scholarly journals Generative adversarial networks for generating synthetic features for Wi-Fi signal quality

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260308
Author(s):  
Mauro Castelli ◽  
Luca Manzoni ◽  
Tatiane Espindola ◽  
Aleš Popovič ◽  
Andrea De Lorenzo
Keyword(s):  
Synthetic Data ◽  
Real Data ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Signal Quality ◽  
Quality Service ◽  
High Quality ◽  
The Real ◽  
Adversarial Networks ◽  
High Quality Service

Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with specialized technology companies that deliver software services in order to monitor the networks and identify faults and respective solutions. A common barrier faced by these specialized companies is the lack of data to develop and test their products. This paper investigates the use of generative adversarial networks (GANs), which are state-of-the-art generative models, for generating synthetic telecommunication data related to Wi-Fi signal quality. We developed, trained, and compared two of the most used GAN architectures: the Vanilla GAN and the Wasserstein GAN (WGAN). Both models presented satisfactory results and were able to generate synthetic data similar to the real ones. In particular, the distribution of the synthetic data overlaps the distribution of the real data for all of the considered features. Moreover, the considered generative models can reproduce the same associations observed for the synthetic features. We chose the WGAN as the final model, but both models are suitable for addressing the problem at hand.

scholarly journals Generative models for fast cluster simulations in the TPC for the ALICE experiment

2019 ◽  
Vol 214 ◽  
pp. 06003 ◽  
Author(s):  
Kamil Deja ◽  
Tomasz Trzcin´ski ◽  
Łukasz Graczykowski
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Synthetic Data ◽  
Real Data ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Detector Response ◽  
Alice Experiment ◽  
The Real ◽  
Speed Up

Simulating the detector response is a key component of every highenergy physics experiment. The methods used currently for this purpose provide high-fidelity results. However, this precision comes at a price of a high computational cost. In this work, we introduce our research aiming at fast generation of the possible responses of detector clusters to particle collisions. We present the results for the real-life example of the Time Projection Chamber in the ALICE experiment at CERN. The essential component of our solution is a generative model that allows to simulate synthetic data points that bear high similarity to the real data. Leveraging recent advancements in machine learning, we propose to use conditional Generative Adversarial Networks. In this work we present a method to simulate data samples possible to record in the detector based on the initial information about particles. We propose and evaluate several models based on convolutional or recursive networks. The main advantage offered by the proposed method is a significant speed-up in the execution time, reaching up to the factor of 102 with respect to the currently used simulation tool. Nevertheless, this speed-up comes at a price of a lower simulation quality. In this work we adapt available methods and show their quantitative and qualitative limitations.

scholarly journals Is Synthetic The New Real? Performance Analysis of Time Series Generation Techniques with Focus on Network Load Forecasting

2021 ◽  
Author(s):  
Muhammad Haris Naveed ◽  
Umair Hashmi ◽  
Nayab Tajved ◽  
Neha Sultan ◽  
Ali Imran
Keyword(s):  
Synthetic Data ◽  
Real Data ◽  
Generative Adversarial Networks ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Adversarial Networks ◽  
Network Load ◽  
Quantitative Metrics ◽  
Real Performance ◽  
Analysis Of Time Series

This paper explores whether Generative Adversarial Networks (GANs) can produce realistic network load data that can be utilized to train machine learning models in lieu of real data. In this regard, we evaluate the performance of three recent GAN architectures on the Telecom Italia data set across a set of qualitative and quantitative metrics. Our results show that GAN generated synthetic data is indeed similar to real data and forecasting models trained on this data achieve similar performance to those trained on real data.

