem. For this purpose, we proposed an algorithm for on-line training with guide data (OLTA-GD). OLTA-GD is good for implementation in portable/wearable computing devices...
Many telecommunication companies today have actively started to transform the way they do business, going beyond communication infrastructure providers are repositioning themselves as data-driven service providers to create new revenue streams. In this paper, we present a novel industrial application where a scalable Big data approach combined with deep learning is used successfully to classify massive...
The Support Vector Machine (SVM) is a classical classification algorithm that has a wide range of application. With kernel function, SVM can dispose the datasets that are not linearly separable in their original feature space, making it more flexible in practical use compared with linear model. However, its complexity in training is an obstacle to large-scale dataset handling. This paper proposes...
Recently, pathological diagnosis plays a crucial role in many areas of medicine, and some researchers have proposed many models and algorithms for improving classification accuracy by extracting excellent feature or modifying the classifier. They have also achieved excellent results on pathological diagnosis using tongue images. However, pixel values can't express intuitive features of tongue images...
Decision making is an important component in a speaker verification system. For the conventional GMM-UBM architecture, the decision is usually conducted based on the log likelihood ratio of the test utterance against the GMM of the claimed speaker and the UBM. This single-score decision is simple but tends to be sensitive to the complex variations in speech signals (e.g. text content, channel, speaking...
This paper proposed a new Vehicle Make Recognition (VMR) method using the PCANet features extracted from vehicle front view images. The PCANet architecture processes every input vehicle image through only three very simple data processing components: cascaded principle component analysis (PCA), binary hashing, and block-wise histograms, and generates a sparse vector as the feature representation....
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