Digital Commons Network^™

Exploring Ai Technology In Grammar Performance Testing For Children With Learning Disabilities, Dimitra V. Katsarou, Evangelos Mantsos, Soultana Papadopoulou, Maria Sofologi, Efthymia Efthymiou, Ilias Vasileiou, Kalliopi Megari, Maria Theodoratou, Georgios A. Kougioumtzis

All Works

The study explores the application of artificial intelligence (AI) in addressing grammar challenges among children with learning disabilities, aiming to assess the efficacy of an AI-driven tool for personalized interventions. A sample of 100 children aged 8–12, diagnosed with learning disabilities, was recruited from special education programs. Participants were divided into an experimental group (n = 50), which used an AI-based grammar assessment tool with personalized feedback, and a control group (n = 50), which completed conventional paper-based grammar tests without feedback. The AI tool administered adaptive grammar tasks, including sentence correction and verb conjugation, and performance was evaluated over …

Stimulating Environmental And Health Protection Through Utilizing Statistical Methods For Climate Resilience And Policy Integration, Sanaa Kaddoura, Rafiq Hijazi, Nadia Dahmani, Reem Nassar

All Works

Climate change, a critical global challenge, is evident in rising global temperatures, shifting precipitation trends, and extreme weather events, including floods, heatwaves, and rising sea levels. The impacts of climate change not only endanger physical health but also affect mental well-being, particularly among populations experiencing frequent or severe climate-related events. Understanding individual perceptions of climate risks and adaptive capacities is crucial for developing strategies that promote health resilience and environmental protection. This paper examines how risk perceptions, direct experiences with extreme weather, and perceived adaptive capacities influence climate change protection measures and support for relevant policies. Data were gathered from …

Quantifying The Role Of Active Listening And Reassurance In Virtual Health Coach Interactions, Ghulam Hussain, Brian Keegan, Robert Ross

Conference papers

Conversational Agents have the potential to support healthcare through coaching exercise routines, but are still lacking in demonstrating authentic social behaviours to support engagement. To this end, we present a series of experiments that we conducted in order to investigate how automated health care coaches can be more effective when their interaction style is tailored to demonstrate qualities associated with a good bedside manner, namely active listening and reassurance. To test this, we first developed a dataset of 135 dialogue excerpts from three distinct sources, i.e., original, handcrafted and LLMs, the latter two of which were tuned to demonstrate specific …

Pathways To Chronic Disease Detection And Prediction: Mapping The Potential Of Machine Learning To The Pathophysiological Processes While Navigating Ethical Challenges, Ebenezer Afrifa-Yamoah, Eric Adua, Emmanuel Peprah-Yamoah, Enoch O. Anto, Victor Opoku-Yamoah, Emmanuel Acheampong, Michael J. Macartney, Rashid Hashmi

Research outputs 2022 to 2026

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these “omics” studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for …

Forward-Secure Hierarchical Delegable Signature For Smart Homes, Jianfei Sun, Guowen Xu, Yang Yang, Xuehuan Yang, Xiaoguo Li, Cong Wu, Zhen Liu, Guomin Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Aiming to provide people with great convenience and comfort, smart home systems have been deployed in thousands of homes. In this paper, we focus on handling the security and privacy issues in such a promising system by customizing a new cryptographic primitive to provide the following security guarantees: (1) fine-grained, privacy-preserving authorization for smart home users and integrity protection of communication contents; (2) flexible self-sovereign permission delegation; (3) forward security of previous messages. To our knowledge, no previous system has been designed to consider these three security and privacy requirements simultaneously. To tackle these challenges, we put forward the first-ever …

Artificial Intelligence In Surgical Coding: Evaluating Large Language Models For Current Procedural Terminology Accuracy In Hand Surgery, Emily Isch, Jamie Lee, D. Mitchell Self, Abhijeet Sambangi, Theodore E. Habarth-Morales, John R. Vaile, E. J. Caterson

Department of Surgery Faculty Papers

PURPOSE: The advent of large language models (LLMs) like ChatGPT has introduced notable advancements in various surgical disciplines. These developments have led to an increased interest in the use of LLMs for Current Procedural Terminology (CPT) coding in surgery. With CPT coding being a complex and time-consuming process, often exacerbated by the scarcity of professional coders, there is a pressing need for innovative solutions to enhance coding efficiency and accuracy.

