Digital Commons Network^™

Impact Of An Online Decision Support Tool For Ductal Carcinoma In Situ (Dcis) Using A Pre-Post Design (Aft-25), Elissa Ozanne, Kellyn Maves, Angela Tramontano, Thomas Lynch, Alastair Thompson, Ann Partridge, Elizabeth Frank, Deborah Collyar, Desiree Basila, Donna Pinto, Terry Hyslop, Marc Ryser, Shoshana Rosenberg, E. Shelley Hwang, Rinaa Punglia

Department of Pharmacology and Experimental Therapeutics Faculty Papers

BACKGROUND: The heterogeneous biology of ductal carcinoma in situ (DCIS), as well as the variable outcomes, in the setting of numerous treatment options have led to prognostic uncertainty. Consequently, making treatment decisions is challenging and necessitates involved communication between patient and provider about the risks and benefits. We developed and investigated an interactive decision support tool (DST) designed to improve communication of treatment options and related long-term risks for individuals diagnosed with DCIS.

FINDINGS: The DST was developed for use by individuals aged > 40 years with DCIS and is based on a disease simulation model that integrates empirical data and …

Investigating The Intersection Of Ai And Cybercrime: Risks, Trends, And Countermeasures, Sanaika Shetty, Kyung-Shick Choi, Insun Park

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

Trends From 20 Years Of Artificial Intelligence In Financial Services In Africa, Nthabiseng Moela, Lerato Matlala, Jackie Ma, Dipuo Maphutha, Hossana Twinomurinzi

African Conference on Information Systems and Technology

The need for financial inclusion in Africa, particularly for marginalised groups like women and small businesses, highlights the importance of leveraging Artificial Intelligence (AI). This study provides a bibliometric analysis of AI's integration into African financial services from 2003 to 2023. The key results show a significant increase in AI use, particularly in fraud detection, credit risk prediction, and stock market volatility forecasting, with 49% of the research coming from South Africa, Nigeria, and Tunisia. However, areas like financial development management, inflation control, and gender disparities in loan access remain underexplored. The emphasis has been on the technical implementation of …

Use Your Own Device (Uyod): Framework For Building A Human Firewall, Tapiwa Gundu, Kevin Kativu

African Conference on Information Systems and Technology

The Use Your Own Device (UYOD) paradigm is increasingly common in modern digital workplaces, offering benefits like flexibility and cost savings but also introducing significant cybersecurity risks. This study develops a comprehensive framework for constructing a robust human firewall aimed at mitigating these risks. The research employs a systematic literature review (SLR) methodology, starting with a corpus of 198 articles, from which 17 high-quality studies were selected for in-depth analysis. Key components identified include cybersecurity awareness training, regular simulated attacks, clear policies and procedures, technology competence training, and fostering a security-first culture. Findings reveal that integrating these components into an …

The Use Of Social Media For Organisational Learning: A Systematic Review Of Literature, Harry Moongela, Marie Hattingh

African Conference on Information Systems and Technology

Social media (categorised as Web 2.0 platforms) has been reported to significantly impact and improve organisational learning (OL), but there seems to be a lack of systematic literature reviews (SLR) covering the use of social media to facilitate OL. Thus, this article conducted a SLR for both social media and OL by presenting the significant concepts, models and frameworks from literature. A total number of 48 articles were found for the analysis method and the findings reveal important concepts such as types of OL, levels of OL and frameworks incorporating both social media and OL. The results also show that …

Neurosymbolic Cognitive Methods For Enhancing Foundation Model-Based Reasoning, Kaushik Roy, Siyu Wu, Alessandro Oltramari

Faculty Publications

Foundation models have emerged as powerful tools, exhibiting extraordinary performance across various tasks, such as language processing, visual recognition, code generation, and human-centered engagement. However, recent studies have highlighted their limitations when grounded, abstract, and generalized reasoning capabilities are required. Complex tasks often involve multiple hierarchical reasoning steps, which are typical features of human thinking processes. In fact, in this chapter we claim that cognitively-inspired computational models, such as the so-called Common Model of Cognition, are key to enable complex reasoning within foundation model-based artificial intelligence (AI) systems. We investigate neurosymbolic approaches for mapping AI system components to those of …

