SAR Target Classification with Limited Data via Data Driven Active Learning

We present CAiRE, an end-to-end generative empathetic chatbot designed to recognize user emotions and respond in an empathetic manner. Our system adapts the Generative Pre-trained Transformer (GPT) to empathetic response generation task via transfer learning. CAiRE is built primarily to focus on empathy integration in fully data-driven generative dialogue systems. We create a web-based user interface which allows multiple users to asynchronously chat with CAiRE. CAiRE also collects user feedback and continues to improve its response quality by discarding undesirable generations via active learning and negative training.

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Data-driven experimental design and model development using Gaussian process with active learning

Cognitive Psychology ◽

10.1016/j.cogpsych.2020.101360 ◽

2021 ◽

Vol 125 ◽

pp. 101360

Author(s):

Jorge Chang ◽

Jiseob Kim ◽

Byoung-Tak Zhang ◽

Mark A. Pitt ◽

Jay I. Myung

Keyword(s):

Experimental Design ◽

Active Learning ◽

Gaussian Process ◽

Model Development ◽

Data Driven

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Evaluating Data-Driven Forecasting Methods for Predicting SARS-CoV2 Cases: Evidence From 173 Countries

10.1101/2020.08.03.20167189 ◽

2020 ◽

Author(s):

Ghufran Ahmad ◽

Furqan Ahmed ◽

Suhail Rizwan ◽

Javed Muhammad ◽

Hira Fatima ◽

...

Keyword(s):

Moving Average ◽

Data Availability ◽

Data Driven ◽

Percentage Error ◽

Pictorial Representation ◽

Limited Data ◽

Autoregressive Integrated Moving Average ◽

Forecasting Methods ◽

Global Pandemic ◽

Future Burden

AbstractThe WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries, and has been declared as a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 173 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), random walk forecasts (RWF) with and without drift. We also evaluate the accuracy of these forecasts using the Mean Absolute Percentage Error (MAPE). The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generated heat maps to provide a pictorial representation of the countries at risk of having an increase in cases in the coming 4 weeks for June. Due to limited data availability during the ongoing pandemic, less data-hungry forecasting models like ARIMA and ETS can help in anticipating the future burden of SARS-CoV2 on healthcare systems.

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Data-driven Verification using Efficient Active Learning

10.2514/6.2022-2086 ◽

2022 ◽

Author(s):

Venkata Vaishnav Tadiparthi ◽

Raktim Bhattacharya

Keyword(s):

Active Learning ◽

Data Driven

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Using Pre-trained Language Model to Enhance Active Learning for Sentence Matching

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3480937 ◽

2022 ◽

Vol 21 (2) ◽

pp. 1-19

Author(s):

Guirong Bai ◽

Shizhu He ◽

Kang Liu ◽

Jun Zhao

Keyword(s):

Active Learning ◽

Language Model ◽

Experimental Results ◽

Language Models ◽

Data Driven ◽

Learning Approach ◽

Sentence Matching ◽

Learning Language

Active learning is an effective method to substantially alleviate the problem of expensive annotation cost for data-driven models. Recently, pre-trained language models have been demonstrated to be powerful for learning language representations. In this article, we demonstrate that the pre-trained language model can also utilize its learned textual characteristics to enrich criteria of active learning. Specifically, we provide extra textual criteria with the pre-trained language model to measure instances, including noise, coverage, and diversity. With these extra textual criteria, we can select more efficient instances for annotation and obtain better results. We conduct experiments on both English and Chinese sentence matching datasets. The experimental results show that the proposed active learning approach can be enhanced by the pre-trained language model and obtain better performance.

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Reconstruction of Governing Equations from Vibration Measurements for Geometrically Nonlinear Systems

Lubricants ◽

10.3390/lubricants7080064 ◽

2019 ◽

Vol 7 (8) ◽

pp. 64 ◽

Cited By ~ 1

Author(s):

Marco Didonna ◽

Merten Stender ◽

Antonio Papangelo ◽

Filipe Fontanela ◽

Michele Ciavarella ◽

...

Keyword(s):

System Identification ◽

Differential Equations ◽

Duffing Oscillator ◽

Vibration Signal ◽

Data Driven ◽

Geometrically Nonlinear ◽

Limited Data ◽

Weakly Coupled ◽

Driven System ◽

Quasi Linear System

Data-driven system identification procedures have recently enabled the reconstruction of governing differential equations from vibration signal recordings. In this contribution, the sparse identification of nonlinear dynamics is applied to structural dynamics of a geometrically nonlinear system. First, the methodology is validated against the forced Duffing oscillator to evaluate its robustness against noise and limited data. Then, differential equations governing the dynamics of two weakly coupled cantilever beams with base excitation are reconstructed from experimental data. Results indicate the appealing abilities of data-driven system identification: underlying equations are successfully reconstructed and (non-)linear dynamic terms are identified for two experimental setups which are comprised of a quasi-linear system and a system with impacts to replicate a piecewise hardening behavior, as commonly observed in contacts.

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