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Automation To Autonomy: Temporal Dynamics Of Trust And Visual Attention Allocation Did Not Evolve, Tetsuya Sato, Eric Chancey, Yusuke Yamani Jan 2025

Automation To Autonomy: Temporal Dynamics Of Trust And Visual Attention Allocation Did Not Evolve, Tetsuya Sato, Eric Chancey, Yusuke Yamani

Psychology Faculty Publications

Emerging work environments are expected to implement autonomy that performs various functions without human input. Previous works has shown that trust in automation is negatively correlated with visual attention allocation, indicating that trust is a dynamic construct. Moreover, trust in automation and trust in autonomy appears to evolve in similar ways. However, recent work has demonstrated differences between trust in automation and trust in autonomy within Kaber’s (2018) theoretical framework (Sato et al., 2023b). Yet, it is uncertain whether the development of trust and visual attention allocation differs between automation and autonomy. The present study examined the temporal dynamics of …

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Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, And Challenges, Anurag Satpathy, Manmath Narayan Sahoo, Chittaranjan Swain, Paolo Bellavista, Mohsen Guizani, Khan Muhammad, Sambit Bakshi Jan 2025

Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, And Challenges, Anurag Satpathy, Manmath Narayan Sahoo, Chittaranjan Swain, Paolo Bellavista, Mohsen Guizani, Khan Muhammad, Sambit Bakshi

Computer Science Faculty Research & Creative Works

Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In …

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Content Subversion Against 1 Information-Based Systems, Junjie Xiong, Ian Markwood, Dakun Shen, Yao Liu, Zhuo Lu Jan 2025

Content Subversion Against 1 Information-Based Systems, Junjie Xiong, Ian Markwood, Dakun Shen, Yao Liu, Zhuo Lu

Computer Science Faculty Research & Creative Works

We present a novel class of content subversion attacks against information-based services, causing documents to appear to humans dissimilar to the underlying content extracted by information-based services. We demonstrate the significant impact of these attacks on real-world systems through five distinct variants. Our first attack allows academic paper writers and reviewers to collude via subverting the automatic reviewer assignment systems in current use by academic conferences including INFOCOM, which we reproduced. Our second attack renders ineffective plagiarism detection software, particularly Turnitin, targeting specific small plagiarism similarity scores to appear natural and evade detection. In our third attack, we place masked …

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Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Alexander H. Nelson, Han-Seok Seo, Xin Li, Page Daniels Dobbs, Khoa Luu Jan 2025

Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Alexander H. Nelson, Han-Seok Seo, Xin Li, Page Daniels Dobbs, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Social group activity recognition is crucial for various applications including surveillance, human-robot interaction, and behavioral analysis. Current approaches often require extensive manual annotations and rely heavily on pre-trained detectors, limiting their practical applications. Additionally, existing methods struggle to effectively model long-term spatiotemporal relationships in group activities. This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we create local and global views with varying frame rates. Our self-supervised objective ensures that features extracted from contrasting views of the same video are consistent across …

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Topo-Vm-Unetv2: Encoding Topology Into Vision Mamba Unet For Polyp Segmentation, Diego Adame, Jose Angel Nunez, Fabian Vazquez Jr., Nayeli Gurrola, Huimin Li, Haoteng Tang Jan 2025

Topo-Vm-Unetv2: Encoding Topology Into Vision Mamba Unet For Polyp Segmentation, Diego Adame, Jose Angel Nunez, Fabian Vazquez Jr., Nayeli Gurrola, Huimin Li, Haoteng Tang

Computer Science Faculty Publications

Convolutional neural network (CNN) and Transformer-based architectures are two dominant deep learning models for polyp segmentation. However, CNNs have limited capability for modeling long-range dependencies, while Transformers incur quadratic computational complexity. Recently, State Space Models such as Mamba have been recognized as a promising approach for polyp segmentation because they not only model long-range interactions effectively but also maintain linear computational complexity. However, Mamba-based architectures still struggle to capture topological features (e.g., connected components, loops, voids), leading to inaccurate boundary delineation and polyp segmentation. To address these limitations, we propose a new approach called Topo-VM-UNetV2, which encodes topological features into …

