Data-Centric AI
The evolution of Artificial Intelligence (AI) has been driven by two core components: data and algorithms. Historically, AI research has predominantly followed the Model-Centric paradigm, which focuses on developing and refining models, while ...
Enhancing data preparation: insights from a time series case study
Data play a key role in AI systems that support decision-making processes. Data-centric AI highlights the importance of having high-quality input data to obtain reliable results. However, well-preparing data for machine learning is becoming ...
DIAMANTE: A data-centric semantic segmentation approach to map tree dieback induced by bark beetle infestations via satellite images
Forest tree dieback inventory has a crucial role in improving forest management strategies. This inventory is traditionally performed by forests through laborious and time-consuming human assessment of individual trees. On the other hand, the ...
CONCORD: enhancing COVID-19 research with weak-supervision based numerical claim extraction
The COVID-19 Numerical Claims Open Research Dataset (CONCORD) is a comprehensive, open-source dataset that extracts numerical claims from academic papers on COVID-19 research. A weak-supervision model is employed for this extraction, taking ...
Multi-view subspace text clustering
Text clustering has become an important challenge in artificial intelligence since several applications require to automatically organize documents into homogeneous topics. Given the availability of several text representation models, text ...
Conversing with business process-aware large language models: the BPLLM framework
Traditionally, process-aware Decision Support Systems (DSSs) have been enhanced with AI functionalities to facilitate quick and informed decision-making. In this context, AI-Augmented Business Process Management Systems have emerged as innovative ...
Improving the clarity of questions in Community Question Answering networks
Every day, thousands of questions are asked on the Community Question Answering network, making these questions and answers extremely valuable for information seekers around the world. However, a significant proportion of these questions do not ...
Generative adversarial meta-learning knowledge graph completion for large-scale complex knowledge graphs
In the study of large-scale complex knowledge graphs, due to the incompleteness of knowledge and the existence of low-frequency knowledge samples, existing knowledge graph complementation methods are often limited by the amount of data and ignore ...
Improving graph collaborative filtering with view explorer for social recommendation
Social recommender systems (SRS) have garnered adequate attention due to the supplementary information provided by social network, which aids in making recommendations. However, social network information contains noise, which can be detrimental ...
SESAME - self-supervised framework for extractive question answering over document collections
Question Answering is one of the most relevant areas in the field of Natural Language Processing, rapidly evolving with promising results due to the increasing availability of suitable datasets and the advent of new technologies, such as ...
Improved machine learning technique for feature reduction and its application in spam email detection
This paper introduces MPAG, a new feature selection method aimed at overcoming the limitations of the conventional Marine Predators Algorithm (MPA). The MPA may experience stagnation and become trapped in local optima during optimization. To ...
Enhancing E-learning effectiveness: a process mining approach for short-term tutorials
The rise of e-learning systems has revolutionized education, enabling the collection of valuable students’ activity data for continuous improvement. While existing studies have predominantly focused on prolonged learning paths, short-term ...