An Ethically Grounded Llm-Based Approach To Insider Threat Synthesis And Detection,
2025
Dakota State University
An Ethically Grounded Llm-Based Approach To Insider Threat Synthesis And Detection, Haywood Gelman, John Hastings, David Kenley
Research & Publications
Insider threats are a growing organizational problem due to the complexity of identifying their technical and behavioral elements. A large research body is dedicated to the study of insider threats from technological, psychological, and educational perspectives. However, research in this domain has been generally dependent on datasets that are static and limited access which restricts the development of adaptive detection models. This study introduces a novel, ethically grounded approach that uses the large language model (LLM) Claude Sonnet 3.7 to dynamically synthesize syslog messages, some of which contain indicators of insider threat scenarios. The messages reflect real-world data distributions by …
Enhancing The Accuracy Of Image Classification For Degenerative Brain Diseases With Cnn Ensemble Models Using Mel-Spectrograms,
2025
Dong-A University, Busan
Enhancing The Accuracy Of Image Classification For Degenerative Brain Diseases With Cnn Ensemble Models Using Mel-Spectrograms, Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim
Mathematics & Statistics Faculty Publications
Alzheimer’s disease (AD) and Parkinson’s disease (PD) are prevalent neurodegenerative disorders among the elderly, leading to cognitive decline and motor impairments. As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. Using voice data offers advantages in accessibility compared to brain imaging analysis. This study introduces a novel ensemble-based classification model that utilizes Mel spectrograms and Convolutional Neural Networks (CNNs) to distinguish between healthy individuals (NM), AD, and PD patients. A total of 700 voice …
Extending A Pretrained Language Model (Bert) Using An Ontological Perspective To Classify Students' Scientific Expertise Level From Written Responses,
2025
Old Dominion University
Extending A Pretrained Language Model (Bert) Using An Ontological Perspective To Classify Students' Scientific Expertise Level From Written Responses, Heqiao Wang, Kevin C. Haudek, Amanda D. Manzanares, Chelsie L. Romulo, Emily A. Royse, Caterina B. Azzarello
Human Movement Studies & Special Education Faculty Publications
The complex and interdisciplinary nature of scientific concepts presents formidable challenges for students in developing their knowledge-in-use skills. The utilization of computerized analysis for evaluating students' contextualized constructed responses offers a potential avenue for educators to develop personalized and scalable interventions, thus supporting the current teaching and learning of science. While prior research in artificial intelligence has demonstrated the effectiveness of algorithms, including Bidirectional Encoder Representations from Transformers (BERT), in tasks like automated classifications of constructed responses, these efforts have predominantly leaned towards text-level features, often overlooking the exploration of conceptual ideas embedded in students' responses from a cognitive perspective. …
Point Cloud-Based Diffusion Models For The Electron-Ion Collider,
2025
Old Dominion University
Point Cloud-Based Diffusion Models For The Electron-Ion Collider, Jack Y. Araz, Vinicius Mikuni, Felix Ringer, Nobuo Sato, Fernando Torales Acosta, Richard Whitehill
Physics Faculty Publications
At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We …
Using Visual Prompts To Analyze Ejection Fraction In Echocardiograms,
2025
West Virginia University
Using Visual Prompts To Analyze Ejection Fraction In Echocardiograms, Kevin Reisch
Graduate Theses, Dissertations, and Problem Reports (ETD)
Left ventricular ejection fraction (LVEF) is a critical biomarker for heart failure, but manual estimation from echocardiograms is time-consuming. Artificial intelligence can be used to accelerate this process, allowing clinicians to focus on other critical tasks. Current methods typically train models from scratch on echocardiogram datasets; however, this approach is limited by the scarcity of large medical imaging datasets, which are expensive and difficult to acquire. We present a transfer learning approach that leverages pretrained models from massive datasets, enabling continuous improvement as foundation models advance. Our method employs visual prompting to generate trainable masks for echocardiogram videos, transforming the …
Openmuse: Integrating Open-Source Models Into Music Creation Workflows,
2025
Dartmouth College
