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Nicholas Fang

dblp:96/3462 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Usability and user experience research · 77% User interface design and tools · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
User interface design and tools
web site design
0.012001
What makes Web sites credible?: a report on a large quantitative study · CHI 2001

Methods — techniques the papers use, named apart from their topics

quantitative study · 0.0factor analysis · 0.0
YearPublicationVenuePosition
2024 EMBOSR: Embodied Spatial Reasoning for Enhanced Situated Question Answering in 3D Scenes
abstract
3D Embodied Spatial Reasoning, emphasizing an agent’s interaction with its surroundings for spatial information inference, is adeptly facilitated by the process of Situated Question Answering in 3D Scenes (SQA3D). SQA3D requires an agent to comprehend its position and orientation within a 3D scene based on a textual situation and then utilize this understanding to answer questions about the surrounding environment in that context. Previous methods in this field face substantial challenges, including a dependency on constant retraining on limited datasets, which leads to poor performance in unseen scenarios, limited expandability, and inadequate generalization. To address these challenges, we present a new embodied spatial reasoning paradigm for enhanced SQA3D, fusing the capabilities of foundation models with the chain of thought methodology. This approach is designed to elevate adaptability and scalability in a wide array of 3D environments. A new aspect of our model is the integration of a chain of thought reasoning process, which significantly augments the model’s capability for spatial reasoning and complex query handling in diverse 3D environments. In our structured experiments, we compare our approach against other methods with varying architectures, demonstrating its efficacy in multiple tasks including SQA3D and 3D captioning. We also assess the informativeness contained in the generated answers for complex queries. Ablation studies further delineate the individual contributions of our method to its overall performance. The results consistently affirm our proposed method’s effectiveness and efficiency.
Nicholas Fang, Yu-Shen Liu
IROS3
2001 What makes Web sites credible?: a report on a large quantitative study
abstract
The credibility of web sites is becoming an increasingly important area to understand. To expand knowledge in this domain, we conducted an online study that investigated how different elements of Web sites affect people's perception of credibility. Over 1400 people participated in this study, both from the U.S. and Europe, evaluating 51 different Web site elements. The data showed which elements boost and which elements hurt perceptions of Web credibility. Through analysis we found these elements fell into one of seven factors. In order of impact, the five types of elements that increased credibility perceptions were “real-world feel”, “ease of use”, “expertise”, “trustworthiness”, and “tailoring”. The two types of elements that hurt credibility were “commercial implications&rdquo ;and “amateurism”. This large-scale study lays the groundwork for further research into the elements that affect Web credibility. The results also suggest implications for designing credible Web sites.
B. J. Fogg, Jonathan Marshall, Othman Laraki, Alex Osipovich, Chris Varma, Nicholas Fang, Jyoti Paul, Akshay Rangnekar, John Shon, Preeti Swani, Marissa Treinen
CHI6