Probabilistic Forecasting of Sensory Data With Generative Adversarial Networks – ForGAN

Koochali, Alireza; Schichtel, Peter; Dengel, Andreas; Ahmed, Sheraz

doi:10.1109/access.2019.2915544

Cited by 79 publications

(35 citation statements)

References 77 publications

(69 reference statements)

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“…ForGAN. It utilizes conditional generative adversarial network to learn the data generating distribution and compute probabilistic forecasts [18].…”

Section: Baseline Methodsmentioning

confidence: 99%

“…We observe that DESSERT outperforms all the baselines across four evaluation metrics (both correlation-based and error-based). DESSERT achieves 0.67 Pearson's and 31.08 Mean Absolute Percentage Error (MAPE), which is significantly better than the best baseline [18] ( = 0.557, MAPE=43.47). We also present a detailed ablation study of DESSERT.…”

Section: Introductionmentioning

confidence: 96%

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Would Your Tweet Invoke Hate on the Fly? Forecasting Hate Intensity of Reply Threads on Twitter

Dahiya

Sharma

Sahnan

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Curbing hate speech is undoubtedly a major challenge for online microblogging platforms like Twitter. While there have been studies around hate speech detection, it is not clear how hate speech finds its way into an online discussion. It is important for a content moderator to not only identify which tweet is hateful, but also to predict which tweet will be responsible for accumulating hate speech. This would help in prioritizing tweets that need constant monitoring. Our analysis reveals that for hate speech to manifest in an ongoing discussion, the source tweet may not necessarily be hateful; rather, there are plenty of such non-hateful tweets which gradually invoke hateful replies, resulting in the entire reply threads becoming provocative.In this paper, we define a novel problemgiven a source tweet and a few of its initial replies, the task is to forecast the hate intensity of upcoming replies. To this end, we curate a novel dataset constituting ∼ 4.5 contemporary tweets and their entire reply threads. Our preliminary analysis confirms that the evolution patterns along time of hate intensity among reply threads have highly diverse patterns, and there is no significant correlation between the hate intensity of the source tweets and that of their reply threads. We employ seven state-of-the-art dynamic models (either statistical signal processing or deep learning based) and show that they fail badly to forecast the hate intensity. We then propose DESSERT, a novel deep state-space model that leverages the function approximation capability of deep neural networks with the capacity to quantify the uncertainty of statistical signal processing models. Exhaustive experiments and ablation study show that DESSERT outperforms all the baselines substantially. Further, its deployment in an advanced AI platform designed to monitor real-world problematic hateful content has improved the aggregated insights extracted for countering the spread of online harms.T. Chakraborty would like to acknowledge the support of Logically, the Ramanujan Fellowship, and the Infosys Centre for AI, IIIT Delhi. We also thank Sarah Masud for her help in writing the paper. CCS CONCEPTS• Computing methodologies → Machine learning algorithms;• Information systems → Social tagging systems; • Humancentered computing → Social network analysis.

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“…ForGAN. It utilizes conditional generative adversarial network to learn the data generating distribution and compute probabilistic forecasts [18].…”

Section: Baseline Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Would Your Tweet Invoke Hate on the Fly? Forecasting Hate Intensity of Reply Threads on Twitter

Dahiya

Sharma

Sahnan

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

show abstract

“…Based on the result of this paper, the best performance is with a shallow LSTM; however, GAN has an acceptable performance. ForGAN is a model based on a conditional GAN with an LSTM/GRU layer and was proposed by Koochali et al ( 2019 ) as a novel approach for forecasting future values. Another GAN model for time series prediction introduced by Zec et al ( 2019 ).…”

Section: Time Series Prediction Methods For Mitigating Time Delay mentioning

confidence: 99%

A Brief Survey of Telerobotic Time Delay Mitigation

2020

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There is a substantial number of telerobotics and teleoperation applications ranging from space operations, ground/aerial robotics, drive-by-wire systems to medical interventions. Major obstacles for such applications include latency, channel corruptions, and bandwidth which limit teleoperation efficacy. This survey reviews the time delay problem in teleoperation systems. We briefly review different solutions from early approaches which consist of control-theory-based models and user interface designs and focus on newer approaches developed since 2014. Future solutions to the time delay problem will likely be hybrid solutions which include modeling of user intent, prediction of robot movements, and time delay prediction all potentially using time series prediction methods. Hence, we examine methods that are primarily based on time series prediction. Recent prediction approaches take advantage of advances in nonlinear statistical models as well as machine learning and neural network techniques. We review Recurrent Neural Networks, Long Short-Term Memory, Sequence to Sequence, and Generative Adversarial Network models and examine each of these approaches for addressing time delay. As time delay is still an unsolved problem, we suggest some possible future research directions from information-theory-based modeling, which may lead to promising new approaches to advancing the field.

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“…Although both models used LSTM for the Generator and Discriminator, RCGAN applied conditional GAN for additional information. ForGAN by Koochali (2019) is not a synthetic data generating model, and yet, it still used conditional GAN to forecast some sensor data and network traffic [10]. Zhang (2018) built a CGAN to generate smart grid data, only that it used CNN instead of LSTM for time-series data [11].…”

Section: Related Workmentioning

confidence: 99%

Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

Park

Yoo

2021

Applied Sciences

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The financial sector accumulates a massive amount of consumer data that contain the most sensitive information daily. These data are strictly limited outside the financial institutions, sometimes even within the same organization, for various reasons such as privacy laws or asset management policy. Financial data has never been more valuable, especially when assessed jointly with data from different industries, including healthcare, insurance, credit bureau, and research institutions. Therefore, it is critical to generate synthetic datasets that retain the statistical or latent properties of the real datasets as well as the privacy protection guaranteed. In this paper, we apply Generative Adversarial Nets (GANs) to generating synthetic consumer credit data to be used for various educational purposes, specifically in developing machine learning models. GAN is preferable to other pseudonymization methods such as masking, swapping, shuffling, or perturbation, for it does not suffer from adding more attributes or data. This study is significant because it is the first attempt to generate the synthetic data of real-world credit data in practical use. The results find that synthetic consumer credit data using GAN shows a substantial utility without severely compromising privacy and would be a useful resource for big data training programs.

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Probabilistic Forecasting of Sensory Data With Generative Adversarial Networks – ForGAN

Cited by 79 publications

References 77 publications

Would Your Tweet Invoke Hate on the Fly? Forecasting Hate Intensity of Reply Threads on Twitter

Would Your Tweet Invoke Hate on the Fly? Forecasting Hate Intensity of Reply Threads on Twitter

A Brief Survey of Telerobotic Time Delay Mitigation

Synthesizing Individual Consumers′ Credit Historical Data Using Generative Adversarial Networks

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