About some problems of use of firearms by employees of security divisions of the Federal Penitentiary Service of Russia

2020 ◽  
pp. 51-59
Author(s):  
Ольга Николаевна Мазеина ◽  
Антон Михайлович Лысухин
Keyword(s):  
Necessary Conditions ◽  
Legal Protection ◽  
Quality Service ◽  
Legal Basis ◽  
High Quality ◽  
Coercive Measures ◽  
The Real ◽  
The Right ◽  
High Quality Service ◽  
Exhaustive List

В статье рассматриваются актуальные проблемы применения огнестрельного оружия сотрудниками подразделений охраны Федеральной службы исполнения наказаний. Обозначены нормативно-правовые акты, которые наделяют сотрудников правом применять различные меры принуждения к правонарушителям и освещают ситуации, в которых возможно использование и применение огнестрельного оружия. Юридическим основанием возникновения обязанности своевременно и правомерно использовать огнестрельное оружие в целях устранить источники опасности для сотрудников определяется реальная угроза. В статье рассматриваются вопросы, касающиеся особенностей несения службы: сложности, напряженности, экстремальности, динамичности, непредсказуемости оперативной обстановки. При этом акцентируется внимание на готовности сотрудников к качественному несению службы, способности применить оружие в необходимых условиях - условиях реальной угрозы, исходящей от исчерпывающего перечня источников опасности, непосредственно угрожающих жизни и здоровью. Основными проблемами применения огнестрельного оружия сотрудниками подразделений охраны представлены: превышение пределов правомерного применения; необходимость доказывания законности применения оружия в следственных органах и судах; страх применения огнестрельного оружия; неготовность сотрудников к применению оружия в отношении людей; дефицит времени, связанный с принятием решения о применении оружия; профессионально значимые качества сотрудников и др. Предложены меры и мероприятия по повышению надежности и эффективности служебной деятельности, четкого выполнения поставленных задач, правовой защищенности сотрудников, несущих службу с оружием. The article deals with the actual problems of using firearms by employees of security units of the Federal penitentiary service of Russia. The authors of the article indicate the legal acts that give employees the right to apply various coercive measures to offenders and highlight the situations in which the use and use of firearms is possible. The legal basis for the obligation to use firearms in a timely and lawful manner in order to eliminate sources of danger for employees is determined by the real threat. The article deals with issues related to the peculiarities of service: complexity, tension, extremity, dynamism, and unpredictability of the operational situation. At the same time, attention is focused on the readiness of employees for high - quality service, the ability to use weapons in the necessary conditions-conditions of a real threat emanating from an exhaustive list of sources of danger that directly threaten life and health. The main problems with the use of firearms by employees of security units are: exceeding the limits of lawful use; the need to prove the legality of the use of weapons in investigative bodies and courts; fear of the use of firearms; lack of readiness of employees to use weapons against people; lack of time associated with making a decision on the use of weapons; professionally significant qualities of employees, etc.the Authors suggest measures and measures to improve the reliability and efficiency of official activities, clear performance of tasks, legal protection of employees serving with weapons.

scholarly journals Generative Adversarial Networks for Anonymized Healthcare of Lung Cancer Patients

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2220
Author(s):  
Luis Gonzalez-Abril ◽  
Cecilio Angulo ◽  
Juan-Antonio Ortega ◽  
José-Luis Lopez-Guerra
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Computational Models ◽  
Synthetic Data ◽  
Real Data ◽  
Learning Technologies ◽  
Data Availability ◽  
Generative Adversarial Networks ◽  
Lung Cancer Patients ◽  
Adversarial Networks

The digital twin in health care is the dynamic digital representation of the patient’s anatomy and physiology through computational models which are continuously updated from clinical data. Furthermore, used in combination with machine learning technologies, it should help doctors in therapeutic path and in minimally invasive intervention procedures. Confidentiality of medical records is a very delicate issue, therefore some anonymization process is mandatory in order to maintain patients privacy. Moreover, data availability is very limited in some health domains like lung cancer treatment. Hence, generation of synthetic data conformed to real data would solve this issue. In this paper, the use of generative adversarial networks (GAN) for the generation of synthetic data of lung cancer patients is introduced as a tool to solve this problem in the form of anonymized synthetic patients. Generated synthetic patients are validated using both statistical methods, as well as by oncologists using the indirect mortality rate obtained for patients in different stages.

scholarly journals Image Augmentation based on GAN deep learning approach with Textual Content Descriptors

