Physical Sciences and Mathematics Commons^™

Self-Chats From Large Language Models Make Small Emotional Support Chatbot Better, Zhonghua Zheng, Lizi Liao, Yang Deng, Libo Qin, Liqiang Nie

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations. However, deploying such LLMs like GPT-3 (175B parameters) is resource-intensive and challenging at scale. In this study, we utilize LLMs as “Counseling Teacher” to enhance smaller models’ emotion support response abilities, significantly reducing the necessity of scaling up model size. To this end, we first introduce an iterative expansion framework, aiming to prompt the large teacher model to curate an expansive emotion support dialogue dataset. This curated dataset, termed ExTES, encompasses a broad spectrum of scenarios and is crafted with meticulous strategies …

Incorporating Ai-Assisted Sensing Into The Metaverse: Opportunities For Interactions, Esports, And Security Enhancement, Yi Wu

Doctoral Dissertations

With the rapid growth and development of Virtual Reality (VR) and Augmented Reality (AR), extensive research has been carried out in the domain of the Metaverse, including immersive gaming, human-computer interaction, eSports, and the associated security & privacy concerns.

My research explores the potential of incorporating Artificial Intelligence (AI)-assisted sensing technologies to facilitate a more immersive, convenient, authentic, and secure virtual experience. This dissertation mainly focus on the following topics: (1) how to perform facial expression tracking to improve the users' awareness in the Metaverse; (2) fitness tracking for immersive eCycling; (3) running gait analysis for immersive indoor running, and …

Codont5: A Multi-Task Codon Language Model For Species-To-Species Translation, Ashley N. Babjac

Doctoral Dissertations

DNA (DeoxyriboNucleic Acid) carries the genetic information for the biological processes and function of all organisms. It is composed of nucleotides, which can be grouped into 3-mer triplets called codons. It is well known that codons encoding the same amino acid, referred to as "synonymous" codons, are selected with differing frequencies between organisms. Prior research has revealed there are codons used with much higher frequency than others, causing to them being "preferred" in highly expressed genes. This has led to the development of multiple computational models that do a good job predicting gene expression in some protein-coding genes; however, their …

Understanding Traits To Support Crowdworkers' Flexibility, Senjuti Dutta

Doctoral Dissertations

Crowdworkers are drawn to the profession in part due to the flexibility it affords. However, the current design of crowdsourcing platforms limits this flexibility. Therefore, it is important to support the overall flexibility of crowdworkers. Incorporating a variety of device types in the workflow plays an important role in supporting the flexibility of crowdworkers, however each device type requires a tailored workflow. The standard workflow of crowdworkers consists of stages of work such as managing and completing tasks. I hypothesize that different devices will have unique traits for task completion and task management. Therefore in this dissertation, I explore what …

Enabling Reproducibility, Scalability, And Orchestration Of Scientific Workflows In Hpc And Cloud-Converged Infrastructure, Paula Fernanda Olaya

Doctoral Dissertations

Scientific communities across different domains increasingly run complex workflows for their scientific discovery. Scientists require that these workflows ensure robustness; where workflows must be reproducible, scale in performance; and exhibit trustworthiness in terms of the computational techniques, infrastructures, and people. However, as scientists leverage advanced techniques (big data analytics, AI, and ML) and infrastructure (HPC and cloud), their workflows grow in complexity, leading to new challenges in scientific computing; hindering robustness.

In this dissertation, we address the needs of diverse scientific communities across different fields to identify three main challenges that hinder the robustness of workflows: (i) lack of traceability, …

General Relativistic Gravity In Core-Collapse Supernova Simulations, James Nicholas Roberts Ii

Doctoral Dissertations

Core-collapse supernovae (CCSNe) are some of the most extreme and complex phenomena in the universe. The toolkit for high-order neutrino-radiation hydrodynamics (thornado) is being developed to simulate CCSNe which will provide insight into the mechanisms underlying these events. The thornado framework is a collection of modules used to calculate the effects of gravity, hydrodynamics, neutrino transport, and nuclear physics through the Weaklib equation of state table. This dissertation will present the development of the Poseidon code, which provides the general relativistic gravity solver for the thornado framework.

