Data generators: a short survey of techniques and use cases with focus on testing

Popic, Srdan; Pavkovic, Bogdan; Velikic, Ivan; Teslić, Nikola

doi:10.1109/icce-berlin47944.2019.8966202

Cited by 15 publications

(13 citation statements)

References 31 publications

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“…The latter, will first extract three one-dimensional signals out of the three feature maps. Then it will calculate the following nine features: (1) Number of zero-crossings in radial velocity, (2, 3) arguments of maximum and minimum radial velocity, (4, 5) maximum and minimum radial velocity, (6) difference between maximum and minimum angle in azimuth, (7) difference between maximum and minimum angle in elevation, (8) difference of angle in azimuth when radial velocity reached its maximum and minimum value, (9) difference of angle in elevation when radial velocity reached its maximum and minimum value. Finally, for classification we used a Multi-Layer Perceptron (MLP), with one hidden…”

Section: Processing Pipeline For Gesture Recognitionmentioning

confidence: 99%

Synthetic Radar Dataset Generator for Macro-Gesture Recognition

et al. 2021

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Recent developments in mmWave technology allow the detection and classification of dynamic arm gestures. However, achieving a high accuracy and generalization requires a lot of samples for the training of a machine learning model. Furthermore, in order to capture variability in the gesture class, the participation of many subjects and the conduct of many gestures with different arm speed are required. In case of macro-gestures, the position of the subject must also vary inside the field of view of the device. This would require a significant amount of time and effort, which needs to be repeated in case that the sensor hardware or the modulation parameters are modified. In order to reduce the required manual effort, here we developed a synthetic data generator that is capable of simulating seven arm gestures by utilizing Blender, an open-source 3D creation suite. We used it to generate 600 artificial samples with varying speed of execution and relative position of the simulated subject, and used them to train a machine learning model. We tested the model using a real dataset recorded from ten subjects, using an experimental sensor. The test set yielded 84.2% accuracy, indicating that synthetic data generation can significantly contribute in the pre-training of a model.

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Section: Processing Pipeline For Gesture Recognitionmentioning

confidence: 99%

Synthetic Radar Dataset Generator for Macro-Gesture Recognition

et al. 2021

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“…[ Popić et al, 2019] reviewed data generator techniques and use cases. For the data modelling process, they present several methods such as [Hoag and Thompson, 2007] and [Rabl and Poess, 2011].…”

Section: Data Generation For Fdiamentioning

confidence: 99%

An Approach for Testing False Data Injection Attack on Data Dependent Industrial Devices

Briland¹,

Bouquet²

2021

jucs

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False data injection is an attack in which an attacker injects fabricated data into a system with the objective to change the behaviour and the decision-making of the system. Many industrial data-based devices are vulnerable to such attacks, this work presents an approach for testing False Data Injection Attack. This approach uses a Domain-Specific Language to generate altered data with two objectives, to provide sophisticated attacks scenarios to increase the resilience of vulnerable systems against False Data Injection Attack and to train detection tools.

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“…Generation of synthetic datasets for testing has importance in many areas of computing, such as data visualization, data mining, software engineering, and artificial intelligence. Sran Popić et al [7] wrote a survey about works in the area of synthetic data generation that focuses on application testing, highlighting the system architectures and the intended usage of the applications, showing the pros and cons of the surveyed techniques. Demillo and Offut [8] have described a failure-based application to generate synthetic data units for performing tests for software modules.…”

Section: Related Workmentioning

confidence: 99%

“…In this typical data flow, a user could choose to share the model with fellow researchers to reproduce experiments (6). The researchers receiving this data model could generate their own dataset following the same distribution defined by the generators (7). While the underlying randomness of the generation process implies that two datasets created from the same model are not identical, the data points are equivalent as they share the same characteristics and behavior (e.g., same correlations, probabilities, outliers).…”

Section: Proposed Applicationmentioning

confidence: 99%

Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

et al. 2020

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Data generators are applications that produce synthetic datasets, which are useful for testing data analytics applications, such as machine learning algorithms and information visualization techniques. Each data generator application has a different approach to generate data. Consequently, each one has functionality gaps that make it unsuitable for some tasks (e.g., lack of ways to create outliers and non-random noise). This paper presents a data generator application that aims to fill relevant gaps scattered across other applications, providing a flexible tool to assist researchers in exhaustively testing their techniques in more diverse ways. The proposed system allows users to define and compose known statistical distributions to produce the desired outcome, visualizing the behavior of the data in real-time to analyze if it has the characteristics needed for efficient testing. This paper presents in detail the tool functionalities and how to create datasets, as well as a usage scenario to illustrate the process of data creation. INDEX TERMS Synthetic dataset generator, benchmark datasets creation, data creation system.

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Data generators: a short survey of techniques and use cases with focus on testing

Cited by 15 publications

References 31 publications

Synthetic Radar Dataset Generator for Macro-Gesture Recognition

Synthetic Radar Dataset Generator for Macro-Gesture Recognition

An Approach for Testing False Data Injection Attack on Data Dependent Industrial Devices

Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

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