2021 ◽  
Vol 3 (3) ◽  
pp. 210-225
Author(s):  
Judy Simon
Keyword(s):  
Computer Vision ◽  
Synthetic Data ◽  
Original Data ◽  
Generative Models ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Biological Vision ◽  
Adversarial Networks ◽  
Textual Content

Computer vision, also known as computational visual perception, is a branch of artificial intelligence that allows computers to interpret digital pictures and videos in a manner comparable to biological vision. It entails the development of techniques for simulating biological vision. The aim of computer vision is to extract more meaningful information from visual input than that of a biological vision. Computer vision is exploding due to the avalanche of data being produced today. Powerful generative models, such as Generative Adversarial Networks (GANs), are responsible for significant advances in the field of picture creation. The focus of this research is to concentrate on textual content descriptors in the images used by GANs to generate synthetic data from the MNIST dataset to either supplement or replace the original data while training classifiers. This can provide better performance than other traditional image enlarging procedures due to the good handling of synthetic data. It shows that training classifiers on synthetic data are as effective as training them on pure data alone, and it also reveals that, for small training data sets, supplementing the dataset by first training GANs on the data may lead to a significant increase in classifier performance.

scholarly journals Achieving Causal Fairness through Generative Adversarial Networks

2019 ◽  
Author(s):  
Depeng Xu ◽  
Yongkai Wu ◽  
Shuhan Yuan ◽  
Lu Zhang ◽  
Xintao Wu
Keyword(s):  
Causal Model ◽  
Real Data ◽  
Causal Modeling ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Modeling Framework ◽  
High Quality ◽  
Causal Graph ◽  
Adversarial Networks ◽  
Real Distribution

Achieving fairness in learning models is currently an imperative task in machine learning. Meanwhile, recent research showed that fairness should be studied from the causal perspective, and proposed a number of fairness criteria based on Pearl's causal modeling framework. In this paper, we investigate the problem of building causal fairness-aware generative adversarial networks (CFGAN), which can learn a close distribution from a given dataset, while also ensuring various causal fairness criteria based on a given causal graph. CFGAN adopts two generators, whose structures are purposefully designed to reflect the structures of causal graph and interventional graph. Therefore, the two generators can respectively simulate the underlying causal model that generates the real data, as well as the causal model after the intervention. On the other hand, two discriminators are used for producing a close-to-real distribution, as well as for achieving various fairness criteria based on causal quantities simulated by generators. Experiments on a real-world dataset show that CFGAN can generate high quality fair data.

scholarly journals Is Synthetic The New Real? Performance Analysis of Time Series Generation Techniques with Focus on Network Load Forecasting

2021 ◽  
Author(s):  
Muhammad Haris Naveed ◽  
Umair Hashmi ◽  
Nayab Tajved ◽  
Neha Sultan ◽  
Ali Imran
Keyword(s):  
Synthetic Data ◽  
Real Data ◽  
Generative Adversarial Networks ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Adversarial Networks ◽  
Network Load ◽  
Quantitative Metrics ◽  
Real Performance ◽  
Analysis Of Time Series

This paper explores whether Generative Adversarial Networks (GANs) can produce realistic network load data that can be utilized to train machine learning models in lieu of real data. In this regard, we evaluate the performance of three recent GAN architectures on the Telecom Italia data set across a set of qualitative and quantitative metrics. Our results show that GAN generated synthetic data is indeed similar to real data and forecasting models trained on this data achieve similar performance to those trained on real data.

scholarly journals Dynamic GAN for High-Quality Sign Language Video Generation from Skeletal poses using Generative Adversarial Networks

2021 ◽  
Author(s):  
B Natarajan ◽  
Elakkiya R
Keyword(s):  
Sign Language ◽  
Performance Metrics ◽  
Random Noise ◽  
Video Quality ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Generation Process ◽  
High Quality ◽  
Adversarial Networks ◽  
Proposed Model