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Towards Human Explainable Digital Forensics: Generating Human Interpretable Evidence For Semantic Understanding In Manipulated Images And Text, Yuwei Chen Jan 2025

Towards Human Explainable Digital Forensics: Generating Human Interpretable Evidence For Semantic Understanding In Manipulated Images And Text, Yuwei Chen

Electronic Theses & Dissertations (2024 - present)

Detecting and characterizing manipulations in digital media continues to pose a significant challenge within the field of digital forensics. Despite notable advancements, the discipline often remains in a reactive stance against emerging threats. Current state-of-the-art methods, typically evaluated within academic settings, fails to mirror the complexities of real-world disinformation scenarios. These methods generally prioritize high performance based on quantitative metrics, yet they demonstrate a considerable dependency on training data and lack adaptability to new novel attack signatures. With the rapid evolution of attack methodologies, the dependency on highly accurate models that do not generalize or adapt well to unseen threats …

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Signal-Based Error Handling: Case Study Using The Bathymetric Attributed Grid Library, Anthony R. Papetti Jan 2025

Signal-Based Error Handling: Case Study Using The Bathymetric Attributed Grid Library, Anthony R. Papetti

Honors Theses and Capstones

No abstract provided.

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Panoscu: A Simulation-Based Dataset For Panoramic Indoor Scene Understanding, Mariia Khan, Yue Qiu, Yuren Cong, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn Jan 2025

Panoscu: A Simulation-Based Dataset For Panoramic Indoor Scene Understanding, Mariia Khan, Yue Qiu, Yuren Cong, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn

Research outputs 2022 to 2026

Panoramic images offer a comprehensive spatial view that is crucial for indoor robotics tasks such as visual room rearrangement, where an agent must restore objects to their original positions or states. Unlike existing 2D scene change understanding datasets, which rely on single-view images, panoramic views capture richer spatial context, object relationships, and occlusions—making them better suited for embodied artificial intelligence (AI) applications. To address this, we introduce Panoramic Scene Change Understanding (PanoSCU), a dataset specifically designed to enhance the visual object rearrangement task. Our dataset comprises 5,300 panoramas generated in an embodied simulator, encompassing 48 common indoor object classes. PanoSCU …

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End-To-End License Plate Detection And Recognition In Iraq Using A Detection Transformer And Ocr, Younis Al-Arbo, Hanaa F. Mahmood, Asmaa Alqassab Jan 2025

End-To-End License Plate Detection And Recognition In Iraq Using A Detection Transformer And Ocr, Younis Al-Arbo, Hanaa F. Mahmood, Asmaa Alqassab

Mesopotamian Journal of Computer Science

Automatic License Plate Recognition (ALPR), DEtection TRansformer (DETR), Deep Learning for Object Detection, Optical Character Recognition (OCR), Region-Specific Vehicle Identification

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Using Wearable Technology For Context-Aware Of Pilgrimage Management System During/Post-Pandemic Of Covid-19, Fatina Shukur, Ahmed Al-Fatlawi, Safaa Jasim Mosa, Nasir Ibrahim Jan 2025

Using Wearable Technology For Context-Aware Of Pilgrimage Management System During/Post-Pandemic Of Covid-19, Fatina Shukur, Ahmed Al-Fatlawi, Safaa Jasim Mosa, Nasir Ibrahim

Mesopotamian Journal of Computer Science

Hajj is an annual event placed in Saudi Arabia. It is one of the largest religious gatherings, such that it gathers millions of pilgrims from all around the world. The pandemic of Covid-19 is not over yet, and could likely be part of our life for a long time. Also, it is negatively affecting such mass gatherings. The aim of this research is to facilitate the overall hajj event while maintaining peoples’ health, safety, and security. Therefore, we use a technology that helps to reduce direct contact between pilgrims themselves as well as with other entities, such that human interaction …