METHODS: This observational study evaluated the effectiveness of five publicly available large language models-Perplexity.AI, Bard, BingAI, ChatGPT 3.5, and ChatGPT 4.0-in accurately identifying CPT codes for hand surgery procedures. A …

Standardizing Canine Breed Data In Veterinary Records Is Challenging, But Computer Vision Offers An Alternative Perspective On Breed Assignment, Glenvelis Perez, Yixuan He, Zihan Lyu, Yilin Chen, Nicholas Howe, Halie M. Rando

Computer Science: Faculty Publications

Dog breed is fundamental health information, especially in the context of breed-linked diseases. The standard-ization of breed terminology across health records is necessary to leverage the big data revolution for veterinary research. Breed can also inform clinical decision making. However, client-reported breeds vary in their reliability depending on how breed was determined. Surprisingly, research in computer science reports that AI can assign breed to dogs with over 90% accuracy from a photograph. Here, we explore the extent to which current research in AI is relevant to breed assignment or validation in veterinary contexts. This review provides a primer on approaches …

Imageinthat: Manipulating Images To Convey User Instructions To Robots, Karthik Mahadevan, Blaine Lewis, Jiannan Li, Bilge Mutlu, Anthony Tang, Tovi Grossman

Research Collection School Of Computing and Information Systems

Foundation models are rapidly improving the capability of robots in performing everyday tasks autonomously such as meal preparation, yet robots will still need to be instructed by humans due to model performance, the difficulty of capturing user preferences, and the need for user agency. Robots can be instructed using various methods---natural language conveys immediate instructions but can be abstract or ambiguous, whereas end-user programming supports longer-horizon tasks but interfaces face difficulties in capturing user intent. In this work, we propose using direct manipulation of images as an alternative paradigm to instruct robots, and introduce a specific instantiation called ImageInThat which …

Selecting Comparative Sets Of Reviews Across Multiple Items, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

While choosing among several products, users may look up reviews from each product they are considering. Due to the large number of reviews of products, selecting representative reviews from one product alone is already a challenging problem. In this work, we further aim to conduct review selection for multiple products simultaneously for comparative purposes. We formulate objective functions that synchronize the review selection and design efficient algorithms to optimize for the objective functions. To narrow down the potentially long list of comparison items into a shorter list of more similar items, we construct a graph representing items’ similarity and design …

Varium: Variational Autoencoder For Multi-Interest Representation With Inter-User Memory, Nhu Thuat Tran, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Frameworks for discovering multiple user interest factors based on Variational AutoEncoder (VAE) has demonstrated competitive recommendation performance. However, as VAE only considers one user as input at a time, sharing across like-minded users may not be adequately facilitated. Moreover, interest sharing between users is not always available and thus, poses a challenge for VAE to explicitly model this information. To resolve this, we introduce an inter-user memory-based mechanism to unsupervisedly discover latent interest sharing between users under VAE framework. Concretely, we design a memory including an array of prototypes, each hypothetically representing a group of users sharing a particular interest. …

Lightprof: A Lightweight Reasoning Framework For Large Language Model On Knowledge Graph, Tu Ao, Yanhua Yu, Yuling Wang, Yang Deng, Zirui Guo, Liang Pang, Pinghui Wang, Tat-Seng Chua, Xiao Zhang, Zhen Cai

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning. However, delays in knowledge updates may cause them to reason incorrectly or produce harmful results. Knowledge Graphs (KGs) provide rich and reliable contextual information for the reasoning process of LLMs by structurally organizing and connecting a wide range of entities and relations. Existing KG-based LLM reasoning methods only inject KGs’ knowledge into prompts in a textual form, ignoring its structural information. Moreover, they mostly rely on close-source models or open-source models with large parameters, which poses challenges to high resource consumption. To address this, we propose a …

How To Securely Delegate And Revoke Partial Authorization Credentials, Meng Sun, Junzuo Lai, Wei Wu, Ye Yang, Cheng-Kang Chu, Robert H. Deng

Research Collection School Of Computing and Information Systems

An attribute-based credential (ABC) system allows a user, obtaining a credential on a set of attributes from an issuer, to anonymously prove a subset of attributes to a service provider. Nowadays, delegation is an important requirement of ABC, which allows a user to delegate his credentials to other users. However, traditional delegatable ABC systems only support delegating a credential with all attributes. In many scenarios, an appropriate delegation is a user can delegate his credential on parts of attributes to others. Another requirement is revocation of credentials in case of unexpected events. In this article, we propose a delegatable and …

Cachealarm: Monitoring Sensitive Behaviors Of Android Apps Using Cache Side Channel, Jianwen Tian, Haoyu Ma, Debin Gao, Xiaohui Kuang

