Digital Commons Network^™

Challenges In Synchronous And Remote Collaboration Around Visualization, Matthew Brehmer, Maxime Cordeil, Christophe Hurter, Takayuki Itoh, Wolfgang Büschel, Mahmood Jasim, Arnaud Prouzeau, David Saffo, Lyn Bartram, Sheelagh Carpendale, Chen Zhu-Tian, Andrew Cunningham, Anthony Tang, Samuel Huron, Masahiko Itoh, Arpit Joshi, Kiyoshi Kiyokawa, Hideaki Kuzuoka, Bongshin Lee, Guillermo Molina León

Research Collection School Of Computing and Information Systems

We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human-computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence …

Thinktank-Me: A Multi-Expert Framework For Middle East Event Forecasting, Haoxuan Li, He Chang, Yunshan Ma, Yi Bin, Yang Yang, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing LLM-based approaches employ single-model architectures that generate predictions along a singular explicit trajectory, constraining their ability to capture diverse geopolitical nuances across complex regional contexts. To address this limitation, we introduce ThinkTank-ME, a novel Think Tank framework for Middle East event forecasting that emulates collaborative expert analysis in real-world strategic decision-making. To facilitate expert specialization and rigorous evaluation, we construct POLECAT-FOR-ME, a Middle East–focused event forecasting benchmark. Experimental results demonstrate the superiority of multi-expert collaboration in handling complex temporal geopolitical forecasting …

Discrete Diffusion For Bundle Construction, Teng Tu, Ai Li, Yunshan Ma, Shuo Xu, Xiaohao Liu, Haokai Ma, Liang Pang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

As a central task in product bundling, bundle construction aims to select a subset of items from large item catalogs to build an entire bundle or, more practically, complete a partial bundle. Existing methods often rely on the sequential construction paradigm that predicts items one at a time, nevertheless, this paradigm is fundamentally unsuitable for the essentially unordered bundles. In contrast, non-sequential methods model a bundle as a set, but still face two dimensionality curses: the combinatorial space grows exponentially with both bundle length and catalog size. Accordingly, we identify two technical challenges: 1) how to effectively and efficiently model …

Finding Missing Input Validation In Tees Via Llm-Assisted Symbolic Execution, Chengyan Ma, Jieke Shi, Ruidong Han, Ye Liu, Yuqing Niu, David Lo

Research Collection School Of Computing and Information Systems

Trusted Execution Environments (TEEs) provide hardware-enforced isolation that protects sensitive code and data from untrusted software. Despite their strong security guarantees, analyzing TEE applications remains challenging due to the high cost and complexity of configuring complete TEE build and runtime environments, as well as the limited observability imposed by hardware isolation. This paper presents SymTEE, a novel large language model (LLM)-assisted symbolic execution framework for detecting missing input validation issues in TEE applications without requiring real TEE setups. SymTEE begins by leveraging Abstract Syntax Tree (AST) analysis to extract TEE code slices that may lack sufficient input validation, and then …

Columnas: The Honors Program Newsletter At Bentley University, Amanda Li, Wilson Jan, Michael Raphael, Alexandra Rieckehoff, Karina Wu, Michael Shehata, Nilufar Noorian, Eloise Weintraub

Honors Program

INSIDE THE MODERN WORLD

Page 2: Stepping Out by Amanda Li

Page 3: Inside the Corporate Slop Bowl by Wilson Jan

Page 4: The Silencing: An Evaluation of the Global Attacks on the Right to Protest by Michael Raphael

THE SOUND OF CHANGE

Page 5: The Social, Cultural, and Economic Impact of Bad Bunny by Alexandra Rieckehoff

Page 6: Streaming Changed Music, But Is It Fair to Artists? by Karina Wu

Page 7: Feeling the Music: How Haptic Wearables Are Changing the Way We Experience Sound by Michael Shehata

SHIFTING SYSTEMS

Page 8: The Story Behind Davos, One of the …

Storycomposerai: Supporting Human-Ai Story Co-Creation Through Decomposition And Linking, Shuo Niu, Dylan Clements, Marina Margalit Nemanov, Hyungsin Kim