The Poseidon code solves for the general relativistic metric using the xCFC formulation …

Data Collector Selection Ranking-Based Method For Collaborative Multi-Tasks In Ubiquitous Environments, Belal Z. Hassan, Ahmed. A. A. Gad-Elrab, Mohamed S. Farag, S. E. Abu-Youssef

Al-Azhar Bulletin of Science

In Ubiquitous Computing and the Internet of Things, the sensing and control of objects involve numerous devices collecting and transmitting data. However, connecting these devices without fostering collaboration leads to suboptimal system performance. As the number of connected sensing devices in Internet of Things increases, efficient task accomplishment through collaboration becomes imperative. This paper proposes a Data Collector Selection Method for Collaborative Multi-Tasks to address this challenge, considering task preferences and uncertainty in data collectors' contributions. The proposed method incorporates three key aspects: (1) Using Fuzzy Analytical Hierarchy Process to determine optimal weights for task preferences; (2) Ranking data collectors …

Anopas: Practical Anonymous Transit Pass From Group Signatures With Time-Bound Keys, Rui Shi, Yang Yang, Yingjiu Li, Huamin Feng, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

An anonymous transit pass system allows passengers to access transport services within fixed time periods, with their privileges automatically deactivating upon time expiration. Although existing transit pass systems are deployable on powerful devices like PCs, their adaptation to more user-friendly devices, such as mobile phones with smart cards, is inefficient due to their reliance on heavy-weight operations like bilinear maps. In this paper, we introduce an innovative anonymous transit pass system, dubbed Anopas, optimized for deployment on mobile phones with smart cards, where the smart card is responsible for crucial lightweight operations and the mobile phone handles key-independent and time-consuming …

Style: Improving Domain Transferability Of Asking Clarification Questions In Large Language Model Powered Conversational Agents, Yue Chen, Chen Huang, Yang Deng, Wenqiang Lei, Dingnan Jin, Jia Liu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Equipping a conversational search engine with strategies regarding when to ask clarification questions is becoming increasingly important across various domains. Attributing to the context understanding capability of LLMs and their access to domain-specific sources of knowledge, LLM-based clarification strategies feature rapid transfer to various domains in a posthoc manner. However, they still struggle to deliver promising performance on unseen domains, struggling to achieve effective domain transferability. We take the first step to investigate this issue and existing methods tend to produce one-size-fits-all strategies across diverse domains, limiting their search effectiveness. In response, we introduce a novel method, called STYLE, to …

Chain-Of-Exemplar: Enhancing Distractor Generation For Multimodal Educational Question Generation, Haohao Luo, Yang Deng, Ying Shen, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Multiple-choice questions (MCQs) are important in enhancing concept learning and student engagement for educational purposes. Despite the multimodal nature of educational content, current methods focus mainly on text-based inputs and often neglect the integration of visual information. In this work, we study the problem of multimodal educational question generation, which aims at generating subject-specific educational questions with plausible yet incorrect distractors based on multimodal educational content. To tackle this problem, we introduce a novel framework, named Chain-of-Exemplar (CoE), which utilizes multimodal large language models (MLLMs) with Chain-of-Thought reasoning to improve the generation of challenging distractors. Furthermore, CoE leverages three-stage contextualized …

On The Multi-Turn Instruction Following For Conversational Web Agents, Yang Deng, Xuan Zhang, Wenxuan Zhang, Yifei Yuan, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Web agents powered by Large Language Models (LLMs) have demonstrated remarkable abilities in planning and executing multi-step interactions within complex web-based environments, fulfilling a wide range of web navigation tasks. Despite these advancements, the potential for LLM-powered agents to effectively engage with sequential user instructions in real-world scenarios has not been fully explored. In this work, we introduce a new task of Conversational Web Navigation, which necessitates sophisticated interactions that span multiple turns with both the users and the environment, supported by a specially developed dataset named Multi-Turn Mind2Web (MT-Mind2Web). To tackle the limited context length of LLMs and the …

