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Proceedings of the International Florida Artificial Intelligence Research Society Conference
ISSN 2334-0754 · LibraryPress@UF · 797 artikel di indeks
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Beyond Accuracy
This paper evaluates multimodal AI models (Gemini and ChatGPT) for visual-based aerial trajectory classification, comparing them against a neural network baseline. Beyond standard metrics, we introduce behavioral criteria to assess reliability in safety-critical contexts. Results show that…
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Addressing a Bias in Evaluating of Student Self-Explanations of Worked Programming Examples
Worked examples are step-by-step solutions to problems in a specific domain, offered to students to acquire domain-specific problem-solving skills. The power of worked examples could be magnified by combining them with self-explanations, which ask students to explain rather than passively study…
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Comparative Study of Different Learning Paradigms for Zero-Shot Sentiment Analysis of the Low-Resource African Language Oromo
In this paper, we address zero-shot sentiment analysis for Oromo, a low-resource language spoken in East Africa, as part of SemEval-2023 Task 12 (Zero-Shot on Oromo). Leveraging large-scale language models, including BERT and its multilingual variants, we investigate four learning paradigms…
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Multi-Stream Fusion of Spatial, Frequency, and Attention Features for Robust Deepfake Detection in Low-Resolution Images
The increasing realism of generative models makes deepfake detection challenging under the low resolution and compression artifacts which are common in real-world media. While many detectors perform well on high-quality images, their performance degrades when fine-grained spatial details are…
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Operationalization-Based Modeling of Software Non-Functional Requirement Relationships
Software Non-Functional Requirements (NFRs), also known as Quality Attributes (QAs), such as security, performance, and usability, play a critical role in shaping software architecture. However, their relationships are often complex, context-dependent, and difficult to anticipate early in design…
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A Scalable Approach to Solving Simulation-Based Network Security Games
We introduce MetaDOAR, a lightweight meta-controller that augments the Double Oracle / PSRO paradigm with a learned, partition-aware filtering layer and Q-value caching to enable scalable multi-agent reinforcement learning on very large cyber-network environments. MetaDOAR learns a compact state…
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Prediction of Solar Flares Using Photospheric Magnetic Field Parameters with Deep Learning
Solar flares, particularly those of the M- and X-class, have a significant impact on human life because of their potential to disrupt critical infrastructure and communication systems on Earth. Accurate prediction of solar flares is crucial for mitigating these risks, but the black-box nature of…
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InsightBoard
Modern machine learning systems deployed in safety-critical domains require visibility not only into aggregate performance but also into how training dynamics affect subgroup fairness over time. Existing training dashboards primarily support single-metric monitoring and offer limited support for…
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Robotic Fall Prediction with Egocentric Vision and Proprioception
Legged robots have received increasing attention in recent years thanks to ever-improving deep learning techniques, holding promise for programmable robots that can match the capabilities of animals and people. Body pose estimation is a widely used technique, which has been well-developed and…
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Forecasting Geomagnetic Disturbances with Interpretable Deep Learning
Geomagnetic disturbances significantly impact Earth, affecting spacecraft operations, power grids, communication systems, among others. The Kp index, a widely used geomagnetic disturbance measure, requires accurate prediction to achieve effective space weather monitoring. In this paper, we present…
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AstroAid
Selecting observation targets in astronomy requires reasoning over constraints like visibility, brightness, and scientific value. In domains such as variable star monitoring, where thousands of targets exist and time is limited, making informed choices is essential but often overwhelming…
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Dwell Time Estimation Using Periodic Image Captures and Deep Learning
The Innovative Truck Parking Availability System (iTPAS) employs computer vision algorithms (such as YOLOv8) to monitor truck parking occupancy in real-time at highway rest areas in Florida. Although iTPAS successfully detects instantaneous occupancy, it cannot determine how long vehicles remain…
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Explaining Why Instrumental Rationality is Insufficient for Ethical Behavior
As technologies based on AI expand in complexity, autonomy, and domains of application, the need for ethical considerations is ubiquitous. From self-driving vehicles and autonomous recruiting processes to eldercare robots and recidivism prediction software, philosophers, computer scientists and…
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A Narrative-Driven Computational Framework for Clinician Burnout Surveillance
Clinician burnout threatens patient safety, care quality, and workforce sustainability, especially in high-acuity ICUs. Existing detection approaches rely on retrospective surveys or coarse EHR metadata, limiting their ability to capture the evolution of burnout-related stress. We analyze 10,000…
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Codify
Programming education poses significant challenges for many students due to varying priorities. Traditional classroom instruction often lacks the scalability required to provide personalized support. This paper introduces AI Tutor, an intelligent tutoring system designed to enhance programming…
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Advancing Fairness and Explainability in AI for Autism Diagnosis
Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition that is often underdiagnosed, and AI presents a promising approach for scalable, early detection using behavioral and neuroimaging data. Despite advances in this area, the lack of comprehensive datasets, along with…
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A Seven-Layer Lifecycle Framework for Fair, Robust, and Safe AI
As AI systems move into high-stakes settings, failures in fairness, robustness, and safety can lead to tangible harm. We present a concise seven-layer lifecycle framework that integrates (i) data and training interventions, (ii) evaluation stress tests and subgroup reporting, (iii) deployment…
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The LLM-Augmented Clustering for Customer Support Ticket Triage
Automatically clustering customer support tickets into coherent issue groups is critical for efficient triage, root- cause analysis, and resource allocation. However, support ticket text is short, noisy, and exhibits high lexical variance for semantically identical issues, making traditional…
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Multi-Label Heart Disease Classification Using Electrocardiograms and Machine Learning
Heart disease is at present the leading cause of death globally, and the urgent need exists for accurate, scalable diagnostic tools in a timely manner. Electrocardiograms (ECGs) are a non-invasive means of quickly and easily recording the electrical action of the heart. This enables the…
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Steganography with Large Language Models
Large language model (LLM) steganography generates fluent cover text that encodes a secret message, with the secret key often given as a natural language prompt or seed. Recent rank based LLM stegosystems achieve high capacity and strong distributional indistinguishability, but little is known…