Computer Science

GenAI's ability to produce text and images is increasingly incorporated into human-AI co-creation tasks such as storytelling and video editing. However, integrating GenAI into these tasks requires enabling users to retain control over editing individual story elements while ensuring that generated visuals remain coherent with the storyline and consistent across multiple AI-generated outputs. This work examines a paradigm of creative decomposition and linking, which allows creators to clearly communicate creative intent by prompting GenAI to tailor specific story elements, such as storylines, personas, locations, and scenes, while maintaining coherence among them. We implement and evaluate StoryComposerAI, a system that exemplifies …

Scenarioxp: A Complete Scenario-Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Doctoral Dissertations and Master's Theses

Today is an age of exciting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

Developing A Multi-Band Gnss Remote Sensing Station To Measure Precipitable Water Vapor For Flash Flood Nowcasting, Madison Gleydura

Doctoral Dissertations and Master's Theses

Flash flood nowcasting in Central and Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at long range and signal blockage by mountains. GNSS-meteorology offers an established alternative for measuring precipitable water vapor and is currently integrated into several numerical weather models. Recent research demonstrates that commercial-grade GNSS receivers can produce tropospheric products comparable to those from geodetic-grade equipment. The gaps in mountain coverage can be addressed by developing a low-cost, self-contained embedded system that …

Fisheasy, Jake Rankin

Presentations - 2026

Problem: 
Inexperienced and experienced anglers lack necessary tools to begin fishing

Motivation:
Fishing is a fun hobby everyone should be able to enjoy – the lack of resources makes that difficult

Solution:
An application that integrates learning tutorials with helpful practices tools for both novice and experienced anglers

Sentra, David Aguilar

Posters - 2026

In the fast-paced space of event organization, fostering continuous collaboration among participants is essential. However, organizers often lose valuable time monitoring multiple, disconnected systems once an event is underway. Enter Sentra: an all-in-one Discord bot tailored specifically for weekend events like hackathons. Sentra bridges the gap between participants and organizers by consolidating seamless team matchmaking, robust support ticketing, and automated AI moderation into a single, unified interface.

A Real Account Of Deep Fakes, Benjamin L.W Sobel

Michigan Law Review

Laws regulating pornographic deepfakes are written to prohibit “digital forgeries,” “false” images, or media “indistinguishable” from “authentic” recordings. Yet the typical anti-deepfake law covers materials that aren’t forgeries, aren’t false, and that reasonable observers can easily distinguish from authentic recordings. Though drafted as if they regulate statements of fact, anti-deepfake laws actually target certain outrageous depictions per se—and rightly so, because pornographic deepfakes cause harm irrespective of their truth or falsity. However, the inapposite language of facts results in statutes with crucial ambiguities. Moreover, because anti-deepfake laws ban outrageous depictions irrespective of the factual assertions they make, they differ fundamentally …

The Psychology Behind Ai-Generated Phishing And Social Engineering Attacks, A’Shya Reynolds

School of Cybersecurity Master's Level Projects and Papers

Cybercrime has evolved significantly with the integration of artificial intelligence (AI), transforming traditional phishing and social engineering attacks into highly sophisticated and personalized threats. While early phishing attempts relied on generic messaging and low success rates, modern AI-driven attacks leverage advanced data analytics, natural language processing, and behavioral prediction to manipulate victims more effectively.

This research examines how cybercriminals utilize AI to enhance psychological manipulation techniques in phishing and social engineering attacks, increasing victim susceptibility. Drawing from interdisciplinary literature in cybersecurity and psychology, this study explores key psychological mechanisms, including cognitive biases, emotional triggers, and decision-making processes that influence victim …

Llm-Driven Mission Control And Autonomous Planning For Search-And-Rescue Uavs: A Simulation-Based Evaluation, Naser Bader Alsaedi

