Integrating Authentication Schemes In Augmented And Virtual Reality Classrooms,
2024
University of Denver
Integrating Authentication Schemes In Augmented And Virtual Reality Classrooms, Naheem Noah
Electronic Theses and Dissertations
Augmented Reality and Virtual Reality (AR/VR) technologies are revolutionizing educational experiences, but their widespread adoption hinges on addressing critical security and usability challenges, particularly in the domain of user authentication. This research presents an investigation into the security landscape of AR/VR and explores a graphical authentication scheme called “Things” that enhances both security and usability in immersive learning environments. Through a systematic evaluation of popular AR/VR devices and applications, potential vulnerabilities and limitations were identified, such as high usage of pin/passwords which are susceptible to shoulder-surfing attacks, lack of multi-factor authentication, and unclear data-sharing practices. A review of existing knowledge-based …
Changes In Near-Field Perception And Reaching Behavior In Virtual Environments Over Time,
2024
Clemson University
Changes In Near-Field Perception And Reaching Behavior In Virtual Environments Over Time, Kristopher C. Kohm
All Dissertations
Near-field perception and reaching capabilities are fundamental for most interactions in immersive virtual environments (IVEs). To perform actions in IVEs accurately and efficiently, virtual reality (VR) users need to be able to adapt to changes in their perception. Some of these perceptual differences may be inherent to virtual environments, such as the difference in depth perception between the virtual and non-virtual worlds. Others may be deliberate alterations to the user's action capabilities or to their surroundings to make interactions easier. Both the alterations and the user's ability to adjust to them may change over time as they gain experience in …
Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study,
2024
Clemson University
Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study, Samuel Nelson Koscelny
All Theses
High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the …
Exploring A Multimodal Fusion-Based Deep Learning Network For Detecting Facial Palsy,
2024
Singapore Management University
Exploring A Multimodal Fusion-Based Deep Learning Network For Detecting Facial Palsy, Heng Yim Nicole Oo, Min Hun Lee, J. H. Lim
Research Collection School Of Computing and Information Systems
Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessment by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes unstructured data (i.e. an image frame with facial line segments) and structured data (i.e. features of facial expressions) to detect facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of 21 facial palsy patients. Our experimental results show that among various data modalities (i.e. unstructured data - RGB images …
How To Avoid Jumping To Conclusions: Measuring The Robustness Of Outstanding Facts In Knowledge Graphs,
2024
Singapore Management University
How To Avoid Jumping To Conclusions: Measuring The Robustness Of Outstanding Facts In Knowledge Graphs, Hanhua Xiao, Yuchen Li, Yanhao Wang, Panagiotis Karras, Kyriakos Mouratidis, Natalia Rozalia Avlona
Research Collection School Of Computing and Information Systems
An outstanding fact (OF) is a striking claim by which some entities stand out from their peers on someattribute. OFs serve data journalism, fact checking, and recommendation. However, one could jump to conclusions by selecting truthful OFs while intentionally or inadvertently ignoring lateral contexts and data that render them less striking. This jumping conclusion bias from unstable OFs may disorient the public, including voters and consumers, raising concerns about fairness and transparency in political and business competition. It is thus ethically imperative for several stakeholders to measure the robustness of OFs with respect to lateral contexts and data. Unfortunately, a …
Unifying Global-Local Representations In Salient Object Detection With Transformers,
2024
Singapore Management University
Unifying Global-Local Representations In Salient Object Detection With Transformers, Sucheng Ren, Nanxuan Zhao, Qiang Wen, Guoqiang Han, Shengfeng He
Research Collection School Of Computing and Information Systems
The fully convolutional network (FCN) has dominated salient object detection for a long period. However, the locality of CNN requires the model deep enough to have a global receptive field and such a deep model always leads to the loss of local details. In this paper, we introduce a new attention-based encoder, vision transformer, into salient object detection to ensure the globalization of the representations from shallow to deep layers. With the global view in very shallow layers, the transformer encoder preserves more local representations to recover the spatial details in final saliency maps. Besides, as each layer can capture …