Openmuse: Integrating Open-Source Models Into Music Creation Workflows, Tyler K. Vergho
Dartmouth College Master’s Theses
This master's thesis introduces OpenMUSE (Open Multimodal Unified Sound Engine), a platform that demonstrates the potential of open-source AI music generation by integrating state-of-the-art deep learning models into a unified system. By unifying ten different open-source models, including MusicGen, AudioLDM2, and custom-trained text-to-symbolic music generation models, OpenMUSE aims to create a user-friendly interface that empowers artists to produce complex, adaptive musical compositions. The system enhances accessibility by providing a simple web interface and natural language controls, while improving controllability through features like melody conditioning and semantic audio editing. Specifically, OpenMUSE offers a digital audio workstation (DAW)-inspired interface that lowers the …
Data Injustice In Global Justice,
2025
Maurer School of Law - Indiana University
Data Injustice In Global Justice, Asaf Lubin, Cherry Tang
Articles by Maurer Faculty
In May 2020, the United Nations Secretary-General unveiled a sweeping “Data Strategy for Action by Everyone, Everywhere,” seeking to unlock the UN’s “full data potential.” The International Criminal Court’s Office of the Prosecutor followed suit, declaring in 2023 its intent to acquire advanced cyber forensic tools so as to hold the “widest range of digital evidence globally.” Across international institutions, data-driven governance has become the norm, with humanitarian agencies and tribunals transforming into “data hubs and information clearinghouses.” This Article critiques the unfettered datafication of global justice by international courts and organizations. These entities have aggressively expanded their data-driven operations …
Quantum-Augmented Ai/Ml For O-Ran: Hierarchical Threat Detection For Synergistic Intelligence And Interpretability,
2025
Hampton University
Quantum-Augmented Ai/Ml For O-Ran: Hierarchical Threat Detection For Synergistic Intelligence And Interpretability, Tan Le, Vanessa Le, Sachin Shetty
VMASC Publications
Open Radio Access Networks (O-RAN) enhance modularity and telemetry granularity but also widen the cybersecurity attack surface across disaggregated control, user and management planes. We propose a hierarchical defense framework with three coordinated layers—anomaly detection, intrusion confirmation, and multiattack classification—each aligned with O-RAN’s telemetry stack. Our approach integrates hybrid quantum computing and machine learning, leveraging amplitude- and entanglement-based feature encodings with deep and ensemble classifiers. We conduct extensive benchmarking across synthetic and real-world telemetry, evaluating encoding depth, architectural variants, and diagnostic fidelity. The framework consistently achieves near-perfect accuracy, high recall, and strong class separability. Multi-faceted evaluation across decision boundaries, probabilistic …
Cultivating Confidence,
2025
Embry-Riddle Aeronautical University
Cultivating Confidence, Leila Halawi, Mark Miller, Sam Holley
Publications
Artificial intelligence (AI) is pervasive in scholarly publications, internet sites, and public discourse. AI is a term with broad scope that refers to machines that can learn and perform tasks that typically require human intelligence. The specter of AI intruding into many aspects of aviation has raised alarms, concerns, and prodigious misunderstanding of potential and contemplated applications in systems and processes. The EASA AI Roadmap (EASA, 2023 ), a linear projection with three levels EASA, 2023 extending into 2050, places the human-AI teaming period (through 2035) at Level 2. This suggests a ten-year span to develop the interactive issues to …
Embracing Ai In Higher Education: Redefining Teaching And Learning In The Digital Era,
2025
Oglethorpe University
Embracing Ai In Higher Education: Redefining Teaching And Learning In The Digital Era, Najem Tala, Leila Halawi
Publications
This research explores the transformative potential of Artificial Intelligence (AI) in education, focusing on its ability to address emerging challenges and revolutionize teaching practices. We critically assess the current educational landscape, evaluate AI's role as a catalyst for educational reform, and examine implementation challenges. Our analysis emphasizes the evolving impact of AI on personalized learning, student engagement, and administrative efficiency and contributes to the growing body of literature on educational technology. The discussion highlights both the opportunities and limitations of AI in education, pointing to critical areas that remain underexplored.