Rethinking Retrieval Augmented Fine-Tuning In An Evolving Llm Landscape, Nicholas Sager, Timothy Cabaza, Matthew Cusack, Ryan Bass, Joaquin Dominguez

SMU Data Science Review

This study explores the utilization of Retrieval Augmented Fine-Tuning (RAFT) to enhance the performance of Large Language Models (LLMs) in domain-specific Retrieval Augmented Generation (RAG) tasks. By integrating domain-specific information during the retrieval process, RAG aims to reduce hallucination and improve the accuracy of LLM outputs. We investigate the use of RAFT, an approach that enhances LLMs by incorporating domain-specific knowledge and effectively handling distractor documents. This paper validates previous work, which found that RAFT can considerably improve the performance of Llama2-7B in specific domains. We also expand upon previous work into new state-of-the-art open-source models and other datasets with …

Assessing The Accuracy And Utility Of Chatgpt Responses To Patient Questions Regarding Posterior Lumbar Decompression, Alec Giakas, Rajkishen Narayanan, Teeto Ezeonu, Jonathan Dalton, Yunsoo Lee, Tyler Henry, John Mangan, Gregory Schroeder, Alex Vaccaro, Christopher Kepler

Department of Orthopaedic Surgery Faculty Papers

Aim: To examine the clinical accuracy and applicability of ChatGPT answers to commonly asked questions from patients considering posterior lumbar decompression (PLD). Methods: A literature review was conducted to identify 10 questions that encompass some of the most common questions and concerns patients may have regarding lumbar decompression surgery. The selected questions were then posed to ChatGPT. Initial responses were then recorded, and no follow-up or clarifying questions were permitted. Two attending fellowship-trained spine surgeons then graded each response from the chatbot using a modified Global Quality Scale to evaluate ChatGPT’s accuracy and utility. The surgeons then analyzed each question, …

Pooling And Winsorizing Machine Learning Forecasts To Predict Stock Returns With High-Dimensional Data, Erik Mekelburg, Jack Strauss

Finance: Faculty Scholarship

We evaluate US market return predictability using a novel data set of several hundred ag- gregated firm-level characteristics. We apply LASSO, Elastic Net, Random Forest, Neural Net, Extreme Gradient Boosting, and Light Gradient Boosting Machine methods and find these models experience large prediction errors that lead to forecast failures. However, winsorizing and pooling machine learning model forecasts provides consistent out-of-sample predictability. To assess robustness, we apply machine learning methods to high-dimensional data for Canada, China, Germany and the UK as well as the Goyal-Welch data. All machine learning models we consider, except for the ensemble pooled methods, fail to significantly …

Recasting The Mould – Librarianship Of The Future: Leveraging Automation, Apis, And Ai, Samantha Seah

Research Collection Library

With leaps in artificial intelligence made in recent years redefining the information landscape and introducing new means of information production, librarianship also must evolve to include new literacies. One way librarians can equip and empower ourselves is by understanding the building blocks of how machines and automation work. Perhaps more important than learning specific programming languages, learning computational thinking provides us with more ways to spot and evaluate problems and devise solutions without extensive coding knowledge. My presentation will take the improvement of membership processing as an example using Power Automate, a low-code Microsoft tool mimicking block programming. The tool …

Enabling Emg-Based Silent Speech Transcription Through Speech-To-Text Transfer Learning, Alexander T. Garcia

Master's Theses

In recent years, advances in deep learning have allowed various forms of electrographic signals, such as electroencephalography (EEG) and electromyography (EMG), to be used as a viable form of input in artificial intelligence applications, particularly for applications in the medical field. One such topic that EMG inputs have been used is in silent speech interfaces, or devices capable of processing speech without an audio-based input. The goal of this thesis is to explore a novel method of training a machine learning model to be used for silent speech interface development: using transfer learning to leverage a pre-trained speech recognition model …

Quantum-Enhanced Simulation-Based Optimization For Newsvendor Problems, Monit Sharma, Hoong Chuin Lau, Rudy Raymond