We Train Ai, Why Not Humans, Too? An Exploration Of Human-Ai Team Training For Future Workplace Viability,
2024
Clemson University
We Train Ai, Why Not Humans, Too? An Exploration Of Human-Ai Team Training For Future Workplace Viability, Caitlin M. Lancaster
All Dissertations
The integration of Artificial Intelligence (AI) in the workforce is transforming team dynamics, leading to the emergence of Human-AI Teams (HATs). These teams offer opportunities to capitalize on human strengths with AI's prowess, offering significant opportunities for innovation and efficiency. Effective HAT functioning requires aligning human expectations with AI capabilities and bridging knowledge gaps between teammates. Despite this potential, key integration challenges remain, such as developing shared mental models, addressing skill limitations, and overcoming negative AI perceptions. Existing training efforts often apply human-human teaming principles directly to HATs, overlooking AI's role as a teammate and limiting the development of HAT-specific …
G2face: High-Fidelity Reversible Face Anonymization Via Generative And Geometric Priors,
2024
South China University of Technology
G2face: High-Fidelity Reversible Face Anonymization Via Generative And Geometric Priors, Haoxin Yang, Xuemiao Xu, Cheng Xu, Huaidong Zhang, Jing Qin, Yi Wang, Pheng-Ann Heng, Shengfeng He
Research Collection School Of Computing and Information Systems
Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G 2 Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face …
Nonfactoid Question Answering As Query-Focused Summarization With Graph-Enhanced Multihop Inference,
2024
Singapore Management University
Nonfactoid Question Answering As Query-Focused Summarization With Graph-Enhanced Multihop Inference, Yang Deng, Wenxuan Zhang, Weiwen Xu, Ying Shen, Wai Lam
Research Collection School Of Computing and Information Systems
Nonfactoid question answering (QA) is one of the most extensive yet challenging applications and research areas in natural language processing (NLP). Existing methods fall short of handling the long-distance and complex semantic relations between the question and the document sentences. In this work, we propose a novel query-focused summarization method, namely a graph-enhanced multihop query-focused summarizer (GMQS), to tackle the nonfactoid QA problem. Specifically, we leverage graph-enhanced reasoning techniques to elaborate the multihop inference process in nonfactoid QA. Three types of graphs with different semantic relations, namely semantic relevance, topical coherence, and coreference linking, are constructed for explicitly capturing the …
Heterogeneous Graph Transformer With Poly-Tokenization,
2024
Singapore Management University
Heterogeneous Graph Transformer With Poly-Tokenization, Zhiyuan Lu, Yuan Fang, Cheng Yang, Chuan Shi
Research Collection School Of Computing and Information Systems
Graph neural networks have shown widespread success for learning on graphs, but they still face fundamental drawbacks, such as limited expressive power, over-smoothing, and over-squashing. Meanwhile, the transformer architecture offers a potential solution to these issues. However, existing graph transformers primarily cater to homogeneous graphs and are unable to model the intricate semantics of heterogeneous graphs. Moreover, unlike small molecular graphs where the entire graph can be considered as the receptive field in graph transformers, real-world heterogeneous graphs comprise a significantly larger number of nodes and cannot be entirely treated as such. Consequently, existing graph transformers struggle to capture the …
Empathyear : An Open-Source Avatar Multimodal Empathetic Chatbot,
2024
Singapore Management University
Empathyear : An Open-Source Avatar Multimodal Empathetic Chatbot, Hao Fei, Han Zhang, Bin Wang, Lizi Liao, Qian Liu, Erik Cambria
Research Collection School Of Computing and Information Systems
This paper introduces EmpathyEar, a pioneering open-source, avatar-based multimodal empathetic chatbot, to fill the gap in traditional text-only empathetic response generation (ERG) systems. Leveraging the advancements of a large language model, combined with multimodal encoders and generators, EmpathyEar supports user inputs in any combination of text, sound, and vision, and produces multimodal empathetic responses, offering users, not just textual responses but also digital avatars with talking faces and synchronized speeches. A series of emotion-aware instruction-tuning is performed for comprehensive emotional understanding and generation capabilities. In this way, EmpathyEar provides users with responses that achieve a deeper emotional resonance, closely emulating …
