Search Results for author:
Found 41 papers, 18 papers with code
PANAS-t: A Pychometric Scale for Measuring Sentiments on Twitter
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
Comparing and Combining Sentiment Analysis Methods
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).
Fashion Conversation Data on Instagram
The fashion industry is establishing its presence on a number of visual-centric social media like Instagram.
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder
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.
The Fallacy of Echo Chambers: Analyzing the Political Slants of User-Generated News Comments in Korean Media
This study analyzes the political slants of user comments on Korean partisan media.
Lightweight and Robust Representation of Economic Scales from Satellite Imagery
Satellite imagery has long been an attractive data source that provides a wealth of information on human-inhabited areas.
A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm
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.
The Conflict Between People's Urge to Punish AI and Legal Systems
Regulating artificial intelligence (AI) has become necessary in light of its deployment in high-risk scenarios.
BaitWatcher: A lightweight web interface for the detection of incongruent news headlines
In digital environments where substantial amounts of information are shared online, news headlines play essential roles in the selection and diffusion of news articles.
"Trust me, I have a Ph.D.": A Propensity Score Analysis on the Halo Effect of Disclosing One's Offline Social Status in Online Communities
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
Responsible AI and Its Stakeholders
Responsible Artificial Intelligence (AI) proposes a framework that holds all stakeholders involved in the development of AI to be responsible for their systems.
COVID-19 Vaccine Acceptance in the US and UK in the Early Phase of the Pandemic: AI-Generated Vaccines Hesitancy for Minors, and the Role of Governments
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.
Risk Communication in Asian Countries: COVID-19 Discourse on Twitter
This finding calls for a need to analyze the public discourse by new measures, such as topical dynamics.
Social and Information Networks
Collecting the Public Perception of AI and Robot Rights
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."
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection
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.
Active Learning for Human-in-the-Loop Customs Inspection
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.
Improving Unsupervised Image Clustering With Robust Learning
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
Image Clustering
on CIFAR-100
(Train Set metric, using extra
training data)
Descriptive AI Ethics: Collecting and Understanding the Public Opinion
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.
Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making
How to attribute responsibility for autonomous artificial intelligence (AI) systems' actions has been widely debated across the humanities and social science disciplines.
Elsa: Energy-based learning for semi-supervised anomaly detection
Anomaly detection aims at identifying deviant instances from the normal data distribution.
Evaluating the Robustness of Trigger Set-Based Watermarks Embedded in Deep Neural Networks
Trigger set-based watermarking schemes have gained emerging attention as they provide a means to prove ownership for deep neural network model owners.
Classification of Goods Using Text Descriptions With Sentences Retrieval
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.
Knowledge Sharing via Domain Adaptation in Customs Fraud Detection
Knowledge of the changing traffic is critical in risk management.
The Conflict Between Explainable and Accountable Decision-Making Algorithms
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.
Prediction of Football Player Value using Bayesian Ensemble Approach
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.
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
This paper presents FedX, an unsupervised federated learning framework.
Self-explaining deep models with logic rule reasoning
We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision.
Detecting Contextomized Quotes in News Headlines by Contrastive Learning
Quotes are critical for establishing credibility in news articles.
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications.
Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harm
Building upon research suggesting that people blame AI systems, we investigated how several factors influence people's reactive attitudes towards machines, designers, and users.
GraphFC: Customs Fraud Detection with Label Scarcity
Custom officials across the world encounter huge volumes of transactions.
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration
The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising.
FedDefender: Client-Side Attack-Tolerant Federated Learning
Evaluations of real-world scenarios across multiple datasets show that the proposed method enhances the robustness of federated learning against model poisoning attacks.
Towards Attack-tolerant Federated Learning via Critical Parameter Analysis
Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other.
Factuality Challenges in the Era of Large Language Models
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention.
Explainable Product Classification for Customs
The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices.
Self Supervised Vision for Climate Downscaling
The improved downscaling performance and no dependence on high-resolution ground truth data make the proposed method a valuable tool for climate research and mark it as a promising direction for future research.
I Am Not Them: Fluid Identities and Persistent Out-group Bias in Large Language Models
The results indicate that when imbued with a particular social identity, ChatGPT discerns in-group and out-group, embracing in-group values while eschewing out-group values.
FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning
Federated learning combines local updates from clients to produce a global model, which is susceptible to poisoning attacks.
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification
Unsupervised image classification is a challenging computer vision task.
Ranked #1 on
Image Clustering
on STL-10
(Backbone metric, using extra
training data)
A Risk Communication Event Detection Model via Contrastive Learning
This paper presents a time-topic cohesive model describing the communication patterns on the coronavirus pandemic from three Asian countries.
