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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 ↗5969 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
Behavioural biometrics-based continuous authentication using transformer networks for detecting insider-threat session hijacking ↗
Abstract Insider threats, in which a legitimate user’s already-authenticated session is taken over by another enrolled individual, evade one-time login authentication because the session is trusted before the takeover occurs. Continuous authentication, which re-verifies the user throughout a session from behavioural signals, is a natural defence. We study behavioural-biometric continuous authentication from keystroke dynamics and ask whether a Transformer network can detect insider session hijacking more reliably than established detectors. Using the public CMU keystroke-dynamics benchmark of…
- OpenAlex · Published · First archived
Machine learning-driven optimization of lead-free Rb2PtxPd1-xBr6 dual-absorber photodetectors ↗
Lead-free halide perovskites are a promising class of materials for environmentally friendly optoelectronics, although the optimization of their complex device architectures presents a considerable challenge. Here, a high-performance photodetector is engineered using a dual-absorber layer architecture composed of Rb 2 Pt 0.75 Pd 0.25 Br 6 and Rb 2 Pt 0.25 Pd 0.75 Br 6 . An integrated methodology combining simulations using SCAPS-1D with machine learning and SHAP interpretability analysis enables the systematic optimization of absorber layer thickness, doping concentration, and defect density.…
- OpenAlex · Published · First archived
Physics informed machine learning predicts binding free energy changes in protein-RNA complexes upon mutations ↗
Protein-RNA interactions are central to gene regulation, yet predicting mutation-induced changes in binding free energy (ΔΔG) remains difficult due to sparse, heterogeneous measurements and strong imbalance in mutation types. Here we present Pred-MutPRI, a physics-informed machine learning framework for ΔΔG prediction in protein–RNA complexes. We curated a unified dataset of experimentally measured ΔΔG values with standardized thermodynamic conventions, and introduced thermodynamic permutation (TP) to generate cycle-consistent training pairs that expand substitution-type coverage while reducin…
- OpenAlex · Published · First archived
Benchmarking token mixers for efficient transformer-based learned video compression ↗
Learned video compression has rapidly evolved, with recent approaches demonstrating potential in complex context modeling. However, these performance gains often come at the cost of significant complexity in both framework design and computation-heavy modules, obscuring the efficiency contribution of the core architectural components. This work utilizes the video compression transformer (VCT) as a controlled testbed to isolate and quantify the contribution of the transformer-based entropy model by its core context modeling schemes. The authors propose an abstracted transformer entropy model (A…
- OpenAlex · Published · First archived
Machine learning models for predicting thermal conductivity of nano‑enhanced phase-change materials ↗
Abstract The low thermal conductivities of phase-change materials (PCMs) remain a major obstacle to their efficient application in thermal energy storage. Nano‑enhanced phase-change materials (NEPCMs) present a promising alternative; therefore, accurate prediction of their thermal conductivities is of great importance. In this work, six machine learning (ML) models were developed to predict the effective thermal conductivities of NEPCMs: multilayer perceptron neural network (MLPNN), radial basis function neural network, least squares support vector machine, decision tree, k‑nearest neighbors,…
- OpenAlex · Published · First archived
Molecular modelling of high-entropy alloys: from quantum to atomistic, and machine learning ↗
Abstract High-entropy alloys (HEAs), characterized by complex multielement compositions, offer broad opportunities to tune mechanical, corrosion, thermal, wetting, and functional properties, while their vast configurational space makes molecular modelling essential. This review evaluates progress across computational thermodynamics, density functional theory, atomistic simulations, and machine learning approaches, highlighting multiscale integration, methodological advances, current challenges, validation needs, and future directions for predictive HEA modelling and materials design across ind…
- OpenAlex · Published · First archived
Novel intelligent design method of soft magnetic metallic glasses with excellent magnetic properties by utilizing machine learning technology ↗
In this study, a machine-learning (ML)-assisted compositional screening strategy is developed for Fe-based amorphous/nanocrystalline soft magnetic alloys. Typical ML algorithms including random forest (RF), k-nearest neighbor (kNN), and support vector machine (SVM) are applied to establish the ML-assisted compositional screening model of the Fe–based amorphous/nanocrystalline alloy, and grid search, data augmentation together with normalization are utilized to optimize the model parameters and original dataset to maximize the predictive performance of the ML models. Results indicate that the R…
- OpenAlex · Published · First archived
Understanding discrepancy in AI-human assessment systems due to hidden calibration inconsistency ↗
Empirical studies have evaluated overall fairness and reliability of AI grading systems at aggregate levels, while investigation on their consistency remains scarce. This study investigated systematic discrepancy between AI and human grades across varying conditions. It further examined potential algorithmic bias and predictors of significant grading divergence through interaction effects. Using records from assessment of professional competence (APC), descriptive and inferential statistical modelling techniques were analyzed. The findings showed significant AI-human grading discrepancy across…
- OpenAlex · Published · First archived
Admissibility defines structural limits on optimisation in artificial intelligence ↗
Abstract Many persistent failures of contemporary artificial intelligence are treated as failures of optimisation, data coverage, or statistical robustness. This paper argues that a recurring class of these failures arises earlier: the observation regime under which a system acts may fail to preserve the distinctions required by the predicate it is asked to decide. We make this condition precise. A predicate $$\Phi :X\rightarrow \{0,1\}$$ is decidable from observations $$M:X\rightarrow O$$ only when $$\Phi $$ factors through the quotient that M induces on X . This is an elementary factorisatio…
- OpenAlex · Published · First archived
An integrative theoretical model of AI-supported cybersecurity governance and organizational resilience ↗
Abstract This paper develops an integrative theoretical model explaining how and when AI-supported cybersecurity governance may contribute to organizational resilience. Using a theory-synthesis design and an integrative literature review, it connects scholarship on organizational resilience, cybersecurity governance, artificial intelligence governance, human-AI decision-making, adversarial machine learning, risk governance, organizational learning, and dynamic capabilities. AI-supported cybersecurity governance is conceptualized as a formal organizational arrangement through which heterogeneou…
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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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