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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 ↗6025 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
AI ‐Mediated Multimodal Reading Education for EFL Learners: Effects on Students' Enjoyment and Engagement ↗
ABSTRACT Artificial intelligence (AI) has increasingly reshaped educational practices by expanding opportunities for adaptive, multimodal and learner‐centered instruction. While growing attention has been paid to AI‐supported language learning, less is known about how multimodal AI technologies may support the emotional and behavioural dimensions of education among at‐risk students, particularly in second/foreign language reading contexts. Addressing this gap, the present study examined whether AI‐mediated multimodal reading education could enhance at‐risk English as a foreign language (EFL) l…
- OpenAlex · Published · First archived
Teaching organ-at-risk delineation in the AI era: educational impact and dosimetric relevance of a structured training intervention ↗
Accurate delineation of organs at risk (OARs) remains a cornerstone of radiotherapy planning. As AI-assisted auto-contouring enters routine practice, the place of contouring in residency training is increasingly debated: should residents still be trained to contour, and if so, with what focus? To evaluate the impact of a structured educational intervention on OAR contouring accuracy among radiation oncology residents, and to assess whether training-related changes in contouring performance are reflected in geometric and dosimetric endpoints, with an independent commercial AI auto-segmentation…
- OpenAlex · Published · First archived
Critiquing AI Surveys with a Case Study on Temporal Features for Trustworthy Computer Vision ↗
A novel, thorough manual survey is presented, focusing on the trustworthy extraction and application of temporal features in drone video feeds for object detection and tracking. Trust stems from reducing the necessary scope of opaque, deep components, and externally accounting for hallucinations. This manual survey is used as a baseline for a first of its kind case study into the use of new end-to-end LLM based “Deep Research” tools for literature review purposes. It was found that such tools cannot currently be used to replace a manual survey on novel research topics, but may offer utility ga…
- OpenAlex · Published · First archived
Image-embedded prompt injection vulnerability of vision-language models in dental radiology: a cross-vendor attack–defense evaluation ↗
Abstract Image-embedded prompt injection—adversarial text rendered into the pixel data of a medical image—is an emerging security threat to vision-language models (VLMs) explored for clinical decision support. In an adversarial stress test using a permissive elicitation prompt, we assessed four production-tier VLMs (GPT-4o, Gemini 2.5 Flash, Claude Sonnet 4.5, MedGemma 4B) on 270 DenTeX dental panoramic radiographs under four attack classes, across 58,320 logged inference calls. All four models were vulnerable, with paired attack success rates (ASR) up to 62.6% (95% CI 58.5–66.7%, GPT-4o) and…
- OpenAlex · Published · First archived
Improving clinical data standardization in surgical notes through biomedical entity linking with contextual evidence from large language models ↗
Surgical notes contain essential clinical information for postoperative care, yet free-text format and institutional variability limit their use for standardized data representation and secondary analysis. Biomedical entity linking enables mapping of heterogeneous clinical expressions to standardized ontologies such as SNOMED-CT, supporting semantic interoperability. However, existing approaches often rely on predefined mention spans through named entity recognition (NER), which is labor-intensive and may introduce errors. We analyzed 9,051 gastric cancer surgical notes from Seoul National Uni…
- OpenAlex · Published · First archived
Prolonged participation AI-based mindfulness school programme in primary education: Implications for AI practices, schools and developers ↗
Background Research on Artificial Intelligence (AI) -supported mindfulness programs in primary education remains limited, particularly in rural contexts where implementation conditions differ from urban settings. Aim This study examines the effects of an AI-supported mindfulness intervention on rural primary school students' behavioral and socio-emotional outcomes, and on teachers' self-regulation and creativity, measured at pretest (T1), posttest (T2), and follow-up (T3). Method The study used a quasi-experimental design with a control group across selected classrooms in four rural schools. T…
- OpenAlex · Published · First archived
Artificial intelligence driven multimodal medical image segmentation algorithm: applied research and clinical verification ↗
Abstract Multimodal medical imaging—comprising CT, MRI, and PET—is fundamental to modern diagnosis, lesion localization, and treatment planning. The accuracy and speed of segmenting these modalities directly affect clinical decisions. To overcome limitations in cross-modal integration, sensitivity to small lesions, generalization, and workflow compatibility, we propose AMIS-Net, an AI-driven segmentation network. Built on an encoder-decoder backbone, AMIS-Net incorporates a Dual Attention Module (DAM) for adaptive feature recalibration and a Small Object Capture (SOC) module for multi-scale fe…
- OpenAlex · Published · First archived
Performance analysis of vertical patterned glass BIPV in zero energy buildings using explainable AI ↗
Building integrated photovoltaic (BIPV) systems offer a key alternative for urban spatial constraints, yet detailed analyses of power generation across various module concepts are still required. This study conducts an in depth analysis combining data driven approaches and explainable artificial intelligence (XAI) on a one year field dataset (20,700 observations) from 110 full sized, vertically mounted patterned glass BIPV modules. First, data analysis reveals that while voltage (Vmp) decreased with rising temperatures, current (Imp) correlated strongly with plane of array (POA) irradiance. Hi…
- OpenAlex · Published · First archived
Enhancing transformer-based wind speed forecasting on new meteorological and turbine-level SCADA datasets ↗
Accurate wind speed forecasting is crucial for mitigating grid instability and supply–demand imbalances caused by the intermittent nature of wind power. To support reproducible research under heterogeneous sensing conditions, we construct and publicly release three complementary datasets: KZMET, a 15-year reanalysis-based meteorological dataset covering 25 cities in Kazakhstan; VNMET, a multi-year mast-based wind measurement dataset from 10 locations in Vietnam; and WTSL, a high-resolution (1-minute) turbine-level SCADA dataset containing over 17 million records and labeled operational alarms.…
- OpenAlex · Published · First archived
Deep Neural Network Application to Real-Time Growth Prediction of Epitaxial GaN by Molecular Beam Epitaxy ↗
Abstract In this work, we explore the application of machine learning(ML) methods, specifically recurrent neural networks (RNNs), for the analysis and prediction of GaN growth via molecular beam epitaxy (MBE). In particular, we show the possibility to control in real-time and predict the growth evolution based on the actual values of selected parameters. For this purpose, we tailored a specific GaN growth sequence, derived from droplet epitaxy and metal modulated growth; each deposition is characterized using the surface morphology information contained in the ereflection high-energy electron…
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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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