Digital Commons Network^™

Rl4co: An Extensive Reinforcement Learning For Combinatorial Optimization Benchmark, Federico Berto, Et. Al

Research Collection School Of Computing and Information Systems

Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency. However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers. To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines. Built on efficient software libraries and best practices in …

Eformer: An Effective Edge-Based Transformer For Vehicle Routing Problems, Dian Meng, Zhiguang Cao, Yaoxin Wu, Yaqing Hou, Hongwei Ge, Qiang Zhang

Research Collection School Of Computing and Information Systems

Recent neural heuristics for the Vehicle Routing Problem (VRP) primarily rely on node coordinates as input, which may be less effective in practical scenarios where real cost metrics--such as edge-based distances--are more relevant. To address this limitation, we introduce EFormer, an Edge-based Transformer model that uses edge as the sole input for VRPs. Our approach employs a precoder module with a mixed-score attention mechanism to convert edge information into temporary node embeddings. We also present a parallel encoding strategy characterized by a graph encoder and a node encoder, each responsible for processing graph and node embeddings in distinct feature spaces, …

Learning Frame-Level Classifiers For Video-Based Real-Time Assessment Of Stroke Rehabilitation Exercises From Weakly Annotated Datasets, Ana Rita Cóias, Min Hun Lee, Alexandre Bernardino, Asim Smailagic, Mariana Mateus, David Fernandes, Sofia Trapola

Research Collection School Of Computing and Information Systems

Autonomous rehabilitation support solutions, such as virtual coaches, should provide real-time feedback to improve motor function and maintain patient engagement. However, fully annotated dataset collection for real-time exercise assessment is time-consuming and costly, posing a barrier to evaluating proposed methods. In this work, we present a novel framework that learns a frame-level classifier using weakly annotated videos for real-time assessment of compensatory motions in stroke rehabilitation exercises by generating pseudo-labels at a frame level. We consider three approaches: 1) a baseline approach that uses a source dataset to train a frame-level classifier, 2) a transfer learning approach that uses target …

Use Of A Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework For Low-Resource Settings: Development And Validation Study, Shao Wei Sean Lam, Min Hun Lee, Michael Dorosan, Samuel Altonji, Hiang Khoon Tan, Walter T. Lee

Research Collection School Of Computing and Information Systems

Background: Early-stage diagnosis of laryngeal cancer significantly improves patient survival and quality of life. However, the scarcity of specialists in low-resource settings hinders the timely review of flexible nasopharyngoscopy (FNS) videos, which are essential for accurate triage of at-risk patients.Objective: We introduce a preliminary AI-based screening framework to address this challenge for the triaging of at-risk patients in low-resource settings. This formative research addresses multiple challenges common in high-dimensional FNS videos: (1) selecting clear, informative images; (2) deriving regions within frames that show an anatomical landmark of interest; and (3) classifying patients into referral grades based on the FNS video …

Reimagining Education With Ai, Margherita Pagani, Steven M. Miller, Jerry Wind

Research Collection School Of Computing and Information Systems

This chapter examines AI’s transformative potential in education, focusing on Generative AI (GenAI) and Large Language Models (LLMs) while at the same time emphasizing the importance of grounding and guiding AI efforts with learning science and education research findings. It synthesizes analyses and expert recommendations, highlighting opportunities like personalized learning and enhanced teacher productivity, alongside challenges such as over-reliance on AI. Practical steps for instructors include adopting a question-first approach, utilizing AI for personalized feedback, designing AI-enhanced learning experiences, fostering critical thinking, and ensuring ethical AI use. The chapter concludes with strategic recommendations for leveraging AI to sustainably improve educational …

Taclr: A Scalable And Efficient Retrieval-Based Method For Industrial Product Attribute Value Identification, Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen

Research Collection School Of Computing and Information Systems

Product Attribute Value Identification (PAVI) involves identifying attribute values from product profiles, a key task for improving product search, recommendation, and business analytics on e-commerce platforms. However, existing PAVI methods face critical challenges, such as inferring implicit values, handling outof-distribution (OOD) values, and producing normalized outputs. To address these limitations, we introduce Taxonomy-Aware Contrastive Learning Retrieval (TACLR), the first retrieval-based method for PAVI. TACLR formulates PAVI as an information retrieval task by encoding product profiles and candidate values into embeddings and retrieving values based on their similarity. It leverages contrastive training with taxonomy-aware hard negative sampling and employs adaptive inference …

