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Side-channel analysis of cryptographic systems can allow for the recovery of secret information by an adversary even where the underlying algorithms have been shown to be provably secure. This is achieved by exploiting the unintentional leakages inherent in the underlying implementation of the algorithm in software or hardware. Within this field of research, a class of attacks known as profiling attacks,...
Traditional multi-class image classification needs a large number of training samples for building a classifier model. However, it is very time-consuming and costly to obtain labels for a large number of training samples from human experts. Active learning is a feasible solution. This paper proposes a maximum classification optimization method (MCO) for actively selecting unlabeled images to acquire...
This paper presents a universally-designed web-based Workspace that facilitates the manipulation of elementary level mathematics: arithmetic and algebra. The Workspace features a multi-layer design that supports diversity along different dimensions: students with different degrees of visual acuity (from sighted to blind); different levels of proficiency in the subject; and different interaction modes...
Human activity detection from videos is very challenging, and has got numerous applications in sports evalution, video surveillance, elder/child care, etc. In this research, a model using sparse representation is presented for the human activity detection from the video data. This is done using a linear combination of atoms from a dictionary and a sparse coefficient matrix. The dictionary is created...
In information retrieval, efficient accomplishing the nearest neighbor search on large scale database is a great challenge. Hashing based indexing methods represent each data instance as a binary string to retrieve the approximate nearest neighbors. In this paper, we present a semi-randomized hashing approach to preserve the Euclidean distance by binary codes. Euclidean distance preserving is a classic...
Most of the traditional indoor location algorithms based on the distance loss model always filter the received signal strength, and then we can use the distance loss model to infer the distance between the nodes and achieve location eventually. The accuracy of the traditional indoor location algorithm is very unstable due to multipath propagation effects and complex signal attenuation law in the indoor...
This paper describes a novel approach to construct a mapping function between a given speaker pair using probability density functions (PDF) of matrix variate. In voice conversion studies, two important functions should be realized: 1) precise modeling of both the source and target feature spaces, and 2) construction of a proper transform function between these spaces. Voice conversion based on Gaussian...
For typical indoor positioning systems employing a training/positioning model based on Wi-Fi fingerprints, significant training costs extremely restrict this kind of indoor localization system to be widely deployed and implemented with real location based applications. In this paper, we present a crowd-based approach to solve this problem, which automatically collects and constructs fingerprints database...
This paper presents a data driven KL-Divergence based target cost and concatenation cost calculation method for a hybrid speech synthesis with unit selection and Hidden Markov Model (HMM)-based speech synthesis. In the training stage, a set of context-dependent HMMs are estimated according to the acoustic features and label information of the database. In the synthesis stage, the pre-selection for...
In order to solve the problem that the output of ball mill pulverizing system is difficult to directly measured in thermal power plant with double inlet and double outlet ball mill pulverizing system which is a large delay, strong nonlinear system. It introduces the pruning method to improve the incremental least square support vector machine's sparsity that based on the incremental least square support...
The main aim of this paper is to advance the state of the art in automated prostate segmentation using T2 weighted MR images, by introducing a hybrid topological MRI prostate segmentation method which is based on a set of pre-labeled MR atlas images. The proposed method has been experimentally tested on a set of 30 MRI T2 weighted images. For evaluation the automated segmentations of the proposed...
Assessing reachability for a dynamical system, that is deciding whether a certain state is reachable from a given initial state within a given cost threshold, is a central concept in controls, robotics, and optimization. Direct approaches to assess reachability involve the solution to a two-point boundary value problem (2PBVP) between a pair of states. Alternative, indirect approaches involve the...
Cloud SLAs are contractually binding agreements between cloud service providers and cloud consumers. For cloud service providers, it is essential to prevent SLA violations as much as possible to enhance customer satisfaction and avoid penalty payments. Therefore, it is desirable for providers to predict possible violations before they happen. We propose an approach for predicting SLA violations, which...
In this paper, we propose a novel pipeline for automated scribal attribution based on the Quill feature: 1) We compensate the Quill feature histogram for pen changes and page warping. 2) We add curvature as a third dimension in the feature histogram, to better separate characteristics like loops and lines. 3) We also investigate the use of several dissimilarity measures between the feature histograms...
Author name disambiguation has been one of the hardest problems faced by digital libraries since their early days. Historically, supervised solutions have empirically outperformed those based on heuristics, but with the burden of having to rely on manually labelled training sets for the learning process. Moreover, most supervised solutions just apply some type of generic machine learning solution...
In this paper we proposed a static analysis framework to classify the android malware. The three different feature likely (a) opcode (b) method and (c) permissions are extracted from the each android .apk file. The dominant attributes are aggregated by modifying two different ranked feature methods such as ANOVA to Extended ANOVA (X-ANOVA) and Wann-Whiteney U-test to Extended U-Test (X-U-Test). These...
An important factor of a corpus is its domain, usually the quality of a SMT system trained on an in-domain corpus increases by adding out-of-domain sentences to its training corpus. In this paper we have shown out-of-domain corpora may also contains sentences which are proper for improving the quality of in-domain corpus. These sentences have words and phrases that occur in indomain corpora so, their...
Many of the phrase pairs extracted in the phrase-based machine translation systems have low quality and are not relevant. So their existence in the phrase table not only enlarges it, but also could reduce the translation quality. There are many methods presented to prune these noisy phrase pairs, using the statistics derived from the phrase table. In this paper we proposed a new pruning method that...
Satire exposes humanity's vices and foibles through the use of irony, wit, and sometimes sarcasm too. It is also frequently used in online communities. Recognition of satire can help in many NLP applications like dialogue system and review summarization. In this paper we filter online news articles as satirical or true news documents using SVM (Support Vector Machine) classification method combined...
Emotional Polarity Classification is an important task in Sentiment Analysis area. It is applied in many real problems such as reviews of consumer products and services, financial markets, and forensic analysis. The scientists from the areas of text mining and nature language processing have studied how to solve emotional polarity classification problem. They used a variety of methods, from simple...
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