Discovery Of Photosynthetic Oxic N2-Fixation In Cyanobacteria Using Wet Lab And Machine Learning Approaches,
2025
South Dakota State University
Discovery Of Photosynthetic Oxic N2-Fixation In Cyanobacteria Using Wet Lab And Machine Learning Approaches, James A. Young Iii
Electronic Theses and Dissertations
No abstract provided.
Method Entity And Relation Extraction Based On Automatically Generated Syntactic Templates: A Case Study Of Csdn Artificial Intelligence Blog,
2025
Department of Information Management, Peking University, Beijing 100871
Method Entity And Relation Extraction Based On Automatically Generated Syntactic Templates: A Case Study Of Csdn Artificial Intelligence Blog, Kuiliang Li, Bolin Huang
Journal of Scientific Information Research
[Purpose/significance]There are many relationships between method entities and application scenarios, problems,organizations and other entities. Extracting these entity relationships helps to capture the development trend of technology and promote the improvement of innovation ability.[Method/process]This paper discusses a method for extracting method entities and relations based on automatically generated syntactic templates. By designing a new adaptive template, the method improves flexibility and adaptability, reducing dependence on large-scale labeled data. Using a small number of seed triples, the method iteratively generates syntactic templates and extracts method entities and relations for the CSDN artificial intelligence topic blog. It also improves the extraction quality using …
The Evolution Of Research Methods In The Digital Humanities Perspective: A Quantitative Analysis Based On Cnki Data And A Large Language Model,
2025
1.School of Information Management, Nanjing Agricultural University, Nanjing 210095
2.Research Center for Humanities and Social Computing, Nanjing Agricultural University, Nanjing 210095
The Evolution Of Research Methods In The Digital Humanities Perspective: A Quantitative Analysis Based On Cnki Data And A Large Language Model, Guangyao Sun, Dongbo Wang
Journal of Scientific Information Research
[Purpose/significance]This paper aims to explore the evolution trend of research methods in the field of digital humanities with the help of large language model technology. [Method/process]This paper mainly focuses on the data of CNKI journal articles, selects the general Chinese large language model GLM-4, uses prompt engineering and chain of thought to extract and cluster the abstract data, of papers and analyzes its evolution trend through quantitative processing. [Result/conclusion]The study shows that GLM-4 can well identify and extract research methods from complex abstract data. Analyzing the evolution trend in chronological order, it is found that research methods such as "interview …
Research On Automated Generation And Evaluation Of Patent Claimsbased On Gpt-4,
2025
Shanghai Maritime University Library(Archive), Shanghai 201306
Research On Automated Generation And Evaluation Of Patent Claimsbased On Gpt-4, Junhua Li, Qian Yuan, Xiang Yan, Changhong Lv
Journal of Scientific Information Research
[Purpose/significance]This study aims to automatically generate claims using the GPT-4 model, in order to reduce the writing difficulty for inventor and improve the work efficiency and quality. [Method/process]The article constructs Prompts suitable for automatically generating patent claims and implements four prompting strategies: ZeroShot, Exact-Drafting, Stepwise-Claim, and Exact-Step Claim. By inputting patent specifications and technical disclosure documents into the GPT-4 model and using Prompts to guide its output, the automated generation of patent claims is achieved. The ROUGE and BERTScore evaluation metrics were used to assess the quality of the text, and the generated text was analyzed in comparison with the …
Research On Emerging Technology Topic Identification Based On Bertopic,
2025
Department of Information Management, Peking University, Beijing 100871
Research On Emerging Technology Topic Identification Based On Bertopic, Dakun Wang, Bolin Hua
Journal of Scientific Information Research
[Purpose/significance]Identifying and foreseeing emerging technologies, bring technological first-mover advantages to enterprises and governments, and grasp technological development trends in a timely manner. [Method/process]This study uses BERTopic's topic modeling method to obtain domain topic distribution, and merges paper and patent topics based on the cosine similarity of topic vectors to identify emerging topics. [Result/conclusion]Using the BERTopic topic modeling method combined with index evaluation can effectively identify emerging topics and emerging terms.Taking the field of new energy vehicles as an example to carry out empirical research, using two methods: divided verification period and data verification method, 12 of the 16 identified topics …