Watme: Towards Lossless Watermarking Through Lexical Redundancy, Liang Chen, Yatao Bian, Yang Deng, Deng Cai, Shuaiyi Li, Peilin Zhao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Text watermarking has emerged as a pivotal technique for identifying machine-generated text. However, existing methods often rely on arbitrary vocabulary partitioning during decoding to embed watermarks, which compromises the availability of suitable tokens and significantly degrades the quality of responses. This study assesses the impact of watermarking on different capabilities of large language models (LLMs) from a cognitive science lens. Our finding highlights a significant disparity; knowledge recall and logical reasoning are more adversely affected than language generation. These results suggest a more profound effect of watermarking on LLMs than previously understood. To address these challenges, we introduce Watermarking with …

A New Hope: Contextual Privacy Policies For Mobile Applications And An Approach Toward Automated Generation, Shidong Pan, Zhen Tao, Thong Hoang, Dawen Zhang, Tianshi Li, Zhenchang Xing, Xiwei Xu, Mark Staples, Thierry Rakotoarivelo, David Lo

Research Collection School Of Computing and Information Systems

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application’s graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SEEPRIVACY, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding …

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 …

Interpretable Tensor Fusion, Saurabh Varshneya, Antoine Ledent, Philipp Liznerski, Andriy Balinskyy, Purvanshi Mehta, Waleed Mustafa, Marius Kloft

Research Collection School Of Computing and Information Systems

Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type. However, practical applications may encompass data of diverse types, such as text, images, and audio. We introduce interpretable tensor fusion (InTense), a multimodal learning method training a neural network to simultaneously learn multiple data representations and their interpretable fusion. InTense can separately capture both linear combinations and multiplicative interactions of the data types, thereby disentangling higher-order interactions from the individual effects of each modality. InTense provides interpretability out of the box by assigning relevance scores to modalities and their associations, respectively. The approach is …

Path-Choice-Constrained Bus Bridging Design Under Urban Rail Transit Disruptions, Yiyang Zhu, Jian Gang Jin, Hai Wang

Research Collection School Of Computing and Information Systems

Although urban rail transit systems play a crucial role in urban mobility, they frequently suffer from unexpected disruptions due to power loss, severe weather, equipment failure, and other factors that cause significant disruptions in passenger travel and, in turn, socioeconomic losses. To alleviate the inconvenience of affected passengers, bus bridging services are often provided when rail service has been suspended. Prior research has yielded various methodologies for effective bus bridging services; however, they are mainly based on the strong assumption that passengers must follow predetermined bus bridging routes. Less attention is paid to passengers’ path choice behaviors, which could affect …

Advancing Telehealth Through Artificial Intelligence: Incorporating Emotional Intelligence And Addressing Cybersecurity Challenges, Mahima Rajendra Pulgaonkar

Electronic Theses, Projects, and Dissertations

This culminating experience project explores the integration of Emotional Artificial Intelligence (Emotional AI) into telehealth systems, addressing the dual challenges of enhancing patient care and mitigating cybersecurity risks. The research questions are: (Q1) How can Emotionally Intelligent AI improve telehealth systems' ability to recognize and respond to mental health symptoms? and (Q2) What are the specific cybersecurity challenges associated with AI in telehealth and how can they be mitigated? The findings for each question are: Q1: Emotionally Intelligent AI can significantly enhance telehealth by providing personalized, empathetic interactions that improve patient engagement, adherence to treatment plans, and early detection of …