Theses

Unmanned aerial vehicles (UAVs) are increasingly used in search‑and‑rescue (SAR) missions, yet many systems still rely on fragmented software where mission design, perception, and flight control are configured separately. This thesis examines whether a unified AI‑driven framework can reduce configuration effort and operator workload in UAV‑based SAR operations. The proposed system integrates natural‑language mission specification using a large language model (LLM) (LLaMA 3.1), autonomous coverage planning, YOLOv8‑based victim detection, and PX4/MAVSDK control within a single architecture. Operators describe missions through free‑form text or a graphical interface; the model converts these descriptions into structured mission parameters that are automatically planned and …

Ai-Powered Knowledge Management Systems Across Industries: A Systematic Review Of Applications, Implementation Barriers, And Ethical Challenges, Edmund Evangelista, Ghazala Rizvi

All Works

This systematic literature review (SLR) evaluates the existing literature on the benefits, implementation challenges, and ethical concerns associated with Artificial Intelligence (AI)-driven Knowledge Management Systems (KMS) across industries. The SLR followed PRISMA guidelines to identify studies from Scopus, Web of Science, JSTOR, and Google Scholar, using inclusion and exclusion criteria. Critical Appraisal Skills Programme (CASP) checklists were used to assess methodological quality and risk of bias in the included studies, and a structured narrative synthesis was employed to synthesize the findings. The review of 21 articles reveals benefits like improved knowledge capture and creation, storage, retrieval, personalization, and efficient dissemination, …

Preserving The Past, Innovating The Future: Integrating Metaverse, Blockchain, And Generative Ai For Tourism And Cultural Heritage Preservation, Mousa Al-Kfairy, Amna Ahmed Aaber Ahmed Alqubaisi, Omar Alfandi

All Works

The preservation and accessibility of cultural heritage are seriously threatened by urbanization, mass tourism, neglect, and natural disasters. This study utilizes cutting-edge technologies to address these issues by presenting a novel framework that combines generative AI, blockchain, and the metaverse. The Metaverse provides immersive virtual experiences that lessen the physical strain on delicate locations by enabling the creation of lifelike digital replicas of cultural heritage sites. By creating immutable records, blockchain technology ensures the legitimacy, ownership, and traceability of digital assets, enabling open access and NFT monetization. Rebuilding lost or damaged artifacts, creating lifelike 3D models, and customizing user interactions …

Generative Ai In Enterprises: Optimizing Applications With Large Language Models, Donghao Huang

Dissertations and Theses Collection (Open Access)

This dissertation investigates how to deploy Large Language Models (LLMs) effectively in enterprise settings, where accuracy, reliability, cost, privacy, and operational constraints often matter more than benchmark performance alone. Drawing on seventeen peer-reviewed publications (eleven published and six accepted for publication), the work develops and validates optimization strategies across three connected themes: retrieval-augmented generation (RAG), agentic AI for workflow automation, and deployment guidelines for real-world enterprise environments.

First, we study RAG optimization through systematic evaluation of open and proprietary models, highlighting conditions under which efficient open-weight models can match or exceed proprietary alternatives. To address a pervasive failure mode in …

Explainable Artificial Intelligence In The Image Domain And Its Applications To The Medical Field, Mirtha Lucas

Theses and Dissertations from DePaul University

This dissertation investigates the development of Explainable Artificial Intelligence (XAI) methods for deep learning models in the image domain, with a particular focus on medical imaging applications. Although neural networks achieve high predictive performance, their lack of interpretability limits their adoption in critical domains such as healthcare, where transparency and trust are essential. This work addresses this challenge by proposing novel approaches that improve the interpretability and reliability of model predictions.   A primary contribution is the introduction of Riemann–Stieltjes Integrated Grad-CAM (RSI Grad-CAM), a gradient-based attribution method that generates more relevant and spatially localized saliency maps. The method is evaluated …

Hypersiniel: Guaranteed Output Delivery Comes (Almost) Free In Private Delegation Of Zksnarks, Yunbo Yang, Yuejia Cheng, Junkai Liang, Kailun Wang, Xuanming Liu, Xiaoguo Li, Jianfei Sun, Jiachen Shen, Xiaolei Dong, Zhenfu Cao, Meng Hao, Guomin Yang, Deng, Robert H., Kui Ren

Research Collection School Of Computing and Information Systems

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness.Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to …

