Dynamic Human Trust Modeling of Autonomous Agents With Varying Capability and Strategy
Jason Dekarske (University of California, Davis), Zhaodan Kong (University of California, Davis), Sanjay Joshi (University of California, Davis)
https://arxiv.org/abs/2404.19291 https://arxiv.org/pdf/2404.19291
arXiv:2404.19291v1 Announce Type: new
Abstract: Objective We model the dynamic trust of human subjects in a human-autonomy-teaming screen-based task.
Background Trust is an emerging area of study in human-robot collaboration. Many studies have looked at the issue of robot performance as a sole predictor of human trust, but this could underestimate the complexity of the interaction.
Method Subjects were paired with autonomous agents to search an on-screen grid to determine the number of outlier objects. In each trial, a different autonomous agent with a preassigned capability used one of three search strategies and then reported the number of outliers it found as a fraction of its capability. Then, the subject reported their total outlier estimate. Human subjects then evaluated statements about the agent's behavior, reliability, and their trust in the agent.
Results 80 subjects were recruited. Self-reported trust was modeled using Ordinary Least Squares, but the group that interacted with varying capability agents on a short time order produced a better performing ARIMAX model. Models were cross-validated between groups and found a moderate improvement in the next trial trust prediction.
Conclusion A time series modeling approach reveals the effects of temporal ordering of agent performance on estimated trust. Recency bias may affect how subjects weigh the contribution of strategy or capability to trust. Understanding the connections between agent behavior, agent performance, and human trust is crucial to improving human-robot collaborative tasks.
Application The modeling approach in this study demonstrates the need to represent autonomous agent characteristics over time to capture changes in human trust.
The population of small near-Earth objects: composition, source regions and rotational properties
Juan A. Sanchez, Vishnu Reddy, Audrey Thirouin, William F. Bottke, Theodore Kareta, Mario De Florio, Benjamin N. L. Sharkey, Adam Battle, David C. Cantillo, Neil Pearson
https://arxiv.org/abs/2404.18263
Metrization of Gromov-Hausdorff-type topologies on boundedly-compact metric spaces
Ryoichiro Noda
https://arxiv.org/abs/2404.19681 https://arxiv.org/pdf/2404.19681
arXiv:2404.19681v1 Announce Type: new
Abstract: We present a new general framework for metrization of Gromov-Hausdorff-type topologies on non-compact metric spaces. We also give easy-to-check conditions for separability and completeness and hence the measure theoretic requirements are provided to study convergence of random spaces with additional random objects. In particular, our framework enables us to define a metric inducing a suitable Gromov-Hausdorff-type topology on the space of rooted boundedly-compact metric spaces with laws of stochastic processes and/or random fields, which was not clear how to do in previous frameworks. In addition to general theory, this paper includes several examples of Gromov-Hausdorff-type topologies, verifying that classical examples such as the Gromov-Hausdorff topology and the Gromov-Hausdorff-Prohorov topology are contained within our framework.
A Universe of Sound - processing NASA data into sonifications to explore participant response: #sonified NASA data of three astronomical objects presented as aural visualizations, then surveyed blind or low-vision and sighted individuals to elicit feedback on the experience of these pieces as it relates to enjoyment, education, and trust of the scientific data."
Spatio-seasonal risk assessment of upward lightning at tall objects using meteorological reanalysis data
Isabell Stucke, Deborah Morgenstern, Georg J. Mayr, Thorsten Simon, Achim Zeileis, Gerhard Diendorfer, Wolfgang Schulz, Hannes Pichler
https://arxiv.org/abs/2403.18853
Cyclic sieving on permutations -- an analysis of maps and statistics in the FindStat database
Ashleigh Adams, Jennifer Elder, Nadia Lafreni\`ere, Erin McNicholas, Jessica Striker, Amanda Welch
https://arxiv.org/abs/2402.16251
Generative AI can not generate its way out of prejudice
The concept of "generative" suggests that the tool can produce what it is asked to produce. In a study uncovering how stereotypical global health tropes are embedded in AI image generators, researchers found it challenging to generate images of Black doctors treating white children. They used Midjourney, a tool that after hundreds of attempts would not generate an output matching the prompt. I tried their experiment with…
The power of relativistic jets: a comparative study
Luigi Foschini, Benedetta Dalla Barba, Merja Tornikoski, Heinz Andernach, Paola Marziani, Alan P. Marscher, Svetlana G. Jorstad, Emilia J\"arvel\"a, Sonia Ant\'on, Elena Dalla Bont\`a