Human Centered Approaches And Taxonomies For Explainable Artificial Intelligence,
2024
Technological University Dublin
Human Centered Approaches And Taxonomies For Explainable Artificial Intelligence, Helen Sheridan, Emma Murphy, Dympna O'Sullivan
Conference papers
Recent interest within the research community related to explainable artificial intelligence (XAI) has led to a profuse amount of literature on the subject. Those who wish to tackle the domain from an HCI focus may be presented with overwhelming material, most of which does not pertain to human aspects of XAI. Taxonomies can serve to categorize a subject into topic areas and distill content into an overview of the field. This late breaking work intends to help those within the HCI community with a focus on XAI to understand relevant aspects of human centered XAI. We also present a taxonomy …
Creating And Delivering Audio Descriptions For Videos,
2024
Singapore Management University
Creating And Delivering Audio Descriptions For Videos, Rosiana Natalie
Dissertations and Theses Collection (Open Access)
Despite anti-discrimination regulations mandating the provision of audio descriptions (ADs), the majority of online video content remains inaccessible to blind and low-vision (BLV) individuals. This is because these ADs are either absent or fail to adequately address the diverse and unique needs of the audience. Traditionally, content creators have relied on professionals to author ADs. However, this gold standard may not be accessible for some content creators because this method is still costly and has a long turnaround time. Moreover, when ADs are available, they tend to be static and unalterable, failing to cater to the unique preferences of BLV …
My Ai Companion: An Examination Of The Removal Of Erotic Role Play From Replika Through User Discussion On Reddit,
2024
University of Nebraska-Lincoln
My Ai Companion: An Examination Of The Removal Of Erotic Role Play From Replika Through User Discussion On Reddit, Chelsee M. Allen
Department of Sociology: Dissertations, Theses, and Student Research
The development of artificial intelligence (AI) software has expanded rapidly in recent years, and thus has emerged the importance of exploring human relationships with AI chatbots. Replika, an app which uses AI to mimic human conversation, removed a function called Erotic Role Play (ERP) that allowed for sexual conversation with users’ customizable chatbots in February of 2023. This exploratory qualitative study examines the aftermath of ERP’s removal through an analysis of user interactions on Reddit. Five overarching themes emerged through the analysis of top posts to a Replika-specific subreddit, encompassing topics around mental health, stigma, coping, sex work and gendered …
An Exploratory Study Of Conventional Machine Learning And Large Language Models For Sentiment Analysis,
2024
Singapore Management University
An Exploratory Study Of Conventional Machine Learning And Large Language Models For Sentiment Analysis, Cui Zou, Jingyuan Cai, Langtao Chen, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Sentiment analysis is the use of natural language processing to identify affective states and determine people’s opinions in various analytical applications such as customer reviews and social media analyses. Large language models (LLMs) such as GPT-4o demonstrate impressive performance in text generation tasks. Despite numerous studies in the extant literature, few have compared the performance of conventional machine learning models with LLMs for sentiment analysis. This study aims to fill this gap by conducting an evaluation of these models using a balanced dataset of 2,000 IMDb movie reviews. Our study shows that GPT-4o achieves the highest performance, while GPT-3.5 and …
A Computational Aesthetic Design Science Study On Online Video Based On Triple-Dimensional Multimodal Analysis,
2024
Singapore Management University
A Computational Aesthetic Design Science Study On Online Video Based On Triple-Dimensional Multimodal Analysis, Zhangguang Kang, Fiona Fui-Hoon Nah, Keng Siau
Research Collection School Of Computing and Information Systems