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Neuron Semantic-Guided Test Generation For Deep Neural Networks Fuzzing, Li Huang, Weifeng Sun, Meng Yan, Zhongxin Liu, Yan Lei, David Lo Jan 2025

Neuron Semantic-Guided Test Generation For Deep Neural Networks Fuzzing, Li Huang, Weifeng Sun, Meng Yan, Zhongxin Liu, Yan Lei, David Lo

Research Collection School Of Computing and Information Systems

In recent years, significant progress has been made in testing methods for deep neural networks (DNNs) to ensure their correctness and robustness. Coverage-guided criteria, such as neuron-wise, layer-wise, and path-/trace-wise, have been proposed for DNN fuzzing. However, existing coverage-based criteria encounter performance bottlenecks for several reasons: Testing Adequacy: Partial neural coverage criteria have been observed to achieve full coverage using only a small number of test inputs. In this case, increasing the number of test inputs does not consistently improve the quality of models. Interpretability: The current coverage criteria lack interpretability. Consequently, testers are unable to identify and understand which …

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A Case Study Using The Transparency Framework And Artificial Intelligence To Promote Effective Writing And Student Success In A Writing-Intensive Course, Elizabeth A. Brown, Maria Kronenburg, Ashlee Steeley, Diana Tagbor Jan 2025

A Case Study Using The Transparency Framework And Artificial Intelligence To Promote Effective Writing And Student Success In A Writing-Intensive Course, Elizabeth A. Brown, Maria Kronenburg, Ashlee Steeley, Diana Tagbor

Health Behavior, Policy & Management Faculty Publications

Program evaluation data suggest that undergraduate students struggle with writing in a clear and concise manner and appropriately citing. Faculty implemented the plan-do study-act cycle to pilot the Transparency in Learning and Teaching (TILT) project framework and to explore the use of artificial intelligence (AI) and discuss approaches to using AI, along with the TILT framework, in a writing-intensive course to identify the pros and cons of using ChatGPT in an online classroom. The TILT framework reinforces adult learning by helping students clearly understand the assignment's purpose and establish a clear relationship between assignment and students' professional lives. Faculty encouraged …

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A Qualitative Analysis Of College Students' Interest In Mhealth Solutions, Leslie Hoglund, Craig M. Becker, Cara Tonn Jan 2025

A Qualitative Analysis Of College Students' Interest In Mhealth Solutions, Leslie Hoglund, Craig M. Becker, Cara Tonn

Health Behavior, Policy & Management Faculty Publications

This study explores college students' perceptions of an AI-driven mHealth application designed to promote well-being. With rising mental health challenges in academic settings, students increasingly seek digital tools that provide holistic support for physical, mental, and financial health. Through focus groups, this qualitative study examines students' preferences for personalized health tracking, educational content, and flexible reminders within a private, supportive community. Key findings emphasize students' desire for a balanced, all-in-one app that integrates health and wellness tools without overwhelming them with notifications. Students also highlighted the importance of social media integration for outreach, though concerns were raised about potential stress …

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On The Validity Of Traditional Vulnerability Scoring Systems For Adversarial Attacks Against Llms, Atmane Ayoub Mansour Bahar, Ahmad Samer Wazan Jan 2025

On The Validity Of Traditional Vulnerability Scoring Systems For Adversarial Attacks Against Llms, Atmane Ayoub Mansour Bahar, Ahmad Samer Wazan

All Works

This research investigates the effectiveness of established vulnerability metrics, such as the Common Vulnerability Scoring System (CVSS), in evaluating attacks on Large Language Models (LLMs), with a focus on Adversarial Attacks (AAs). The study explores the influence of different metric factors in determining vulnerability scores, providing new perspectives on potential enhancements to these metrics. Approach - This study adopts a quantitative approach, calculating and comparing the coefficient of variation of vulnerability scores across 56 adversarial attacks on LLMs. The attacks, sourced from various research papers, and obtained through online databases, were evaluated using multiple vulnerability metrics. Scores were determined by …

