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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 ↗5971 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
Predicting compressive strength of recycled aggregate concrete using AI with experimental validation ↗
Growing concerns about environmental impact and rising construction costs have encouraged the use of recycled aggregates in concrete. This study offers an experimental and AI-driven assessment of the compressive strength of recycled aggregate concrete. Concrete was prepared by varying recycled fine and coarse aggregates, water-cement ratios, plasticizer content, and parent concrete strength. The measured 28-day compressive strength ranged from 31.8 to 45.2 MPa, and a steady reduction was observed as recycled aggregate levels and water absorption increased. The results highlight how aggregate q…
- OpenAlex · Published · First archived
The Dual Use of Generative Artificial Intelligence in Cybersecurity: A Survey from Deepfake Attacks to Intelligent Defense ↗
The rapid iteration of generative Artificial Intelligence (AI) is reshaping the cybersecurity offense-defense landscape. This paper systematically reviews research progress in this field from 2021 to 2026, focusing on the dual use of generative AI in cybersecurity. On the one hand, malicious actors exploit generative AI to create deepfake content, automate phishing emails, and conduct social engineering attacks, significantly improving the personalization and scalability of attacks. On the other hand, the same technological system demonstrates significant effectiveness in defense scenarios suc…
- OpenAlex · Published · First archived
MT-brainVLM: a multi-task 3D vision–language model for hierarchical report generation with dual-path Shapley explainability ↗
Hierarchically organised report generation from high-dimensional 3D inputs is a central yet underexplored capability of multi-task vision–language models (VLMs), obstructed by three problems: unifying report generation with structurally incompatible auxiliary heads under one weight-shared backbone, natively handling partially available modalities, and providing axiomatically grounded, numerically verifiable attributions for multi-output predictions. We propose MT-BrainVLM, an end-to-end multi-task 3D VLM coupling a modality-aware 3D U-Net encoder and mask-gated fusion, a BLIP-2-style Q-Former…
- OpenAlex · Published · First archived
Machine Learning-Based Discrimination of Pre- and Post-Cleaning Marine Turbocharger Recordings Using Rotational-Speed Signals ↗
Monitoring marine turbocharger condition under real-ship operation is complicated by operating-condition variability and limited labelled maintenance data. This study assesses whether mean-centred angle-domain rotational-speed fluctuations can discriminate between six recordings acquired before and after one turbocharger cleaning event. The primary evaluation used exhaustive paired complete-record holdout, where one pre-cleaning and one post-cleaning recording are excluded from preprocessing, feature ranking, and model fitting in each split. Secondary purged blocked validation assessed tempora…
- OpenAlex · Published · First archived
DAIMON: Designing AI-Augmented Research Dashboards to Enable Novel Human-AI Collaborative Workflows in Longitudinal Sensing Studies ↗
Researchers conduct longitudinal passive sensing studies in in-the-wild settings, often spanning months or years, to uncover naturalistic behavioral patterns. These studies are not “set-and-forget” deployments; they require continuous monitoring as technical failures and declining participant compliance can lead to substantial missing data, undermining study validity and downstream models. Thus, conducting these studies involves multiple detail-oriented, cognitively demanding, and time-consuming tasks, making it a burdensome and stressful process. Existing research dashboards, the primary tool…
- OpenAlex · Published · First archived
Constrained multi-objective optimization based on Lasso double machine learning ↗
Constrained multi-objective optimization requires a delicate balance between objective improvement and constraint satisfaction throughout the search process. While existing stage-wise methods utilize infeasible solutions, they rely on rigid heuristic rules and fail to quantify the actual utility of these solutions. To address this limitation, we propose a novel approach leveraging double machine learning to dynamically assess the value of infeasible solutions. Specifically, we formulate a causal framework where the lagged infeasible-solution ratio serves as the treatment, and the subsequent fi…
- OpenAlex · Published · First archived
HAR-ASIC: A Configurable Edge-AI Human Activity Recognition Circuit for Ultra-Low Power Wearable Devices ↗
We present an application-specific integrated circuit (ASIC) using digital hardware design to maximise energy-efficiency of human activity recognition (HAR) in autonomous wearable devices. The HAR-ASIC was designed in 22 nm technology and includes a battery of configurable, hardware-optimised feature extraction functions along with a decision tree ensemble classifier, thus serving as an edge AI device for local sensor data processing. We validate the HAR-ASIC based on post-layout process data with five HAR datasets and show the design scalability for configurations to maximise energy saving an…
- OpenAlex · Published · First archived
A State‐of‐the‐Art Comprehensive Review: Application of Artificial Intelligence in Predicting the Load‐Bearing Capacity of Piles ↗
ABSTRACT Artificial intelligence models have widely replaced traditional statistical modeling tools as reliable alternatives for capacity estimation. Estimating the load‐bearing capacity of piles using analytical formulas can yield a range of results, which can be attributed to the simplifying assumptions incorporated into the models. Another innovative method for predicting the load‐bearing capacity of piles is the pile dynamic analysis test, which is based on the theory of one‐dimensional wave propagation. Simpler methods such as field tests like the cone penetration test and standard penetr…
- OpenAlex · Published · First archived
Empathy in the Soil, Agents on the Desktop: Why Plant Pathology Needs a Scientist-in-the-Loop AI Paradigm ↗
Plant pathology faces a cognitive bottleneck caused by the growing volume of biological data. Addressing it requires a shift from passive chatbots to autonomous AI research agents that operate in Goal-Action-State cycles. Researchers can set up a local, desktop-based agentic framework that combines semantic discovery, LLM-Wiki knowledge synthesis, and in silico prevalidation, managed under a human-directed three-tier organizational model. Strict “Scientist-in-the-Loop” boundaries, which means that a human researcher sets the goals, reviews AI-generated output before it is treated as evidence,…
- OpenAlex · Published · First archived
Noninvasive Interstitial Glucose Estimation Using Wearables and Machine Learning in Healthy Individuals and Individuals With Obesity: Observational Cohort Study ↗
Abstract Background Continuous glucose monitoring (CGM) can facilitate weight management and lower the risk of metabolic diseases by providing real-time feedback on glycemic responses, thereby enabling more informed lifestyle decisions. However, current CGM systems remain constrained by invasiveness, cost, and short sensor lifespan, limiting their practicality for guiding individualized postprandial low-glycemic diets. Objective Extending earlier proof-of-concept findings, this study aimed to validate an interstitial glucose (IG) machine learning algorithm in real-world environments using mult…
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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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