Starpose: 3d Human Pose Estimation Via Spatial-Temporal Autoregressive Diffusion, Haoxin Yang, Weihong Chen, Xuemiao Xu, Cheng Xu, Peng Xiao, Cuifeng Sun, Shaoyu Huang, Shengfeng He

Research Collection School Of Computing and Information Systems

Monocular 3D human pose estimation remains a challenging task due to inherent depth ambiguities and occlusions. Compared to traditional methods based on Transformers or Convolutional Neural Networks (CNNs), recent diffusionbased approaches have shown superior performance, leveraging their probabilistic nature and high-fidelity generation capabilities. However, these methods often fail to account for the spatial and temporal correlations across predicted frames, resulting in limited temporal consistency and inferior accuracy in predicted 3D pose sequences. To address these shortcomings, this paper proposes StarPose, an autoregressive diffusion framework that effectively incorporates historical 3D pose predictions and spatialtemporal physical guidance to significantly enhance both the …

Sifar: A Simple Faster Accelerated Variance‑Reduced Gradient Method, Zhize Li

Research Collection School Of Computing and Information Systems

In this paper, we propose a simple faster accelerated gradient method called SIFAR for solving the finite-sum optimization problems. Concretely, we consider both general convex and strongly convex settings: i) For general convex finite-sum problems, SIFAR improves previous state-of-the-art result given by Varag. In particular, for large-scale problems or the convergence error is not very small, SIFAR obtains the first optimal result O(n), matching the lower bound. ii) For strongly convex finite-sum problems, we also show that SIFAR can achieve the optimal convergence rate matching the lower bound. Besides, SIFAR enjoys a simpler loopless algorithmic structure while previous algorithms use …

From Risk To Resilience: Towards Assessing And Mitigating The Risk Of Data Reconstruction Attacks In Federated Learning, Xiangrui Xu, Zhize Li, Yufei Han, Bin Wang, Jiqiang Liu, Wei Wang

Research Collection School Of Computing and Information Systems

Data Reconstruction Attacks (DRA) pose a significant threat to Federated Learning (FL) systems by enabling adversaries to infer sensitive training data from local clients. Despite extensive research, the question of how to characterize and assess the risk of DRAs in FL systems remains unresolved due to the lack of a theoretically-grounded risk quantification framework. In this work, we address this gap by introducing Invertibility Loss (InvLoss) to quantify the maximum achievable effectiveness of DRAs for a given data instance and FL model. We derive a tight and computable upper bound for InvLoss and explore its implications from three perspectives. First, …

L2m2: A Hierarchical Framework Integrating Large Language Model And Multi‑Agent Reinforcement Learning, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Lin Li, Xin Zhao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning (MARL) has demonstrated remarkable success in collaborative tasks, yet faces significant challenges in scaling to complex scenarios requiring sustained planning and coordination across long horizons. While hierarchical approaches help decompose these tasks, they typically rely on hand-crafted subtasks and domain-specific knowledge, limiting their generalizability. We present L2M2, a novel hierarchical framework that leverages large language models (LLMs) for high-level strategic planning and MARL for low-level execution. L2M2 enables zero-shot planning that supports both end-to-end training and direct integration with pre-trained MARL models. Experiments in the VMAS environment demonstrate that L2M2's LLM-guided MARL achieves superior performance while requiring …

Ai-Assisted Risk Assessment In Generative Ai Governance, Wu Jiaqi Young, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Effective risk assessment is paramount for responsible generative AI (GenAI) deployment. Traditional governance approaches that rely on manual reviews are inadequate given the scale and velocity of GenAI outputs. A risk-based approach incorporating real-time monitoring and governance is paramount. In this research, we examine how the efficacy of suggestive versus supportive explanations for AI’s risk assessment of GenAI outputs is moderated by user domain expertise and AI’s risk assessment in determining user acceptance. We hypothesize that cognitive involvement increases with AI’s risk assessment, with higher risks triggering more critical evaluation. By drawing on the elaboration likelihood model, we hypothesize that …

Freqllm: Frequency-Aware Large Language Models For Time Series Forecasting, Shunan Wang, Min Gao, Zongwei Wang, Yibing Bai, Feng Jiang, Guansong Pang

