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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 ↗5944 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
LAFNet: a lightweight neural network with fuzzy attention mechanism for rail surface defect classification ↗
Abstract Rapid and accurate detection of rail anomaly is crucial for railway transportation safety. Although existing deep learning methods have achieved certain progress, they generally suffer from large model parameter sizes and high computational costs, making it difficult to meet the requirements of real-time performance and resource constraints for on-site deployment. To address these challenges, this study proposes a lightweight neural network model (LAFNet) for rail anomaly classification. The model first adopts a three-layer convolutional structure to extract deep features. Then, an en…
- OpenAlex · Published · First archived
AAIGF-E: An AI and Agentic Intelligence Governance Framework for Electric-Sector Smart-City Infrastructure ↗
AI is being deployed in smart-grid and smart-city energy infrastructure faster than governance frameworks can validate it. Existing instruments—NERC CIP, NIST AI RMF 1.0, and ISA/IEC 62443—address cybersecurity compliance and AI trustworthiness principles but do not provide operational control architecture for AI systems influencing grid stability, distributed energy resource (DER) dispatch, or real-time reliability decisions. Compliance with these instruments is not equivalent to operational assurance: their requirements do not by themselves establish validated input integrity controls, adver…
- OpenAlex · Published · First archived
Integrating Statistical Models and Machine Learning for Derivatives Portfolio Valuation and Risk Assessment ↗
Regulatory scrutiny of banks’ internal models has intensified, demanding improvements in quantitative risk methodologies and the implementation of new regulatory measures. Repeated portfolio revaluation makes these calculations computationally challenging, notably under the Fundamental Review of the Trading Book (FRTB). In this context, we examine the computational implementation of market risk models, focusing on the practical calculation of Value at Risk (VaR) and Expected Shortfall (ES). Given the complexity of trading portfolios, these models rely on proxies and simplifying assumptions for…
- OpenAlex · Published · First archived
Sentiment Analysis of Acceptance TVET Online Courses on the Skill Academy App from Google Play: Leveraging Text Mining with Comparison Machine Learning Model ↗
Background Online Technical and Vocational Education and Training (TVET) has expanded rapidly in Indonesia through platforms such as Skill Academy. User reviews provide valuable insights into user acceptance, but their volume makes manual analysis impractical. Therefore, this study applies text mining to analyze user sentiment and compare eight machine learning models for sentiment classification. Methods The study used 3,000 reviews collected via web scraping using the google-play-scraper library. The data was then anonymized, cleaned, translated into English, and automatically sentiment-labe…
- OpenAlex · Published · First archived
AI-Enabled Hierarchical Network Selection in Integrated 6G TN-NTN Architectures ↗
Sixth-generation (6G) wireless systems target ubiquitous connectivity by integrating terrestrial network (TNs) and non-terrestrial network (NTN) cooperation, including low-Earth-orbit (LEO) satellites, high-altitude platform stations (HAPS), and unmanned aerial vehicles (UAVs). In such a vertically stratified architecture characterized by massive multi-connectivity, the selection of the most suitable tier or tier-combination for each mobile user is a complicated task, as the decision depends jointly on the propagation environment, user requirements, and the instantaneous characteristics of the…
- OpenAlex · Published · First archived
Automated Brief Hospital Course Summarization in Cardiac Surgery Using a Lightweight Large Language Model–Based Framework: Development and Evaluation Study on the Medical Information Mart for Intensive Care-IV ↗
Background Physician documentation requirements are a known contributor to clinician burnout, with the manual creation of brief hospital course (BHC) summaries being particularly time-consuming. Automating BHC summarization may mitigate this workload and reduce documentation errors. However, current natural language processing (NLP) methods are often limited to single-document inputs, and large language models (LLMs) face privacy and deployment challenges. Furthermore, existing methods often require manual information extraction and struggle to maintain temporal accuracy. Objective We develope…
- OpenAlex · Published · First archived
From Raw Signals to Understood States: A Perspective on Neuro-Symbolic and LLM-Driven Intelligent Sensing of Human Affective and Cognitive States ↗
Intelligent sensor technologies now provide unprecedented multimodal access to human affective and cognitive processes, spanning physiological, ocular, facial, kinematic, ambient, and behavioral streams. Yet, despite dramatic advances in acquisition and deep representation learning, the pipeline from raw signals to psychologically meaningful, actionable understanding remains fragile. Deep-only architectures excel at pattern extraction but struggle with contextual reasoning, uncertainty communication, and human-facing explanation; symbolic-only frameworks resist noisy, high-dimensional streams.…
- OpenAlex · Published · First archived
QuadFusion-IDS: Enhancing Intrusion Detection with Unified Hybrid Deep and Machine Learning Techniques in Cybersecurity Systems ↗
As network traffic becomes increasingly complex and cyberattack behaviors continue to evolve, conventional intrusion detection approaches face challenges in accurately identifying malicious and legitimate network activities. This study proposes QuadFusion-IDS, a hybrid binary intrusion detection framework that integrates FT-Transformer with Autoencoder (FT-Transformer + AE), multi-head attention deep neural network (MHA-DNN), dilated temporal convolutional network (Dilated TCN), and XGBoost to capture complementary feature representations, temporal dependencies, and nonlinear traffic patterns.…
- OpenAlex · Published · First archived
Document-Level Claim Extraction and Decontextualization Method Based on Large Language Models ↗
Claim extraction and decontextualization are fundamental steps in automated fact-checking tasks. Claim extraction aims to identify core check-worthy claims from unverified text, while decontextualization seeks to eliminate the contextual dependencies of core claims so that they can be understood independently of the original text. In practical applications, information to be verified is usually presented in the form of documents, and existing document-level claim extraction and decontextualization methods often rely on complex multi-model architectures. This paper proposes LLM-CED, a document-…
- OpenAlex · Published · First archived
Risk-Controlled Adaptive-Resolution Fault Diagnosis of Power Transformers Using Dissolved Gas Analysis: Lightweight Hierarchical Learning, Credibility, and Deployment Support ↗
Dissolved gas analysis (DGA) is widely used for transformer condition assessment, but fixed-resolution classifiers may release overly specific fault labels when the available evidence is weak. This study develops a risk-controlled adaptive-resolution framework in which a lightweight hierarchical residual neural encoder generates fine-grained and electrical/thermal parent-level candidates, and separate credibility estimates determine whether the released result should remain fine-grained, fall back to the parent mechanism, or abstain. The 25-dimensional DGA representation is encoded by three re…
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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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