Applying Large Language Models (LLMs) to whole-page semantic element detection in e-commerce is prohibitively expensive: typical product pages contain hundreds to thousands of DOM elements, and LLM costs scale linearly with input tokens. We propose a hybrid approach that uses a Graph Neural Network (GraphSAGE) as a pre-filter to reduce the candidate set before LLM processing. Our preliminary results on the Klarna Product Page Dataset show approximately 90
The quality of life of aging adults has improved over time through the incorporation of technological innovations in their homes, such as voice assistants or monitoring systems. However, current systems do not detect certain patterns that indicate dementia or mild cognitive decline due to limitations in the level of detail of the collected data or in the interpretability of the information already available. In this context, IoT and IoMT devices can be used to collect information that allows automatic detection of possible patterns or alterations. Integration of collected data with artificial intelligence models can improve the interpretability of the data and the detection of behavior patterns that indicate cognitive decline. This thesis aims to design an architecture that uses data from IoT/IoMT devices to identify behavior patterns using artificial intelligence models, with the aim of preventing dementia or mild cognitive decline in aging people through early detection and monitoring strategies.
While digital accessibility is a fundamental human right, modern web systems often fail to meet legal standards due to inadequate developer tools and persistent barriers for users with visual impairments. This research proposes an Accessibility by Design framework that integrates accessibility considerations into both the development and consumption phases of web systems. The framework is composed of two complementary components: an AI-assisted development environment that leverages Large Language Models to support developers in auditing and generating accessible web components and a web-based multimodal system that translates visual STEM data into multisensory representations through sonification. The study evaluates how integrating accessibility considerations into product design enhances usability while ensuring equal access for all users regardless of their abilities.
Open Government Data (OGD) publishing is often manual and locally heterogeneous, yielding inconsistent dataset quality and weak traceability of pre-publication decisions. SODPUB is a BPMN-orchestrated workflow-as-a-service that targets interoperability-oriented improvement before portal upload. Data providers submit CSV datasets via REST; the pipeline applies checks derived from W3C guidance, normalises tabular structure, generates CSV on the Web metadata, and produces DCAT-compatible catalog descriptions. Each proposed change is explained and versioned, and the workflow exports a machine-readable process trace aligned with PROV-O so that decisions remain auditable. The current implementation provides an executable BPMN skeleton, initial rule catalogue, and RDF trace generation; REST endpoints and portal connectors are under active development. Planned evaluation measures include acceptance rates of proposed fixes by portal staff, reduction of detectable structural and metadata issues, time per publication, and perceived transparency. The expected outcome is a reusable reference workflow that improves quality while preserving publisher release responsibility.
Evaluating Knowledge Graph Question Answering (KGQA) systems is increasingly unreliable due to static benchmarks, evolving Knowledge Graphs (KG), and memorization effects in Large Language Models (LLM). We present DynBench Generator, a web frontend for automatically generating new KGQA benchmark datasets that both preserve the original question and query complexity and are resistant to LLM memorization. In this demo, we showcase an interactive platform that enables on-demand generation and inspection of memorization-resistant question-query pair for fair and reproducible KGQA evaluation.
