HeKKSaGOn AI Symposium

Contribution List

16. AI Usage Cards: Disclosing AI-generated Content in Scientific Work

Terry Lima Ruas (CIDAS)

9/19/23, 2:25 PM

Data Science

Plenary

We introduce AI Usage Cards, a standardized way to report the use of AI in scientific research. Our model and cards allow users to reflect on key principles of responsible AI usage. They also help the research community trace, compare, and question various forms of AI usage and support the development of accepted community norms.

15. AI Service Centre for Sensitive and Critical Infrastructures

Stefanie Mühlhausen (GWDG)

9/19/23, 3:45 PM

Data Science

Plenary

KISSKI, a pioneering force in AI research and services, centres its efforts on sensitive and critical infrastructures. Collaborating with experts spanning medicine, energy, and AI domains, KISSKI is dedicated to forging a robust AI service centre. Its holistic range of provisions, encompassing hardware, software, consultancy, and training, is poised to empower SMEs, start-ups, and research...

24. Toward Practical Use of Automated Short Answer Scoring: Challenges and Prospects

Hiroaki Funayama (Tohoku University)

9/19/23, 4:45 PM

Data Science

Plenary

The rapid advancement of Artificial Intelligence (AI) technologies has increased interest in their application within educational contexts. In particular, Short Answer Scoring (SAS), which focuses on the automated assessment of brief, descriptive answers, is attracting increasing attention. The primary reasons for researching SAS include reducing grading costs and facilitating real-time,...

12. Natural language understanding and language education support technology

Yuichiroh Matsubayashi (Tohoku University)

9/20/23, 9:00 AM

Data Science

Keynotes

The rapid advancement of large-scale language models (LLMs) is
bringing about a transformative era in language processing technology.
Traditionally, developing models that are capable of effectively
handling language expressions with the same level of freedom and
complexity as human intelligence has been extremely difficult.
Specifically, in the field of semantic parsing, there has been...

10. Vision and language models meet visual abductive reasoning for predicting driving hazards

Masanori Suganuma (Tohoku University)

9/20/23, 11:00 AM

Data Science

Keynotes

We stand at the threshold of a transformative period, defined by the remarkable advancements in large language models (LLMs). Given their prowess, there's a burgeoning interest in expanding LLMs to vision and language (VL) tasks, where models harness the capabilities of LLMs to analyze both visual and textual data concurrently.
In this talk, I will introduce our research that delves into...

21. 3D Data Processing with Deep Learning

Naoya Chiba (Tohoku University)

9/20/23, 11:45 AM

Data Science

Keynotes

Since the use of point cloud data by deep learning has become widespread, research on neural networks for handling point clouds and methods for handling 3D data has been focused in this research area. When data points are not arranged on a grid and are not sequential, they are referred to as "irregular data structures". For such data, including point clouds, graphs, and tabular data, it is...

13. Elastic-Net Penalized Quantile Regression for Data Mining Application

Ahmed Alsayed (University of Milan)

9/20/23, 1:45 PM

Data Science

Plenary

Recently, the penalized regularization regression methods have been extensively studied in the literature, but it is still difficult to generalize a method to be applied in various applications, particularly that suffer from heterogeneity and collinearity. Due to that, we compare the developed machine learning methods using numerical simulations for various senario. The applied methods in this...

20. AI/ML in cardiovascular medicine

Tim Friede (UMG)

9/20/23, 2:05 PM

Data Science

Plenary

In medicine, artificial intelligence (AI) and machine learning (ML) promise vast advances. We start by providing an overview of current applications of AI and ML in cardiovascular medicine. Furthermore, we will provide an example for the development and validation of an explainable ML model from cardiovascular medicine. Finally, we discuss the role of statistics in AI/ML applications.

17. Utilization of AI Technology in Dental Practice ~myDentalAI~

Tomonori Hayami (Cybermedia Center, Osaka University)

9/20/23, 4:05 PM

Data Science

Plenary

With the progress of machine learning and artificial intelligence, various research is being conducted in dentistry to support diagnosis and treatment, prevent medical errors, and improve patients' QOL by introducing these technologies. In order to infer the contents of dental treatment and acquire feature representations of surgeons’ behavior, the Joint Research Department for Oral Data...

3. Test Abstract

Sven Bingert (GWDG)

Data Science

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