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The purpose of this paper is to evaluate the feasibility of grammatical evolution (GE) in combining meteorological models into more accurate single forecast of rainfall amount. A set of GE experiments was performed comparing six proposed ensemble forecast grammars on three benchmark problems. We also proposed a manner of designing benchmark problems by creating arbitrary combinations of meteorological...
This paper provides further evidence on the predictive power of online community traffic with regard to stock prices. Using the largest dataset to date, spanning 8 years and almost the complete set of SP500 stocks, we train a classifier using a set of features entirely extracted from web-traffic data of financial online communities. The classifier is shown to outperform the predictive power of a baseline...
In this paper, a methodology which aims to solve the accuracy susceptibility problem in PMC-driven energy estimation models is developed. The application characteristics representativeness of PMCs and training benchmarks to generate an accurate and stable energy estimation model are considered. The results show a good accuracy and stability with average relative error of 4.8%.
Since time constraints are a very critical aspect of an embedded system, performance evaluation can not be postponed to the end of the design flow, but it has to be introduced since its early stages. Estimation techniques based on mathematical models are usually preferred during this phase since they provide quite accurate estimation of the application performance in a fast way. However, the estimation...
In cloud-based Web application hosting environments, virtualization offers the potential to exploit dynamic resource provisioning and scaling to maintain service level agreements while minimizing resource utilization for a given workload. However, optimal proactive resource provisioning and scaling for a specific Web application require, at the least, a profile of the application's current workload...
Architectural Vulnerability Factor (AVF) [3] quantifies the probability that a raw soft error finally produces a visible error in the program output. It is often used by computer designers as an important reliability metric at the architectural level. However, the AVF measurement is extremely expensive in terms of hardware and computation. In this paper, we characterize and predict a program's AVF...
Modern compilers provide optimization options to obtain better performance for a given program. Effective selection of optimization options is a challenging task. Recent work has shown that machine learning can be used to select the best compiler optimization options for a given program. Machine learning techniques rely upon selecting features which represent a program in the best way. The quality...
The high social costs associated with bankruptcy have spurred searches for better prediction capability. We propose a nonparametric approach for bankruptcy prediction, using data envelopment analysis (DEA) model to identify the boundaries of bankruptcy and non-bankruptcy. The benchmarks of non-bankruptcy and bankruptcy can construct two piecewise frontiers to dominate two convexity classes. Overlap...
In this paper we present a system for online power prediction in vir-tualized environments. It is based on Gaussian mixture models that use architectural metrics of the physical and virtual machines (VM) collected dynamically by our system to predict both the physical machine and per VM level power consumption. A real implementation of our system shows that it can achieve average prediction error...
The goal of this work is to identify famous tenors from commercial recordings. Our approach is based on training expressive singer-specific models and using them to classify new musical fragments interpreted by singers that perform arias from the training set. In this paper we focus on expressive timing variations and build the models by applying machine learning techniques to a body of data consisting...
The early-stage design of a new microprocessor involves the evaluation of a wide range of benchmarks across a large number of architectural configurations. Several methods are used to cut down on the required simulation time. Typically, however, existing approaches fail to capture true program behaviour accurately and require a non-negligible number of training simulations to be run. We address these...
The shrinking processor feature size, lower threshold voltage and increasing clock frequency make modern processors highly vulnerable to transient faults. Architectural vulnerability factor (AVF) reflects the possibility that a transient fault eventually causes a visible error in the program output, and it indicates a system's susceptibility to transient faults. Therefore, the awareness of the AVF...
Most dynamic optimizers use feedback-directed adaptive optimization techniques. These techniques are expensive because of the profiling overhead. Although the recent trend has been toward the application of machine learning heuristics in compiler optimization, its role in identification and prediction of hotspots has been ignored. This approach evaluates a support vector machine (SVM) based machine...
Uniprocessor simulators track resource utilization cycle by cycle to estimate performance. Multiprocessor simulators, however, must account for synchronization events that increase the cost of every cycle simulated and shared resource contention that increases the total number of cycles simulated. These effects cause multiprocessor simulation times to scale superlinearly with the number of cores....
The industry is now in agreement that the future of architecture design lies in multiple cores. As a consequence, all computer systems today, from embedded devices to petascale computing systems, are being developed using multicore processors. Although researchers in industry and academia are exploring many different multicore hardware design choices, most agree that developing portable software that...
This paper investigates the possibility of a pseudo-online adaptive training schema for Mamdani-type neuro-fuzzy models that have robust linguistic interpretability. As such verbatim models are incapable of complex constructs available to Takagi-Sugeno-type neuro-fuzzy models, a heuristic approach is developed to allow the rule bases to adapt accordingly to fundamental shifts in the characteristics...
The design of computer architectures is a very complex problem. The multiple parameters make the number of possible combinations extremely high.Many researchers have used simulation, although it is a slow solution since evaluating a single point of the search space can take hours. In this work we propose using evolutionary multilayer perceptron (MLP) to compute the performance of an architecture parameter...
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