ABSTRACT
Mingling, the activity of ad-hoc, private, opportunistic conversations ahead of, during, or after breaks, is an important socializing activity for attendees at scheduled events, such as in-person conferences. The Covid-19 pandemic had a dramatic impact on the way conferences are organized, so that most of them now take place in a hybrid mode where people can either attend on-site or remotely. While on-site attendees can resume in-person mingling, hybrid modes make it challenging for remote attendees to mingle with on-site peers. In addressing this problem, we propose a collaborative mixed-reality (MR) concept, including a prototype, called HybridMingler. This is a distributed MR system supporting ambient awareness and allowing both on-site and remote conference attendees to virtually mingle. HybridMingler aims to provide both on-site and remote attendees with a spatial sense of co-location in the very same venue location, thus ultimately improving perceived presence. © 2023 Owner/Author.
ABSTRACT
For humans, the COVID-19 pandemic and Coronavirus have undeniably been a nightmare. Although there are effective vaccines, specific drugs are still urgent. Normally, to identify potential drugs, one needs to design and then test interactions between the drug and the virus in an in silico manner for determining candidates. This Drug-Target Interaction (DTI) process, can be done by molecular docking, which is too complicated and time-consuming for manual works. Therefore, it opens room for applying Artificial Intelligence (AI) techniques. In particular, Graph Neural Network (GNN) attracts recent attention since its high suitability for the nature of drug compounds and virus proteins. However, to introduce such a representation well-reflecting biological structures of biological compounds is not a trivial task. Moreover, since available datasets of Coronavirus are still not highly popular, the recently developed GNNs have been suffering from overfitting on them. We then address those issues by proposing a novel model known as Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism. On one hand, our model can learn more precise features of compounds and proteins. On the other hand, we introduce a new gating mechanism to create better atom representation from non-neighbor information. Once applying transfer learning from very large databanks, our model enjoys promising performance, especially when experimenting with Coronavirus. © 2022 IEEE.
ABSTRACT
Our study aims to fill the gap in estimating the impacts of political connections and bank funding diversity on the risk-taking behaviors of Vietnamese commercial banks. By employing the Bayesian methodology, our paper can overcome the small sample issues to reduce the bias in estimation results. We construct a data sample that includes 38 commercial banks in Vietnam from 2003 to 2020. Our results suggest several findings in the Vietnamese banking sector. Firstly, our findings suggest that politically connected banks have 0.4% non-performing loans higher than unconnected peers. Secondly, we find a positive relationship between funding diversity and non-performing loans of commercial banks in Vietnam. Interestingly, our findings report that the commercial banks, especially the politically connected banks, reduced non-performing loans by 0.2% and 0.4% for a year before the recent two National Congress of the Communist Party of Vietnam, respectively. It could be due to the notion that the bank managers secure their job and political promotions by reducing non-performing loans before the National Congress of the Communist Party of Vietnam. Finally, our study argues that the commercial bank had a lower level of non-performing loans during the Covid19 pandemic because the government offered stimulus supports to the local economy. Our study has substantial implications for bank managers and authorities in emerging markets. © 2022, Economic Laboratory for Transition Research. All rights reserved.