Research Collection School Of Computing and Information Systems

Malware attack has been a serious threat to the security and privacy of both individual and corporation users of the Android platform. Business entities seek to protect themselves by means of monitoring privacy-related sensitive behaviors conducted on company-issued Android devices. However, due to Android’s own access control and privacy protection policies, this is difficult to be done with third-party apps using only normal privileges. Existing works proposed using side-channel readings from leaky APIs and system virtual files to speculate runtime app behaviors, which could be unreliable due to future system updates (that ban exploited resources), hardware jittering, etc. In this …

Privacy-Preserving Fine-Grained Data Sharing With Dynamic Service For The Cloud-Edge Iot, Jianfei Sun, Yangyang Bao, Weidong Qiu, Rongxing Lu, Songnian Zhang, Yunguo Guan, Xiaochun Cheng

Research Collection School Of Computing and Information Systems

The cloud-edge computing model has been expected to play a revolutionary role in promoting the quality of future generation large-scale Internet of Things (IoT) services. However, security and privacy in data sharing remain crucial issues hindering the success of cloud-edge IoT services. While some solutions based on attribute-based encryption (ABE) have been proposed to address these issues, they still face practical challenges such as attribute privacy leakage, resource-constrained devices, dynamic user groups, inflexible and inefficient service response. To address these challenges, this paper proposes a privacy-preserving fine-grained data sharing scheme with dynamic service (PF2DS), which implements access control by calculating …

Divide-And-Conquer: Confluent Triple-Flow Network For Rgb-T Salient Object Detection, Hao Tang, Zechao Li, Dong Zhang, Shengfeng He, Jinhui Tang

Research Collection School Of Computing and Information Systems

RGB-Thermal Salient Object Detection (RGB-T SOD) aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images. A key challenge lies in bridging the inherent disparities between RGB and Thermal modalities for effective saliency map prediction. Traditional encoder-decoder architectures, while designed for cross-modality feature interactions, may not have adequately considered the robustness against noise originating from defective modalities, thereby leading to suboptimal performance in complex scenarios. Inspired by hierarchical human visual systems, we propose the ConTriNet, a robust Confluent Triple-Flow Network employing a "Divide-and-Conquer"strategy. This framework utilizes a unified encoder with specialized decoders, each addressing different subtasks …

Enhancing Item‑Level Bundle Representation For Bundle Recommendation, Xiaoyu Du, Kun Qian, Yunshan Ma, Xinguang Xiang

Research Collection School Of Computing and Information Systems

Bundle recommendation approaches offer users a set of related items on a particular topic. The current state-of-the-art (SOTA) method utilizes contrastive learning to learn representations at both the bundle and item levels. However, due to the inherent difference between the bundle-level and item-level preferences, the item-level representations may not receive sufficient information from the bundle affiliations to make accurate predictions. In this article, we propose a novel approach, Enhanced Bundle Recommendation (EBRec), which incorporates two enhanced modules to explore inherent item-level bundle representations. First, we propose to incorporate the bundle-user-item (B-U-I) high-order correlations to explore more collaborative information, thus to …

Enhanced Sample Selection With Confidence Tracking: Identifying Correctly Labeled Yet Hard-To-Learn Samples In Noisy Data, Weiran Pan, Wei Wei, Feida Zhu, Yong Deng

Research Collection School Of Computing and Information Systems

We propose a novel sample selection method for image classification in the presence of noisy labels. Existing methods typically consider small-loss samples as correctly labeled. However, some correctly labeled samples are inherently difficult for the model to learn and can exhibit high loss similar to mislabeled samples in the early stages of training. Consequently, setting a threshold on per-sample loss to select correct labels results in a trade-off between precision and recall in sample selection: a lower threshold may miss many correctly labeled hard-to-learn samples (low recall), while a higher threshold may include many mislabeled samples (low precision). To address …

Evaluating Software Development Agents: Patch Patterns, Code Quality, And Issue Complexity In Real-World Github Scenarios, Zhi Chen, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with Software Development Agents representing the next major leap. These agents, capable of reasoning, planning, and interacting with external environments, offer promising solutions to complex software engineering tasks. However, while much research has evaluated code generated by large language models (LLMs), comprehensive studies on agent-generated patches, particularly in real-world settings, are lacking. This study addresses that gap by evaluating 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues from SWE-Bench Verified, focusing on their impact on code quality. Our analysis shows no single …

