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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 ↗5974 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
A zero-shot LLM framework for analyzing user preference on product attributes: a case study of smartwatch reviews ↗
The rise of zero-shot learning through Large Language Models (LLMs) opens new opportunities for analyzing consumer feedback without relying on annotated data. This study proposes a zero-shot framework to uncover user preferences on product attributes, using 12,900 Amazon smartwatch reviews as a case study. The framework integrates semantic keyword extraction, LLM-based attribute classification, sentiment analysis, and Importance–Performance Analysis (IPA) to convert unstructured textual reviews into structured consumer preference insights. Validation against human annotations demonstrates stro…
- OpenAlex · Published · First archived
Numerical and machine learning analysis of Rb2AgInBr6 lead-free double perovskite solar cells ↗
Abstract To address the environmental concerns and stability limitations associated with conventional lead-based perovskite photovoltaics, this study proposes a lead-free double perovskite solar cell based on an optimized $$\hbox {ITO}$$ / $$\hbox {C}_{60}$$ / $$\hbox {Rb}_{2} \hbox {AgInBr}_{6}$$ / $$\hbox {Cu}_{2}\hbox {NiSnS}_{4}$$ / $$\hbox {Pt}$$ heterostructure. A hybrid computational framework combining SCAPS-1D numerical simulation under AM1.5G illumination with machine-learning (ML) analysis was developed to systematically investigate the influence of key device parameters, including…
- OpenAlex · Published · First archived
Correlation-Guided Graph Transformer Networks for Cold-Start Group Event Recommendation in Social Networks ↗
The emergence of social platforms has seen the importance of group event recommendation increase as a result of the fast rate of growth. In comparison to single-user recommendation, the group recommendation has to capture overall preferences and overcome the extreme sparsity, long-tail distributions, and cold-start problems. This paper suggests Correlation-Guided Graph Transformer Network (CG-GTN) to address these problems. The framework proposes the use of correlation to construct bi-partite graphs by using a convex combination of behavioral similarity, contextual similarity and time proximit…
- OpenAlex · Published · First archived
A multi-objective bayesian optimized neural network approach for intrusion detection ↗
Cyberattacks in Internet of Things (IoT) and vehicular contexts are becoming more sophisticated, which in turn emphasises the need for an ideal Intrusion Detection System (IDS) that provides high detection accuracy and computational efficiency. This research proposes MambaEcoNet-SC1D, a novel IDS model that encompasses multi-stage feature learning with intelligent optimization. In this multi-stage pipeline, Sparse CNN-1D first extracts compact local traffic features, the Eco Transformer captures contextual dependencies through energy-efficient distance-based attention, and the SSM-based Mamba…
- OpenAlex · Published · First archived
Federated knowledge-guided 3D Transformer learning for bone tumor segmentation from partially annotated computed tomography data ↗
Accurate 3D delineation of bone tumors and metastatic lesions from volumetric CT is essential for diagnosis, staging, radiotherapy planning, and treatment monitoring, yet manual contouring is time consuming and variable across observers. Although modern 3D CNN/Transformer segmenters achieve strong centralized performance, they typically assume pooled multi-institution data and consistent label definitions. In practice, clinical imaging archives are privacy-isolated, exhibit pronounced non-IID appearance shift (scanner/protocol and case-mix variation), and provide heterogeneous and partially an…
- OpenAlex · Published · First archived
Artificial intelligence-based assessment of gastric mucosal exposure and its association with lesion detection: a multicenter study ↗
Completely and clearly visualizing the mucosa during esophagogastroduodenoscopy (EGD) improves high-risk lesion detection. Here we propose a new EGD quality control indicator and present the optimal gastric mucosal exposure area (OGMEA) system to automatically assess mucosal exposure and examine its association with the detection of early gastric cancer and other gastric lesions. A segmentation model was developed to assess mucosal exposure. Expert endoscopists defined a standard for 19 gastric anatomical sites. Images exceeding the OGMEA threshold were considered adequately-exposed. The OGMEA…
- OpenAlex · Published · First archived
Next-generation thermal runaway monitoring in batteries using real-time gas sensing and AI-driven diagnostics ↗
Electric vehicles (EVs) are central to a sustainable future worldwide, with batteries as their core component, including Li-ion, solid-state, NiMH, and Lead-acid batteries, each with its own advantages and disadvantages. However, thermal runaway (TR) in these batteries poses a major safety challenge, hindering large-scale EV adoption. TR is an irreversible series of exothermic reactions that can cause explosions, fires, and the release of toxic gases. Conventional battery management systems (BMS) that rely on voltage, current, and temperature often fail to detect early-stage TR events because…
- OpenAlex · Published · First archived
Hybrid deep learning for three-way classification of human-written, AI-generated, and AI-rephrased text ↗
Abstract The rapid proliferation of large language models (LLMs) has made it increasingly difficult to distinguish AI-generated and AI-rephrased text from authentic human writing, posing serious risks to academic integrity, journalism, and e-commerce credibility. This paper presents a rigorous benchmark evaluation for three-way AI text origin classification designed to detect human-written, AI-generated, and AI-rephrased text by integrating handcrafted linguistic features, transformer-based contextual embeddings, and ensemble learning strategies. A corpus of 161,788 samples is curated from Ama…
- OpenAlex · Published · First archived
Improving human performance in deepfake detection: limits of aggregation and human–AI collaboration ↗
Abstract Given that deepfake videos are now easy to generate and increasingly high in quality, the present research investigated human detection performance and several routes to improvement. Across four experiments, participants demonstrated above-chance detection accuracy when viewing YouTube clips and their corresponding deepfakes, but performance remained modest and error-prone. In Experiment 1, aggregating responses across the full sample produced reliable improvements in accuracy, consistent with a ‘wisdom of the crowd’ effect. However, in Experiment 2, participants showed no evidence of…
- OpenAlex · Published · First archived
Version context and control construction in machine learning detection of malicious package updates across npm and PyPI ↗
Abstract Open-source package registries such as npm and PyPI are increasingly targeted by software supply-chain attacks in which a trusted package is compromised through a later release. This study reconstructs each candidate release together with its immediate predecessor in npm and PyPI and evaluates machine-learning detection under package-disjoint validation. Against never-compromised controls selected within ecosystem and matched on candidate archive file count, the model reaches ROC-AUC = 0.801 ± 0.006 and nested grouped F1 = 0.792 (95% CI 0.730–0.845). Performance falls to ROC-AUC = 0.5…
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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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