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Intrinsic Motivation, Future Orientation, And Financial Stress: A Student-Centered Model Of Metaverse Classroom Adoption In Low-Income Contexts, Mousa Al-Kfairy, Omar Alfandi, Saed Alrabaee Jan 2025

Intrinsic Motivation, Future Orientation, And Financial Stress: A Student-Centered Model Of Metaverse Classroom Adoption In Low-Income Contexts, Mousa Al-Kfairy, Omar Alfandi, Saed Alrabaee

All Works

The rapid rise of immersive technologies has placed metaverse-based classrooms at the center of higher education innovation. Yet, little is known about how students in low-income contexts perceive and adopt these platforms, particularly when motivation, career goals, and financial pressures intersect. This study develops and tests a student-focused model that integrates intrinsic motivation, future time perspective, career relevance, and financial stress to explain behavioral intention toward metaverse adoption. A survey of 292 university students in Jordan—a lower-income national setting—was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results showed all hypothesized paths were significant. Future time perspective and career …

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Deep Learning Models Based On Cnn, Rnn, And Lstm For Rainfall Forecasting: Jordan As A Case Study, La'aly A. Al-Samrraie, Ayman M. Abdalla, Khalideh Al Bkoor Alrawashdeh, Abeer Al Bsoul, Mohammad Abu Awad, Kamel Alzboon, Ahmed A. Al-Taani Jan 2025

Deep Learning Models Based On Cnn, Rnn, And Lstm For Rainfall Forecasting: Jordan As A Case Study, La'aly A. Al-Samrraie, Ayman M. Abdalla, Khalideh Al Bkoor Alrawashdeh, Abeer Al Bsoul, Mohammad Abu Awad, Kamel Alzboon, Ahmed A. Al-Taani

All Works

This study is the first to compare deep learning models for rainfall prediction across several Jordanian cities representing diverse climates using 11 years of recorded climate data, something that previous studies have not addressed in the Jordanian context. The climate records for four Jordanian cities (Amman, Irbid, Karak, and Ajloun) were recorded hourly. The data was divided into training sets (80%) and test sets (20%), with and without the application of correlation analysis, feature selection, and data standardization steps applied. Three neural network models, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) were …

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Incremental Cluster Validity Index-Based Offline Clustering For Machine Learning, Leonardo Enzo Brito Da Silva, Donald C. Wunsch Ii, Nagasharath Rayapati Jan 2025

Incremental Cluster Validity Index-Based Offline Clustering For Machine Learning, Leonardo Enzo Brito Da Silva, Donald C. Wunsch Ii, Nagasharath Rayapati

Patents

A neural network model replaces the supervised labeling component of a supervised learning system with an incremental cluster validity index-based unsupervised labeling component. An implementation is presented combining fuzzy adaptive resonance theory predictive mapping (ARTMAP) and incremental cluster validity indices (iCVI) for unsupervised machine learning purposes, namely the iCVIARTMAP. An iCVI module replaces the adaptive resonance theory (ART) module B of a fuzzy ARTMAP neural network model and provides assigmnents of input samples to clusters (i.e., labels) at each learning iteration in accordance to any of several possible iCVI methods described. A map field incrementally builds a many-to-one mapping of …

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Problem-Centered Post-Secondary Computer Science Education: A Study Of The Private Artificial Intelligence Curriculum, Golnoush Haddadian, Prajwal Panzade, Daniel Takabi, Min Kyu Kim Jan 2025

Problem-Centered Post-Secondary Computer Science Education: A Study Of The Private Artificial Intelligence Curriculum, Golnoush Haddadian, Prajwal Panzade, Daniel Takabi, Min Kyu Kim

