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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 ↗5506 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.
- Hugging Face · Model / dataset date · First archived
machinekind/polish-synthetic-speech-voxcpm2 — dataset update ↗
# Polish synthetic speech — VoxCPM2, 4 voices **31867 clips, 59.6 h** of modern, diverse Polish: the **same texts read by 4 synthetic voices**, generated with [VoxCPM2](https://github.com/OpenBMB/VoxCPM) (OpenBMB, Apache-2.0) and quality-gated clip by clip. Made to distil a large TTS into small on-device ones for a humanoid robot ([Matcha-TTS-PL v2](https://huggingface.co/machinekind/Matcha-TTS-PL-v2), [try it in the browser](https://huggingface.co/spaces/machinekind/matcha-tts-pl-v2)). | voice | gender | clips | hours | mean UTMOS | similarity to its anchor | designed from the description | |
- OpenAlex · Published · First archived
SF-SRGAN: Shift-Free Transformer GAN with High-Frequency Enhancement for Image Super-Resolution ↗
Image super-resolution refers to the process of recovering high-resolution images from low-resolution inputs. However, existing methods face three critical challenges: GAN-based approaches suffer from training instability and artifacts; shifted-window Transformers have issues of computational cost and fixed receptive fields; and high-frequency detail modeling along with multi-source feature fusion remains insufficient. To address these issues, this paper proposes SF-SRGAN, a high-frequency enhanced shift-free Transformer generative adversarial network. The generator introduces a shift-free var…
- OpenAlex · Published · First archived
A Transformer Network for Settlement Generalization Integrating Raster Maps and Remote Sensing Images ↗
To address the challenges of reliance on manual expertise and insufficient automation and intelligence in settlement generalization during scale transformation, this paper proposes a multi-scale settlement generalization model (DSG-Trans) based on the fusion of dual-source data: raster maps and remote sensing images. Leveraging the cartographic correspondence between remote sensing image resolution and map scale, this model integrates source-scale maps with specific low-resolution remote sensing imagery to automatically generate target-scale raster maps that adhere to cartographic specificatio…
- OpenAlex · Published · First archived
MTFNet: A Lightweight Multi-Scale Time–Frequency Neural Network for Multi-Class Event Recognition in DAS-Based Perimeter Security ↗
Distributed Acoustic Sensing (DAS) has emerged as a pivotal technology for perimeter security. However, existing DAS event recognition methods have inherent limitations. Single-domain modeling has limited feature representation, while conventional multi-domain fusion methods require converting one-dimensional signals into two-dimensional images. This conversion adds computational overhead and may discard part of the original phase and amplitude information. In addition, the robustness of lightweight recognizers under low signal-to-noise ratio (SNR) conditions remains insufficiently examined. T…
- OpenAlex · Published · First archived
VOICE-AE: automated CTCAE scoring from ambient clinical audio using speech recognition and large language models ↗
Abstract Treatment-related adverse event documentation is labor-intensive and subject to inter-observer variability. We developed and evaluated VOICE-AE, a multi-stage pipeline integrating automatic speech recognition, speaker diarization and large language models (LLMs) for automated extraction and grading of Common Terminology Criteria for Adverse Events (CTCAE) toxicities from clinician-patient audio recordings. Encounters of prostate cancer patients undergoing radiotherapy were recorded and processed through VOICE-AE. Six blinded human raters independently graded 10 genitourinary/gastroint…
- OpenAlex · Published · First archived
People change their decisions when an AI system contradicts them, given a counterfactual explanation ↗
We tested whether people change their decisions when an AI system proposes they do so, and the effects of a counterfactual explanation. In eight experiments with 2,369 participants, they made a decision and an AI system recommended they make the opposite one. Participants did not tend to switch their decisions when an AI system proposed they do so, compared to a human expert (Experiment 1), regardless of how it was described (Experiment 1b). Their reluctance occurred whether or not they committed to a decision first (Experiments 2a and 2b). They switched to the AI system’s decision more when i…
- OpenAlex · Published · First archived
Multimodal feature importance ranking improves classroom teaching quality evaluation in human and artificial intelligence collaborative education ↗
Classroom Teaching Quality Evaluation (CTQE) constitutes a core component of human-AI collaborative education systems; however, existing methods exhibit significant deficiencies in the systematic importance ranking of multimodal features. This paper proposes the Multimodal Feature Importance Ranking via Multi-model Ensemble framework (MFIR-ME), which integrates five categories of modal features—acoustic, linguistic, visual, temporal, and behavioral—and incorporates a Cross-Modal Attention Fusion module (CMAF) to achieve dynamically weighted adaptive cross-modal information integration. Four co…
- OpenAlex · Published · First archived
Machine learning-based prediction formulas for buckling resistance of welding-strengthened circular steel tubes ↗
Welding curved cover plates is commonly used to strengthen circular hollow section (CHS) members under service loads, but welding heat input can induce lateral deflection and complex post-strengthening stress states that reduce the buckling resistance. This study develops machine-learning (ML)-based prediction models and a practical buckling-resistance prediction formula for such members. A database of 800 finite-element (FE) samples, derived from 400 structural configurations, was generated using validated sequentially coupled thermo-mechanical analyses covering service loading, welding, cool…
- OpenAlex · Published · First archived
REW-DETR: A Lightweight Real-Time Detection Transformer for Bridge Surface Defect Detection ↗
Timely detection of bridge surface defects is important for structural safety assessment and inspection efficiency. To address defect scale variation, complex background interference, and the high computational cost of RT-DETR-R50, this study proposes REW-DETR, a lightweight bridge surface defect detector. REW-DETR adopts a coordinated lightweight design integrating efficient feature extraction, multi-level channel recalibration, and localization optimization. RepNCSPELAN4–ADown is employed to reconstruct the backbone and fusion stages, ECA is embedded at the P3–P5 feature levels to enhance ch…
- OpenAlex · Published · First archived
Advancing Lumbar Disc Herniation Classification from MRI Using an Efficient and Reproducible Classical Machine Learning Framework ↗
Background/Objectives: A common diagnosis for lumbar disc herniation is an image taken with Magnetic Resonance Imaging (MRI). Due to the need for expertise during manual interpretation of MRI scans by specialists, the process is very time consuming. The use of a machine learning (ML)-based pathway to classify lumbar MRI images as either “normal” or “herniated” represents an efficient way to analyse MRI scans seeking similar pathologies. Methods: The proposed methodology transforms original lumbar MRIs (from colour to greyscale), resizes them, filters, and normalises them (using bilateral algor…
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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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