Digital Commons Network^™

Late-Night And Early-Morning Train Scheduling With Non-Traffic Hour Maintenance Window In Urban Rail Transit Systems, Yaochen Ma, Hai Yang, Hai Wang

Research Collection School Of Computing and Information Systems

Regular maintenance during non-traffic hours (NTH) is vital for the resilience of urban rail transit (URT) systems, yet an insufficient NTH maintenance window poses a challenge for URT systems in various cities. For instance, the Hong Kong MTR Corporation has noted that the required NTH maintenance time often exceeds the available window, prompting service adjustments such as earlier late-night closures and/or later early-morning starts. To address this challenge, this study develops an optimal scheduling framework that links late-night and early-morning URT services through the NTH maintenance window requirement to maximize public welfare. A Decoupled Optimization Model (DOM) first derives closed-form …

Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models, Chunyan Liu, Yan Lei, Huan Xie, Jinping Wang, Yue Yu, David Lo

Research Collection School Of Computing and Information Systems

Learning-based dynamic fault localization techniques play a crucial role in the field of software engineering. These techniques dynamically execute test cases to meticulously extract useful knowledge from the execution information in the program, with the aim of identifying fault locations by leveraging machine learning, deep learning, and large language models. Currently, there is already a flourishing body of research that is intensely focused on learning-based dynamic fault localization. Research literature can be categorized into two main aspects for learning-based dynamic fault localization: data-based enhancements (i.e., the datasets) and model-based enhancements (i.e., the suspiciousness algorithms). Thus, we conduct an extensive literature …

Deep Learning For Video Anomaly Detection: A Review, Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion of architectures of continuously growing capability and capacity, a great variety of deep learning-based methods are constantly emerging for the VAD task, greatly improving the generalization ability of detection algorithms and broadening the application scenarios. Therefore, such a multitude of methods and a large body of literature make a comprehensive survey a pressing necessity. In this article, we present …

Operationalizing Ethics For Ai Agents: How Developers Encode Values Into Repository Context Files, Christoph Treude, Sebastian Baltes, Marc Cheong

Research Collection School Of Computing and Information Systems

As AI coding agents become embedded in software development workflows, developers are beginning to operationalize ethical principles by encoding behavioral rules into repository-level context files for AI agents, such as AGENTS.md files. Rather than examining the ethics of AI agents in the abstract, this vision paper investigates how ethics and values are already being translated for AI agents into actionable instructions that shape agent behavior. Through a preliminary investigation, we find that developers are already embedding guidance related to fairness, accessibility, sustainability, tone, and privacy. These artifacts function as a developer-authored governance layer, translating abstract principles into situated, natural-language directives …

A Dataset Of Agentic Ai Coding Tool Configurations, Matthias Galster, Seyedmoein Mohsenimofidi, Levi Böhme, Jai Lal Lulla, Muhammad Auwal Abubakar, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

Agentic AI coding tools such as Claude Code and OpenAI Codex execute multi-step coding tasks with limited human oversight. To steer these tools, developers create repository-level configuration artifacts (e.g., Markdown files) for configuration mechanisms such as Context Files, Skills, Rules, and Hooks. There is no curated dataset yet that captures these configurations at scale. This dataset, collected from open-source GitHub repositories, fills that gap. We selected 40,585 actively maintained repositories through metadata filtering, classified them using GPT-5.2 to identify 36,710 as belonging to engineered software projects, and systematically detected configuration artifacts in these repositories. The dataset covers 4,738 repositories across …

Oscbench: Benchmarking Object State Change In Text-To-Video Generation, Xianjing Han, Bin Zhu, Shiqi Hu, Franklin Mingzhe Li, Patrick Carrington, Roger Zimmermann, Jingjing Chen

Research Collection School Of Computing and Information Systems

Text-to-video (T2V) generation models have made rapid progress in producing visually high-quality and temporally coherent videos. However, existing benchmarks primarily focus on perceptual quality, text–video alignment, or physical plausibility, leaving a critical aspect of action understanding largely unexplored: object state change (OSC) explicitly specified in the text prompt. OSC refers to the transformation of an object’s state induced by an action, such as peeling a potato or slicing a lemon. In this paper, we introduce OSCBench, a benchmark specifically designed to assess OSC performance in T2V models. OSCBench is constructed from instructional cooking data and systematically organizes action–object interactions into …

Tranx-Adapter: Bridging Artifacts And Semantics Within Mllms For Robust Ai-Generated Image Detection, Wenbin Wang, Yuge Huang, Jianqing Xu, Yue Yu, Jiangtao Yan, Shouhong Ding, Pan Zhou, Yong Luo

