Change detection in Earth observation (EO) is critical for monitoring land surface transformations, yet recent research in the field is constrained by inconsistent evaluation protocols and a narrow focus on predictive accuracy without regard for computational efficiency. To address this, we present a standardized, open-source benchmark for evaluating state-of-the-art (SOTA) deep learning methods for Earth observation change detection. We conduct a comprehensive analysis of ten representative model architectures, ranging from convolutional networks (CNNs) to vision transformers (ViTs), across ten heterogeneous change detection datasets. We rigorously evaluate these models with identical experimental protocols, comparing models trained from scratch against those utilizing pre-trained weights. Furthermore, we evaluate predictive performance alongside computational efficiency, including parameter counts and inference latency. Our findings reveal that well-optimized classical architectures, such as Siamese U-Nets, frequently outperform more complex contemporary models when computational efficiency is factored in, and that pre-training consistently provides a significant performance boost with no additional inference cost. To ensure complete transparency and reproducibility, all experimental resources, including standardized data splits, training scripts, training logs, and model checkpoints are publicly available and adhere to FAIR principles (Findable, Accessible, Interoperable, and Reusable).

Physiologically Based Pharmacokinetic (PBPK) Modelling have emerged as a critical method in pharmaceutical and environmental sciences. Despite its widespread adoption, inconsistent vocabulary used across models, software platforms, and literature remains a significant challenge. Such inconsistency limits the potential for data harmonization and model interoperability. This gap also hinders the adoption of machine-readable semantic technologies that could enhance PBPK model reporting, validation, and interoperability.To address this gap, we developed PBPKO (Physiologically Based Pharmacokinetic Ontology), a dedicated ontology for the kinetic modelling community. PBPKO captures mechanisms and terminology commonly used in PBPK modelling, including concepts related to physiological parameters, biochemical parameters, biological processes, enzymes, transporters, and related concepts. As of release of v2026-07-15, the ontology comprises 858 classes and 7 object properties native to PBPKO. To support semantic interoperability within the broader biomedical ontology ecosystem, PBPKO is aligned with upper-level ontologies such as the Basic Formal Ontology (BFO) and the Relation Ontology (RO), and reuses terms from established resources including Gene Ontology (GO) and the Ontology for Biomedical Investigations (OBI).In this work, we further applied PBPKO to the annotation of PFAS multi-compartment PBPK models as a case study, while also highlighting the parallel development effort of “FAIR PBPK Inspector”, which leverages the ontology for model annotation. Together, these efforts support FAIR sharing of PBPK models and facilitate their regulatory acceptance through improved standardization. PBPK ontology is accessible with CC4 licences on https://github.com/InSilicoVida-Research-Lab/pbpko .

A key aim of the FNS-Cloud project (grant agreement no. 863059) was to overcome fragmentation within food, nutrition and health data through development of tools and services facilitating matching and merging of data to promote increased reuse. However, in an era of increasing data reuse, it is imperative that the scientific quality of data analysis is maintained. Whilst it is true that many datasets can be reused, questions remain regarding whether they should be, thus, there is a need to support researchers making such a decision. This paper describes the development and evaluation of the FNS-Cloud data quality assessment tool for dietary intake datasets. Markers of quality were identified from the literature for dietary intake, lifestyle, demographic, anthropometric, and consumer behavior data at all levels of data generation (data collection, underlying data sources used, dataset management and data analysis). These markers informed the development of a quality assessment framework, which comprised of decision trees and feedback messages relating to each quality parameter. These fed into a report provided to the researcher on completion of the assessment, with considerations to support them in deciding whether the dataset is appropriate for reuse. This quality assessment framework was transformed into an online tool and a user evaluation study undertaken. Participants recruited from three centres (N = 13) were observed and interviewed while using the tool to assess the quality of a dataset they were familiar with. Participants positively rated the assessment format and feedback messages in helping them assess the quality of a dataset. Several participants quoted the tool as being potentially useful in training students and inexperienced researchers in the use of secondary datasets. This quality assessment tool, deployed within FNS-Cloud, is openly accessible to users as one of the first steps in identifying datasets suitable for use in their specific analyses. It is intended to support researchers in their decision-making process of whether previously collected datasets under consideration for reuse are fit their new intended research purposes. While it has been developed and evaluated, further testing and refinement of this resource would improve its applicability to a broader range of users.

Automated algorithm performance prediction in numerical blackbox optimization often relies on problem characterizations, such as exploratory landscape analysis features. These features are typically used as inputs to machine learning models and are represented in a tabular format. However, such approaches often overlook algorithm configurations, a key factor influencing performance. The relationships between algorithm operators, parameters, problem characteristics, and performance outcomes form a complex structure best represented as a graph. This work explores the use of heterogeneous graph data structures and graph neural networks to predict the performance of optimization algorithms by capturing the complex dependencies between problems, algorithm configurations, and performance outcomes. We focus on two modular frameworks, modCMA-ES and modDE, which decompose two widely used derivative-free optimization algorithms: the covariance matrix adaptation evolution strategy (CMA-ES) and differential evolution (DE). We evaluate 324 modCMA-ES and 576 modDE variants on 24 BBOB problems across six runtime budgets and two problem dimensions. Achieving up to 36.6% improvement in MSE over traditional tabular-based methods, this work highlights the potential of geometric learning in black-box optimization.

