Zobrazeno 1 - 10
of 252
pro vyhledávání: '"Kwan Hui"'
Publikováno v:
Frontiers in Education, Vol 8 (2023)
In recent years, eye-tracking (ET) methods have gained an increasing interest in STEM education research. When applied to engineering education, ET is particularly relevant for understanding some aspects of student behavior, especially student compet
Externí odkaz:
https://doaj.org/article/ffa42f35f5ce494cac8213d6ee73faa3
Autor:
Junhua Liu, Yung Chuen Ng, Zitong Gui, Trisha Singhal, Lucienne T. M. Blessing, Kristin L. Wood, Kwan Hui Lim
Publikováno v:
Journal of Big Data, Vol 9, Iss 1, Pp 1-16 (2022)
Abstract Occupational data mining and analysis is an important task in understanding today’s industry and job market. Various machine learning techniques are proposed and gradually deployed to improve companies’ operations for upstream tasks, suc
Externí odkaz:
https://doaj.org/article/4c0efd1178a24f74a6ad8db3eda14365
Publikováno v:
Journal of Big Data, Vol 9, Iss 1, Pp 1-38 (2022)
Abstract Online social networking services like Twitter are frequently used for discussions on numerous topics of interest, which range from mainstream and popular topics (e.g., music and movies) to niche and specialized topics (e.g., politics). Due
Externí odkaz:
https://doaj.org/article/070fc6c246f441739683e31f18679814
Publikováno v:
Journal of Big Data, Vol 8, Iss 1, Pp 1-28 (2021)
Abstract A key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or break
Externí odkaz:
https://doaj.org/article/b0de21dea8bb4a099237839017973a61
Autor:
Li, Menglin, Lim, Kwan Hui
The Financial Relation Extraction (FinRE) task involves identifying the entities and their relation, given a piece of financial statement/text. To solve this FinRE problem, we propose a simple but effective strategy that improves the performance of p
Externí odkaz:
http://arxiv.org/abs/2405.06665
Autor:
Mu, Wenchuan, Lim, Kwan Hui
In deep learning applications, robustness measures the ability of neural models that handle slight changes in input data, which could lead to potential safety hazards, especially in safety-critical applications. Pre-deployment assessment of model rob
Externí odkaz:
http://arxiv.org/abs/2404.16457
Autor:
Mu, Wenchuan, Lim, Kwan Hui
In today's data and information-rich world, summarization techniques are essential in harnessing vast text to extract key information and enhance decision-making and efficiency. In particular, topic-focused summarization is important due to its abili
Externí odkaz:
http://arxiv.org/abs/2404.16411
Autor:
Li, Menglin, Lim, Kwan Hui
To address the challenges of scarcity in geotagged data for social user geolocation, we propose FewUser, a novel framework for Few-shot social User geolocation. We incorporate a contrastive learning strategy between users and locations to improve geo
Externí odkaz:
http://arxiv.org/abs/2404.08662
Multi-turn intent classification is notably challenging due to the complexity and evolving nature of conversational contexts. This paper introduces LARA, a Linguistic-Adaptive Retrieval-Augmentation framework to enhance accuracy in multi-turn classif
Externí odkaz:
http://arxiv.org/abs/2403.16504
Resource allocation in tactical ad-hoc networks presents unique challenges due to their dynamic and multi-hop nature. Accurate prediction of future network connectivity is essential for effective resource allocation in such environments. In this pape
Externí odkaz:
http://arxiv.org/abs/2403.13872