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently shown promise in Time Series Forecasting (TSF) by effectively capturing intricate time-domain dependencies. However, our preliminary experiments reveal that standard LLM-based approaches often fail to capture global correlations, limiting predictive performance. We found that embedding frequency-domain signals smooths weight distributions and enhances structured correlations by clearly separating global trends (low-frequency components) from local variations (high-frequency components). Building on these insights, we propose FreqLLM, a novel framework that integrates frequency-domain semantic alignment into LLMs to refine prompts for improved time series analysis. By bridging the gap between frequency signals and textual embeddings, FreqLLM effectively captures …

Fine‑Tuning Multimodal Large Language Models For Product Bundling, Xiaohao Liu, Jie Wu, Zhulin Tao, Yunshan Ma, Yinwei Wei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recent advances in product bundling have leveraged multimodal information through sophisticated encoders, but remain constrained by limited semantic understanding and a narrow scope of knowledge. Therefore, some attempts employ In-context Learning (ICL) to explore the potential of large language models (LLMs) for their extensive knowledge and complex reasoning abilities. However, these efforts are inadequate in understanding mulitmodal data and exploiting LLMs' knowledge for product bundling. To bridge the gap, we introduce Bundle-MLLM, a novel framework that fine-tunes LLMs through a hybrid item tokenization approach within a well-designed optimization strategy. Specifically, we integrate textual, media, and relational data into a unified …

Anomalygfm: Graph Foundation Model For Zero/Few-Shot Anomaly Detection, Hezhe Qiao, Chaoxi Niu, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years. Existing generalist graph models have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inherently infrequent, irregular and heterogeneous abnormality patterns in graphs from different domains. To address this challenge, we propose AnomalyGFM, a GAD-oriented graph foundation model that supports zero-shot inference and few-shot prompt tuning for GAD in diverse graph datasets. …

Affinitytune: A Prompt-Tuning Framework For Few-Shot Anomaly Detection On Graphs, Jingyan Chen, Guanghui Zhu, Guansong Pang, Chunfeng Yuan, Yihua Huang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) is a critical task with applications in domains such as networking, finance, and bioinformatics. % However, the scarcity of labeled anomalies and the limitations of unsupervised methods hinder effective detection. % While semi-supervised and few-shot learning approaches offer improvements, they struggle with knowledge transfer and rely heavily on labeled data. % Recent advancements in prompt tuning on graphs provide a promising direction, but their application to heterophilous graphs in anomaly detection remains underexplored. % In this work, we propose AffinityTune, a novel framework for few-shot graph anomaly detection based on prompt tuning. % Our approach introduces …

Llm2rec: Large Language Models Are Powerful Embedding Models For Sequential Recommendation, Yingzhi He, Xiaohao Liu, An Zhang, Yunshan Ma, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Traditional sequential recommenders predominantly rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns. However, these embeddings depend solely on past interactions, lacking transferable knowledge to generalize to unseen domains. Recent advances in large language models (LLMs) have motivated text-based recommendation approaches that derive item representations from textual descriptions. While these methods enhance generalization, they fail to encode CF signals-i.e., latent item correlations and preference patterns-crucial for effective recommendation. We argue that an ideal embedding model …

Role Of Social Media Mindfulness In Combatting Fake News Propagation, Gaurav Bansal, Fiona Fui-Hoon Nah, Jason B. Thatcher

Research Collection School Of Computing and Information Systems

The dissemination of fake news by social media users is a key factor in the escalation of misinformation. Research suggests that social media networks are becoming increasingly homophilic, which leads to an overreliance on social media friends that contributes to the spread of fake news. However, little is known about how social media mindfulness can reduce the sharing of fake news. To investigate this research question, we conceptualized a social media mindfulness construct and developed the social media mindfulness scale. We also hypothesize that social media mindfulness lowers overreliance on friends’ knowledge, which increases skepticism about social media news that …

Gnncontext: Gnn-Based Code Context Prediction For Programming Tasks, Xiaoye Zheng, Zhiyuan Wan, Shun Liu, Kaiwen Yang, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

