AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,073 papers
Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-impairing conditions. Current approaches decode predominantly from motor cortical areas, discarding others—such as area 44, part of Broca’s...
While Large Language Models are increasingly embedded within long-horizon Human-AI interactions, they are typically evaluated as stateless systems governed by rigid safety alignments. This study introduces blinded Big-5 psychometric telemetry for quantifying behavioral continuity in extended Human-AI interactions and a...
Large language models (LLMs) can predict interpersonal attraction from conversation transcripts, but it remains unclear what a speech predictor can add beyond transcript-only LLM prediction. Using Japanese speed-dating conversations, we combine predictions from a transcript-only LLM and a supervised speech predictor to...
Embodied conversational agents (ECAs) need effective empathic grounding to foster social support and engagement. Expanding into emotional domains, ECAs now use Large Language Models (LLMs) and multimodal human-agent interactions to enhance their capabilities. Yet, understanding the impact of backchanneling modalities o...
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We examine educational YouTube videos through multimodal metadata, such as transcripts, titles, thumbnails, and viewer comments, to investigate how ChatGPT is framed across creator groups and how those framings relate to audience response and platform reach. Little is known about how large language models are presented...
Circular economy (CE) transitions emerge from the interplay of heterogeneous consumer and firm behaviours, infrastructure dynamics, and policy interventions. However, the tools widely used to assess CE policy, e.g. LCA, MFA, and EEIO models, are static and cannot represent how markets form and evolve over time. Agent-b...
Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human–computer interaction. Most existing Brain–Computer Interface (BCI) approaches rely on memory-intensive fine-tuning of Large Language Models (LLMs) or encoder–decode...
This repository contains the data and resources associated with Istidlal, the first Arabic benchmark for evaluating logical and sequential reasoning in Arabic Large Language Models (LLMs) within the economic and financial domain. The repository includes two main assets: 1. Arabic Economic and Financial News Corpus (AEF...
Current research — English This ongoing research project develops a seven-dimensional Kantian diagnostic framework for large language models, agent systems and deployment configurations. It distinguishes conceptual conditions of rational agency from empirically testable task proxies. The current evidence supports selec...
Existing human-robot interaction systems often lack mechanisms for sustained personalization and dynamic adaptation in multi-user environments, limiting their effectiveness in real-world deployments. We present HARMONI, a multimodal personalization framework that leverages large language models to enable socially assis...
Fe-N-C catalysts are the most promising non-precious alternatives to platinum group metals (PGMs) for the acidic oxygen reduction reaction (ORR) in proton-exchange membrane fuel cells (PEMFCs). However, their practical application is significantly limited by the intrinsic trade-off between activity and stability. Herei...
Multimodal interactive systems increasingly rely on vision-language models (VLMs), such as CLIP, to mediate between users and visual content. These VLMs power image search and retrieval interfaces where users specify what they want in natural language. However, such systems suffer from a basic aspect of human communica...
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
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