Research Collection School Of Computing and Information Systems

Rapid advances in AI-generated image (AIGI) technology enable highly realistic synthesis, threatening public information integrity and security. Recent studies have demonstrated that incorporating texture-level artifact features alongside semantic features into multimodal large language models (MLLMs) can enhance their AIGI detection capability. However, our preliminary analyses reveal that artifact features exhibit high intra-feature similarity, leading to an almost uniform attention map after the softmax operation. This phenomenon causes attention dilution, thereby hindering effective fusion between semantic and artifact features. To overcome this limitation, we propose a lightweight fusion adapter, TranX-Adapter, which integrates a Task-aware Optimal-Transport Fusion that leverages the Jensen-Shannon divergence …

Towards Uniformity And Alignment For Multimodal Representation Learning, Wenzhe Yin, Pan Zhou, Zehao Xiao, Jie Liu, Shujian Yu, Jan-Jakob Sonke, Efstratios Gavves

Research Collection School Of Computing and Information Systems

Multimodal representation learning aims to construct a shared embedding space in which heterogeneous modalities are semantically aligned. Despite strong empirical results, InfoNCE-based objectives introduce inherent conflicts that yield distribution gaps across modalities. In this work, we identify two conflicts in the multimodal regime, both exacerbated as the number of modalities increases: (i) an alignment–uniformity conflict, whereby the repulsion of uniformity undermines pairwise alignment, and (ii) an intra-alignment conflict, where aligning multiple modalities induces competing alignment directions. To address these issues, we propose a principled decoupling of alignment and uniformity for multimodal representations, providing a conflict-free recipe for multimodal learning that …

Variational Speculative Decoding: Rethinking Draft Training From Token Likelihood To Sequence Acceptance, Xiandong Zou, Jianshu Li, Jing Huang, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates inference for (M)LLMs, yet a training-decoding discrepancy persists: while existing methods optimize single greedy trajectories, decoding involves verifying and ranking multiple sampled draft paths. We propose Variational Speculative Decoding (VSD), formulating draft training as variational inference over latent proposals (draft paths). VSD maximizes the marginal probability of target-model acceptance, yielding an ELBO that promotes high-quality latent proposals while minimizing divergence from the target distribution. To enhance quality and reduce variance, we incorporate a path-level utility and optimize via an Expectation-Maximization procedure. The E-step draws MCMC samples from an oracle-filtered posterior, while the M-step maximizes weighted likelihood using …

Air: Improving Agent Safety Through Incident Response, Zibo Xiao, Jun Sun, Junjie Chen

Research Collection School Of Computing and Information Systems

Large Language Model (LLM) agents are increasingly deployed in practice across a wide range of autonomous applications. Yet current safety mechanisms for LLM agents focus almost exclusively on preventing failures in advance, providing limited capabilities for responding to, containing, or recovering from incidents after they inevitably arise. In this work, we introduce AIR, the first incident response framework for LLM agent systems. AIR defines a domain-specific language for managing the incident response lifecycle autonomously in LLM agent systems, and integrates it into the agent's execution loop to (1) detect incidents via semantic checks grounded in the current environment state and …

Rendering Data Unlearnable By Exploiting Llm Alignment Mechanisms, Ruihan Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly trained on massive, heterogeneous text corpora, raising serious concerns about the unauthorised use of proprietary or personal data during model training. In this work, we address the problem of data protection against unwanted model learning in a realistic blackbox setting. We propose Disclaimer Injection, a novel data-level defence that renders text unlearnable to LLMs. Rather than relying on model-side controls or explicit data removal, our approach exploits the models’ own alignment mechanisms: injecting carefully designed alignment-triggers to prevent effective learning. Through layer-wise analysis, we find that finetuning on such protected data induces persistent activation …

Train In Vain: Functionality-Preserving Poisoning To Prevent Unauthorized Use Of Code Datasets, Yuan Xiao, Yuchen Chen, Jiaming Wang, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen

Research Collection School Of Computing and Information Systems

The widespread availability of large-scale code datasets has accelerated the development of code large language models (CodeLLMs), raising concerns about unauthorized dataset usage. Dataset poisoning offers a proactive defense by reducing the utility of such unauthorized training. However, existing poisoning methods often require full-dataset poisoning and introduce transformations that break code compilability. In this paper, we introduce FunPoison, a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. FunPoison leverages reusable statement-level templates with automatic repair and conservative safety checking to ensure side-effect freedom, while a type-aware synthesis module preserves type correctness, suppresses static-analysis warnings, and …

