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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
A Clinical Primer on Computer Vision ↗
Abstract Computer vision is a rapidly evolving field within computer science that focuses on extracting meaningful information from digital images. In medical imaging, computer vision techniques such as object detection, classification, and segmentation are essential for analysing complex anatomical structures. This primer provides an overview of computer vision concepts and methods, highlighting both traditional approaches and modern machine learning techniques. Landmark detection and shape modelling are also discussed as these are particularly relevant for musculoskeletal imaging. Challenges…
- OpenAlex · Published · First archived
Proposing a minimal-coding methodology to customize large language models to automate services in information organizations ↗
Rarely do any studies propose a minimal-coding methodology to customize a Large Language Model (LLM) as part of the AI-driven automation of information services in libraries. This paper fills in the gap by illustrating a four-stage methodology to customize Llama 3 when automating cataloging, chat support, and recommendation services. Firstly, we collected and processed library data, that is, 197 frequently asked questions from the library website of a leading Research-I university in the US, and metadata of 450,320 books and their one-million-plus user ratings from the Book-Crossing dataset. S…
- OpenAlex · Published · First archived
AI-Driven Predictive Maintenance and Power Optimization Strategy for Distributed Hydropower Systems ↗
Distributed hydrokinetic systems produce sequential operational telemetry that can support predictive condition monitoring and power-control optimization. This study combines short-horizon prediction of exact-zero-power onset with field-informed evaluation of a maximum-power-point controller multiplier. The classifier uses power, torque, and torque-to-power ratio from the two preceding per-rotor observations. A leakage-resistant chronological evaluation was performed using 36,636 non-overlapping windows: 279 positive episode onsets and 36,357 normal-operation windows. The untouched final perio…
- OpenAlex · Published · First archived
Integrating real world IoT and AI supported live coding in LMS based programming learning ↗
Web-based technologies have influenced programming education, yet many courses still rely on static materials and delayed feedback, making it difficult for students to test ideas during problem-solving. This limitation is especially evident in Internet of Things (IoT) programming, where code must interact with physical devices. This study examined an LMS-based learning module combining live coding, remote IoT device access, and AI-supported formative feedback. The module was implemented with 97 undergraduate students in an IoT-based web programming course, using a single-group pre-test and pos…
- OpenAlex · Published · First archived
Higher education transformation through artificial intelligence: an empirical study in Saudi Arabia ↗
This study examines associations between AI awareness, readiness, ethical perceptions, attitude, and actual AI usage.Survey data from 424 students at King Fahd University of Petroleum and Minerals (KFUPM) were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4. Two structural models were compared: one specifying direct effects and a second in which attitude served as a partial mediator. In Model 1 (direct effects), only readiness and attitude were significantly associated with AI usage. In Model 2 (mediation), readiness retained both direct and indirect a…
- OpenAlex · Published · First archived
Multidimensional analysis of e-scooter crash severity using cluster correspondence and explainable AI ↗
The rapid expansion of electric scooters (e-scooters) in urban transportation networks has transformed personal mobility across U.S. cities, yet rising crash rates have raised critical safety concerns. This study investigates the factors contributing to e-scooter crash severity through a novel integration of Cluster Correspondence Analysis (CCA) and SHapley Additive exPlanations (SHAP), offering both structural and interpretable insights into injury outcomes. Using 355 e-scooter crash records from Texas between 2021 and 2024, the study identifies four distinct crash typologies based on behavio…
- OpenAlex · Published · First archived
FPGA-Based Neural Network Accelerators for Space Missions: A Survey ↗
Space missions are growing ever more ambitious, placing greater demands on onboard computing. Field-programmable gate arrays (FPGAs) have garnered interest due to their reconfigurability and cost-effectiveness. At the same time, neural network (NN)-based methods are proving invaluable for critical spacecraft tasks such as autonomous operations, remote sensing, selective downlink, and data compression. This survey reviews and classifies the state of the art in FPGA-based NN accelerators for space missions. We examine current trends, highlight key challenges, and suggest directions for future re…
- OpenAlex · Published · First archived
How Agent-Driven Creative Trace Mining Reshapes Design Behavior in a Deployment Study of Intent-Aligned Multimodal Design ↗
Designers continuously capture inspirations across media, yet these creative traces remain underutilized in computational design support. We present DesignJournal, an agent-driven system that mines multimodal creative traces and transforms them into intent-aligned image generation. The system consolidates artifacts into a unified workspace and employs an agentic pipeline for structured grouping, belief-guided prompt refinement, and iterative intent updates. We developed a framework that classifies sequential design actions, deriving a hierarchical action taxonomy from interaction logs. We used…
- OpenAlex · Published · First archived
Different lenses on human-AI collaboration: towards a comprehensive view on hybrid intelligence in education ↗
This paper aims to deepen our understanding of hybrid intelligence in education by investigating human-AI collaboration in four real-world educational use cases. We argue against the simple view of automation and augmentation as distinct processes and foreground the automation-augmentation paradox. This paradox proposes a comprehensive view of automation and augmentation as co-occurrent processes that drive task allocation between humans and AI. We use this paradox as an analytical lens to examine patterns of task changes in human-AI collaboration across four educational use cases. This study…
- OpenAlex · Published · First archived
Context engineering frames prompt engineering within system level orchestration for large language model applications ↗
Abstract By moving from isolated instruction crafting to system-level contextual orchestration, context engineering reframes how large language model (LLM) applications are designed for complex, multi-source workflows. Prompt engineering can deliver strong results for narrow, single-turn tasks, yet it often becomes brittle and costly to maintain as interaction length, tool use, governance constraints, and reliability requirements increase. In this perspective, context engineering is used as a working systems concept for the design and governance of context pipelines that determine how relevant…
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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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