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This paper investigates the performance of two-class classification credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and non-parametric Parzen classifiers are extended, using Bayes' rule, to include either a class imbalance or a Bernoulli prior. This is done with the aim of addressing the low default probability problem. Furthermore, the performance of Parzen...
Large-scale deep convolutional neural networks (CNNs) are widely used in machine learning applications. While CNNs involve huge complexity, VLSI (ASIC and FPGA) chips that deliver high-density integration of computational resources are regarded as a promising platform for CNN's implementation. At massive parallelism of computational units, however, the external memory bandwidth, which is constrained...
In this paper, we introduce memos, which integrates suitable memory management policies and schedules resources over the entire memory hierarchy in hybrid memory system. Powered by an OS kernel level monitoring tool, memos captures memory patterns online, and then leverages them to guide the memory page placement and data mapping. Experimental results show, on average, memos can benefit memory utilization,...
The popularity of GPUs in general purpose computation has prompted efforts to scale up MapReduce systems with GPUs, but lack of efficient I/O handling results in underutilization of shared system resources in existing systems. This paper presents SPMario, a scale-up GPU MapReduce framework to speed up job execution and boost utilization of system resources with the new I/O Oriented Scheduling. The...
FPGA, or Field Programmable Gate Array, has been widely used for several applications such as digital signal and image processing, video processing, software-defined radio, radar processing, medical imaging and so on. Currently, with the significance growth of parallel computing and cloud computing application, FPGA provides another solution for high performance computing instead of CPU or GPGPU due...
Convolution Neural Networks today provide the best results for many image detection and image recognition problems. The network accuracy increase in the past years is obtained through an increase in complexity of the structure and amount of parameters of the deep networks. Memory bandwidth and power consumption constraints are limiting the deployment of such state-of-the-art architecture in low power...
In recent years, violence has considerably increased in the world. In a certain state of Brazil, for example, the homicide rate grew from 16 homicides per 100,000 inhabitants in 2000, to 48 homicides per 100,000 inhabitants in 2014. Police departments worldwide use various types of crime maps, which are generated with diverse techniques, in order to analyze and fight crime. Those types of maps enable...
The estimation of environmental contours of extreme sea states characterized by significant wave height and energy period for the purposes of reliability-based offshore design is a problem that has been tackled in many different ways. Many of the methods used to generate such contours rely on parametric approaches that require an a priori assumption of the relationship between the variables of interest...
For numerous scientific applications Sparse Matrix-Vector multiplication (SpMV) is one of the most important kernels. Unfortunately, due to its very low ratio of computation to memory access SpMV is inherently a memory bound problem. On the other hand, the main memory bandwidth of commercial off-the-shelf (COTS) architectures is insufficient for available computation resources on these platforms,...
A scalable and flexible memory interconnect is a key component for a many-core architecture to take full advantage of the high-bandwidth of multiple memory stacks. In this paper, we discuss both technological and architectural challenges of these processor-to-memory interconnects, and focus on two important issues of many-core memory accesses: traffic hotspots and non-uniform memory access (NUMA)...
Today's datacenter is shared among various applications with different QoS requirements, which poses a great challenge to deliver low delay transport with high throughput. Most of works address this challenge by reducing the in-network delay, but assumes a negligible local delay. However, we show that this assumption does not hold for a multi-tenant datacenter that a physical machine is shared by...
Achieving low and predictable execution time of short jobs in Hadoop clusters has gained a great attention due to their importance on system productivity and user experience. However, one major contributor that makes it challenging is diskI/O interference. We observed that disk writes unintentionally block latency-sensitive short jobs and cause unexpected high latency. Unfortunately, previous research...
Low-power, embedded, GPU System-on-Chip (SoC) devices provide outstanding computational performance, especially for compute-intensive tasks. While clusters of SoCs for High-Performance Embedded Computing (HPEC) are not new, the computational power of these supercomputers has long lacked the efficiency of their more traditional, High-Performance Computing (HPC) counterparts. With the advent of the...
Hurricanes can cause significant damages to the electric power systems and result in widespread and prolonged loss of electric services. A preventive scheduling of available resources in response to these events can be of significant importance in reducing the related undesirable aftermath. An Event-driven Security-Constrained Unit Commitment (E-SCUC), as discussed in this paper, can be used as a...
Cooperative localization capability is a highly desirable characteristic of wireless sensor networks. It has attracted considerable research attention in academia and industry. The sum-product algorithm over a wireless sensor network (SPAWN) is a powerful method to cooperatively estimate the positions of many sensors (agents) using knowledge of the absolute positions of a few sensors (anchors). Drawbacks...
Random forest cannot give accurate and calibrated posterior class probability estimates for its predictions. In this paper, we propose novel probabilities estimators combining random forests with kernel density estimation. Kernel density estimator can manage to obtain smooth non-parametric estimations of class probabilities, but fail to scale up to the high dimensional data. In order to apply kernel...
Diagnosis of rice planthopper pests based on imaging technology is an efficient means to develop intelligent agriculture. Effective contour automation extraction is an important pretreatment technology at the early stage for identifying and classifying rice planthoppers. For the curtain as the background contained texture structures and resulted in a heterogeneous texture in the sensed image, which...
The correntropy provides a robust criterion for outlier-insensitive machine learning, and its maximisation has been increasingly investigated in signal and image processing. In this paper, we investigate the problem of unmixing hyperspectral images, namely decomposing each pixel/spectrum of a given image as a linear combination of other pixels/spectra called endmembers. The coefficients of the combination...
Through multiple levels of abstraction, deep learning takes advantage of multiple layers models to find the complicated structure and learn the high level representations of data. In recent years, deep learning has made great progress in object detection, speech recognition, and many other domains. The robustness of learning systems with deep architectures is however rarely studied and needs further...
The estimation of distribution algorithm is widely used to solve global optimization problems in recent years. The basic idea is using machine learning methods to extract relevant features of the search space among the selected individuals and to construct a probabilistic model for sampling new solutions. As we know, EDAs mainly focus on the global distribution information of population and are lack...
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