School of Cybersecurity Faculty Publications

In response to the demand for Artificial Intelligence (AI) experts, this study introduced a curriculum development initiative. The aim was to design and implement a Private AI curriculum to understand the computer science (CS) students’ evaluations of the curricular activities and their levels of interest and motivation. Twenty-five students, a mix of undergraduates and graduates, were recruited and a scaled-down version of the curriculum was implemented. A parallel mixed-methods approach was employed. The results reinforced the significance of problem-centered curricula in CS context. Students rated the curricular activities highly and demonstrated strong motivation; however, graduates expressed more favorable view of …

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Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar Jan 2025

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

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Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar Jan 2025

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

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Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington Jan 2025

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

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Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty Jan 2025

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

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The Effectiveness Of Tech Support Fraud In Damaging Older Individual’S Financial Security, Vanessa Perera Jan 2025

The Effectiveness Of Tech Support Fraud In Damaging Older Individual’S Financial Security, Vanessa Perera

Theses : Honours

This study discovers the tactics employed to create detrimental effects upon older people impacted from fraudulent tech-support incidents. It examines social engineering, and financial confusion of older people. This is significant considering adaptations towards digital banking and payment management. This research looked at online and active over 65s. Using largely qualitative approaches over 65s were interviewed and responses validated against cyber-professionals’ responses. This identified three key findings: older adults feel confused and misunderstand tech support scams; threat actors build trust by pretending to offer technical help but use this to deceive their victims; and older adults face serious social and …

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Marrying Top-K With Skyline Queries: Operators With Relaxed Preference Input And Controllable Output Size, Kyriakos Mouratidis, Keming Li, Bo Tang Jan 2025

Marrying Top-K With Skyline Queries: Operators With Relaxed Preference Input And Controllable Output Size, Kyriakos Mouratidis, Keming Li, Bo Tang

Research Collection School Of Computing and Information Systems

The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records' attributes (typically, using a top-k query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they …

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Transforming Urban Dynamics: Harnessing Large Language Models For Smarter Mobility, Hao Xue, Ming Jin, Shirui Pan, Flora Salim, Guansong Pang Jan 2025

Transforming Urban Dynamics: Harnessing Large Language Models For Smarter Mobility, Hao Xue, Ming Jin, Shirui Pan, Flora Salim, Guansong Pang

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) has the potential to analyze mobility data and make mobility systems smarter by leveraging diverse data sources such as geospatial data, transportation logs, and real-time sensor data to optimize traffic flow, enhance public transportation systems, and support the development of autonomous vehicles. With the newly emerged generative AI paradigm, exemplified by large language models (LLMs), there is great potential to transform the current AI applications in mobility, transportation, and urban domains. This article provides an overview of recent efforts and aims to shed light on the challenges and future opportunities to facilitate the adaptation of LLMs for …

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A Bibliographic And Topic Modeling Analysis Of The P-Adic Theory Literature Using Latent Dirichlet Allocation, Humberto Llinás, Ismael Gutiérrez, Anselmo Torresblanca, Javier De La Hoz, Brian Llinás Jan 2025

A Bibliographic And Topic Modeling Analysis Of The P-Adic Theory Literature Using Latent Dirichlet Allocation, Humberto Llinás, Ismael Gutiérrez, Anselmo Torresblanca, Javier De La Hoz, Brian Llinás

Computer Science Faculty Publications

P-adic analysis, introduced by Kurt Hensel in the early 20th century, has developed into a fundamental area of mathematical research with broad applications in number theory, algebraic geometry, and mathematical physics. This study aims to examine the thematic evolution and scholarly impact of p-adic research through a comprehensive topic modeling and bibliometric analysis. Using classical bibliometric techniques (e.g., performance analysis, co-authorship, and co-citation networks) combined with Latent Dirichlet Allocation (LDA), we analyzed 7388 peer-reviewed documents published between 1965 and 2024. The computational workflow was conducted using R (version 4.4.1) and VOSviewer (version 1.6.20), which enabled the identification of 20 distinct …