Bridging Draft Policy Misalignment: Group Tree Optimization For Speculative Decoding, Shijing Hu, Jingyang Li, Zhihui Lu, Pan Zhou

Research Collection School Of Computing and Information Systems

Speculative decoding accelerates large language model (LLM) inference by letting a lightweight draft model propose multiple tokens that the target model verifies in parallel. Yet existing training objectives optimize only a single greedy draft path, while decoding follows a tree policy that re-ranks and verifies multiple branches. This draft policy misalignment limits achievable speedups. We introduce Group Tree Optimization (GTO), which aligns training with the decoding-time tree policy through two components: (i) Draft Tree Reward, a sampling-free objective equal to the expected acceptance length of the draft tree under the target model, directly measuring decoding performance; (ii) Group-based Draft Policy …

Budgeting Apps, Financial Literacy, And Financial Control, Max Masabo, Abdullah Mohammad Mahi

Presentations - 2026

Our paper shows the real hidden connections between budgeting app usage and financial wellbeing of U.S Households

Comparative Analysis Of Mlp And Cnn Models For Cardiac Arrhythmia Classification, Veltman Okey-Ejowhor, Vahid Emamian

Posters - 2026

Electrocardiogram (ECG) is a record of the electric activity of the heart over time. ECG analysis plays a pivotal role in diagnosing critical heart conditions. Significant developments have been made in the realm of deep learning and applied artificial intelligence. These deep learning models have been utilized heavily because of their ability to analyze deep morphological features of each signal. The model architecture used in this study is a convolutional neural network (CNN) combined with a multi-layered perceptron (MLP). The MLP acts as an input filter that classifies normal heartbeat signals from abnormal. The CNN is the second filter in …

Foxbuddy, Luis Eduardo Garza Jr.

Posters - 2026

Emergency preparedness remains a significant challenge for individuals due to disorganized resource management and lack of accessible guidance. Additionally, cybersecurity risks during emergencies are often overlooked, leaving individuals vulnerable to digital threats such as phishing, scams, and data exposure. FoxBuddy provides a centralized, user-friendly platform that enhances both physical preparedness and cybersecurity awareness.

Using Ai-Based Predictive Scheduling To Improve Patient Flow And Reduce Wait Times In Healthcare Clinics, Oscar Martinez

Posters - 2026

• Healthcare systems face increasing challenges in patient access and wait times
• Average wait times for specialist care continue to rise, creating:
  • Delays in treatment
  • Reduced patient satisfaction
  • Increased system inefficiencies (Sanford, 2025)
• A major contributor is operational bottlenecks, defined as:
  • Points of congestion that slow or disrupt service flow
• Hospitals typically operate under process layouts, which:
  • Handle diverse patient needs
  • Reduce specialization efficiency
• Contributing factors to bottlenecks:
  • Physician shortages and burnout
  • Administrative burden
  • Inefficient scheduling systems (Moura & Pinho, 2025)
• AI offers potential solutions through:
  • Predictive scheduling
  • Automation of administrative processes
  • Data-driven optimization of patient flow

Ai-Assisted 3d Pre-Visualization: Streamlining Animatics For Filmmakers, Nishit Thapa

Honors Theses

Pre-visualization is a core part of film pre-production. Creators use it to test blocking, lighting, camera movement, and spatial relationships before committing to a shot. Despite this, three-dimensional pre-visualization remains out of reach for many entry-level filmmakers, as the software is expensive and takes substantial time to learn.

Recent AI developments have produced generative systems capable of creating highly re-alistic imagery and video from text prompts. These systems open new possibilities for visual sto-rytelling but are not built for the iterative, hands-on process that pre-visualization requires, where creators must adjust scene parameters, camera logic, and spatial configurations throughout.

