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Basis-path coverage testing is a kind of logic-coverage testing and is based on graph theory and vector theory. Because the combination between path and business logic hasn't been considered, non-executable paths could be generated after executing the Basis-path algorithm. This generation model defines the non-executable path-segment and presents sub-model for acquisition of potential non-executable...
In this paper, we study a hypothesis testing problem of, among n random variables, determining k random variables which have different probability distributions from the rest (n − k) random variables. Instead of using separate measurements of each individual random variable, we propose to use mixed measurements which are functions of multiple random variables. It is demonstrated that equation observations...
Based on image sequence, a void fraction measurement model of gas-liquid two-phase flow in mini-pipe is developed using support vector regression (SVR) and particle swarm optimization (PSO). A high-speed image acquisition system is constructed to capture dynamic gray image sequence of gas-liquid two-phase flow. The area ratio of gas phase in longitudinal section of the pipe for every image of image...
In speaker recognition tasks, one of the reasons for reduced accuracy is due to closely resembling speakers in the acoustic space. In order to increase the discriminative power of the classifier, the system must be able to use only the unique features of a given speaker with respect to his/her acoustically resembling speaker. This paper proposes a technique to reduce the confusion errors, by finding...
Consider the problem of noisy Bayesian active learning given a sample space, a finite label set, and a finite set of label generating functions from the sample space to the label set, also known as the function class. The objective is to identify the function in the function class that generates the labels using as few label queries as possible and with low probability of error despite possible corruption...
This paper formulates the Central Detection Officer (CDO) problem in which a central officer decides if some agents in a network observe data from an anomalous distribution compared to the majority. Since the data statistics are unknown in advance, the goal of the CDO is to identify the data pattern of each agent and detect the presence and locations of anomalies by polling the agents strategically...
This paper deals with rotation and scale invariant texture classification problem at the machine learning level by modelling these variations in the texture data as a covariate shift. Covariate shift between the training and testing data is minimised by estimating importance weights for the training data which are then incorporated in a standard machine learning algorithm like support vector machines...
A mechanism for distributed semantic similar resource search is proposed in P2P network. The mechanism is based on the content addressable network (CAN). CAN, one of P2P networks, has the natural ability to support the semantic similar search with the semantic vector space model (SVSM) of resources. However, there exists a mismatching problem between the low-dimension CAN network and the high-dimension...
In this paper, we propose voice conversion based on articulatory-movement (AM) to vocal tract parameter (VTP) mapping. An artificial neural network (ANN) is applied to map AM to VTP and to convert the source speaker's voice to the target speaker's voice. The proposed system is not only text independent voice conversion, but can also be used for an arbitrary source speaker. This means that our approach...
Anomaly detection is an important aspect of data analysis. Kernel methods have been shown to exhibit good anomaly detection performance, however, they have high computational complexity. When anomaly detection is performed on a data stream, computational complexity is a key issue. Our approach uses the kernel hypersphere, which does not require a computationally complex operation in order to form...
Wireless Sensor Networks (WSNs) become more and more popular nowadays. They are applied not only in civilian but also in military. However, when having numerous data, we have to preserve them secretly and can acknowledge which information we need. In this paper, while keeping data privacy, we focus on four queries: value query, range query, outlier query and hot items. Moreover, we can identify outlier...
In this paper, we present a test data generation algorithm of combinatorial testing based on Differential Evolution, and introduce a selection and substitution based on the degree of unfinished interaction, in order to optimize the test case selected in further. An IPO-like strategy for generating the test suite is used to reduce the dimension for differential evolutionary computation in the optimization...
While modern research in face recognition has focused on new feature representations, alternate learning methods for fusion of features, most have ignored the issue of unmodeled correlations in face data when combining diverse features such as similar visual regions, attributes, appearance frequency, etc. Conventional wisdom is that by using sufficient data and machine, one can learn the systematic...
We introduce new secure privacy-preserving protocols for outsourcing continuous authentication of smartphone users. Our protocols allow a smartphone to privately perform continuous and unobtrusive authentication using touch behaviors. Through our protocols, the smartphone does not need to disclose touch information to the authentication server. Further, neither the server nor the smartphone have access...
The paradigm of stimulus-driven emotional learning is embraced within a more integral field of machine learning. A computational model is suggested, where the actions of stimuli are represented by matrices acting on agent's state vector. The model is validated against several classical experiments in the area of classical conditioning. Eventually, ways of further development are indicated and the...
In this paper we describe a method for predicting the subjective quality of a new mountain bike route for a particular subject based on routes previously ridden and ranked by the subject. GPS tracks of the previously ridden routes are over laid on rasterized topographic maps and topographic features are extracted in the vicinity of the routes using image processing techniques. The subject ranks each...
Reversible logic has been proposed as one solution to the problem of ever increasing power consumption. Work in areas such as synthesis techniques in reversible logic is growing, as is work on testing approaches. Numerous fault models have been proposed, but many researchers are still utilising models proposed for traditional logic. We provide an overview of the various fault models and testing approaches...
In this paper, a computationally efficient approach to transcription of monophonic melodies from a raw acoustic signal is presented. Two different instance-based pitch classification methods are proposed, the choice of which depends on the size of the available training database. In the first method, the conventional K-Nearest Neighbor algorithm is trained on a large database of piano tones and employed...
In core based design (i.e. System on Chip) testing, IEEE 1500 standard has become a widely used option because of its completeness and easy to use approach, but this standard is only supported in the test mode as it stays transparent in the functional mode. In this paper, a proposed method to enhance the IEEE 1500 standard for functional testing in order to increase observability during functional...
A review of the published research confirms that recognition of printed Arabic Word continues to present challenges. This is specially the case when segmentation is problematic. A word level recognition system is presented here that does not rely on any segmentation or require baseline detection of ascenders and descenders. A Discrete Hidden Markov classifier along with a block-based Discrete Cosine...
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