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Deep neural network (DNN) acoustic models can be adapted to under-resourced languages by transferring the hidden layers. An analogous transfer problem is popular as few-shot learning to recognise scantily seen objects based on their meaningful attributes. In similar way, this paper proposes a principled way to represent the hidden layers of DNN in terms of attributes shared across languages. The diverse...
Steganalysis aims at detecting the presence of steganography with or without knowledge of steganographic technique used. Feature based Steganalysis determines the presence of secret data by comparing the statistical features of both cover and stego images. Using a large number of features for Steganalysis relative to training set size may reduce classification accuracy and also increase computational...
In this paper, we propose a new high quality pseudo-relevance feedback documents selection approach that uses machine learning based classifier for selecting a set of good feedback documents for boosting the effectiveness of Query Expansion (QE). Our proposed classification technique utilizes very small amount of labelled data set for training purpose that is very appropriate to select a set of good...
With the increased popularity of smart phones, there is a greater need to have a robust authentication mechanism that handles various security threats and privacy leakages effectively. This paper studies continuous authentication for touch interface based mobile devices. A Hidden Markov Model (HMM) based behavioral template training approach is presented, which does not require training data from...
Problem solving is an important skill for engineering graduates to develop. However, in most traditional engineering classrooms, students practice solving well structured problems which is not sufficient because engineers need to be able to solve real world problems which are ill structured. Therefore it is important for engineering students to be trained in ill-structured problem solving. In this...
This paper presents a novel probability based approach for distinguishing between keyword and stopword from a text corpus. This has a lot of applications including automatic construction of stopword list. First objective of this paper is to investigate the role of probability distribution for distinguishing between keyword and stopword. Second objective is to compare the performance of probability...
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