Learning To Identify Seen, Unseen And Unknown In The Open World: A Practical Setting For Zero-Shot Learning, Sethupathy Parameswaran, Yuan Fang, Chandan Gautam, Savitha Ramasamy, Xiaoli Li

Research Collection School Of Computing and Information Systems

As vision-language models advance, addressing the Zero-Shot Learning (ZSL) problem in the open world becomes increasingly crucial. Specifically, a robust model must handle three types of samples during inference: seen classes with visual and semantic information provided in training, unseen classes with only the semantic information in training, and unknown samples with no prior information from training. Existing methods either handle seen and unseen classes together (ZSL) or seen and unknown classes (known as Open-Set Recognition, OSR). However, none addresses the simultaneous handling of all three, which we term Open-Set Zero-Shot Learning (OZSL). To address this problem, we propose a …

Dr. Tongue: Sign-Oriented Multi-Label Detection For Remote Tongue Diagnosis, Yiliang Chen, Steven S. C. Ho, Cheng Xu, Yao Jie Xie, Wing Fai Yeung, Shengfeng He, Jing Qin

Research Collection School Of Computing and Information Systems

Tongue diagnosis is a vital tool in Western and Traditional Chinese Medicine, providing key insights into a patient's health by analyzing tongue attributes. The COVID-19 pandemic has heightened the need for accurate remote medical assessments, emphasizing the importance of precise tongue attribute recognition via telehealth. To address this, we propose a Sign-Oriented multi-label Attributes Detection framework. Our approach begins with an adaptive tongue feature extraction module that standardizes tongue images and mitigates environmental factors. This is followed by a Sign-oriented Network (SignNet) that identifies specific tongue attributes, emulating the diagnostic process of experienced practitioners and enabling comprehensive health evaluations. To …

A Review On The Use Of Immersive Technology In Space Research, Mohammad Amin Kuhail, Aymen Zekeria Abdulkerim, Erik Thornquist, Saron Yemane Haile

All Works

Immersive technologies, such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), create digital experiences by merging real and virtual worlds, offering enhanced spatial engagement and sensory immersion. This study examines immersive technologies’ possible advancements to space research, along with application examples in data visualization, astronaut training, and mission planning. Based on the analysis of 44 papers, immersive technologies can assist in diverse areas as varied as procedure guidance, astronaut training, and health-related aspects involving using devices such as HTC Vive, Microsoft HoloLens, and Oculus. The most critical challenges are, by far, difficulties in the selection of participants …

Identifying Human Factor Causes Of Remotely Piloted Aircraft System Safety Occurrences In Australia, John Murray, Steven Richardson, Keith Joiner, Graham Wild

Research outputs 2022 to 2026

Remotely piloted aircraft are a fast-emerging sector of the aviation industry. Although technical failures have been the largest cause of accident occurrences for Remotely Piloted Aircraft Systems (RPASs), if they are to follow the path of conventionally crewed aviation, Human Factors (HFs) will increasingly contribute to accidents as the technology of RPASs improves. Examining an RPAS accident database from 2008–2019 for HF-caused accidents and coding to the Human Factors Analysis and Classification System (HFACS) taxonomy, an exploration of RPAS HFs is carried out and the predominant HF issues for RPAS pilots identified. The majority of HF accidents were coded to …

Token Turing Machines Are Efficient Vision Models, Purvish Jajal, Nick Eliopoulos, Benjamin Shiue-Hal Chou, George K. Thiruvathukal, James C. Davis, Yung-Hsiang Lu

Computer Science: Faculty Publications and Other Works

We propose Vision Token Turing Machines (ViTTM), an efficient, low-latency, memory-augmented Vision Transformer (ViT). Our approach builds on Neural Turing Machines (NTM) and Token Turing Machines (TTM), which were applied to NLP and sequential visual understanding tasks. ViTTMs are designed for non-sequential computer vision tasks such as image classification and segmentation. Our model creates two sets of tokens: process tokens and memory tokens; process tokens pass through encoder blocks and read-write from memory tokens at each encoder block in the network, allowing them to store and retrieve information from memory. By ensuring that there are fewer process tokens, we are …

Provable Security In Idealised Models, Chandranan Dhar

Doctoral Theses

This thesis is a compilation of provable security analyses of various cryptographic constructions in idealised models. The first construction examined is the ABR hash. We revisit the existing proof of the ABR hash in the random oracle model and identify significant errors in the proof. Although we are unable to correct the original proof, we establish the security of the ABR tree of height 3 from scratch, addressing the first non-trivial case. As our second contribution, we conduct a tight and comprehensive security analysis of the Ascon AEAD mode in the random permutation model. We show that the efficiency of …