Itimo: An Llm-Empowered Synthesis Dataset For Travel Itinerary Modification, Zhuoxuan Huang, Yunshan Ma, Hongyu Zhang, Hua Ma, Zhu Sun

Research Collection School Of Computing and Information Systems

Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixed itinerary planning, leaving modification underexplored due to the scarcity of shape need-to-modify itinerary data. To bridge this gap, we formally define the itinerary modification task and propose a general pipeline to construct the corresponding dataset, namely iTIMO. This pipeline frames the generation of shape need-to-modify itinerary data as an intent-driven perturbation task. It instructs large language models to perturb real-world itineraries using three operations: REPLACE, ADD, and DELETE. Each perturbation is grounded in three intents: …

Dual-Diffusional Generative Fashion Recommendation, Mingzhe Yu, Lei Wu, Qianru Sun, Yunshan Ma

Research Collection School Of Computing and Information Systems

Personalized generative recommender systems have emerged as a promising solution for fashion recommendation. However, existing methods primarily rely on implicit visual embeddings from historical interactions, which often contain preference-irrelevant information and result in insufficient user behavior modeling. Moreover, these models typically generate only item images, providing limited interpretability. To address these limitations, we propose DualFashion, a Dual-Diffusional Generative Fashion Recommendation Architecture that jointly models image and text modalities for personalized and explainable recommendation. DualFashion adopts a dual-diffusion Transformer with image and text branches, where structured attribute-level captions and visual outfit information are jointly used as conditioning signals to model user …

Avadclip: Audio-Visual Collaboration For Robust Video Anomaly Detection, Peng Wu, Wanshun Su, Guansong Pang, Yujia Sun, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

With the increasing adoption of video anomaly detection in intelligent surveillance domains, conventional visual-only detection approaches often struggle with information insufficiency and high false-positive rates in complex environments. To address these limitations, we present a novel weakly supervised framework that leverages audio-visual collaboration for robust video anomaly detection. Capitalizing on the exceptional cross-modal representation learning capabilities of Contrastive Language-Image Pretraining (CLIP) across visual, audio, and textual domains, our framework introduces two major innovations: an efficient audio-visual fusion that enables adaptive cross-modal integration through lightweight parametric adaptation while maintaining the frozen CLIP backbone, and a novel audio-visual prompt that dynamically enhances …

Robust Graph Learning On The Web: Challenges, Methods, And Applications, Ao Xiang, Yang Liu, Guansong Pang, Yuanhao Ding, Hezhe Qiao, Dawei Cheng, Qing He

Research Collection School Of Computing and Information Systems

Graph learning is transforming web intelligence, powering applications from recommender systems to anomaly detection. However, most existing approaches implicitly assume ideal conditions where training and testing data are accurate, complete, and free from manipulation. In reality, web environments rarely exhibit such stability. Dynamic user behavior, incomplete or outdated content, adversarial interference, and sudden distribution shifts can all erode the reliability of even state-of-the-art models, leading to biased or unsafe outcomes. This tutorial provides a comprehensive survey of emerging strategies for robust graph learning on the web. We first present a structured taxonomy of the principal robustness threats specific to web …

Verbalizing Lightgcn: Direct Learning Of Textual Representations From User-Item Interaction Graph Via Llms, Manh-Khanh Ngo Huu, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In this work, we propose VerbaLightGCN, a novel LLM-based recommendation framework that integrates the semantic understanding of LLMs with user-item interaction modeling. Traditional collaborative filtering (CF) models typically embed user and item IDs into a latent space to capture interaction signals. However, pretrained LLMs cannot natively interpret these learned embeddings. To bridge this gap, VerbaLightGCN adopts a CF-as-text paradigm, in which collaborative signals are encoded in textual form and directly learned from the user–item interaction graph, and are then combined with semantic information to construct user and item profiles that function as latent embeddings. Inspired by LightGCN, our method retains …

Divide-And-Conquer: Generating Ui Code From Screenshots, Yuxuan Wan, Chaozheng Wang, Yi Dong, Wenxuan Wang, Shuqing Li, Yintong Huo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Websites are critical in today’s digital world, with over 1.11 billion currently active and approximately 252,000 new sites launched daily. Converting website layout design into functional UI code is a time-consuming yet indispensable step of website development. Manual methods of converting visual designs into functional code present significant challenges, especially for non-experts. To explore automatic design-to-code solutions, we first conduct a motivating study on GPT-4o and identify three types of issues in generating UI code: element omission, element distortion, and element misarrangement. We further reveal that a focus on smaller visual segments can help multimodal large language models (MLLMs) mitigate …

