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In this study we use triangular basis function set to solve second kind fuzzy integral equation that can be converted to a system of two integral equations in crisp case. We also consider collocation method for approximately solving the equation.
Software fault prediction models are employed to optimize testing resource allocation by identifying fault-prone classes before testing phases. We apply three different ensemble methods to develop a model for predicting fault proneness. We propose a framework to validate the source code metrics and select the right set of metrics with the objective to improve the performance of the fault prediction...
Machine learning is an increasingly important form of cognitive computing, making progress in several application areas. Machine learning often involves big data sets and is computationally challenging, requiring efficient use of resources. The use of cloud computing as the platform for machine learning offers advantages of scalability and efficient use of hardware. It may, however, be difficult to...
Automatic hand detection and accurate hand pose estimation from depth data in real system are challenging and vital tasks for human-computer interaction. In this paper, we introduce a Convolutional Neural Network (CNN) as Deep learning regression framework while employing an embedding denoising auto-encoder in the bottom layer of the network to learn latent representation of hand pose and account...
In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted as a discrepancy metric between the distributions of source and target domains. However, existing MMD-based domain adaptation methods generally ignore the changes of class prior distributions, i.e., class weight bias across domains. This remains an open problem but ubiquitous for domain adaptation, which can be caused by...
In industrial power generation plants, subsystem monitoring and analytics play a vital role in quantifying the knowledge about different factors that impact their overall performance. Multi-dimensional performance metrics, e.g. thermal efficiency, in-service time, mean-time-to-failure etc., are calculated that may have different data constraints, modelling techniques, and execution frameworks. Automating...
Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting factor is the high energy consumption of CNN processing due to its high computational...
Statistical machine learning models that operate on manifold-valued data are being extensively studied in vision, motivated by applications in activity recognition, feature tracking and medical imaging. While non-parametric methods have been relatively well studied in the literature, efficient formulations for parametric models (which may offer benefits in small sample size regimes) have only emerged...
For the mathematical model of tug handling simulator, the locally optimal locally weighted learning (LWL) is proposed. Firstly, samples space rearrangement is taken to diminish the one-to-many mapping and non-separable of ship motion states. Secondly, distance metric is learned by leave-one-out cross validation for every sample, and this approach improves the nonlinearity mapping ability and robustness...
Cloud environments are criticized for their volatility in performance aspects, making it extremely difficult for time- critical applications owners to perform the decisive step for migration and owners of SaaS to present performance vs cost tradeoffs to their customers when acting as IaaS customers. The aim of this work is to present an architectural approach based on which a)IaaS providers may enhance...
In this paper, a novel saliency map generation approach based on the saccade target theory is proposed. A probabilistic model of transsaccadic integration is built based on four cues that influence human visual attention: foveaperiphery resolution discrepancy, visual memory, oculomotor bias and inhibition of return (IOR), where visual memory is formulated as combination of the visual short-term memory...
In current interval-valued linear regression models, meaningless predictions may be generated because the lower bounds of the predicted intervals may be greater than their upper bounds. To avoid this problem, we propose a constrained interval-valued linear regression model based on random set theory. However, due to the introduction of constraints in this model, the expectation of the errors is no...
The use of adaptive traffic lights controllers represents an affordable approach to improve vehicular traffic conditions. In isolated intersections or local areas with a limited number of traffic lights, numerous works from the related open literature have achieved excellent results in key performance metrics. Nevertheless, an urban network environment poses important challenges for adaptive control,...
The metric learning problem is concerned with learning a distance function tuned to a particular task, and has been shown to be useful when used in conjunction with nearest-neighbor methods and other techniques that rely on distances or similarities. This paper proposes an ensemble learning technique which combines the efforts of multiple metric learning algorithms like Large Margin Nearest Neighbours...
The increasing availability of point cloud data in recent years is demanding for high performance denoising methods and compression schemes. When point cloud data is directly obtained from depth sensors or extracted from images acquired from different viewpoints, imprecisions on the depth acquisition or in the 3D reconstruction techniques result in noisy point clouds which may include a significant...
Model-based software development promises improvements in terms of quality and cost by raising the abstraction level of the development from code to models, but also requires mature techniques and tools. Although Eclipse Modelling Framework (EMF) introduces a default persistence mechanism for models, namely XMI, its usage is often limited as model size increases. To overcome this limitation, during...
We propose a formal methods approach to control traffic signals optimally from specifications described by metric temporal logic (MTL). Since real-time optimization is computationally infeasible beyond small-scale networks, we use a divide and conquer approach. We decompose the network into smaller subnetworks and synthesize assume-guarantee contracts for their interconnections. We show how to exploit...
Service Level Agreement (SLA) is gaining more and more interest since the dynamic aspect of the cloud computing can adversely influence the guarantee of the Quality of Service (QoS). Proving an SLA violation is considered to be a complex operation to the cloud consumer. This task gets more and more difficult to the consumers as they use services from multiple providers, each with its own monitoring...
Recent technology advancements in the areas of compute, storage and networking, along with the increased demand for organizations to cut costs while remaining responsive to increasing service demands have led to the growth in the adoption of cloud computing services. Cloud services provide the promise of improved agility, resiliency, scalability and a lowered Total Cost of Ownership (TCO). This research...
In the process of big data analysis and processing, a key concern blocking users from storing and processing their data in the cloud is their misgivings about the security and performance of cloud services. There is an urgent need to develop an approach that can help each cloud service provider (CSP) to demonstrate that their infrastructure and service behavior can meet the users' expectations. However,...
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