A code context model comprises source code elements and their relations relevant to a programming task. The capture and use of code context models in software tools can benefit software development practices, such as code navigation and search. Prior research has explored approaches that leverage either the structural information of code or interaction histories of developers with integrated development environments to automate the construction of code context models. However, these approaches primarily capture shallow syntactic and lexical features of code elements, with limited ability to capture contextual and structural dependencies among neighboring code elements. In this paper, we propose GNNContext, …

Collisionrepair: First‑Aid And Automated Patching For Storage Collision Vulnerabilities In Smart Contracts, Yu Pan, Wanjing Han, Yue Duan, Mu Zhang

Research Collection School Of Computing and Information Systems

Storage collision vulnerabilities, a significant security risk in upgradeable smart contracts, often arise when a user-facing proxy contract and a backend logic contract share storage space. While static analysis techniques can detect such issues, they often over-approximate program states, leading to false positives and requiring developers to manually verify each issue, giving attackers time to exploit any overlooked vulnerabilities. To address this, we propose COLLISIONREPAIR, an automated patching technique for mitigating storage collision risks. COLLISIONREPAIR monitors storage access sequences between proxy and logic contracts by defining an "ownership" property for storage locations. It then replays historical transactions to recover existing …

Prism: To Fortify Widget Based User‑App Data Exchanges Using Android Virtualization Framework, Yingtat Ng, Zhe Chen, Haiqing Qiu, Xuhua Ding

Research Collection School Of Computing and Information Systems

We present Prism, an UI hardening technique for an Android app to safeguard its widgets against a corrupted kernel. Prism ensures secure interface rendering and allows for visual authentication, which developers could use to enable user intent confidentiality protection. Our design leverages the recent Android Virtualization Framework with minimal changes to the existing UI framework and graphics subsystem. It is much easier to deploy and use Prism on Android phones than TrustZone-based secure UI schemes, because the apps are not admitted to the Secure World and retain their full rights to manage and control their own interfaces. We have implemented …

Tetd: Trusted Execution In Trust Domains, Zhanbo Wang, Jiaxin Zhan, Xuhua Ding, Fengwei Zhang, Ning Hu

Research Collection School Of Computing and Information Systems

Intel TDX empowers cloud service providers to construct confidential virtual machines called trust domains (TDs) on x86 platforms. Similar to its counterparts from AMD and Arm, TDX's hardware based protection over integrity and secrecy of virtual machine memory and vCPU states inevitably hinders legitimate virtual machine management such as introspection. At the presence of compromised high-privileged software (e.g., the guest kernel), neither the cloud service provider nor the TD owner can securely carry out a task within the TD. To tackle this problem, we propose TETD, an in-TD trusted execution technique without trusting any TD system software. Our design does …

Oblivious Digital Tokens, Mihael Liskij, Xuhua Ding, Gene Tsudik, David A. Basin

Research Collection School Of Computing and Information Systems

A computing device typically identifies itself by exhibiting unique measurable behavior or by proving its knowledge of a secret. In both cases, the identifying device must reveal information to a verifier. Considerable research has focused on protecting identifying entities (provers) and reducing the amount of leaked data. However, little has been done to conceal the fact that the verification occurred.We show how this problem naturally arises in the context of digital emblems, which were recently proposed by the International Committee of the Red Cross to protect digital resources during cyber-conflicts. To address this new and important open problem, we define …

Zero-Shot Generalist Graph Anomaly Detection With Unified Neighborhood Prompts, Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However, existing GAD methods are one-model-for-one-dataset approaches, i.e., training a separate model for each graph dataset. This largely limits their applicability in real-world scenarios. To overcome this limitation, we propose a novel zero-shot generalist GAD approach UNPrompt that trains a one-for-all detection model, requiring the training of one GAD model on a single graph dataset and then effectively generalizing to detect anomalies in other graph datasets without any retraining or fine-tuning. The key insight …

Achilles: A Formal Framework Of Leaking Secrets From Signature Schemes Via Rowhammer, Junkai Liang, Zhi Zhang, Xin Zhang, Qingni Sheng, Yansong Gao, Xinliang Yuan, Haiyang Xue, Pengfei Wu, Zhonghai. Wu