Research Collection School Of Computing and Information Systems

Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence it is estimated through simulation. Exploiting the enhanced efficiency of Quantum Amplitude Estimation (QAE) compared to classical Monte Carlo simulation, it frequently outpaces classical simulation-based optimization, resulting in notable performance enhancements in various scenarios. In this work, we make use of a quantum-enhanced algorithm for simulation-based optimization and apply it to solve a variant of the classical …

Efficient And Secure Federated Learning Against Backdoor Attacks, Yinbin Miao, Rongpeng Xie, Xinghua Li, Zhiquan Liu, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

Due to the powerful representation ability and superior performance of Deep Neural Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from both academic and industrial fields. However, its transmitted plaintext data causes privacy disclosure. FL based on Local Differential Privacy (LDP) solutions can provide privacy protection to a certain extent, but these solutions still cannot achieve adaptive perturbation in DNN model. In addition, this kind of schemes cause high communication overheads due to the curse of dimensionality of DNN, and are naturally vulnerable to backdoor attacks due to the inherent distributed characteristic. To solve these issues, …

The Impact Of Managerial Myopia On Cybersecurity: Evidence From Data Breaches, Wen Chen, Xing Li, Haibin Wu, Liandong Zhang

Research Collection School Of Accountancy

Using a sample of U.S. firms for the period 2005–2017, we provide evidence that managerial myopic actions contribute to corporate cybersecurity risk. Specifically, we show that abnormal cuts in discretionary expenditures, our proxy for managerial myopia, are positively associated with the likelihood of data breaches. The association is largely driven by firms that appear to cut discretionary expenditures to meet short-term earnings targets. In addition, the association is stronger for firms with greater short-term equity incentives, higher earnings response coefficients, low levels of institutional block ownership, or large market shares. Finally, firms appear to increase discretionary expenditures upon the announcement …

How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation, Cen Zhang, Yaowen Zheng, Mingqiang Bai, Yeting Li, Wei Ma, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Fuzz drivers are essential for library API fuzzing. However, automatically generating fuzz drivers is a complex task, as it demands the creation of high-quality, correct, and robust API usage code. An LLM-based (Large Language Model) approach for generating fuzz drivers is a promising area of research. Unlike traditional program analysis-based generators, this text-based approach is more generalized and capable of harnessing a variety of API usage information, resulting in code that is friendly for human readers. However, there is still a lack of understanding regarding the fundamental issues on this direction, such as its e ectiveness and potential challenges. To …

Text-Driven Video Prediction, Xue Song, Jingjing Chen, Bin Zhu, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Current video generation models usually convert signals indicating appearance and motion received from inputs (e.g., image and text) or latent spaces (e.g., noise vectors) into consecutive frames, fulfilling a stochastic generation process for the uncertainty introduced by latent code sampling. However, this generation pattern lacks deterministic constraints for both appearance and motion, leading to uncontrollable and undesirable outcomes. To this end, we propose a new task called Text-driven Video Prediction (TVP). Taking the first frame and text caption as inputs, this task aims to synthesize the following frames. Specifically, appearance and motion components are provided by the image and caption …

Technoculture And Language Models In Archaeology: Reconstructing And Preserving Cultural Narratives Through Digital Humanities, James Hutson

Faculty Scholarship

Technoculture, which examines the intersection of culture and technology, has increasingly permeated archaeological practice, transforming both scholarly research and public engagement [1-3]. The introduction of digital tools such as virtual reality (VR), geographic information systems (GIS), and large language models (LLMs) has democratized access to archaeological knowledge, enabling communities to engage more actively with their cultural heritage [4-6]. This short article explores the mutual influence of technocultural studies and AI technologies on archaeology, with a focus on the preservation and reconstruction of cultural narratives through digital means.

