Digital Commons Network^™

Stylometric And Formal Patterns In The Scholarly Impact Of Scientific Literature, Joshua Ange, Eric Godat, Rajani Sudan

SMU Journal of Undergraduate Research

Scientific communication is typically tied to promoting public engagement and interest in science, increasing scientific literacy, and playing an essential role in policymaking. The success of public communication of scientific findings is largely associated with secondary characteristics of research (e.g. the style of writing and presentation), rather than the primary content or research quality. But it is unclear to what extent the success of scientific literature intended for working scientists is influenced by those same secondary characteristics. Does the writing style of scientific articles impact their success in academic spheres? In this study, we explore the stylometric and formal characteristics …

Assessment And Evidence Practices In Cybersecurity Education: A Systematic Review (2015–2025), James K. Mayberry

Journal of Cybersecurity Education, Research and Practice

This study presents a PRISMA-based systematic review of 412 cybersecurity education intervention studies, coding assessment methods, evidence types, claimed outcomes, use of established assessment instruments, and artifact availability. Despite frequent claims of skill development and workforce preparation, 45.4% of studies reported no identifiable assessment. Knowledge tests appeared in 11.4% of studies, while performance assessments appeared in 10.2%. From 2015 to 2025, assessment practices remained dominated by post-only designs or no assessment, with no statistically detectable increase in pre/post-capable designs. Use of established assessment instruments was rare, with 94.2% of assessed studies using ad hoc measures or not identifying an established …

Coordinating Meaning With Ai System Cards: A Thematic Analysis, Jennifer Rene French Cyrek

Doctoral Dissertations and Projects

As the meaning of AI risk remains unsettled across sociotechnical and public discourse, AI system cards are an emergent, yet understudied, genre of technical documentation through which AI technology developers publicly frame new AI system capabilities including risks. This thematic content analysis study examines how AI technology developers coordinate meaning regarding risk and responsible development in stewardship of AI. Guided by a constitutive view of communication and systems theory, second-order cybernetics, and the cybernetic tradition, this study analyzes a purposive corpus of AI system cards collected from 2023-2025 using thematic content analysis and the hierarchy of meaning heuristic from coordinated …

Ai-Powered Knowledge Engines As Research Infrastructure For Systematic Knowledge Discovery, Gary Welz

Publications and Research

This paper proposes knowledge engines as a framework for understanding how intelligent systems — both human and artificial — systematically discover, integrate, and generate knowledge. We argue that history’s greatest scientific minds functioned as knowledge engines, processing information through iterative cycles of ingestion, analysis, synthesis, and communication, guided by curiosity and willingness to challenge established beliefs.

We propose a taxonomy of nine integrated capabilities — ingestion, digestion, analysis, calculation, comparison, connection, association, analogy, and multimodal communication — that any serious knowledge engine must combine systematically. The argument is deliberately integrative: achieving ambitious research goals requires orchestrating all nine capabilities within …

Can An Ai System Be Creative? A Critical Perspective From Art And Engineering, Ivan Magrin-Chagnolleau

Presidential Fellows Articles and Research

This paper examines the question of whether artificial intelligence (AI) systems can be creative, approached from the dual perspective of a researcher trained in electrical engineering, pattern recognition, machine learning, and neural networks, who has also spent most of his life engaged in the arts as actor, stage and film director, writer, composer, and visual artist, and in philosophy. Drawing on Margaret Boden’s foundational framework — both her three properties of creativity (novelty, surprise, and value) and her three types of creative processes (combinatorial, exploratory, and transformational) — the paper argues that AI systems are structurally incapable of creativity in …

Reproduction Beyond Benchmarks: Constbert And Colbert-V2 Across Backends And Query Distributions, Utshab Kumar Ghosh, Ashish David, Shubham Chatterjee

Computer Science Faculty Research & Creative Works

Reproducibility must validate architectural robustness, not just numerical accuracy. We evaluate ColBERT-v2 and ConstBERT across five dimensions, finding that while ConstBERT reproduces within 0.05% MRR@10 on MS-MARCO, both models show a drop of 86-97% on long, narrative queries (TREC ToT 2025). Ablations prove this failure is architectural: performance plateaus at 20 words because the MaxSim operator's uniform token weighting cannot distinguish signal from filler noise. Furthermore, undocumented backend parameters create an 8-point gap due to ConstBERT's sparse centroid coverage, and fine-tuning with 3x more data actually degrades performance by up to 29%. We conclude that architectural constraints in multi-vector retrieval …

Depro: Understanding The Role Of Llms In Debugging Competitive Programming Code, Nabiha Parvez, Md Tanvin Sarkar Pallab, Mia Mohammad Imran, Tarannum Shaila Zaman

