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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 ↗5982 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
Intelligent security policy generation algorithm based on graph neural network and reinforcement learning for SD-WAN ↗
With the evolution of enterprise networks to multi-site and cloud architectures, Software-Defined Wide Area Network (SD-WAN) is prone to the problems of insufficient global policy regulation and frequent policy adjustment under dynamic security threats, which in turn leads to increased delay, aggravated packet loss and unstable network operation. Existing research mostly focuses on traffic optimization or local security detection, lacking global topology security situation modeling for SD-WAN and intelligent policy generation mechanisms that balance security benefits and policy stability. To a…
- OpenAlex · Published · First archived
How Historical and Cultural Resources Shape Street Vitality in Historic Urban Areas: Interpretable Machine Learning Evidence from Multi-Source Data in Chengdu ↗
Historic urban areas are important repositories of urban history, cultural memory, and local identity. Yet how historical and cultural resources (HCRs) are associated with street vitality, particularly in comparison with streetscape factors, remains insufficiently understood. Taking Chengdu’s historic urban area as a case study, this study integrates streetscape images, point-of-interest (POI) data, and HCR data. It employs machine learning with model interpretation techniques to identify key predictors and nonlinear relationships. The study evaluates the associations of HCRs with street vital…
- OpenAlex · Published · First archived
Evaluating Large Language Models with a Unified Prompt-Based Pipeline for Turkish News Summarization and Tag Extraction ↗
News texts are critically important in facilitating readers quick access to accurate information in today’s substantially increasing information-driven world. The very fast pace of news flow and the abundance of highly informative data across various fields require the analysis of summarization and tag extraction tasks. This study aims to measure the multitasking performance of summarization and tag extraction tasks in Turkish news texts using a unified pipeline with a small datasets and different prompt techniques. Summarization and tag extraction tasks were performed with a single pipeline u…
- OpenAlex · Published · First archived
Reducing spiking neural network inference latency for behavior cloning with input adaptive leakage ↗
Abstract Behavior cloning enables efficient imitation of complex human skills from a limited number of demonstrations. However, artificial neural networks (ANNs), which are commonly employed for policy learning, suffer from their high computational demands and energy consumption. Spiking neural networks (SNNs) offer a promising alternative because of their event-driven and energy-efficient characteristics. A common training approach for SNNs is to convert the weights from a pretrained ANN into an equivalent SNN. However, due to rate coding, such a converted SNN typically requires multiple infe…
- OpenAlex · Published · First archived
Clinical readiness of artificial intelligence for treat-to-target management in inflammatory bowel disease: from scores to decisions ↗
Artificial intelligence (AI) is increasingly used in inflammatory bowel disease (IBD), particularly for endoscopy, histology, transmural imaging, and prediction of treatment response. However, strong diagnostic or predictive performance does not necessarily confer clinical utility. This narrative review traces the development of AI in IBD from objective disease assessment and outcome prediction to therapeutic decision support within a treat-to-target framework. Endoscopic AI has the most mature evidence base, while applications in histology and transmural imaging are at earlier stages of clini…
- OpenAlex · Published · First archived
A Hybrid BDI + RAG Multi-Agent Architecture: Plan-Based Reasoning over Multimodal Retrieval ↗
Retrieval-Augmented Generation (RAG) gives language models access to external text and image collections, but it does not make the decisions taken on that content reproducible. Classical Belief–Desire–Intention (BDI) agents provide explicit plans and traceable decisions, although they normally expect beliefs in symbolic form. We connect these capabilities in a hybrid architecture in which RAG updates agent beliefs while BDI plans remain responsible for reasoning and coordination. The plans are implemented in AgentSpeak on JASON/JADE, agents communicate through FIPA-ACL, and probabilistic model…
- OpenAlex · Published · First archived
Foot Posture Outweighs Habitual Footwear in Discriminating Concurrent Lower-Limb Pain in Children: A Cross-Sectional Interpretable Machine-Learning Study ↗
Lower-limb pain is common in school-aged children, but the relative contributions of foot posture, adiposity, and footwear remain uncertain, and prediction models may be inflated by target leakage. We aimed to quantify how well foot posture, adiposity, and footwear discriminate current lower-limb pain, and whether machine learning outperforms penalised logistic regression. We analysed a cross-sectional school-based dataset of 4408 children aged 4–14 years from Türkiye. Eight classifiers were trained with five-fold cross-validation and evaluated once in a held-out 20% test set; pain-site inform…
- OpenAlex · Published · First archived
A Hybrid UKF–ST-Graph Transformer Framework for Cooperative Localization in Ship Ad-Hoc Networks ↗
In sea environment the localization of ships is very important for traffic management. AutomaticIdentification System (AIS) dataset is widely used to get information of ship. This paper proposed ahybrid localization framework for Ship Ad hoc Networks (SANETs) using AIS dataset. This studyintegrates an Unscented Kalman Filter (UKF) with Spatio-Temporal Graph Transformer (ST-GT)built on SANETs to provide cooperative localization. First, UKF is utilized raw AIS data to getinitial position estimates for ship. Then, a SANETs based on spatial proximity is constructed to fromfeatures such as node deg…
- OpenAlex · Published · First archived
Predicting the Construction Age of Vernacular Buildings from Facade Features Using Machine Learning ↗
Determining the construction age of vernacular buildings is essential for the conservation and documentation of historical and cultural heritage. Traditional approaches, however, rely heavily on questionnaire surveys and expert judgment, which can be time-consuming and may be affected by incomplete historical information and variability in expert interpretation. To reduce reliance on direct expert assessment of construction age and improve the reproducibility of the dating process, this paper presents a machine-learning-assisted method for predicting construction age from manually coded facade…
- OpenAlex · Published · First archived
UniOVA: Universal On-demand Video Analytics with Edge-Cloud Collaborative Multimodal LLM ↗
Video analytics is ubiquitous in modern society, and the emergence of Multimodal Large Language Models (MLLMs) has made its application even more extensive. A common way to support MLLM-based video analytics is to continuously stream video to the cloud and then extracts visual features and samples frames for analysis. However, this cloud-centric architecture can introduce high transmission overhead. Simultaneously, the fixed-rate or uniform sampling approaches employed in these systems will miss critical visual evidence relevant to the query, resulting in reduced accuracy.; AB@In this paper, w…
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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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