Effectiveness Of Vocational Therapy On Psychotic Symptoms Of Patients With Schizophrenia, Iman Ahmed Jumaa, Naamah Shlaibah Humaidi

Iraqi Postgraduate Medical Journal

AbstractBackground: Schizophrenia is a severe mental disorder often resulting in significant functional impairment. While antipsychotic medications are the cornerstone of treatment, complementary therapies are needed to address residual symptoms and improve functional outcomes.Aims and Objectives: This study aimed to evaluate the effect of adding a structured vocational therapy program to standard drug therapy in patients with schizophrenia.Materials and Methods: A prospective was conducted with 100 inpatients at Al-Rashad Hospital. Participants were divided into two cohorts: an intervention cohort (n=50) that received vocational therapy plus drug therapy and a control cohort (n=50) that received drug therapy alone. The Positive and Negative …

Neonatal And Maternal Outcomes In Pregnancies With Sonographically Diagnosed Nuchal Cord: A Prospective Cohort Study, Eman Muhammad Ramadan, Samar Sarsam

Iraqi Postgraduate Medical Journal

ABSTRACT:Background: Nuchal cords, where the umbilical cord encircles the fetal neck, are a common obstetric finding, potentially impacting neonatal and maternal outcomes. Their clinical significance remains debated.Aim of the Study: To evaluate the prevalence of nuchal cords in pregnancies at Al Elwiya maternity teaching hospital and to evaluate the association between nuchal cord presence and effects on neonatal and maternal outcomes.Patients and Methods: A prospective, single-center, cohort study was conducted at Al-Elwiya Maternity Teaching Hospital from first of January to first of December 2024. It included 600 pregnant women in the study 107 had an ultrasound diagnosed with nuchal cords. …

Use Of The Bethesda System For Reporting Thyroid Cytopathology In Thyroid Nodules Incoordination To The Appropriate Surgical Intervention, Rusul Abid Kareem, Basim Rassam Ghadhban

Iraqi Postgraduate Medical Journal

Background: The thyroid nodule refers to an abnormal growth of thyroid cells. Although the vast majority of thyroid nodules are benign, a small proportion of thyroid nodules are malignant. In order to diagnose and treat malignant thyroid nodules at the earliest stage, most of them need radiological and cytological evaluation. Fine-needle aspiration (FNA) using The Bethesda System for reporting thyroid cytopathology.Aim of study: To evaluate the significance of the Bethesda classification in detecting malignant thyroid nodule and relate the appropriate surgical intervention accordingly.Methods: prospective, case control study was conducted in Baghdad teaching hospital, 2nd floor. from 1st of December 2021 …

Evaluating The Usefulness Of 24-Hour Video Eeg Monitoring In Dr. Sa’Ad Alwitry’S Hospital, Baghdad, Iraq, Ebtehal Dhiyaa, Akram Mohammad Al-Mahdawi, Muataz Fairooz Abd

Iraqi Postgraduate Medical Journal

Background: Video electroencephalography (VEEG) monitoring, which combines EEG with synchronized video recording, is a valuable but resource-intensive tool for evaluating seizure disorders. While its role in diagnosis and classification is established, its specific impact on altering provisional diagnoses and guiding treatment remains underexplored.Objective: To assess the diagnostic utility of 24-hour inpatient VEEG monitoring in confirming or modifying provisional diagnoses and reducing misdiagnosis in patients with paroxysmal events.Methods: A retrospective analysis was conducted on 192 patients who underwent VEEG monitoring at Dr. Saad Alwitry Hospital between January and December 2018. Patient records were reviewed to assess the impact of VEEG on …

Esophageal Stricture Post Congenital Tracheoesophageal Fistula Repair, Zaid A. Zaid, Momtaz Al-Nima

Iraqi Postgraduate Medical Journal

Background: It’s a narrowing of > 50% of the esophagus lumen or as a narrowing detected on a contrast study , or at esophagostomy in combination with symptomsTracheoesophageal fistula (TEF) is a relatively rare congenital anomaly occurring in one in 2500-3000 live births (3)Moreover, the life expectancy of the patients without proper treatment may be measured in weeksAim of study: evaluation of the demographics, surgical details, and postoperative outcomes of pediatric patients undergoing surgery for Tracheoesophageal Fistula (TEF) repair.Methods: The study was conducted at Pediatric Teaching Hospital/Medical City from December, 2022, to November , 2023. A total of 35 cases post …

Family Medicine In Iraq: Bridging The Gap Between Training And Practice For Health System Reform, Eman A. Al-Kaseer

Iraqi Postgraduate Medical Journal

No abstract provided.