Computer Science Faculty Research & Creative Works

Debugging consumes a substantial portion of the software development lifecycle, yet researchers do not yet understand well the effectiveness of Large Language Models (LLMs) in this task. Competitive programming offers a rich benchmark for such evaluation, given its diverse problem domains and strict efficiency requirements. We present an empirical study of LLM-based debugging on competitive programming problems and introduce DePro, a test-case-driven approach that assists programmers by correcting existing code rather than generating new solutions. DePro combines brute-force reference generation, stress testing, and iterative LLM-guided refinement to efficiently identify and resolve errors. Experiments on 13 faulty user submissions from Codeforces …

Llm-Enabled Open-Source Systems In The Wild: An Empirical Study Of Vulnerabilities In Github Security Advisories, Fariha Tanjim Shifat, Hariswar Baburaj, Ce Zhou, Jaydeb Sarker, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

Large language models (LLMs) are increasingly embedded in open-source software (OSS) ecosystems, creating complex interactions among natural language prompts, probabilistic model outputs, and execution-capable components. However, it remains unclear whether traditional vulnerability disclosure frameworks adequately capture these model-mediated risks. To investigate this, we analyze 295 GitHub Security Advisories published between January 2025 and January 2026 that reference LLM-related components, and we manually annotate a sample of 100 advisories using the OWASP Top 10 for LLM Applications 2025.We find no evidence of new implementation-level weakness classes specific to LLM systems. Most advisories map to established CWEs, particularly injection and deserialization weaknesses. …

Compressed Cinema As A Study In Llm Latent Spaces, Mallen Clifton

ELO (un)supervised 2026

In his article “Spec Acts” (2021), Matthew Kirschenbaum analyzes the AI-generated novel 1 the Road to develop his titular concept of the spec act, “the future in its multitudes collapsing into an actionable present.” With the proliferation of texts produced by generative AI and subsequent critical analyses of them, one element in particular calls for further theorization: “the future in its multitudes,” or more directly, the latent space. This echoes arguments by critics such as Antonio Somaini, who offered his own “Theory of Latent Spaces” last year. However, where Somaini’s attention is towards visual culture, I turn mine to the …

"Todo: Fix The Mess Gemini Created": Towards Understanding Genai-Induced Self-Admitted Technical Debt, Abdullah Al Mujahid, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

As large language models (LLMs) such as ChatGPT, Copilot, Claude, and Gemini become integrated into software development workflows, developers increasingly leave traces of AI involvement in their code comments. Among these, some comments explicitly acknowledge both the use of generative AI and the presence of technical shortcomings. Analyzing 6,540 LLM-referencing code comments from public Python and JavaScript-based GitHub repositories (November 2022-July 2025), we identified 81 that also self-admit technical debt (SATD). Developers most often describe postponed testing, incomplete adaptation, and limited understanding of AI-generated code, suggesting that AI assistance affects both when and why technical debt emerges. We term GenAI-Induced …

Llm-As-A-Judge For Infection Prevention And Control And Antimicrobial Resistance Impact: Comparing Three Main Llms Vs. Human Experts' Assessment, Marcello Di Pumpo, Leonardo Villani, Maria Rosaria Gualano, Danilo Buonsenso, Francesca Raffaelli, Daniele Donà, Patrizia Laurenti, Vittorio Maio, Stefania Boccia, Walter Ricciardi

College of Population Health Faculty Papers

BACKGROUND: Large language models (LLMs) are increasingly used to generate health information, yet their reliability as evaluators remains unclear. This study investigated the feasibility of an LLM-as-a-judge methodology in the context of infection prevention and antimicrobial resistance (AMR), comparing automated ratings with human expert benchmarks.

METHODS: We performed a secondary analysis of an expert-annotated dataset of health messages. Three leading LLMs (ChatGPT, Claude, Gemini) independently evaluated the same messages using an adapted DISCERN tool across five domains: information reliability, quality, AMR impact, persuasiveness, and overall score. We utilized descriptive statistics, intra-rater reliability tests, and mixed-effects ordinal regression to analyze divergence …

Contemporary Cybersecurity Challenges In Emerging Technologies: A Systematic Literature Analysis, Faztudo Languisse Prof

Journal of Cybersecurity Education, Research and Practice

The accelerating convergence of artificial intelligence (AI), the Internet of Things (IoT), cloud computing, blockchain, and quantum computing has fundamentally transformed the global threat landscape, introducing cybersecurity challenges of unprecedented complexity and scale. This systematic literature review synthesizes findings from peer-reviewed publications, institutional reports, and regulatory documents published primarily between 2020 and 2025 to provide an integrated analysis of contemporary cybersecurity challenges across five key emerging technology domains. The review identifies critical vulnerabilities inherent to each domain, documents the evolution of threat actors and attack methodologies — including AI-powered ransomware, adversarial machine learning, and harvest-now-decrypt-later quantum attacks — and evaluates …

