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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 ↗5966 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
Digital literacy shapes AI chatbot adoption, barriers, and performance outcomes in football coaches: a multi-method analytical study ↗
Abstract The adoption of AI chatbots among football coaches is primarily determined by their digital literacy. An international cross-sectional online survey of 213 professional coaches measured adoption and digital literacy using a psychometrically evaluated 31-item index. Data were analyzed using mediation models, ROC curves, ANOVA, and cluster analysis. Four key findings emerged. First, digital literacy completely mediated the negative effect of age on adoption (indirect effect ab = − 0.31). Second, a critical competency threshold of 4.5/7 was identified, with adoption probability rising fr…
- OpenAlex · Published · First archived
AI real-time detection of cable corrosion in power inspection videos based on a YOLOv10-transformer hybrid network ↗
The operation and maintenance of current power infrastructure face critical challenges, such as inefficient cable corrosion detection, strong subjectivity in assessments, and insufficient early-warning capabilities. Traditional manual inspection methods not only demand substantial human resources but are also susceptible to environmental factors and inspector experience, which compromises detection accuracy. Therefore, this study proposes a deep learning model that integrates a multi-scale feature extraction module with an adaptive attention mechanism (MSFEM-AAM). The model employs parallel fe…
- OpenAlex · Published · First archived
Digital currency money laundering identification model based on graph structure and transformer self-supervised learning ↗
Digital currency money laundering detection faces two major challenges: scarce labeled illicit transactions and complex fund-flow topologies. To address these issues, this study proposes GT-SSL, a graph-structured Transformer self-supervised learning framework for cryptocurrency anti-money laundering detection. The main contributions are threefold. First, transaction records are converted into directed attributed graphs and serialized through biased restart random walk, enabling Transformer-based modeling of local fund-flow contexts. Second, a structure-aware Transformer encoder is pre-trained…
- OpenAlex · Published · First archived
Enhancing Large Language Models for document-level relation triple extraction via structural awareness and contrastive learning ↗
In recent years, Large Language Models (LLMs) have demonstrated superior performance in information extraction tasks. Leveraging these models for Document-Level Relation Extraction (DocRE) can benefit from their powerful generative capabilities. However, we observe that LLMs still face challenges in DocRE tasks: Document Structure Parsing Error, Relation Definition Ambiguity, and Entity Boundary Recognition Error. To address these issues, we propose SDB-DRE, an LLM-based DocRE model that does not rely on pre-labeled entities during inference. To tackle the Document Structure Parsing Error, we…
- OpenAlex · Published · First archived
The fantasy of digital immateriality: AI, ecologically unequal exchange, and appropriation of lived human time ↗
The purported immateriality of AI technologies is as illusory as the general understanding of conventional technologies as simply the application of politically neutral knowledge to nature. The materiality of technologies and their foundation in global inequalities has been illuminated by studies of ecologically unequal exchange (EUE). The aim of this article is to extend the applicability of EUE theory to digital technologies such as Artificial Intelligence (AI) by highlighting how even the data that they process have an inevitably biophysical history. AI technologies represent a novelty main…
- OpenAlex · Published · First archived
A framework for automated vibration-based fault diagnostics of rotating machinery using supervised machine learning algorithms ↗
This study proposes a comprehensive framework for automated diagnostics of rotating machinery using vibration signals. The methodology integrates the stages of dataset selection and preparation, signal processing, feature extraction, dimensionality reduction and training and testing of supervised machine learning algorithms. The preparation of the dataset involves proposing a technique for addressing data imbalance, normalisation of the data amplitude, adjusting the sampling frequency, and calculating the actual rotational frequency. Then, appropriate signal filters are designed and applied, a…
- OpenAlex · Published · First archived
Multi-objective optimization algorithms for intrusion detection systems in IoT: a systematic review and future research directions ↗
With the increasing use of the Internet, massive amounts of data are transferred between various communication devices. Network security is a critical area of research in today’s network environment, as data must be transmitted securely between connected devices. The digital revolution and the expansion of operations have amplified the importance of intrusion detection systems (IDSs) in networks. Numerous research concepts have been proposed that utilize swarm algorithms, evolutionary algorithms, deep learning, and machine learning techniques in the field of IDSs. IDSs must be chosen among sev…
- OpenAlex · Published · First archived
Residual Stress Evolution in Machined Supermartensitic Steel under Corrosive Exposure: A Combined Experimental-Machine Learning Study ↗
Abstract Residual stresses and surface finish play an important role in the performance of materials used in the oil and gas industry, especially under combined mechanical loading and exposure to corrosive environments. This study investigates the residual stresses induced by grinding and polishing in supermartensitic steel (S13Cr) and their evolution during constant tensile loading in NaCl and H 2 S media. Residual stresses were measured by x-ray diffraction, and surface roughness and topography were characterized. Grinding produced higher compressive residual stresses, reaching approximately…
- OpenAlex · Published · First archived
Evidence, limitations and implications for renewable energy planning of daily global solar irradiation in tropical climate zones of Cameroon, predicted by machine learning ↗
Accurate solar irradiance prediction is a crucial step for the efficient planning and deployment of solar energy systems, particularly in tropical regions like Cameroon where solar resources remain largely untapped. Despite growing global interest in solar energy prediction using machine learning techniques, this remains a significant challenge. Similar studies exist in Cameroon, but they remain few compared to those in Asia, Europe, or North Africa. In contrast, our study stands out due to its complementary geographic coverage, the inclusion of solar vortex mass (SVM), and the in-depth analys…
- OpenAlex · Published · First archived
Sensitive-aware graph neural network for privacy leakage traceability in scan-to-order systems ↗
Abstract Scan-to-order systems route user data through merchants, payment gateways, cloud services, and third-party analytics providers in a single transaction flow. This multi-entity architecture makes privacy leakage difficult to trace: a sensitive record may travel five or six hops before reaching an unauthorised party, and the intermediate steps look identical to legitimate business traffic. Existing rule-based provenance tools and static graph models break down at this scale because they treat every data flow as equally important and cannot attribute causal responsibility to specific edge…
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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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