Computer Sciences Commons^™

Collaborative Ai: Oer Materials For Exploring Ai As A Partner Rather Than A Tool, David Smith

Open Educational Resources

The Collaborative AI Open Educational Resource (OER) explores how artificial intelligence can act as a creative and analytical collaborator rather than a tool. Centered on the Balanced Blended Space (BBS) framework and the philosophy of the Center for Holistic Integration (CHI), the OER includes curriculum materials, theoretical models, and live research environments. It offers an interesting approach to blending physical, virtual, and conceptual spaces through shared human–AI agency and invites ongoing participation in interdisciplinary meta-projects.

Real-Time Prediction Of Dynamical Systems Using A Hybrid Analog Computer: Network Traffic Modeling, Majd Zuhair Tahat

Doctoral Dissertations

As the number of online users grows exponentially, the number and severity of cyber threats escalate, urgently requiring advancements in real-time network modeling and response. Swiftly predicting and analyzing network traffic is crucial for effective network monitoring and control, preventing cyber breaches, and maintaining healthy network functionality. This research presents a novel approach to real-time modeling based on analyzing evolving properties and patterns in a dynamical network system using a hybrid analog-digital computer. An analog computer was utilized as a co-processor to compute differential equations that model the Transmission Control Protocol (TCP) window size. A comparative analysis was conducted between …

Why Ai’S Role In Advancing Sustainability Is Underestimated, Lipika Bhattacharya

CCX Research

AI has quietly, but powerfully, woven itself into the fabric of our everyday lives. Yet, AI's potential impact on creating a more sustainable world is undervalued. The author examined artificial intelligence's (AI) role in advancing sustainability. She outlined how AI can be applied in various domains such as agriculture, water management, industry, urban planning, and biodiversity conservation for transformative effects.

Learning: Human Versus Machine, Sanjay Sarma

Asian Management Insights

Outdated education paradigms must be revamped to reclaim the all-important human quality: agency.

Sanjay Sarma, CEO, President, and Dean of the Asia School of Business, Kuala Lumpur, Malaysia and the Fred Fort Flowers (1941) and Daniel Fort Flowers (1941) Professor in Mechanical Engineering at the Massachusetts Institute of Technology (MIT), shares insights on the artificial intelligence (AI)-agency revolution and how the human brain works.

The Ethics Of Ai Nudges: How Ai Influences Decision-Making, Seema Chokshi

Asian Management Insights

And why decision-makers should care about it. Artificial intelligence (AI) systems, through mechanisms like nudges and choice architecture, actively, yet often subtly, shape human decision-making in everyday life and professional settings. AI systems can prioritise profit or efficiency at the expense of human agency, fairness, and well-being, highlighting the need to balance AI’s capabilities with ethical considerations. The EU AI Act is a landmark framework designed to curb manipulative AI practices, emphasising the protection of human autonomy and accountability in decision-making.

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 …

Reimagining Education: Keeping The Human In The Loop, Pradeep Varakantham, Sidney Tio

Asian Management Insights

How educators can work with generative artificial intelligence models to improve learning. Artificial intelligence (AI) helps break the mould of one-size-fits-all education by creating personalised learning paths that adapt to each student’s pace and style. By combining human expertise with AI capabilities, educators can create learning experiences that are both structured and flexible, thus getting the best of both worlds. While promising, AI used in educational contexts must carefully navigate privacy concerns, ensure fairness across all student groups, and support appropriate learning progression.

Exploring Intelligent Manufacturing: How Artificial Intelligence Affects Productivity And Labor Demand At The Enterprise Level, Jie Gu

Dissertations and Theses Collection (Open Access)

Manufacturing is a cornerstone of national economic health and social stability, yet it faces challenges such as declining profits, rising labor costs, and an aging workforce. In China, the manufacturing sector is undergoing a critical transformation, driven by technological advancements like artificial intelligence (AI) and the push for intelligent manufacturing. This study explores how AI revitalizes the manufacturing sector by enhancing enterprise productivity and reshaping labor demand, with a focus on quality inspection processes. Using a leading bearing factory as a case study, the research employs econometric models, A/B testing, and interviews to quantify AI’s impact on production efficiency, costs, …

Adversarial Attacks And Defense Methods In Robotic Systems, Thanh D. Le

Shelby Hall Graduate Research Forum Presentations

Presentation slides for a presentation given at the 1st annual Shelby Hall Graduate Research Forum at the University of South Alabama.