Performance Evaluation Of Approximate Nearest Neighbor Search On Nvidia Bluefield-3 Dpu, Sophia Bhoria, Nathan Tibbetts, Arjun Kirubakaran, Alima Subedi, Satish Puri

Computer Science Faculty Research & Creative Works

Advanced SmartNICs known as Data Processing Units (DPU) enable in-network data analytics, being equipped with standard processors and accelerators capable of doing custom computation on-NIC. These SmartNICs are advantageous because the host CPU can delegate simpler data analytics tasks, like filtering, to the NIC where the data first arrives. Only data needing further refinement must be passed on to the host CPU. Our benchmarks focus on NVIDIA's commercially available Bluefield-3 DPU. Similarity search, particularly Approximate Nearest Neighbor (ANN) search, is an important domain with wide usage across numerous applications. We explore ANN search on SmartNICs, providing insight into the performance …

Challenges In Scaling R-Tree Spatial Search On Processing-In-Memory, Tasmia Jannat, Michael Gowanlock, Satish Puri

Computer Science Faculty Research & Creative Works

Spatial query processing is important in scientific, geospatial, and data-intensive applications. R-trees are widely used to index spatial objects, but their query-dependent traversal creates irregular work across different regions. This poster studies the challenges of scaling R-tree spatial search on a commercial Processing-in-Memory (PIM) system. Although PIM reduces CPU to memory data movement by executing search near memory, it does not remove full-pipeline overheads: the host still manages data placement, query batching, kernel launches, result retrieval, and aggregation. Our results show strong DPU-side search acceleration, with PIM kernel speedup ranging from about 20 x to 73 x, but end-to-end speedup …

Learning Programming In Informal Spaces: Using Emotion As A Lens To Understand Novice Struggles On R/Learnprogramming, Alif Al Hasan, Subarna Saha, Mia Mohammad Imran

Computer Science Faculty Research & Creative Works

Novice programmers experience emotional difficulties in informal online learning environments, where Confusion and Frustration can hinder motivation and learning outcomes. This study investigates novice programmers' emotional experiences in informal settings, identifies causes of emotional struggle, and explores design opportunities for affect-aware support systems. We manually annotated 1,500 posts from r/learnprogramming using the Learning-Centered Emotions framework, applying clustering, and axial coding. Confusion, Curiosity, and Frustration dominated emotional experiences, sometimes co-occurring and linked to early learning stages. Positive emotions were infrequent. The primary emotional triggers included ambiguous errors, unclear learning pathways, and misaligned resources. We identify five key areas where novice programmers …

Dynlp: Parallel Dynamic Batch Update For Label Propagation In Graph-Based Semi-Supervised Learning, S. M. Shovan, Arindam Khanda, S. M. Ferdous, Sajal K. Das, Mahantesh Halappanavar

Computer Science Faculty Research & Creative Works

Semi-supervised learning aims to infer class labels using only a small fraction of labeled data. In graph-based semi-supervised learning, this is typically achieved through label propagation to predict labels of unlabeled nodes. However, in real-world applications, new data often arrives in batches, and stale data often becomes irrelevant. Each time a new batch appears, reapplying the traditional label propagation algorithm to recompute all labels is redundant, computationally intensive, and inefficient. To address the absence of an efficient label propagation update method, we propose DynLP, a novel GPU-centric Dynamic Batched Parallel Label Propagation algorithm that performs only the necessary updates, propagating …

Heterogeneous Graph-Augmented Contrastive Learning For Extreme Multi-Class Fiqh Classification, Ali A. Jalil

Al-Bahir

• Background/Introduction: Fine-grained text classification in the field of Islamic Jurisprudence (Fiqh) is difficult because of the structural interdependence of the legal concepts and the extremely multi-class long-tail data distribution (667 classes with 5,979 samples, 52.2% of which contain less than 5 samples). The main problem with traditional flat classifiers is that they assume that target classes are independent and orthogonal output neurons which discards very important relational semantics.
• Objectives: This paper seeks to remediate this extreme imbalance and maintain structural taxonomy by modeling the structural space of classification label space itself as an object to be learned, while giving a …

Chi Meta-Project Ecosystem Overview - Spring 2026, David B. Smith

Publications and Research

This paper offers a high-level account of the Center for Holistic Integration’s (CHI) meta-project ecosystem as visualized in the included system map. CHI provides an organizational structure framed around persistent meta-projects that support and extend individual initiatives across curriculum, scholarly and applied research, infrastructure, artistic production, AI development, cultural inquiry, and external partnerships. Rather than presenting the map as a static inventory of projects, the paper examines how its core domains function as living systems through which knowledge, tools, documentation, participants, and collaborations can accumulate over time. It also considers how CHI-mediated connectivity, institutional integration, and external funding allow the …