Abstract The emergence of unsupervised generative models has resulted in greater performance in image and video generation tasks. However, existing generative models pose huge challenges in high-quality video generation process due to blurry and inconsistent results. In this paper, we introduce a novel generative framework named Dynamic Generative Adversarial Networks (Dynamic GAN) model for regulating the adversarial training and generating photorealistic high-quality sign language videos from skeletal poses. The proposed model comprises three stages of development such as generator network, classification and image quality enhancement and discriminator network. In the generator fold, the model generates samples similar to real images using random noise vectors, the classification of generated samples are carried out using the VGG-19 model and novel techniques are employed for improving the quality of generated samples in the second fold of the model and finally the discriminator networks fold identifies the real or fake samples. Unlike, existing approaches the proposed novel framework produces photo-realistic video quality results without using any animation or avatar approaches. To evaluate the model performance qualitatively and quantitatively, the proposed model has been evaluated using three benchmark datasets that yield plausible results. The datasets are RWTH-PHOENIX-Weather 2014T dataset, and our self-created dataset for Indian Sign Language (ISL-CSLTR), and the UCF-101 Action Recognition dataset. The output samples and performance metrics show the outstanding performance of our model.

scholarly journals DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vajira Thambawita ◽  
Jonas L. Isaksen ◽  
Steven A. Hicks ◽  
Jonas Ghouse ◽  
Gustav Ahlberg ◽  
...  
Keyword(s):  
Population Studies ◽  
Medical Science ◽  
Synthetic Data ◽  
Real Data ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Similar Information ◽  
Normal Ecgs ◽  
Privacy Issues ◽  
Python Package

AbstractRecent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic data generated to represent real data carrying similar information and distribution may alleviate the privacy issue. In this study, we present generative adversarial networks (GANs) capable of generating realistic synthetic DeepFake 10-s 12-lead electrocardiograms (ECGs). We have developed and compared two methods, named WaveGAN* and Pulse2Pulse. We trained the GANs with 7,233 real normal ECGs to produce 121,977 DeepFake normal ECGs. By verifying the ECGs using a commercial ECG interpretation program (MUSE 12SL, GE Healthcare), we demonstrate that the Pulse2Pulse GAN was superior to the WaveGAN* to produce realistic ECGs. ECG intervals and amplitudes were similar between the DeepFake and real ECGs. Although these synthetic ECGs mimic the dataset used for creation, the ECGs are not linked to any individuals and may thus be used freely. The synthetic dataset will be available as open access for researchers at OSF.io and the DeepFake generator available at the Python Package Index (PyPI) for generating synthetic ECGs. In conclusion, we were able to generate realistic synthetic ECGs using generative adversarial neural networks on normal ECGs from two population studies, thereby addressing the relevant privacy issues in medical datasets.

scholarly journals A Non-Parametric Generative Model for Human Trajectories

2018 ◽  
Author(s):  
Kun Ouyang ◽  
Reza Shokri ◽  
David S. Rosenblum ◽  
Wenzhuo Yang
Keyword(s):  
Human Mobility ◽  
Real Data ◽  
Generative Models ◽  
Generative Model ◽  
Generative Adversarial Networks ◽  
Semantic Features ◽  
Location Data ◽  
Adversarial Networks ◽  
Data Points ◽  
Non Parametric

Modeling human mobility and synthesizing realistic trajectories play a fundamental role in urban planning and privacy-preserving location data analysis.  Due to its high dimensionality and also the diversity of its applications, existing trajectory generative models do not preserve the geometric (and more importantly) semantic features of human mobility, especially for longer trajectories. In this paper, we propose and evaluate a novel non-parametric generative model for location trajectories that tries to capture the statistical features of human mobility {\em as a whole}.  This is in contrast with existing models that generate trajectories in a sequential manner.  We design a new representation of locations, and use generative adversarial networks to produce data points in that representation space which will be then transformed to a time-series location trajectory form.  We evaluate our method on realistic location trajectories and compare our synthetic traces with multiple existing methods on how they preserve geographic and semantic features of real traces at both aggregated and individual levels.  The empirical results prove the capability of our model in preserving the utility of real data.

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