This paper presents a time-topic cohesive model describing the communication patterns on the coronavirus pandemic from three Asian countries.
Unsupervised image classification is a challenging computer vision task.
Ranked #3 on Unsupervised Image Classification on CIFAR-10 (using extra training data)
To predict each player's market value, we propose an improved LightGBM model by optimizing its hyperparameter using a Tree-structured Parzen Estimator (TPE) algorithm.
Furthermore, we argue that XAI could result in incorrect attributions of responsibility to vulnerable stakeholders, such as those who are subjected to algorithmic decisions (i. e., patients), due to a misguided perception that they have control over explainable algorithms.
The task of assigning and validating internationally accepted commodity code (HS code) to traded goods is one of the critical functions at the customs office.
Trigger set-based watermarking schemes have gained emerging attention as they provide a means to prove ownership for deep neural network model owners.
Anomaly detection aims at identifying deviant instances from the normal data distribution.
How to attribute responsibility for autonomous artificial intelligence (AI) systems' actions has been widely debated across the humanities and social science disciplines.
There is a growing need for data-driven research efforts on how the public perceives the ethical, moral, and legal issues of autonomous AI systems.
Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results.
Ranked #1 on Unsupervised Image Classification on STL-10
We study the human-in-the-loop customs inspection scenario, where an AI-assisted algorithm supports customs officers by recommending a set of imported goods to be inspected.
Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes.
Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities."
This finding calls for a need to analyze the public discourse by new measures, such as topical dynamics.
Social and Information Networks
This study presents survey results of the public's willingness to get vaccinated against COVID-19 during an early phase of the pandemic and examines factors that could influence vaccine acceptance based on a between-subjects design.
The results show that exposing academic degrees is likely to lead to higher audience votes as well as larger discussion size, compared to the users without the disclosed identities, in a community that covers peer-reviewed scientific articles.
Social and Information Networks
In digital environments where substantial amounts of information are shared online, news headlines play essential roles in the selection and diffusion of news articles.
Unsupervised embedding learning aims to extract good representation from data without the need for any manual labels, which has been a critical challenge in many supervised learning tasks.
Satellite imagery has long been an attractive data source that provides a wealth of information on human-inhabited areas.
Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume.
Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs).
Online social networks have become a major communication platform, where people share their thoughts and opinions about any topic real-time.
Social and Information Networks Physics and Society