Research Collection School Of Computing and Information Systems

Signature schemes are a fundamental component of cyber-security infrastructure. While they are designed to be mathematically secure against cryptographic attacks, they are vulnerable to Rowhammer fault-injection attacks. Since all existing attacks are ad-hoc in that they target individual parameters of specific signature schemes, it remains unclear about the impact of Rowhammer on signature schemes as a whole.In this paper, we present Achilles, a formal framework that aids in leaking secrets in various real-world signature schemes via Rowhammer. Particularly, Achilles can be used to find potentially more vulnerable parameters in schemes that have been studied before and also new schemes that …

Improved Secure Two-Party Computation From A Geometric Perspective, Hao Guo, Liqiang Peng, Haiyang Xue, Li Peng, Weiran Liu, Zhe Liu, Lei. Hu

Research Collection School Of Computing and Information Systems

Multiplication and other non-linear operations are widely recognized as the most costly components of secure two-party computation (2PC) based on linear secret sharing. Moreover, the comparison protocol (or Wrap protocol) is essential for various operations such as truncation, signed extension, and signed non-uniform multiplication. This paper aims to optimize these protocols by avoiding invoking the costly comparison protocol, thereby improving their efficiency.We propose a novel approach to study 2PC from a geometric perspective. Specifically, we interpret the two shares of a secret as the horizontal and vertical coordinates of a point in a Cartesian coordinate system, with the secret itself …

Consistent Client Simulation For Motivational Interviewing-Based Counseling, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Phey Ling Kit, Jenny Xiuhui Giam, John Pinto, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Simulating human clients in mental health counseling is crucial for training and evaluating counselors (both human or simulated) in a scalable manner. Nevertheless, past research on client simulation did not focus on complex conversation tasks such as mental health counseling. In these tasks, the challenge is to ensure that the client’s actions (i.e., interactions with the counselor) are consistent with with its stipulated profiles and negative behavior settings. In this paper, we propose a novel framework that supports consistent client simulation for mental health counseling. Our framework tracks the mental state of a simulated client, controls its state transitions, and …

Equivalence And Similarity Refutation For Probabilistic Programs, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two fundamental relational properties of probabilistic programs that are essential for their correctness both in implementation and in compilation. In this work, we present a new method for static equivalence and similarity refutation. Our method refutes equivalence and similarity by computing a function over program outputs whose expected value with respect to the output distributions of two programs is different. The function is computed simultaneously with an upper expectation supermartingale and a lower expectation submartingale for …

On The Other Hand: The Unity Of Contraries In A World Of Conflict, Michael Kearney

Faculty Work Comprehensive List

"How difficult will it be to keep our eyes set on God, not idols, in a world of generative AI, political polarization, rampant individualism, and anxiety about the future?"

Posting about ­­­­­­­­skilled communication from In All Things, an online hub that offers insight into maintaining and faithful and orthodox Reformed Christian worldview while fearlessly engaging in every aspect of contemporary life – until all is made new.

On the Other Hand

Empowering The User Through Inclusive Technical Writing, Robert Enriquez

English Theses & Dissertations

This thesis examines the issues in technical writing due to a disconnect between writer and reader/user. I am basing this study on my real-life job. I am a manager at a well-known sneaker retailer. In August 2024, there was a Workforce Management (WFM) software that the sneaker retailer launched in the retail stores. The WFM itself was glitched and unfinished. Not much disclaimer, training or preparation was given other than these procedural documents the sneaker retailer gave. If all the store managers had to depend on was technical documentation to prepare for this already glitched WFM, then I argue there …

Advanced Analytical Biosensing For Cancer Detection And Neural Diagnostics Using Tapered Optical Fiber (Tof), Protonic, And High Throughput Microplate –Based Technologie, Bayan Hassan Alharbi

Chemistry & Biochemistry Theses & Dissertations

This dissertation investigates the development and application of advanced biosensing technologies to enhance early disease detection, neurological diagnostics, and bioactive compound evaluation. The research spans four key areas. First, it introduces tapered optical fiber (TOF)-based plasmonic biosensors for the non-invasive detection of prostate cancer, demonstrating high sensitivity and specificity compared to conventional diagnostic methods.

Second, it explores the use of fluorescent biosensors to test the Transmembrane Electrostatically Localized Proton (TELP) theory, shedding light on the role of localized protons in neuronal signaling and energy transfer. Third, the work presents a high-throughput, microplate-based biosensing platform for analyzing mitochondrial function under nanosecond …