The Future Of Ai Regulation In Drug Development: A Comparative Analysis,
2025
Duke Law School
The Future Of Ai Regulation In Drug Development: A Comparative Analysis, Gabriela Lenarczyk, Timo Minssen, Nicholson Price, Arti Rai
Faculty Scholarship
As artificial intelligence (AI) transforms drug development, regulatory frameworks are evolving to oversee its implementation, particularly at the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This paper makes three contributions to understanding emerging regulatory approaches. First, we offer a comparative analysis of how these agencies have responded to AI-driven advances, incorporating new US executive orders and the European Union (EU)’s AI Act. Second, we propose a novel analytical framework to understand regulatory divergence: the FDA’s flexible, dialog-driven model contrasts with the EMA’s structured, risk-tiered approach, reflecting broader institutional and political-economic differences. While the former encourages …
Artificial Intelligence And Procedural Due Process,
2025
Duke Law School
Artificial Intelligence And Procedural Due Process, Brandon L. Garrett
Faculty Scholarship
Artificial intelligence (AI) violates procedural due process rights if the government uses it to deprive people of life, liberty, and property without adequate notice or an opportunity to be heard. A wide range of government agencies deploy AI systems, including in courts, law enforcement, public benefits administration, and national security. If the government refuses to disclose the reasons why it denied a person bail, public benefits, or immigration status, serious due process concerns arise. If the government delegates such tasks to an AI system, the due process analysis does not change. One asks whether a person received adequate notice and …
Automating International Human Rights Adjudication,
2025
Duke Law School
Automating International Human Rights Adjudication, Veronika Fikfak, Laurence R. Helfer
Faculty Scholarship
International human rights courts and treaty bodies are increasingly turning to automated decision-making (“ADM”) technologies to expedite and enhance their review of individual complaints. These tribunals have yet to consider many of the legal, normative, and practical issues raised by the use of different types of automation technologies for these purposes. This article offers a comprehensive and balanced assessment of the benefits and challenges of introducing ADM into international human rights adjudication. We argue in favor of using ADM to digitize documents and for internal case management purposes and to make straightforward recommendations regarding registration, inadmissibility, and the calculation of …
Binoculars To Bytes: Development And Field Validation Of An Ai-Driven System For Avian Monitoring,
2025
University of Montana, Missoula
Binoculars To Bytes: Development And Field Validation Of An Ai-Driven System For Avian Monitoring, Christian J. Dupree
Graduate Student Theses, Dissertations, & Professional Papers
Autonomous camera-trap arrays coupled with artificial-intelligence (AI) vision can lift bird monitoring beyond the spatial, temporal, and labor limits of traditional field surveys. We present Binoculars to Bytes (B2B), an open-source pipeline that turns 180–360° time-lapse imagery into analysis-ready avian data. At Freezout Lake Wildlife Management Area (Montana, USA) the system ran four-hour morning deployments during spring and fall migrations (2023–2024). A YOLO-NAS detector, incrementally refined with a “Specialized Localized Iterative Model” workflow, quadrupled local accuracy and, after confidence-based species-binning, cut false-positive rates in half. Daily AI species lists were benchmarked against contemporaneous eBird citizen-science checklists and recovered ≥ …
Interactive Video Search With Multi-Modal Llm Video Captioning,
2025
Singapore Management University
Interactive Video Search With Multi-Modal Llm Video Captioning, Yu-Tong Cheng, Jiaxin Wu, Zhixin Ma, Jiangshan He, Xiao-Yong Wei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Cross-modal representation learning is essential for interactive text-to-video search tasks. However, the representation learning is limited by the size and quality of video-caption pairs. To improve the search accuracy, we propose to enlarge the size of available video-caption pairs by leveraging multi-model LLM on video captioning. Specifically, we use LLM to generate video captions for a large video collection (i.e., WebVid dataset) and use the generated video-caption pairs to pre-train a text-to-video search model. Additionally, we use LLM to generate fine-grained captions for test video collections to enable text-to-caption retrieval. Furthermore, we build a semantic overview of the retrieved rank …