The first aspect of this intersection lies in how technocultural tools are creating …

Pias: Privacy-Preserving Incentive Announcement System Based On Blockchain For Internet Of Vehicles, Yonghua Zhan, Yang Yang, Hongju Cheng, Xiangyang Luo, Zhuangshuang Guan, Robert H. Deng

Research Collection School Of Computing and Information Systems

More vehicles are connecting to the Internet of Things (IoT), transforming Vehicle Ad hoc Networks (VANETs) into the Internet of Vehicles (IoV), providing a more environmentally friendly and safer driving experience. Vehicular announcement networks show promise in vehicular communication applications. However, two major issues arise when establishing such a system. Firstly, user privacy cannot be guaranteed when messages are forwarded anonymously, thus the reliability of these messages is in question. Secondly, users often lack interest in responding to announcements. To address these problems, we introduce a Blockchain-based incentive announcement system called PIAS. This system enables anonymous message commitment in a …

Sound And Complete Witnesses For Template-Based Verification Of Ltl Properties On Polynomial Programs, Krishnendu Chatterjee, Amir Goharshady, Ehsan Goharshady, Mehrdad Karrabi, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We study the classical problem of verifying programs with respect to formal specifications given in the linear temporal logic (LTL). We first present novel sound and complete witnesses for LTL verification over imperative programs. Our witnesses are applicable to both verification (proving) and refutation (finding bugs) settings. We then consider LTL formulas in which atomic propositions can be polynomial constraints and turn our focus to polynomial arithmetic programs, i.e. programs in which every assignment and guard consists only of polynomial expressions. For this setting, we provide an efficient algorithm to automatically synthesize such LTL witnesses. Our synthesis procedure is both …

Enhancing Multi-Agent System Testing With Diversity-Guided Exploration And Adaptive Critical State Exploitation, Xuyan Ma, Yawen Wang, Junjie Wang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Multi-agent systems (MASs) have achieved remarkable success in multi-robot control, intelligent transportation, and multiplayer games, etc. Thorough testing for MAS is urgently needed to ensure its robustness in the face of constantly changing and unexpected scenarios. Existing methods mainly focus on single-agent system testing and cannot be directly applied to MAS testing due to the complexity of MAS. To our best knowledge, there are fewer studies on MAS testing. While several studies have focused on adversarial attacks on MASs, they primarily target failure detection from an attack perspective, i.e., discovering failure scenarios, while ignoring the diversity of scenarios. In this …

Ft2ra: A Fine-Tuning-Inspired Approach To Retrieval-Augmented Code Completion, Qi Guo, Shangqing Liu, Xiaofei Xie, Ze Tang Tang

Research Collection School Of Computing and Information Systems

The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these models, especially large models, poses a significant challenge when it comes to fine-tuning them for specific downstream tasks. As an alternative approach, retrieval-based methods have emerged as a promising solution, augmenting model predictions without the need for fine-tuning. Despite their potential, a significant challenge is that the designs of these methods often rely on heuristics, leaving critical questions about what information should be stored or retrieved and how to interpolate such information for …

Face It Yourselves: An Llm-Based Two-Stage Strategy To Localize Configuration Errors Via Logs, Shiwen Shi, Yintong Huo, Yuxin Su, Yichen Li, Dan Li, Zibin Zheng

Research Collection School Of Computing and Information Systems

Configurable software systems are prone to configuration errors, resulting in significant losses to companies. However, diagnosing these errors is challenging due to the vast and complex configuration space. These errors pose significant challenges for both experienced maintainers and new end-users, particularly those without access to the source code of the software systems. Given that logs are easily accessible to most end-users, we conduct a preliminary study to outline the challenges and opportunities of utilizing logs in localizing configuration errors. Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause …

An Empirical Study Of Static Analysis Tools For Secure Code Review, Wachiraphan Charoenwet, Patanamon Thongtanunam, Van-Thuan Pham, Christoph Treude

Research Collection School Of Computing and Information Systems

Early identification of security issues in software development is vital to minimize their unanticipated impacts. Code review is a widely used manual analysis method that aims to uncover security issues along with other coding issues in software projects. While some studies suggest that automated static application security testing tools (SASTs) could enhance security issue identification, there is limited understanding of SAST’s practical effectiveness in supporting secure code review. Moreover, most SAST studies rely on synthetic or fully vulnerable versions of the subject program, which may not accurately represent real-world code changes in the code review process. To address this gap, …