Contemplating Existence: Ai And The Meaning Of Life, Emily Barnes, James Hutson

Faculty Scholarship

This article explores the intersection of artificial intelligence (AI) with existential philosophy, examining how AI technologies influence human conceptualizations of purpose and meaning. Despite rapid advancements in AI, the domain's implications for existential thought remain underexplored. By integrating interdisciplinary perspectives from psychology, philosophy, and AI ethics, this study elucidates how AI can shape, challenge, or enhance our understanding of life's purpose. It investigates theoretical frameworks and practical implementations of AI engaging in existential questions, analyzing both the capabilities and limitations of AI systems such as ChatGPT in simulating human existential thought. The ethical implications of AI's role in existential inquiries …

Improving Cyber Defense Using Detailed Bayesian Models Of Attacker Reconnaissance., Nazia Sharmin

Open Access Theses & Dissertations

The continued success of cyber-attacks motivates the need for continued innovation in cyber defense. In particular, there is a need for novel methods to mitigate attacker reconnaissance, usually the first stage in planning an attack. One of the few general approaches in this stage is using deception and information manipulation to affect what the attacker can learn about a system or network. Existing work in moving target defense, game-theoretic models of cyber deception/camouflage, and adversarial learning has provided a framework for optimizing deception strategies. However, most of the current literature is based on limited models of how attackers actually perform …

Neural Networks For Decisions Under Uncertainty, Edwin Tomy George

Open Access Theses & Dissertations

Neural networks are used in many real-world applications, ranging from classification tasks to medical diagnostics. For each task, a neural network is typically able to make predictions due to its ability to extract meaningful patterns from processing large amounts of data. Thus, given the increases in available data in recent decades, the performance of neural networks in making accurate predictions has greatly increased. However, this data often comes with ingrained uncertainties due to measurement errors or the inherent variability of individual data points. Neural networks can learn despite the errors in the overall data, but what if we want them …

Semantic Linguistic User Profiles For Automatic Computational Narrative Creation For Scientific Models, Angel Uriel Ortega Castillo

Open Access Theses & Dissertations

The outcomes of scientific models can be hard to understand given the need for context, domain knowledge, and many variables and data being used. This research aims to provide users of scientific models with understandable information that can be automatically generated. For the context of this research, understandable is defined as being correctly interpreted (i.e., with respect to the original intent of the data). Scientific information can be conveyed to users in the form of a narrative or visualizations, and these are not necessarily separate from each other, but rather complimentary. One of the objectives of this research is to …

Random Forest For High-Dimensional Data, George Ekow Quaye

Open Access Theses & Dissertations

The exponential growth of data has led to a rapid increase in high-dimensional datasets across various domains, presenting significant challenges in data analysis, particularly in predictive modeling tasks. Traditional Random Forest (RF), while robust, often struggles with datasets filled with numerous noisy or non-informative features, compromising both performance and accuracy. This study introduces an advanced algorithm, High-Dimensional Random Forests (HDRF), designed to address these challenges by integrating robust multivariate feature selection techniques directly into the decision tree construction process. Unlike standard RF, HDRF incorporates ridge regression-based variable screening at each decision split, enhancing its ability to identify and utilize the …

Factors Impacting Users’ Willingness To Adopt And Utilize The Metaverse In Education: A Systematic Review, Mousa Al-Kfairy, Soha Ahmed, Ashraf Khalil

All Works

Purpose: This study explores the factors influencing the adoption and acceptance of Metaverse technologies in educational settings. Despite the growing interest in immersive educational environments provided by the Metaverse, there is a lack of comprehensive understanding regarding the elements that affect user engagement and acceptance. This paper aims to bridge this gap through a systematic review of empirical studies that apply Information Systems theories such as TAM, UTAUT, TPB, and their extensions. Methods: A total of 35 empirical studies were analyzed using a methodical review approach. The research methodologies employed in these studies include surveys, structural equation modeling, and interviews, …

A Comprehensive Dataset For Arabic Word Sense Disambiguation, Sanaa Kaddoura, Reem Nassar