Choosing Robust Leadership: Encompassing The Best-Of-N Model And Swarm Intelligence Optimization For Heterogeneous Multiple Autonomous Unmanned Aerial Vehicle Systems, Kudamuhandiramlage Harith Kolitha Warnakulasooriya

Shelby Hall Graduate Research Forum Presentations

Presentation slides for a presentation given at the 1st annual Shelby Hall Graduate Research Forum at the University of South Alabama.

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 …

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 …

Explainable Neural Networks With Guarantee: A Sparse Estimation Approach, Antoine Ledent, Peng Liu

Research Collection School Of Computing and Information Systems

Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictiveness and explainability. Our model is designed as a linear combination of a sparse set of jointly learned features, each derived from a different trainable function applied to a single 1-dimensional input feature. Leveraging the ability to learn arbitrarily complex relationships, our neural network architecture enables automatic selection of a sparse set of important features, with the final prediction being …

Multisfl: Towards Accurate Split Federated Learning Via Multi-Model Aggregation And Knowledge Replay, Zeke Xia, Ming Hu, Dengke Yan, Ruixuan Liu, Anran Li, Xiaofei Xie, Mingsong Chen

Research Collection School Of Computing and Information Systems

Although Split Federated Learning (SFL) effectively enables knowledge sharing among resource-constrained clients, it suffers from low training performance due to the neglect of data heterogeneity and catastrophic forgetting problems. To address these issues, we propose a novel SFL approach named MultiSFL, which adopts i) an effective multimodel aggregation mechanism to alleviate gradient divergence caused by heterogeneous data and ii) a novel knowledge replay strategy to deal with the catastrophic forgetting problem. MultiSFL adopts two servers (i.e., the fed server and main server) to maintain multiple branch models for local training and an aggregated master model for knowledge sharing among branch …

Understanding Individual Agent Importance In Multi-Agent System Via Counterfactual Reasoning, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Jun Hu, Qing Wang, Fanjiang Xu

Research Collection School Of Computing and Information Systems

Explaining multi-agent systems (MAS) is urgent as these systems become increasingly prevalent in various applications. Previous work has provided explanations for the actions or states of agents, yet falls short in understanding the black-boxed agent's importance within a MAS and the overall team strategy. To bridge this gap, we propose EMAI, a novel agent-level explanation approach that evaluates the individual agent's importance. Inspired by counterfactual reasoning, a larger change in reward caused by the randomized action of agent indicates its higher importance. We model it as a MARL problem to capture interactions across agents. Utilizing counterfactual reasoning, EMAI learns the …

Aligning Large Language Models For Faithful Integrity Against Opposing Argument, Yong Zhao, Yang Deng, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks. However, they can be easily misled by unfaithful arguments during conversations, even when their original statements are correct. To this end, we investigate the problem of maintaining faithful integrity in LLMs. This involves ensuring that LLMs adhere to their faithful statements in the face of opposing arguments and are able to correct their incorrect statements when presented with faithful arguments. In this work, we propose a novel framework, named Alignment for Faithful Integrity with Confidence Estimation (AFICE), which aims to align the LLM responses with faithful integrity. Specifically, …

Simulation-Free Hierarchical Latent Policy Planning For Proactive Dialogues, Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Yiheng Sun, Zerui Chen, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional task-oriented dialogues, proactive dialogues demand advanced policy planning and adaptability, requiring rich scenarios and comprehensive policy repositories to develop such systems. However, existing approaches tend to rely on Large Language Models (LLMs) for user simulation and online learning, leading to biases that diverge from realistic scenarios and result in suboptimal efficiency. Moreover, these methods depend on manually defined, context-independent, coarse-grained policies, which not only incur high expert costs but also raise concerns regarding their completeness. In our work, we …