Ms4 Swmpp 2026, John Kay

Municipal Separate Storm Sewer System (MS4) Permit

Stormwater discharges from Sandia National Laboratories/New Mexico (SNL/NM) are eligible for coverage under the National Pollutant Discharge Elimination System (NPDES) Municipal Separate Storm Sewer System (MS4) Permit. This Stormwater Management Plan (SWMP) was prepared by National Technology and Engineering Solutions of Sandia (NTESS) for the Department of Energy (DOE) National Nuclear Security Administration (NNSA) Sandia Field Office (SFO), owner of SNL/NM, located in Albuquerque, NM.

The Effect Of The Continuous Care Model On Self-Practices And Quality Of Life Among Preeclamptic Women, Amira Ahmed Altweel, Salma Abd-Elatty Moawed, Amr Mahmoud Riad, Shaimaa Hassan Mohamady

Nursing

No abstract provided.

Correlation Between Overthinking And Procrastination Behavior Among Nurses, Mona Hassan Abdelaal

Nursing

ABSTRACT Context: A key challenge in understanding internalizing psychological disorders is identifying the cognitive processes that sustain distress and disrupt effective functioning. Recurrent, hard-to-interrupt thinking patterns may interfere with individuals’ ability to initiate and complete intended tasks. Concurrently, individuals may persistently delay necessary actions despite recognizing their negative consequences, indicating a breakdown between intention and behavior. This pattern of delay appears to be associated with seeking short term emotional relief, reduced consideration of future outcomes, and avoidance of prolonged effort. While these issues have been studied independently, their overlapping mechanisms require further investigation. The potential role of repetitive cognitive processes …

Core Sipp Community Collaboration Report, Yvonnie Baik, Mahtab Soleimani, Camille Velarde, Elena E. Martinez, Theresa H. Cruz

Reports & Documents

The New Mexico Core State Injury Prevention Program (Core SIPP), in partnership with the University of New Mexico Division of Prevention and Population Sciences, implemented the Community Collaboration Initiative to support community-led, upstream approaches to injury and violence prevention. Five community-based organizations across New Mexico received funding to develop and implement projects addressing shared risk and protective factors identified through advisory group input and community listening sessions. Programs emphasized youth leadership, mentorship, cultural identity, healthy relationships, mental and behavioral wellness, community connectedness, and access to supportive adults and resources.

Across the five projects, 68 youth participated in programming. A retrospective …

Block Kinematics, Interseismic Coupling And Fault Slip Rates In The Aegean Region From Gps And Earthquake Slip Vector Data: 2. Hellenic Subduction System, Konstantinos Chousianitis, Volkan Özbey, Sotiris Sboras, Robert Mccaffrey, Semih Ergintav

Geology Faculty Publications and Presentations

The Hellenic subduction system is the primary locus of Nubia‐Eurasia convergence, yet thedistribution of strain accumulation along it is still debated. Here, we invert 832 geodetic velocities and 146earthquake slip vectors to assess the spatial variability of interseismic locking and provide new constraints onthe rate of slip deficit accumulation. We find that the subduction interface beneath southwestern Peloponneseand across the area from offshore south Peloponnese to offshore southwest of western Crete hosts three patchesof low to moderate locking (0%–40%) at 10–50 km depth whose slip rate deficits account for ∼15–30% of thetotal plate convergence. The offshore weakly locked patches show …

Cultural Diplomacy Through Museums: An Analysis Of The Role Of Sheikh Dr. Sultan Al Qasimi In Promoting Intercultural Dialogue Through History, Culture, And Tourism, Abdulla Almheiri, Fareed Awae, Muhammad Talhah Jima’Ain, Hamdan Chehdimae, Mohamad Azrien Mohamed Adnan, Fuad Trayek

All Works

This study aims to analyze the role of cultural diplomacy in the Emirate of Sharjah through museums. Particular emphasis is placed on the important contributions of His Highness Sheikh Dr. Sultan Al Qasimi in promoting intercultural communication and consolidating cultural identity through the support of history, archaeology, and tourism. The study uses a quantitative approach with a survey research design. The perspectives of 285 museum visitors have been analyzed based on arithmetic means and standard deviations to measure their level of awareness regarding the museum’s historical, cultural, and tourism dimensions. The findings indicate a high level of awareness concerning the …