This thesis …

Rethinking News Classification Through A Multi-Dimensional Framework, Luana De Jesus Ferreira

Honors Theses

This thesis proposes a multi-dimensional framework for news classification that evaluates articles across three independent dimensions: headline accuracy, language neutrality, and content reliability. These dimensions produce both a continuous reliability score and a five-tier interpretive scale, while additionally classifying articles by genre and topic. To operationalize this framework, a structured annotation protocol was developed and applied to a dataset of 373 news articles drawn from 79 outlets spanning a wide range of contemporary media ecosystem. A binary Logistic Regression classifier trained on the ISOT Fake News Dataset was then evaluated against this dataset to examine how a model trained on …

Generative Artificial Intelligence With A Human Touch: Building Hana, Conrad Johnson

Faculty Scholarship

This Essay examines how generative artificial intelligence (GenAI) can be integrated into legal education and public interest law practice in a way that meaningfully enhances — rather than diminishes — human judgment, professional responsibility, and access to justice. Drawing on the experience of Columbia Law School’s Lawyering in the Digital Age Clinic, the Essay situates GenAI within an experiential pedagogy that emphasizes competence, ethical awareness, and collaborative problem-solving. It argues that law students and lawyers must move beyond a passive or uncritical use of GenAI tools; toward a deeper understanding of how these systems operate, the risks they pose, and …

Exploring The U.S. Public Service Employees’ Experiences With Gamified Cybersecurity Awareness Training, Bisola Adepoju

Human Resource Development Theses and Dissertations

The purpose of this qualitative study was to explore the U.S. public service employees' experiences with gamified cybersecurity awareness training. The primary research question guiding this inquiry was: How do U.S. public service employees make sense of their lived experiences with gamified cybersecurity awareness training? Two secondary questions examined what aspects of the training employees perceive as meaningful or disengaging, and how they interpret their motivation, engagement, and cybersecurity awareness during training. I chose an interpretive qualitative research design grounded in a constructivist paradigm and purposefully selected 11 U.S. public service employees who had participated in gamified cybersecurity awareness training. …

Ai Models As Cultural Beings: Investigating Ai Cultural Biases And The Impact Of Cultural Alignment On Human-Ai Creative Collaboration, Choon Ngee Tan, Meng Han, Roy Y. J. Chua, Chi-Ying Cheng

Research Collection Lee Kong Chian School Of Business

Existing research on AI cultural biases predominantly focuses on Western models, overlooking critical gaps in non-Western models. We conduct a comparative analysis of AI models – ChatGPT (U.S. developed) and ErnieBot (China developed) – from different cultures to investigate how corresponding cultural biases manifest in their outputs. Additionally, we examine how cultural alignment between human users and AI models impacts their collaborative creative performance and the underlying psychological mechanisms. In Study 1, multi-choice prompt with zero-shot technique was used to evaluate cultural biases in four widely used AI models – ChatGPT-3.5/4, ErnieBot-3.5/4 – comparing their responses to established cultural psychometric …

Conflogger: Enhance Systems’ Configuration Diagnosability Through Configuration Logging, Shiwen Shan, Yintong Huo, Yuxin Su, Zhining Wang, Dan Li, Zibin Zheng

Research Collection School Of Computing and Information Systems

Modern configurable systems offer customization via intricate configuration spaces, yet such flexibility introduces pervasive configuration-related issues such as misconfigurations and latent softwarebugs. Existing diagnosability supports focus on post-failure analysis of software behavior to identify configuration issues, but none of these approaches look into whether the software clue sufficient failure information for diagnosis. To fill in the blank, we propose the idea of configuration logging to enhance existing logging practices at the source code level. We develop ConfLogger, the first tool that unifies configuration-aware static taint analysis with LLM-based log generation to enhance software configuration diagnosability. Specifically, our method 1) identifies …

Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) can exhibit concept-conditioned semantic divergence: common high-level cues (e.g., ideologies, public figures) elicit unusually uniform, stance-like responses that evade token-trigger audits. This behavior falls in a blind spot of current safety evaluations, yet carries major societal stakes, as such concept cues can steer content exposure at scale. We formalize this phenomenon and present RAVEN (Response Anomaly Vigilance), a black-box audit that flags cases where a model is simultaneously highly certain and atypical among peers by coupling semantic entropy over paraphrastic samples with cross-model disagreement. In a controlled LoRA fine-tuning study, we implant a concept-conditioned stance using …