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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 ↗5233 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
Development of ethical evaluation indicators for educational artificial intelligence based on the “Life-Practice” pedagogy ↗
The rapid advancement of artificial intelligence has ushered in a new era of educational artificial intelligence (EAI), bringing both immense potential and significant ethical challenges. To promote responsible development and effectively mitigate potential ethical risks, there is an urgent need to establish clear evaluative criteria for ethical practices involving EAI. However, current generic frameworks often prioritize technical safety over pedagogical generativity. This study addresses this imperative by developing an ethical evaluation model and corresponding system of indicators for EAI.…
- OpenAlex · Published · First archived
Assessing multi-GNSS PPP-AR using IGS all-frequency products and machine-learning-driven attribution analysis ↗
Abstract Recent progress in the estimation of all-frequency bias corrections has substantially enhanced Precise Point Positioning with Ambiguity Resolution (PPP-AR), with the potential to achieve instantaneous convergence to centimetre-level accuracy. To investigate the performance of GNSS PPP-AR using the latest all-frequency products from the International GNSS Service (IGS) and to identify the observational factors associated with its performance, this study proposes a machine-learning-driven attribution analysis framework to evaluate PPP-AR performance and quantify the impact of individual…
- OpenAlex · Published · First archived
Prompt Complexity, Overprompting, and the Honesty of Error in LLM-Based Mechanical Drawing Interpretation: A Structured Empirical Evaluation ↗
Large language models (LLMs) are increasingly used to interpret mechanical engineering drawings, yet the main risk is a wrong answer that reads as correct. We report an exploratory empirical study of how prompt design and evaluation shape the reliability of this interpretation, in two connected stages over the same twenty drawings. Stage 1 runs one prose prompting protocol on two model families and finds that progressive enrichment does not improve accuracy; beyond a threshold, it raises confident fabrication. Stage 2 compares six structured prompt architectures at thirty repetitions per cell,…
- OpenAlex · Published · First archived
Urban Road Crash Severity Prediction Using Explainable Machine Learning and Spatial Hotspot Analysis: Evidence from Loja, Ecuador ↗
Administrative road crash records in intermediate cities may support safety planning but often contain incomplete outcomes and inconsistent coordinates. We analyzed 3349 events recorded in Loja, Ecuador, during 2020–2026 (2026 partial). Severity was reconstructed from injury and fatality counts; crashes involving either were operationally classified as severe. Penalized logistic regression, Random Forest, and XGBoost were trained on 2020–2024 records (n = 2703) and tested on 2025 (n = 533), using a conservative predictor specification to reduce administrative outcome leakage. After correcting…
- OpenAlex · Published · First archived
An interpretable machine learning framework for retrospective attribution of hospitalization costs in patients with chronic kidney disease ↗
Hospitalization costs vary substantially among patients with chronic kidney disease (CKD), yet the factors underlying this heterogeneity are not fully understood. We developed an interpretable machine learning framework to retrospectively characterize differences in hospitalization costs among patients with CKD and externally evaluated its performance. The study included 3,017 hospitalizations from one tertiary hospital and 2,708 hospitalizations from another tertiary hospital for external validation. Patients were analyzed separately according to kidney transplant status, including non-transp…
- OpenAlex · Published · First archived
Robust Short-Term Wind Power Forecasting Using Variational Mode Decomposition and Weighted Machine Learning Models ↗
Wind power forecasting is essential for maintaining grid stability due to the inherent intermittency and nonlinear dynamics of wind energy. This study proposes a robust two-stage hybrid framework for short-term wind power prediction integrating variational mode decomposition (VMD), artificial neural networks (ANNs), and weighted least-squares support vector machine (W-LSSVM). In the first stage, VMD decomposes wind speed time series into band-limited intrinsic mode components to reduce non-stationarity and noise. Each component is predicted using a nonlinear ANN model. In the second stage, a r…
- OpenAlex · Published · First archived
Blast Performance Enhancement of RC Columns Using Machine Learning ↗
Reinforced concrete buildings may experience complete or partial progressive collapse due to column failure under blast loading. Therefore, predicting the structural response of columns under blast loading can support preventive measures that reduce fatalities and financial losses. Using thirteen features related to column and blast properties, this study presents machine learning models to predict the maximum lateral displacement of reinforced concrete (RC) columns strengthened with CFRP sheets under blast loading. A total of 1263 CFRP-strengthened numerical configurations were generated from…
- OpenAlex · Published · First archived
Cultural value configurations and guests’ evaluations of AI-enabled service technologies ↗
As Artificial Intelligence reshapes hospitality service delivery, understanding how culturally diverse guests evaluate AI-enabled encounters is critical. This study examines how cultural value configurations shape evaluations of service robots in hotels. Cultural value configurations are coherent combinations of orientations that jointly influence how individuals interpret and assess service encounters. Using more than 3000 robot-related hotel reviews from guests across 77 countries, we integrate Hofstede’s dimensions with Schwartz’s value theory to explain cross-cultural differences in sentim…
- OpenAlex · Published · First archived
Hybrid Vulnerability Discovery in the Wild: Automated Screening, Fine-Tuned LLMs, and Four Published CVEs ↗
Automated security scanners produce many alerts, but disclosure requires evidence of attacker control, reachability, impact, and reproducibility. We present an evidence-gated workflow that screens recently updated Python AI/ML and data-science packages then uses two separately fine-tuned open-weight agents to distinguish vulnerability plausibility from CVE worthiness: the first tests attacker control and reachability, and the second assesses novelty and impact before human proof-of-concept validation and coordinated disclosure. In a targeted campaign covering 400 packages, screening yielded 12…
- OpenAlex · Published · First archived
Identifying Barriers to Using Generative AI in Education Through AI-Assisted Literature Mining of arXiv Articles ↗
This study presents an exploratory, generative AI (GenAI)-assisted full-text analysis of barriers to using GenAI in education within an arXiv corpus. A total of 19,756 records were retrieved using a keyword search and then screened for relevance using three large language models (LLMs), GPT-4o mini, Gemini 2.0 flash-lite, and Llama3-70B (Groq), through a voting mechanism. This yielded a corpus of 1998 education-relevant full-text articles. Using GPT-4o, learning and teaching scenarios (LTSs) were extracted using a structured 6As framework (Actor, Activity, AI Role, AI Interface, Assessment, Ac…
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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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