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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5182 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Beyond Acceptance and Resistance: Digital Media Students’ Judgements of Generative AI Assistance and Perceived Creative Replacement ↗
This study investigated how Digital Media students judged acceptable and problematic forms of generative artificial intelligence (GenAI) involvement in creative learning. An embedded mixed-methods analysis examined 48 completed and consented baseline survey responses from an accessible population of 120 students at an Australian university. Forty-seven responses were valid for most Likert items, with qualitative content analysis of 37 benefit responses and 41 concern responses. Participants identified potential value in troubleshooting, coding support, grammar checking, information organisatio…
- OpenAlex · Published · First archived
From skills to smart care: The mediating role of digital literacy in the relationship between digital competence and medical artificial intelligence readiness among nursing students ↗
The rapid integration of artificial intelligence (AI) in healthcare requires nurses to possess strong digital competence, advanced digital literacy, and readiness to engage with AI technologies. Despite this need, the ways in which digital competence influences AI readiness, and the mediating role of digital literacy, remain underexplored. This descriptive cross-sectional study examined 500 undergraduate nursing students from two Universities for Health Sciences in Saudi Arabia and Egypt using validated measures of digital competence, digital literacy, and medical AI readiness. Results indicat…
- OpenAlex · Published · First archived
The Tensed Computer Improviser and the workmanship of risk: Small-dataset machine learning and real-time interactive musical improvisation ↗
Abstract Generative artificial intelligence (AI) has entered music overwhelmingly by way of large industrial models trained on vast corpora, in which the outcome is fixed in advance by an apparatus that the musician neither controls nor understands. This article argues that the essential machine-learning operations of dataset assembly, feature extraction and model fitting can instead be utilised transparently as part of an artist’s creative practice. Looking to David Pye’s concepts of workmanship of certainty and workmanship of risk , this article examines the design of the Tensed Computer Imp…
- OpenAlex · Published · First archived
Flexible Insole with Soft, Integrated, Polymeric Pressure Sensors to Discriminate Different Types of Gaits Using Machine Learning Techniques ↗
Wearable sensors offer promising solutions for continuous gait monitoring in healthcare, rehabilitation, and sports. In this paper, a novel, soft, flexible, and easily made wearable sensor to detect the pressure generated by human gait was developed. The sensor consists of a soft polyurethane foam functionalized with a PEDOT:PSS aqueous solution designed to integrate the active material and develop a piezoresistive device. Eight soft sensors have been integrated into an insole, with dedicated readout electronic circuitry, including an accelerometer, a gyroscope, and a Bluetooth communication t…
- OpenAlex · Published · First archived
Shipping in 76 Locales: A Multi-Case Study of LLM-Agent Localization Pipelines for Production Mobile Apps ↗
Large language model (LLM) agents translate software interfaces at scale, yet production evidence on what breaks—and which safeguards catch it—is nearly absent: a 2026 mapping study of localization testing retained 24 primary studies, though none on LLM-driven localization. We report a multi-case study of fourteen shipped mobile apps by one developer (ten iOS, four Android; 40–76 locales; roughly 347,000 units) at five maturity levels. Mining the repositories (catalogs, 4473 commits, process documents, tooling), we code defects into thirteen classes in two waves, the second under a frozen code…
- OpenAlex · Published · First archived
Feature-Wise Linear Modulation-Conditioned Transformer for AIS Trajectory Restoration Incorporating Vessel Geometry and Maneuvering Characteristics ↗
Automatic Identification System (AIS) data are widely used for maritime traffic analysis, vessel monitoring, and intelligent navigation systems. However, missing trajectory segments caused by irregular reporting intervals and communication disruptions can degrade the continuity and reliability of vessel trajectories. This study proposes a vessel-condition-aware AIS trajectory restoration framework that combines Cubic Spline-based preliminary restoration with a Feature-wise Linear Modulation (FiLM)-conditioned Transformer and gated bounded residual correction. For AIS sequences of 200 points, t…
- OpenAlex · Published · First archived
AI‐Powered Smart Displays and AR Glasses: Perspectives and Challenges ↗
This perspective paper reviews recent advancements and explores future, exciting opportunities for both artificial intelligence (AI)‐powered smart displays and AI‐assisted augmented reality (AR) eyeglasses. To begin, we analyze fundamental AI‐enhanced image‐quality techniques, including perceptual resolution upscaling algorithms, tone mapping, reverse tone mapping, and image‐quality analysis. Next, we examine the practical implementations of AI technologies for smart displays, in which both the visual experience and use case of novel display technologies can be improved. Finally, we further ex…
- OpenAlex · Published · First archived
SEFRQO-Plus: a self-evolving query optimizer via large language models and retrieval-augmented generation ↗
Abstract Query optimization is a crucial problem in database systems that has been studied for decades. Learned query optimizers (LQOs) can improve performance over time by incorporating feedback; however, they suffer from cold-start issues and often require retraining when workloads shift or schemas change. We initially proposed SEFRQO, a Self-Evolving Fine-tuned RAG-based Query Optimizer, which leverages fine-tuned LLMs to address these challenges and uses in-context learning to achieve continuous evolution. However, its system design relies on the assumption of repetitive query workloads, r…
- OpenAlex · Published · First archived
Harmonic Distortion and Distribution Transformer Thermal Risk in Low-Voltage Networks: Feeder Impedance Sensitivity for Sustainable Renewable Energy Integration ↗
The increasing penetration of distributed energy resources (DERs) in low-voltage networks is central to the clean energy transition but introduces coupled challenges in voltage regulation, harmonic distortion, and distribution-transformer operation that impact grid sustainability and asset longevity. This paper isolates feeder impedance as a controlled variable and evaluates whether remote-network power-quality severity and transformer consequence follow the same sensitivity trend. A time-domain model used a factory-matched 200 kVA, 11 kV/420 V Dyn11 transformer with 40% DER connected at remot…
- OpenAlex · Published · First archived
A design space for AI integration in early-stage production plant planning ↗
Early-stage production plant planning is characterised by evolving requirements, incomplete process definitions, and information distributed across documents, engineering models, and planning tools. Introducing artificial intelligence in this setting raises a fundamental question: what must be considered before a specific application, data model, or technical solution can be defined? Existing research typically starts from an already identified planning task, a selected artificial intelligence method, or an available technical infrastructure. It therefore provides limited guidance for structur…
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How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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