All Works

This data paper introduces a comprehensive dataset tailored for word sense disambiguation tasks, explicitly focusing on a hundred polysemous words frequently employed in Modern Standard Arabic. The dataset encompasses a diverse set of senses for each word, ranging from 3 to 8, resulting in 367 unique senses. Each word sense is accompanied by contextual sentences comprising ten sentence examples that feature the polysemous word in various contexts. The data collection resulted in a dataset of 3670 samples. Significantly, the dataset is in Arabic, which is known for its rich morphology, complex syntax, and extensive polysemy. The data was meticulously collected …

Tool-Sensed Object Information Effectively Supports Vision For Multisensory Grasping, Ivan Camponogara, Alessandro Farnè, Robert Volcic

All Works

Tools enable humans to extend their sensing abilities beyond the natural limits of their hands, allowing them to sense objects as if they were using their hands directly. The similarities between direct hand interactions with objects (hand-based sensing) and the ability to extend sensory information processing beyond the hand (tool-mediated sensing) entail the existence of comparable processes for integrating tool- and hand-sensed information with vision, raising the question of whether tools support vision in bimanual object manipulations. Here, we investigated participants' performance while grasping objects either held with a tool or with their hand and compared these conditions with visually …

Koopman-Inspired Proximal Policy Optimization (Kippo), Andrei Cozma

Masters Theses

Reinforcement Learning (RL) has made significant strides in various domains, yet developing effective control policies for environments with complex, nonlinear dynamics remains a challenge, particularly for policy gradient methods. These methods often struggle due to high-variance in gradient estimates, non-convex optimization landscapes, and sample inefficiency, resulting in unstable learning, suboptimal policies, and trade-offs between performance and reproducibility. The quest for more robust, stable, and effective methods has led to numerous innovations and remains a critical area of research. Proximal Policy Optimization (PPO) has gained popularity in recent years due to its balance in performance, training stability, and computational efficiency. In …

Fishing Vessel Detection In Exclusive Economic Zones From Low Earth Orbit Satellites With Power And Computational Constraints, Kyler E. Nelson

All Graduate Theses and Dissertations, Fall 2023 to Present

Illegal fishing activities pose a significant threat to the sustainability of marine ecosystems and the economies and societies which rely on them. Detection of fishing vessels engaging in illegal activity is difficult, as many ships engaging in such activity actively avoid detection through radio systems used for maritime traffic monitoring. Satellite imagery provides a promising means for detecting fishing vessels, though designing an effective system is difficult due to limited availability of labeled image datasets of fishing vessels. This research proposes a system to detect illegal fishing activity through the use of a low-power ship detection satellite and proposes a …

Solving Large Job Shop Scheduling Problems: Using Graph Classification Via Graph Neural Networks To Pre-Seed A Genetic Algorithm For Machine Dispatching Rule Optimization, Isaac Schwab

Theses and Dissertations

The job shop scheduling problem is a difficult problem to solve, and it is also difficult to implement solutions found in research into real shops. In this research, a methodology is proposed to develop schedules for real shops. The methodology utilizes a genetic algorithm to select dispatching rules for each machine cell and accesses these schedules through a simulation optimization framework. The simulation framework allows for the study of random elements including variable job processing times and random machine breakdowns. This creates a robust schedule that is easy to understand, and therefore implement, while scaling to large, real-world job shops. …

Exploring The Integration Of Blockchain In Iot Use Cases: Challenges And Opportunities, Ivannah George

Electronic Theses, Projects, and Dissertations

Blockchain and The Internet of Things (IoT) is a significant paradigm which has gained traction in today’s digital age as two complimentary technologies. The combination of IoT's connectivity with blockchain's security creates new opportunities and solves problems associated with centralized systems. This culminating project aims to delve deeper into the integration of blockchain technology in IoT applications based on select use cases to uncover potential benefits and significant challenges of blockchain integration across different sectors. The research objectives to be addressed are: (RO1) How emerging vulnerabilities manifest in the implementation of blockchain within current IoT ecosystems. (RO2) How current opportunities …

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 …