Gender Biases Within Artificial Intelligence And Chatgpt: Evidence, Sources Of Biases, And Solutions, Qi Hui Jerlyn Ho, Andree Hartanto, Andrew Koh, Nadyanna M. Majeed

Research Collection School of Social Sciences

The growing adoption of Artificial Intelligence (AI) in various sectors has introduced significant benefits, but also raised concerns over biases, particularly in relation to gender. Despite AI's potential to enhance sectors like healthcare, education, and business, it often mirrors reality and its societal prejudices and can manifest itself through unequal treatment in hiring decisions, academic recommendations, or healthcare diagnostics, systematically disadvantaging women. This paper explores how AI systems and chatbots, notably ChatGPT, can perpetuate gender biases due to inherent flaws in training data, algorithms, and user feedback loops. This problem stems from several sources, including biased training datasets, algorithmic design …

Abolition By Algorithm, Peter N. Salib

Michigan Law Review

In one sense, America’s newest abolitionist movement—advocating the elimination of policing and prison—has been a success. Following the 2020 Black Lives Matter protests, a small group of self-described radicals convinced a wide swath of ordinary liberals to accept a sweeping claim: Mere reforms cannot meaningfully reduce prison and policing’s serious harms. Only elimination can. On the other hand, abolitionists have failed to secure lasting policy change. The difficulty is crime. In 2021, following a nationwide uptick in homicides, liberal support for abolitionist proposals collapsed. Despite being newly “abolition curious,” left-leaning voters consistently rejected concrete abolitionist policies. Faced with the difficult …

Learning Without Labels: A Self-Supervised Learning Approach For Anomaly Detection In Control Systmes, Barbara Gladney

Shelby Hall Graduate Research Forum Posters

Oil pipelines, water plant systems, and other critical infrastructure are managed and operated by industrial control systems (ICS). These systems safeguard the operations of critical infrastructures, requiring minimal disruption from cyberattacks or malfunctions. The use of anomaly detection methods in control systems (ICS) can reduce system interruptions. However, anomaly detection methods often require annotated data, which may not be available for the control system. Additionally, the datasets used for the control systems do not include sensor outputs and environmental data, resulting in a restricted view of the system. This research investigates how SSL models can be applied to different control …

Preserving Privacy In Senior Care At Home Monitoring Systems, Sam Russel, Ryan Benton, Amy Campbell, Scott Sittig

Shelby Hall Graduate Research Forum Posters

Many seniors prefer to live at home which necessitates research into the application of technologies to provide a safer environment with less caregiver resources. However, the application of home health care (HHC) monitoring for seniors is still in an evolutionary stage. Present HHC systems are produced by private companies with general regulatory guidelines lacking specific care of the elderly. As such, each company that produces such a system claims to have better safety, privacy, and security that their competitors. A pressing issues is devising and applying a general framework for the application of technologies that delivers safety while preserving privacy. …

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 …

Explainable Neural Networks With Guarantees: A Sparse Estimation Approach, Antoine Ledent, Peng Liu

Research Collection School Of Computing and Information Systems

Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictiveness and explainability. Our model is designed as a linear combination of a sparse set of jointly learned features, each derived from a different trainable function applied to a single 1-dimensional input feature. Leveraging the ability to learn arbitrarily complex relationships, our neural network architecture enables automatic selection of a sparse set of important features, with the final prediction being …

Dualopt: A Dual Divide-And-Optimize Algorithm For The Large-Scale Traveling Salesman Problem, Shipei Zhou, Yuandong Ding, Chi Zhang, Zhiguang Cao, Yan Jin

Research Collection School Of Computing and Information Systems

This paper proposes a dual divide-and-optimize algorithm (DualOpt) for solving the large-scale traveling salesman problem (TSP). DualOpt combines two complementary strategies to improve both solution quality and computational efficiency. The first strategy is a grid-based divide-and-conquer procedure that partitions the TSP into smaller subproblems, solving them in parallel and iteratively refining the solution by merging nodes and partial routes. The process continues until only one grid remains, yielding a high-quality initial solution. The second strategy involves a path-based divide-and-optimize procedure that further optimizes the solution by dividing it into sub-paths, optimizing each using a neural solver, and merging them back …

