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Discriminative least squares regression (DLSR) is a simple yet effective method for multi-class classification. One problem of DLSR is that it is lack of robustness to outliers. In order to tackle this difficulty, in this paper, we propose a novel Robust DLSR (RoDLSR) model. The core idea behind RoDLSR is to find and further ignore the outliers among the support vector set. Specifically, we modify...
Model-based approaches to Speaker Verification (SV), such as Joint Factor Analysis (JFA), i-vector and relevance Maximum-a-Posteriori (MAP), have shown to provide state-of-the-art performance for text-dependent systems with fixed phrases. The performance of i-vector and JFA models has been further enhanced by estimating posteriors from Deep Neural Network (DNN) instead of Gaussian Mixture Model (GMM)...
It is well-known that for speaker recognition task, gender-dependent acoustic modeling performs better than gender-independent modeling. The practice is to use the gender ground-truth and to train gender-dependent models. However, such information is not necessarily available, especially if speakers are remotely enrolled. A way to overcome this is to use a gender classification system, which introduces...
Over the last decade, a globalization of the software industry took place, which facilitated the sharing and reuse of code across existing project boundaries. At the same time, such global reuse also introduces new challenges to the software engineering community, with not only components but also their problems and vulnerabilities being now shared. For example, vulnerabilities found in APIs no longer...
The inherent dependencies among video content, personal characteristics, and perceptual emotion are crucial for personalized video emotion tagging, but have not been thoroughly exploited. To address this, we propose a novel topic model to capture such inherent dependencies. We assume that there are several potential human factors, or “topics,” that affect the personal characteristics and the personalized...
Phase reconstruction of complex components in the time-frequency domain is a challenging but necessary task for audio source separation. While traditional approaches do not exploit phase constraints that originate from signal modeling, some prior information about the phase can be obtained from sinusoidal modeling. In this paper, we introduce a probabilistic mixture model which allows us to incorporate...
The performance of GPS (Global Positioning System) in densely built urban areas is greatly undermined due to multipath propagation. Many attempt alternative means for urban navigation, of which the ‘fingerprinting’ method has drawn much attention recently. However, the labor of site surveys and a constantly changing environment prevents ‘fingerprinting’ from being an eificient solution. Therefore,...
Encrypted databases store data as ciphertexts. Encryption schemes with functionalities (such as deterministic and order-preserving schemes) are often used, in order to operate and compare the encrypted data directly. However, the secrecy of the data may decrease because information on plaintexts such as equivalence and order relationships are revealed from ciphertexts. In our previous study, we formalized...
As India is progressing so the infrastructure as well as the transportation such as roads, technologies are also developing. The transportation plays a vital role in Indian economy. As we know many people have lost their lives in the accident. So managing the lane system on the road for the vehicles is very important. We can provide safety by using the lane detection system and also guide people for...
Testing becomes difficult when we cannot easily determine whether the system delivers the correct result or not. To address this issue, we have developed a framework for automated testing of NASA's DAT system using metamorphic testing principles combined with model based testing. Based on the results from using the framework to test DAT we have determined that this is a cost beneficial solution allowing...
Frequent pattern mining discovers associations among different items in large sets of data. In many real-world applications, the presence of an object or a characteristic cannot be given exactly all the time. Instead, they can be better expressed in terms of probability and such data is called uncertain data. Mining frequent patterns from uncertain data is challenging due to presence of existential...
Clinical pathways are popular healthcare management tools to standardise care and ensure quality. Measuring pathway conformance and analysing variances gives valuable feedback in the context of care improvement trajectories. The Business Process Model and Notation (BPMN) language and Task-Time matrices are popular ways to model clinical pathways. A key step in variance analysis involves the computation...
Two open source distributed machine learning/deep learning platforms, namely H2O and Apache SINGA, compared their deep learning performances using multilayer perceptron on the classic MNIST database for hand written digits recognition. However, the results reported by both parties differ and neither of them can repeat the results reported by the other side. This paper is an independent study of the...
In recent years due to the update of relevant graphical technologies acquiring of 3D reconstructed images from 2D views is getting attention exponentially. Many researchers have therefore tried to develop robust as well as efficient methods to reconstruct 3D models from images. In this paper, we have proposed a method for reconstructing 3D view from multiple images. This approach is based on images...
In this article, we propose a Communicating Finite state machines consisting of co-operative system specification that communicate by binary, rendezvous interactions. Our verification approach unfolds the state machines in consistent to synchronization to generate CMPM (Communicating Minimal Prefix Machine). In this proposal, distributed set of concurrent kripke tree structure is shown by using the...
Text Summarization is a way of determining the key concepts that are covered in the given text under consideration. Various techniques have been presented in literature and many are used in commercially available systems. The primary aim of the present work is to find efficacy of using sentiments for text summarization. In this work we present a computationally efficient technique based on sentiments...
Now-a-days, providing a platform, where customers can obtain first-hand assessments of product information and manufacturers can collect customers reviews cum feedbacks to improve the product quality, is the prime need of any organization. These reviews are the people's opinion about particular product. Every website has reviews of their product and those reviews are difficult to analyze. Thus, the...
Classification is the process of finding a model or function that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The goal of classification is to accurately predict the target class for each case in the data. In sequence database having sequences, in which each sequence is a list of...
Although the database schemas are an integral part of information systems, the use of software product lines has been mainly studied for the production of executable code. The impact on data management and in particular the database schemas are poorly documented and little studied in the literature. The paper is an attempt to explore some of the issues of the modeling and implementation of the variability...
Goal: Mammography is the most effective technique for breast cancer screening, and the detection of abnormalities plays a vital role in Computer Aided Detection (CAD) system. This paper proposes a detection method for abnormal mammograms based on improved saliency detection. Methods: The proposed method comprises three main steps: firstly, enhance abnormal mammograms using dual morphological top-hat...
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