What's The Art In Artificial Intelligence?, Emily Verla Bovino

Open Educational Resources

This workbook learns from Black and Indigenous artists working with Artificial Intelligence to confront issues of ethics and aesthetics in its technologies. It features guided learning activities with links to publicly available video lectures and online articles, and includes options for experiential learning through both a tutorial in Midjourney and a visit to the public art collection at York College in Jamaica, Queens. Featured artists include: American Artist, Rizvana Bradley, Beth Coleman, Denise Ferreira da Silva, Suzanne Kite, Sondra Perry, Mimi Onuoha and Alisha B. Wormsley. Works by Martin Puryear and Maren Hassinger are explored in the Midjourney exercise.

About …

A Standardized Methodology For Evaluating A Digital Badging System [ Data Package ], Benjamin T. Pederson, Mark G. Reith, Ralucca Gera, David S. Long, Edward D. White, Jonathan Zemmer

Faculty Publications

Digital badges, a form of micro-credentials, have grown in popularity over the past decade. However, few standard processes exist to assess the potential of digital badging systems within an organization. This study proposes a generalizable methodology for comparing a badging system with other methods of recording skills and competencies. The experimental design is tested using the military's cyber operations community as the target organization. Finally, mixed-method data from thirty-six participants is analyzed in accordance with the methodology. Based on the results, digital badging systems are perceived to be more valuable and usable than a current method of military talent management. …

Stochastic Gradient Descent-Based Inference For Dynamic Network Models With Attractors, Hancong Pan, Xiaojing Zhu, Cantay Caliskan, Dino P. Christenson, Konstantinos Spiliopoulos, Dylan Walker, Eric D. Kolaczyk

Business Faculty Articles and Research

In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate their dynamic interactions. Attractors are added at the latent level to capture the notion of attractive and repulsive forces between nodes, borrowing from dynamical systems theory. However, CLSNA reliance on MCMC estimation makes scaling difficult, and the requirement for nodes to be present throughout the study period limit practical applications. We address these issues by (i) introducing a Stochastic gradient descent (SGD) parameter estimation method, (ii) developing a novel approach for uncertainty quantification using SGD, and (iii) extending the model …

Human-Ai Collaboration In Writing: A Multidimensional Framework For Creative And Intellectual Authorship, James Hutson

Faculty Scholarship

The integration of AI technologies into the writing process has significantly altered traditional notions of authorship, creativity, and intellectual labor. Historically, writing was seen as a human-driven cognitive and creative exercise, but with the rise of generative AI tools such as ChatGPT and Claude, the line between human and AI contributions has become increasingly ambiguous. This paper addresses the limitations of the current sliding scale model, which views AI involvement as ranging from “none” to “complete”. In its place, we propose a new multidimensional framework that more accurately reflects the complexity of human-AI collaboration in writing. The model includes axes …

Beyond The Blue Skies: A Comprehensive Guide For Risk Assessment In Aviation, Leila Halawi, Mark Miller, Sam Holley

Publications

Risk assessment in aviation is a critical process that safeguards the safety and reliability of operations. Aviation operations encompass inherent risks, from mechanical failures to human errors and environmental factors. The significance of these risks may be severe, leading to accidents, injuries, and loss of life. Recognizing and mitigating risks is supreme in this dynamic environment, where emerging technologies and innovation constantly reshape this industry. This chapter includes an in-depth explanation of risk management and analysis, leading to the core elements of risk assessment specifically for aviation operations. We will describe the process and explore some of the applications and …

A Review Of Racial Differences And Disparities In Ecg, Jianwei Zheng, Chizobam Ani, Islam Abudayyeh, Yunfan Zheng, Cyril Rakovski, Ehsan Yaghmaei, Omolola Ogunyemi

Mathematics, Physics, and Computer Science Faculty Articles and Research

The electrocardiogram (ECG) is a widely used, non-invasive tool for diagnosing a range of cardiovascular conditions, including arrhythmia and heart disease-related structural changes. Despite its critical role in clinical care, racial and ethnic differences in ECG readings are often underexplored or inadequately addressed in research. Variations in key ECG parameters, such as PR interval, QRS duration, QT interval, and T-wave morphology, have been noted across different racial groups. However, the limited research in this area has hindered the development of diagnostic criteria that account for these differences, potentially contributing to healthcare disparities, as ECG interpretation algorithms largely developed from major …