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks For In-Home Activity Learning Of Mild Cognitive Impairment, Seng Khoon Teh, Ah-Hwee Tan, Kar Way Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

In-home spatiotemporal data, such as the movement trajectory data and the spatial time series data, contains potential predictive utility for detection of geriatric conditions including Mild Cognitive Impairment (MCI), frailty, and cognitive frailty. However, few have explored spatiotemporal learning models for learning and fusion of such disparate spatiotemporal data, owing to the lack of a generalized machine learning model that can jointly model these different spatiotemporal data types. This work reports a multimodal spatiotemporal machine learning model based on a class of self-organizing neural networks that can integrate different spatiotemporal data types for MCI detection. Specifically, Episodic Memory Adaptive Resonance …

Configuring Agentic Ai Coding Tools: An Exploratory Study, Matthias Galster, Seyedmoein Mohsenimofidi, Jai Lal Lulla, Muhammad Auwal Abubakar, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

Agentic AI coding tools increasingly automate software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. We present a systematic analysis of configuration mechanisms for agentic AI coding tools, covering Claude Code, GitHub Copilot, Cursor, Gemini, and Codex. We identify eight configuration mechanisms spanning from static context to executable and external integrations and, in an empirical study of 2,853 GitHub repositories, examine whether and how they are adopted, with a detailed analysis of Context Files, Skills, and Subagents. First, Context Files dominate the configuration landscape and are often the sole mechanism in …

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 …

A Framework For Top-K Queries With Constrained Preferences, Kyriakos Mouratidis, Nikolaos Chaloulakos, Bo Tang

Research Collection School Of Computing and Information Systems

Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently imprecise due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we define the constrained-preference top-k (CT) query. Given an approximate description of …

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 …

Digital Twin-Assisted Optimization Of 6g Wireless Networks: Ensuring Deterministic Communication, Yingpu Nian, Bo Yi, Xingwei Wang, Sajal K. Das

Computer Science Faculty Research & Creative Works

With the rapid advancement of 6G technology and the increasing use of smart devices, Deterministic 6G Wireless Networks (D6WN) have emerged to meet the growing network transmission demands. In particular, applications such as autonomous vehicles, remote surgery, and industrial automation require extremely low transmission latency to function effectively, highlighting the critical need for D6WN in supporting these time-sensitive use cases. Yet, the conventional TCP/IP framework lacks effective unified traffic and congestion control scheduling, aggravating latency and uncertainty, posing a challenge to ensuring reliable real-time critical applications. To address the challenges of deterministic transmission in D6WN, this paper integrates Digital Twin …

Constrained Assortment Optimization Under The Mixed-Logit Model, Hoang Giang Pham, Tien Mai

Research Collection School Of Computing and Information Systems

In this paper, we study the assortment optimization problem under the mixed-logit customer choice model. While assortment optimization has been a central topic in revenue management for decades, the mixed-logit model is widely regarded as one of the most general and flexible frameworks for modeling and predicting customer purchasing behavior. The assortment optimization problem is known to be NP-hard to be approximated to any constant factor, even in the unconstrained case. To address this challenge, we first explore the submodularity properties of a simplified version of the objective function to derive novel semi-constant factor approximation solutions for assortment problems under …

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 …

Accountable Agents In Software Engineering: An Analysis Of Terms Of Service And A Research Roadmap, Christoph Treude

Research Collection School Of Computing and Information Systems

AI coding assistants and autonomous agents are becoming integral to software development workflows, reshaping how code is produced, reviewed, and maintained. While recent research has focused mainly on the capabilities and impacts of productivity of these systems, much less attention has been paid to accountability: who is responsible when agents generate, modify, or recommend code? In practice, accountability is defined through the Terms of Service (ToS) and related policy documents that govern the use of AI-powered development tools.In this vision paper, we present a comparative analysis of the Terms of Service for widely used AI coding assistants and agent-enabled development …

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 …

Spatiotemporal Sycophancy: Negation-Based Gaslighting In Video Large Language Models, Ziyao Tang, Pengkun Jiao, Bin Zhu, Huiyan Qi, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Video Large Language Models (Vid-LLMs) have demonstrated remarkable performance in video understanding tasks, yet their robustness under conversational interaction remains largely underexplored. In this paper, we identify spatiotemporal sycophancy, a failure mode in which Vid-LLMs retract initially correct, visually grounded judgments and conform to misleading user feedback under negation-based gaslighting. Rather than merely changing their answers, the models often fabricate unsupported temporal or spatial explanations to justify incorrect revisions. To systematically investigate this phenomenon, we propose a negation-based gaslighting evaluation framework and introduce GasVideo-1000, a curated benchmark designed to probe spatiotemporal sycophancy with clear visual grounding and temporal reasoning requirements. …