Evaluating Szz Implementations : An Empirical Study On The Linux Kernel, Yunbo Lyu, Hong Jin Kang, Ratnadira Widyasari, Julia Lawall, David Lo

Research Collection School Of Computing and Information Systems

The SZZ algorithm is used to connect bug-fixing commits to the earlier commits that introduced bugs. This algorithm has many applications and many variants have been devised. However, there are some types of commits that cannot be traced by the SZZ algorithm, referred to as “ghost commits”. The evaluation of how these ghost commits impact the SZZ implementations remains limited. Moreover, these implementations have been evaluated on datasets created by software engineering researchers from information in bug trackers and version controlled histories. Since Oct 2013, the Linux kernel developers have started labelling bug-fixing patches with the commit identifiers of the …

Self-Supervised Spatial-Temporal Normality Learning For Time Series Anomaly Detection, Yutong Chen, Hongzuo Xu, Guansong Pang, Hezhe Qiao, Yuan Zhou, Mingsheng Shang

Research Collection School Of Computing and Information Systems

Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normality learning (OTN) module that captures the temporal correlations within sequences, and a Distance prediction-based Spatial Normality learning …

Fdi : Attack Neural Code Generation Systems Through User Feedback Channel, Zhensu Sun, Xiaoning Du, Xiapu Luo, Fu Song, David Lo, Li Li

Research Collection School Of Computing and Information Systems

Neural code generation systems have recently attracted increasing attention to improve developer productivity and speed up software development. Typically, these systems maintain a pre-trained neural model and make it available to general users as a service (e.g., through remote APIs) and incorporate a feedback mechanism to extensively collect and utilize the users' reaction to the generated code, i.e., user feedback. However, the security implications of such feedback have not yet been explored. With a systematic study of current feedback mechanisms, we find that feedback makes these systems vulnerable to feedback data injection (FDI) attacks. We discuss the methodology of FDI …

Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan

Research Collection School Of Computing and Information Systems

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen …

Putting Gpt-4o To The Sword: A Comprehensive Evaluation Of Language, Vision, Speech, And Multimodal Proficiency, Sakib Shahriar, Brady D. Lund, Nishith Reddy Mannuru, Muhammad Arbab Arshad, Kadhim Hayawi, Ravi Varma Kumar Bevara, Aashrith Mannuru, Laiba Batool

All Works

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model’s language capability. Additionally, GPT-4o’s vision and speech capabilities are tested through image classification and object-recognition tasks, as well as accent classification. The multimodal evaluation assesses the model’s performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning …

Artificial Intelligence In Orthopaedic Education: A Comparative Analysis Of Chatgpt And Bing Ai’S Orthopaedic In-Training Examination Performance, Clark Chen, Vivek Biololikar, Duncan Vannest, James Raphael, Gene Shaffer

Einstein Health Papers

Background: This study evaluated the performance of generative artificial intelligence (AI) models on the Orthopaedic In-Training Examination (OITE), an annual exam administered to U.S. orthopaedic residency programs. Methods: ChatGPT 3.5 and Bing AI GPT 4.0 were evaluated on standardised sets of multiple-choice questions drawn from the American Academy of Orthopaedic Surgeons OITE online question bank spanning 5 years (2018–2022). A total of 1165 questions were posed to each AI system. The performance of both systems was standardised using the latest versions of ChatGPT 3.5 and Bing AI GPT 4.0. Historical data of resident scores taken from the annual OITE technical …

Multi-Modal Alignment Via Hyperbolic Geometry, Suyu Liu

Dissertations and Theses Collection (Open Access)

Strong capabilities of generalization to unseen domains are vital for deep neural networks. While existing methods have shown promising results without source domain access, they mostly rely on models that are extensively pre-trained on source domains or overlook the intricate hierarchical structures inherent in visual and textual features. These limitations may have bad impacts on performances, especially on datasets with many classes. To overcome this, in this paper we propose a novel approach that projects the model onto hyperbolic geometry and employs geometric optimal transport to align cross-modal features in an unsupervised manner. Unlike Euclidean geometry, hyperbolic geometry is characterized …