large language models

Cross-Domain Fake News Classification via RoBERTa Fine-Tuning on Hybrid LIAR-FakeNewsNet Corpus with Deployable Web Inference

The rapid spread of misinformation on social media platforms escalates a serious threat to world socio-political stability, health and financial security. While current automated fake news detection models boast impressive accuracy scores, these are often due to memorisation of the training domain rather than genuine g...

Dataset and experimental materials for evaluating LLM reliability in transport planning

This dataset contains the experimental materials for a study on whether publicly accessible conversational large language models (ChatGPT, Claude, Gemini and Microsoft Copilot) can generate road freight driver schedules that comply with EU driving-time and rest-period rules (Regulation (EC) No 561/2006 and Regulation (...

From Human Speakers to Algorithmic Interfaces: Rethinking Ethos in AI-Mediated Communication

Generative Artificial Intelligence (GAI) systems increasingly function as communicative agents, raising questions about how credibility is constructed in them. Drawing on Aristotle's concept of ethos, this study examines how credibility is performed through interface‑level cues in large language model (LLM) chatbots: C...

Blind Letterform Construction as a Behavioral Marker of Geometric Consistency in Large Language Models

A Large-Scale Multi-Domain Unified Reverse Arabic Dictionary Dataset

Abstract Arabic is a linguistically and culturally rich language with a vast vocabulary that spans scientific, religious, and literary domains. However, large-scale lexical datasets linking Arabic words to precise definitions remain limited. We present MURAD (Multi-domain Unified Reverse Arabic Dictionary), an open lex...

Exploring collaborative problem-solving in AI-assisted robotics education

Prompt engineering (PE) involves creating, improving, and deploying prompts to improve large language model (LLM) performance. Despite its increasing importance, many students lack skills in this area. Although frameworks for prompt design and process exist, theory-based instructional tools for improving prompt enginee...

Network Flow-Based IoT Botnet Behavior Detection and Explanation Using Multi-Agent LLM Reasoning

IoT botnets generate diverse network behaviors, including scanning, command-and-control communication, and distributed denial-of-service activity. Traditional machine learn-ing models can achieve high detection accuracy using engineered network-flow features, but they often provide limited behav-ioral explanation. This...

Artificial Intelligence for Engineering-Grade Parametric CAD: A Critical Review of Representations, Large Language Models, and Agentic Design Systems

Artificial intelligence (AI) is rapidly expanding computer-aided design (CAD) from shape generation toward generative, conversational, and agentic engineering. However, most AI–CAD evaluations remain dominated by geometric similarity and execution success, while industrial CAD depends on editable parameters, design int...

The Missing Link: Why Legal Large Language Models Need Knowledge Graphs

The Impact of Patient Sociodemographic Characteristics on Primary Care Clinician Empathic Communication over Patient Portal Messages

Empathic communication in the clinical setting has been associated with lower blood sugar and cholesterol levels among diabetic patients, decreased anxiety, and increased satisfaction, treatment adherence, and trust. Despite recent growth in patient portal use, the expression of empathic communication through patient p...

Differential Privacy Tradeoffs in Large-Scale ML Model Training

Differential privacy (DP) provides the gold standard for formal privacy guarantees in machine learning, offering mathematically rigorous bounds on the information an adversary can infer about any individual training record from an observed model.However, the practical application of differential privacy to large-scale...

Advancing Machine Learning for Resource-Constrained Environments

This dissertation addresses the fundamental challenge of enabling machine learning intelligence on resource-constrained systems. These systems include embedded edge devices, mobile platforms, and robots operating under both on-device and cloud-based paradigms. The resource-constrained systems must perform low-latency,...