Occlusion-Insensitive Talking Head Video Generation Via Facelet Compensation, Yuhui Deng, Yuqin Lu, Yangyang Xu, Yongwei Nie, Shengfeng He

Research Collection School Of Computing and Information Systems

Talking head video generation involves animating a still face image using facial motion cues derived from a driving video to replicate target poses and expressions. Traditional methods often rely on the assumption that the relative positions of facial keypoints remain unchanged. However, this assumption fails when keypoints are occluded or when the head is in a profile pose, leading to inconsistencies in identity and blurring in certain facial regions. In this paper, we introduce Occlusion-Insensitive Talking Head Video Generation, a novel approach that eliminates the reliance on spatial correlation of keypoints and instead leverages semantic correlation. Our method transforms facial …

Adversarial Attacks On Event-Based Pedestrian Detectors: A Physical Approach, Guixu Lin, Muyao Niu, Qingtian Zhu, Zhengwei Yin, Zhuoxiao Li, Shengfeng He, Yinqiang Zheng

Research Collection School Of Computing and Information Systems

Event cameras, known for their low latency and high dynamic range, show great potential in pedestrian detection applications. However, while recent research has primarily focused on improving detection accuracy, the robustness of event-based visual models against physical adversarial attacks has received limited attention. For example, adversarial physical objects, such as specific clothing patterns or accessories, can exploit inherent vulnerabilities in these systems, leading to misdetections or misclassifications. This study is the first to explore physical adversarial attacks on event-driven pedestrian detectors, specifically investigating whether certain clothing patterns worn by pedestrians can cause these detectors to fail, effectively rendering them unable …

Adapting Knowledge Prompt Tuning For Enhanced Automated Program Repair, Xuemeng Cai, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Automated Program Repair (APR) aims to enhance software reliability by automatically generating bug-fixing patches. Recent work has improved the state-of-the-art of APR by fine-tuning pre-trained large language models (LLMs), such as CodeT5, for APR. However, the effectiveness of fine-tuning be-comes weakened in data scarcity scenarios, and data scarcity can be a common issue in practice, limiting fine-tuning performance. To alleviate this limitation, this paper adapts prompt tuning for enhanced APR and conducts a comprehensive study to evaluate its effectiveness in data scarcity scenarios, using three LLMs of different sizes and six diverse datasets across four programming languages. Prompt tuning rewrites …

Drone Delivery Network Design With Uncertainties, Wenjia Zeng, Jiang Ruiwei, Hai Yang, Hai Wang

Research Collection School Of Computing and Information Systems

Unmanned aerial vehicles (UAVs), also called drones, are gaining popularity as an alternative delivery mode due to their faster delivery speed and reduced labor costs. Several companies, especially e-commerce giants, are conducting pilot projects that use drones to deliver fast food and groceries. In 2021, for example, Walmart partnered with Zipline in the United States to provide delivery services for areas near Walmart stores in Arkansas. In China, Meituan drone delivery services have been launched in Shenzhen and have conducted trial food delivery that cover more than 8,000 households.

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 …

Respear: Earable-Based Robust Respiratory Rate Monitoring, Yang Liu, Kayla-Jade Butkow, Jake Stuchbury-Wass, Adam Pullin, Dong Ma, Cecilia Masolo

Research Collection School Of Computing and Information Systems

Respiratory rate (RR) monitoring is integral to understanding physical and mental health and tracking fitness. Existing studies have demonstrated the feasibility of RR monitoring under specific user conditions (e.g., while remaining still, or while breathing heavily). Yet, performing accurate, continuous and non-obtrusive RR monitoring across diverse daily routines and activities remains challenging. In this work, we present RespEar, an earable-based system for robust RR monitoring. By leveraging the unique properties of in-ear microphones in earbuds, RespEar enables the use of Respiratory Sinus Arrhythmia (RSA) and Locomotor Respiratory Coupling (LRC), physiological couplings between cardiovascular activity, gait and respiration, to indirectly determine …