Computational video aesthetic prediction refers to using models that automatically evaluate the features of videos to produce their aesthetic scores. Current video aesthetic prediction models are designed based on bimodal frameworks. To address their limitations, we developed the Triple-Dimensional Multimodal Temporal Video Aesthetic neural network (TMTVA-net) model. The Long Short-Term Memory (LSTM) forms the conceptual foundation for the design framework. In the multimodal transformer layer, we employed two distinct transformers: the multimodal transformer and the feature transformer, enabling the acquisition of modality-specific patterns and representational features uniquely adapted to each modality. The fusion layer has also been redesigned to compute …
Understanding And Fighting Scams: Media, Language, Appeals And Effects,
2024
City University of Hong Kong
Understanding And Fighting Scams: Media, Language, Appeals And Effects, Shuhua Zhou, Xiao Fan Liu, Fiona Fui-Hoon Nah, S. Harrison, X. Zhang, S. Zhen, D. Yeung, J. Hsiao, R. Lc, A. Chan, X. Wang, C. Jiang, F. Lin, J. Li, A. Wong, L. Chan, B. George, P. Li
Research Collection School Of Computing and Information Systems
Scams are fraudulent activities aiming to deceive individuals into relinquishing money, property, or rights, and they have proliferated in the context of widespread misinformation and disinformation. In this paper, we propose strategies and a research plan to address key questions about the exploitation of new communication technologies by scammers, the prevalence and nature of different scam types, and the language characteristics and appeals used in scamming content. We aim to develop a comprehensive taxonomy of scams and identify factors that contribute to their persuasiveness. Additionally, we propose the use of advanced technologies, including artificial intelligence, physiological measures, and brain mapping, …
Certified Robust Accuracy Of Neural Networks Are Bounded Due To Bayes Errors,
2024
Singapore Management University
Certified Robust Accuracy Of Neural Networks Are Bounded Due To Bayes Errors, Ruihan Zhang, Jun Sun
Research Collection School Of Computing and Information Systems
Adversarial examples pose a security threat to many critical systems built on neural networks. While certified training improves robustness, it also decreases accuracy noticeably. Despite various proposals for addressing this issue, the significant accuracy drop remains. More importantly, it is not clear whether there is a certain fundamental limit on achieving robustness whilst maintaining accuracy. In this work, we offer a novel perspective based on Bayes errors. By adopting Bayes error to robustness analysis, we investigate the limit of certified robust accuracy, taking into account data distribution uncertainties. We first show that the accuracy inevitably decreases in the pursuit of …
Jigsaw: Edge-Based Streaming Perception Over Spatially Overlapped Multi-Camera Deployments,
2024
Singapore Management University
Jigsaw: Edge-Based Streaming Perception Over Spatially Overlapped Multi-Camera Deployments, Ila Gokarn, Yigong Hu, Tarek Abdelzaher, Archan Misra
Research Collection School Of Computing and Information Systems
We present JIGSAW, a novel system that performs edge-based streaming perception over multiple video streams, while additionally factoring in the redundancy offered by the spatial overlap often exhibited in urban, multi-camera deployments. To assure high streaming throughput, JIGSAW extracts and spatially multiplexes multiple regions-of-interest from different camera frames into a smaller canvas frame. Moreover, to ensure that perception stays abreast of evolving object kinematics, JIGSAW includes a utility-based weighted scheduler to preferentially prioritize and even skip object-specific tiles extracted from an incoming stream of camera frames. Using the CityflowV2 traffic surveillance dataset, we show that JIGSAW can simultaneously process 25 …
Learning Topological Representations With Bidirectional Graph Attention Network For Solving Job Shop Scheduling Problem,
2024
Singapore Management University
Learning Topological Representations With Bidirectional Graph Attention Network For Solving Job Shop Scheduling Problem, Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun
Research Collection School Of Computing and Information Systems
Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs). This paper proposes the topology-aware bidirectional graph attention network (TBGAT), a novel GNN architecture based on the attention mechanism, to embed the DG for solving JSSP in a local search framework. Specifically, TBGAT embeds the DG from a forward and a backward view, respectively, where the messages are propagated by following the different topologies of the views and aggregated via graph attention. Then, we propose a novel operator based …