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From Philosophy To Nlu: Evolving Definitions With Research Hypotheses, Jian Wu, Sarah Rajtmajer Jan 2025

From Philosophy To Nlu: Evolving Definitions With Research Hypotheses, Jian Wu, Sarah Rajtmajer

Computer Science Faculty Publications

Over the past decades, alongside advancements in natural language processing, significant attention has been paid to training models to automatically extract, understand, test, and generate hypotheses in open and scientific domains. However, interpretations of the term hypothesis for various natural language understanding (NLU) tasks have migrated from traditional definitions in the natural, social, and formal sciences. Even within NLU, we observe differences defining hypotheses across literature. In this paper, we overview and delineate various definitions of hypothesis. Especially, we discern the nuances of definitions across recently published NLU tasks. We highlight the importance of well-structured and well-defined hypotheses, particularly as …

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Understanding Pii Leakage In Large Language Models: A Systematic Survey, Shuai Cheng, Zhao Li, Shu Meng, Mengxia Ren, Haitao Xu, Shuai Hao, Chuan Yue, Fang Zhang Jan 2025

Understanding Pii Leakage In Large Language Models: A Systematic Survey, Shuai Cheng, Zhao Li, Shu Meng, Mengxia Ren, Haitao Xu, Shuai Hao, Chuan Yue, Fang Zhang

Computer Science Faculty Publications

Large Language Models (LLMs) have demonstrated exceptional success across a variety of tasks, particularly in natural language processing, leading to their growing integration into numerous facets of daily life. However, this widespread deployment has raised substantial privacy concerns, especially regarding personally identifiable information (PII), which can be directly associated with specific individuals. The leakage of such information presents significant real-world privacy threats. In this paper, we conduct a systematic investigation into existing research on PII leakage in LLMs, encompassing commonly utilized PII datasets, evaluation metrics, and current studies on both PII leakage attacks and defensive strategies. Finally, we identify unresolved …

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Humans Vs. Llms On Open Domain Scientific Claim Verification: A Baseline Study, Benjamin Curtis, Stefania Dzhaman, Matthew Maisonave, Jian Wu Jan 2025

Humans Vs. Llms On Open Domain Scientific Claim Verification: A Baseline Study, Benjamin Curtis, Stefania Dzhaman, Matthew Maisonave, Jian Wu

Computer Science Faculty Publications

Verifying scientific claims is challenging for the general public because most people lack domain knowledge. Manual verification by subject domain experts is accurate, but it is obviously not scalable to meet the rising number of scientific claims on the Web. Whether the emerging large language models and large reasoning models can be used for scientific claim verification, and how their performances compare to humans, are still research questions. To this end, we developed a new benchmark MSVEC2 that consists of 138 claims from credible fact verification websites and science news outlets. Two tasks were given to both human and LLM …

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Privshap: A Finer-Granularity Network Linearization Method For Private Inference, Xiangrui Xu, Zhenzhen Wang, Rui Ning, Chunsheng Xiu, Hongyi Wu Jan 2025

Privshap: A Finer-Granularity Network Linearization Method For Private Inference, Xiangrui Xu, Zhenzhen Wang, Rui Ning, Chunsheng Xiu, Hongyi Wu

Computer Science Faculty Publications

Private inference applies cryptographic techniques like homomorphic encryption, garble circuit and secret sharing to keep both sides privacy in a client-server setting during inference. It is often hindered by the high communication overheads, especially at non-linear activation layers such as ReLU. Hence ReLU pruning has been widely recognized as an efficient way to accelerate private inference. Existing approaches to ReLU pruning typically rely on coarse hypothesis, which assume an inverse correlation between the importance of ReLU and linear layers or shallow activation layers have less importance for universal models, to assign the budgets according to the layer while preserving the …

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