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Intrusion detection systems are a central component of cyber security architecture, and their accuracy is a critical performance metric for any security deployment. Most of the current performance analysis of intrusion detection systems relies on empirical profiling of a given algorithm or implementation against a benchmark dataset. Whilst effective to a point, this traditional evaluation methodology...
The incorrect use of cryptography is a common source of critical software vulnerabilities. As developers lack knowledge in applied cryptography and support from experts is scarce, this situation is frequently addressed by adopting static code analysis tools to automatically detect cryptography misuse during coding and reviews, even if the effectiveness of such tools is far from being well understood...
The solution of difficult problems can be realized in shorter time with heuristic algorithms. There are many heuristic algorithms. In this study, artificial bee colony (ABC), biogeography based optimization (BBO), cuckoo bird search algorithm (CSO), differential evolution (DE), imperialist competitive algorithm (ICA) and particle swarm algorithm (PSO) have been chosen due to reasons such as the widespread...
In this study, crabs mating optimization (CRAB) algorithm that is one of the heuristic algorithms, has been developed and a monogamous crab mating optimization (MCO) algorithm has been proposed. In development, the main goal is to develop an algorithm that runs faster than the CRAB algorithm, to ensure obtaining good results like the CRAB algorithm. The developed MCO algorithm is compared with the...
With the growing adoption of cloud computing for business systems, the efforts to keep those environments secure are also increasing. Virtualization infrastructures are key to support such systems, but engineers lack means to help them in selecting the best solutions according to their security requirements. The goal of this work is to define and develop a benchmarking approach to assess and compare...
In many computer vision tasks, for example saliency prediction or semantic segmentation, the desired output is a foreground map that predicts pixels where some criteria is satisfied. Despite the inherently spatial nature of this task commonly used learning objectives do not incorporate the spatial relationships between misclassified pixels and the underlying ground truth. The Weighted F-measure, a...
We propose a new method to analyze the impact of errors in algorithms for multi-instance pose estimation and a principled benchmark that can be used to compare them. We define and characterize three classes of errors - localization, scoring, and background - study how they are influenced by instance attributes and their impact on an algorithm’s performance. Our technique is applied to compare the...
Modelling of a database performance depending on numerous factors is the first step towards its optimization. The linear regression model with optional parameters was created. Regression equation coefficients are optimized with the Flower Pollination metaheuristic algorithm. The algorithm is executed with numerous possible execution parameter combinations and results are discussed. Potential obstacles...
Today, The cloud industry is adopting the container technology both for internal usage and as commercial offering. The use of containers as base technology for large-scale systems opens many challenges in the area of resource management at run-time. This paper addresses the problem of selecting the more appropriate performance metrics to activate auto-scaling actions. Specifically, we investigate...
Widely used benchmarks, such as High Performance Linpack (HPL), do not always provide direct insights are notoriously poor indicators of into the actual application performance of systems. When real applications are used, and there have been are criticisms indicating that the performance of simplified benchmarks such as HPL no longer strongly correlate to real application performance. In contrast,...
Software Quality model is a well-accepted way for assessing high-level quality characteristics (e.g., maintainability) by aggregation from low-level metrics. Aggregation method in a software quality model denotes how to aggregate low-level metrics to high-level quality characteristics. Most of the existing quality models adopt the weighted linear aggregation method. The main drawback of weighted linear...
Most existing research for Android focuses on particular security issues, yet there is little broad understanding of Android application run-time characteristics and their implications. To mitigate this gap, we present the first systematic dynamic characterization study of Android apps that targets a broad understanding of application behaviors in Android. Through lightweight method-level profiling,...
This paper presents the details of a CUDA implementation of the Subgraph Isomorphism Graph Challenge, a new effort aimed at driving progress in the graph analytics field. challenge consists of two graph analytics: triangle counting and k-truss. We present our CUDA implementation of the graph triangle counting operation and of the k-truss subgraph decomposition. Both implementations share the same...
Spectrum-based fault localization (SFL), the technique producing a rank list of statements in descending order of their suspiciousness values, is nowadays widely used in current automated program repair tools. There are two different algorithms for these tools to choose statements selected for modification to produce candidate patches from the list: one is the rank-first algorithm based on suspiciousness...
Early design-space evaluation of computer-systems is usually performed using performance models such as detailed simulators, RTL-based models etc. Unfortunately, it is very challenging (often impossible) to run many emerging applications on detailed performance models owing to their complex application software-stacks, significantly long run times, system dependencies and the limited speed/potential...
The rise of graph analytic systems has created a need for ways to measure and compare the capabilities of these systems. Graph analytics present unique scalability difficulties. The machine learning, high performance computing, and visual analytics communities have wrestled with these difficulties for decades and developed methodologies for creating challenges to move these communities forward. The...
Quality assessment of ultrasound images is difficult since image quality is subjective to the human observer. Nevertheless, image quality metrics are imperative when benchmarking different beamforming techniques. However, if we do not know how a beamformer alters an image, a quality metric might give an incorrect measurement of the image quality. Using the standard Delay-And-Sum (DAS) beamformer as...
Intelligent systems capabilities are increasing and its application to robotics has become largely popular. Some literature has been produced about autonomous controllers for robotics, demonstrating that they can face a variety of domains. However, we can observe that the experiments performed lack of a common scientific methodology. Generally, isolated case studies are presented, without providing...
Big data has exacerbated the so-called “memory wall” problem. To study the memory characteristics of big data applications has become an important issue in the high end computing community. In this paper, we propose a trace-based method based on the trace files generated by simulators, which captures memory access information in different memory hierarchies and aggregates information to get memory...
The quality assessment of edges in an image is an important topic as it helps to benchmark the performance of edge detectors, and edge-aware filters that are used in a wide range of image processing tasks. The most popular image quality metrics such as Mean squared error (MSE), Peak signal-to-noise ratio (PSNR) and Structural similarity (SSIM) metrics for assessing and justifying the quality of edges...
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