https://arxiv.org/abs/2403.17581
Searching for large dark matter clumps using the Galileo Satnav clock variations
Bruno Bertrand, Pascale Defraigne, Aur\'elien Hees, Alexandra Sheremet, Cl\'ement Courde, Julien Chab\'e, Javier Ventura-Traveset, Florian Dilssner, Erik Schoenemann, Luis Mendes, Pac\^ome Delva
https://arxiv.org/abs/2403.17890
Twisted post-Hopf algebras, twisted relative Rota-Baxter operators and Hopf trusses
Jos\'e Manuel Fern\'andez Vilaboa, Ram\'on Gonz\'alez Rodr\'iguez, Brais Ramos P\'erez
https://arxiv.org/abs/2402.16704
Towards Safe Robot Use with Edged or Pointed Objects: A Surrogate Study Assembling a Human Hand Injury Protection Database
Robin Jeanne Kirschner, Carina M. Micheler, Yangcan Zhou, Sebastian Siegner, Mazin Hamad, Claudio Glowalla, Jan Neumann, Nader Rajaei, Rainer Burgkart, Sami Haddadin
https://arxiv.org/abs/2404.04004
Generative AI can not generate its way out of prejudice
The concept of "generative" suggests that the tool can produce what it is asked to produce. In a study uncovering how stereotypical global health tropes are embedded in AI image generators, researchers found it challenging to generate images of Black doctors treating white children. They used Midjourney, a tool that after hundreds of attempts would not generate an output matching the prompt. I tried their experiment with…
Galaxies in the Zone of Avoidance: Misclassifications using machine learning tools
P. Marchant Cort\'es, J. L. Nilo Castell\'on, M. V. Alonso, L. Baravalle, C. Villal\'on, M. A. Sgr\'o, I. V. Daza-Perilla, M. Soto, F. Milla Castro, D. Minniti, N. Masetti, C. Valotto, M. Lares
https://arxiv.org/abs/2403.03098 https://arxiv.org/pdf/2403.03098
arXiv:2403.03098v1 Announce Type: new
Abstract: Automated methods for classifying extragalactic objects in large surveys offer significant advantages compared to manual approaches in terms of efficiency and consistency. However, the existence of the Galactic disk raises additional concerns. These regions are known for high levels of interstellar extinction, star crowding, and limited data sets and studies. In this study, we explore the identification and classification of galaxies in the Zone of Avoidance (ZoA). In particular, we compare our results in the near-infrared with X-ray data. We analize the appearance of the objects classified as galaxies using machine learning by Zhang et al. (2021) in the Galactic disk and make a comparison with the visually confirmed galaxies from the VVV NIRGC (Baravalle et al. (2021). Our analysis, which includes the visual inspection of all sources catalogued as galaxies throughout the Galactic disk using machine learning techniques reveals significant differences. Only 4 galaxies were found in both the near-Infrared and X-ray data sets. Several specific regions of interest within the ZoA exhibit a high probability of being galaxies in X-ray data but closely resemble extended Galactic objects. The results indicate the difficulty of using machine learning methods for galaxy classification in the ZoA mainly due to the scarce information on galaxies behind the Galactic plane in the training set. They also stress the importance of considering specific factors that are present to improve the reliability and accuracy of future studies in this challenging region.
Spectroscopy of a Sample of Unidentified Gamma-ray Fermi Sources
Alberto Ulgiati, Simona Paiano, Aldo Treves, Renato Falomo, Boris Sbarufatti, Fabio Pintore, Thomas D. Russell, Giancarlo Cusumano
https://arxiv.org/abs/2402.12081
Spectroscopy of a Sample of Unidentified Gamma-ray Fermi Sources
Alberto Ulgiati, Simona Paiano, Aldo Treves, Renato Falomo, Boris Sbarufatti, Fabio Pintore, Thomas D. Russell, Giancarlo Cusumano
https://arxiv.org/abs/2402.12081
LISA Definition Study Report
Monica Colpi, Karsten Danzmann, Martin Hewitson, Kelly Holley-Bockelmann, Philippe Jetzer, Gijs Nelemans, Antoine Petiteau, David Shoemaker, Carlos Sopuerta, Robin Stebbins, Nial Tanvir, Henry Ward, William Joseph Weber, Ira Thorpe, Anna Daurskikh, Atul Deep, Ignacio Fern\'andez N\'u\~nez, C\'esar Garc\'ia Marirrodriga, Martin Gehler, Jean-Philippe Halain, Oliver Jennrich, Uwe Lammers, Jonan Larra\~naga, Maike Lieser, Nora L\"utzgendorf…
It's sometimes good to remember that many people have been debunking several
of Nielsen's assertions about usability for more than two decades. Not least the one about only needing to test with five users.
Here's a good study from 2001 (yes, 23 years ago) about page load times. By Christine Perfetti and Lori Landesman.
Remember when Amazon took 36 seconds to load over a 56kbps modem?
Kinematics $\&$ Star Formation in the Hub-Filament System G6.55-0.1
Saurav Sen (TIFR, Mumbai, India), Bhaswati Mookerjea (TIFR, Mumbai, India), Rolf Guesten (MPIfR, Bonn), Friedrich Wyrowski (MPIfR, Bonn), C. H Ishwara Chandra (NCRA-TIFR, Pune, India)
https://arxiv.org/abs/2404.07640…