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Developing device drivers is important for innovative consumer electronics because device driver implements key functionalities of new devices. This paper suggests a test-driven development (TDD) of device drivers, taking advantage of user-level driver. Applying TDD to device drivers is difficult because usually device drivers are implemented inside kernel, and are tightly coupled with complex kernel...
This paper addresses the problem of adaptive chemical detection, using the Receptor Density Algorithm (RDA), an immune inspired anomaly detection algorithm. Our approach is to first detect when and if something has changed in the environment and then adapt the RDA to this change. Statistical hypothesis testing is used to determine whether there has been concept drift in consecutive time windows of...
Based on principal component analysis (PCA) and support vector machine (SVM), a new method for the fault diagnosis of TE Process is proposed. The fault recognition based on kernel principal component analysis (KPCA) is analyzed and SVM is employed as a classifier for fault classification. To establish a more efficient SVM model, genetic algorithm (GA) is used to determine the optimal kernel parameter...
Magnetic Resonance Imaging (MRI) has become an important tool for doctors to diagnose liver cancer for decays. The survival rate of liver cancer patients can be significantly improved by an early diagnosis. In this paper, we present a computer aided kernel based support vector machine (SVM) algorithm for diagnosing liver cancer in early stage by applying our proposed method to the patients' magnetic...
In this work, we put forward a new adaptation criterion, namely the hybrid criterion (HC), which is a mixture of the traditional mean square error (MSE) and the maximum correntropy criterion (MCC). The HC criterion is developed from the viewpoint of the least trimmed squares (LTS) estimator, a high breakdown estimator that can avoid undue influence from outliers. In the LTS estimator, the data are...
A comparative study on face recognition based on two methods, SVM of One-against-One method and SVM of One-against-Rest method, has been carried out in this paper. Our method consists of three parts: Firstly, the image robustness on illumination and posture has been improved by the processing of the Gabor wavelet. Secondly, the bilateral 2DLDA technique on image is adopted to realize the dimension...
Anomaly detection starts from a model of normalbehavior and classifies departures from this model as anomalies. This paper introduces a statistical non-parametric approach for anomaly detection that is based on a multivariate extension of the Poisson point process model for univariateextremes. The method is demonstrated on both a synthetic and a real-world data set, the latter being an unbalanced...
Malware writers use increasingly complex evasion mechanisms to ensure the concealment of malware against standard anti-malware suites. To identify malware through its behaviour, rather than its approach is an interesting venue of exploration. System call traces are highly indicative of a process behaviour. However, it is difficult to acquire system calls of all processes running on a physical machine...
Taxicab demand discovering is one of the most fundamental issues of taxicab services. Most of the regions in one city suffer the demand and supply disequilibrium problem. It causes the difficulty in scheduling taxicabs for taxicab companies. It will be solved by modeling the regional demand of taxicabs by using trajectory data. In this paper, we propose a method to model regional taxicab demand. Firstly,...
Handwriting recognition is the ability of a computer to understand handwritten inputs from users. Generally it includes preprocessing, feature extraction, and classifier training. In this paper, we will develop a handwriting digit recognition system by using Deep Boltzmann Machine (DBM) together with the Support Vector Machine (SVM). DBM is a deep learning technique to learn high level features from...
The development of robotic devices for the rehabilitation of gait is a growing area of interest in the engineering rehabilitation community. The problem with modelling gait dynamics is that everybody walks differently. The approach advocated in this paper addresses this issue by modelling the gait dynamics of individual patients. Specifically, we present a model learner which performs automated system...
Good performance of pedestrian detection in an automatic driving system is a necessary task. Many pedestrian detection algorithm use Histogram Oriented Gradient (HOG) for feature extraction and Support Vector Machine (SVM) for classification. Some papers use additional features with HOG, such as Local Binary Pattern (HOG-LBP), to improve the performance. Neural Network and Extreme Learning Machine...
Distributed networks of robust, low-cost, radiation detectors offer the ability to track and locate materials across a number of applications. Individual sensors must be positioned to collectively optimise both the sensing and communication roles of the network and ruggedized to achieve consistent detection performance under a range of environmental conditions. Here we consider the interplay between...
In decentralized detection, the sensors first make a local decision before transmitting it to the fusion center (FC). The optimal design of the sensors' decision rule as well as the fusion rule requires knowledge of the probability distributions of the sensors' observations. This information, however, may not be available prior to deployment. Moreover, these probability distributions may vary over...
This paper proposes a novel approach for automatic estimation of four important traits of speakers, namely age, height, weight and smoking habit, from speech signals. In this method, each utterance is modeled using the i-vector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a...
We present an approach for on-line recognition of handwritten math symbols using adaptations of off-line features and synthetic data generation. We compare the performance of our approach using four different classification methods: AdaBoost. M1 with C4.5 decision trees, Random Forests and Support-Vector Machines with linear and Gaussian kernels. Despite the fact that timing information can be extracted...
When a software system starts behaving abnormally during normal operations, system administrators resort to the use of logs, execution traces, and system scanners (e.g., anti-malwares, intrusion detectors, etc.) to diagnose the cause of the anomaly. However, the unpredictable context in which the system runs and daily emergence of new software threats makes it extremely challenging to diagnose anomalies...
An automatic Language Identification (LID) is a system designed to recognize a language from a given spoken utterance. The spoken utterances are classified according to the pre-defined set of languages. In this paper a LID system is designed for two different languages namely English and French. The classification of an audio sample is done by extracting Mel frequency cepstral coefficients (MFCCs)...
In this paper, we propose a novel method that is more appropriate than classical large-margin classifiers for open set recognition and object detection problems. The proposed method uses the best fitting hyper planes approach, and the main idea is to find the best fitting hyper planes such that each hyper plane is close to the samples of one of the two classes and as far as possible from the other...
The optimization method based extreme learning machine (optimization-based ELM) is generalized from single-hidden-layer feed-forward neural networks (SLFNs) by making use of kernels instead of neuron-alike hidden nodes. This approach is known for its high scalability, low computational complexity, and mild optimization constrains. The multi-kernel learning (MKL) framework Simple MKL iteratively determines...
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