This paper surveys existing ontologies for visualization, which formally define and organize knowledge about visualization concepts, techniques, and tools. Although visualization is a mature field, the rapid growth of data complexity makes semantically rich frameworks increasingly essential for building intelligent and automated visualization systems. Current ontologies remain fragmented, heterogeneous, and inconsistent in terminology and modeling strategies, limiting their coverage and adoption. We present a systematic analysis of representative ontologies, highlighting shared themes and, most importantly, the gaps that hinder unification. These gaps provide the foundations for developing a comprehensive, generic ontology of visualization, aimed at unifying core concepts and supporting reuse across research and practice.

The task of feature ranking has received considerable attention across various predictive modelling tasks in the batch learning scenario, but not in the online learning setting. Available methods that estimate feature importances on data streams have so far predominantly focused on ranking the features for the tasks of classification and occasionally multi-label classification. We propose a novel online feature ranking method for online multi-target regression iSOUP-SymRF, which estimates feature importance scores based on the positions at which a feature appears in the trees of a random forest of iSOUP-Trees, and additionally extend it to task of online feature ranking for multi-label classification. By utilizing iSOUP-Trees, which can address multiple structured output prediction tasks on data streams, iSOUP-SymRF promises feature ranking across a variety of online structured output prediction tasks. We examine the ranking convergence of iSOUP-SymRF in terms of the methods' parameters, the size of the ensemble and the number of selected features, as well as their stability under different random seeds. Furthermore, to show the utility of iSOUP-SymRF and its rankings we use them in conjunction with two state-of-the-art online multi-target regression and multi-label classification methods, iSOUP-Tree and AMRules, and analyze the impact of adding features according to the rankings obtained from iSOUP-SymRF.

In today’s dynamic business world, organizations are increasingly relying on innovative technologies to improve the efficiency and effectiveness of their human resource (HR) management. Our study uses historical time and attendance data collected with the MojeUre time and attendance system to predict employee absenteeism, including sick and vacation leave, using machine learning methods. We integrate employee demographic data and the absence profiles on timesheets showing daily attendance patterns as fundamental elements for our analysis. We also convert the absence data into a feature-based format suitable for the machine learning methods used. Our primary goal in this paper is to evaluate how well we can predict sick leave and vacation leave over short- and long-term intervals using tree-based machine learning methods based on the predictive clustering paradigm. This paper compares the effectiveness of these methods in different learning settings and discusses their impact on improving HR decision-making processes.

This paper provides an initial exploration of employee absence data for anomaly detection. Utilizing data collected from the MojeUre system, which aggregates employee data from diverse companies, our objective is to uncover hidden patterns and anomalies associated with absences. In this paper, we employ various anomaly detection techniques to identify and characterize unusual patterns in absence data. The comparative analysis in this paper offers valuable initial insights for organizations aiming to leverage data analytics for workforce management and strategic decision-making, particularly in the context of anomaly detection.

An essential characteristic of data streams is the possibility of occurrence of concept drift, i.e., change in the distribution of the data in the stream over time. The capability to detect and adapt to changes in data stream mining methods is thus a necessity. While methods for multi-target prediction on data streams have recently appeared, they have largely remained without such capability. In this paper, we propose novel methods for change detection and adaptation in the context of incremental online learning of decision trees for multi-target regression. One of the approaches we propose is ensemble based, while the other uses the Page–Hinckley test. We perform an extensive evaluation of the proposed methods on real-world and artificial data streams and show their effectiveness. We also demonstrate their utility on a case study from spacecraft operations, where cosmic events can cause change and demand an appropriate and timely positioning of the space craft.

Empirical data plays an important role in evolutionary computation research.To make better use of the available data, ontologies have been proposed in the literature to organize their storage in a structured way.However, the full potential of these formal methods to capture our domain knowledge has yet to be demonstrated.In this work, we evaluate a performance prediction model built on top of the extension of the recently proposed OPTION ontology.More specifically, we first extend the OPTION ontology with the vocabulary needed to represent modular black-box optimization algorithms.Then, we use the extended OPTION ontology, to create knowledge graphs with fixed-budget performance data for two modular algorithm frameworks, modCMA, and modDE, for the 24 noiseless BBOB benchmark functions.We build the performance prediction model using a knowledge graph embedding-based methodology.Using a number of different evaluation scenarios, we show that a triple classification approach, a fairly standard predictive modeling task in the context of knowledge graphs, can correctly predict whether a given algorithm instance will be able to achieve a certain target precision for a given problem instance.This approach requires feature representation of algorithms and problems.While the latter is already well developed, we hope that our work will motivate the community to collaborate on appropriate algorithm representations.

The task of feature ranking has received considerable attention across various prediction tasks in the batch learning scenario, but not in the online learning setting. Available methods that estimate feature importances on data streams have thus far focused on ranking the features for the tasks of classification and occasionally multi-label classification. We propose a novel online feature ranking method for online multi-target regression, iSOUP-SymRF, which estimates feature importance scores based on the positions at which a feature appears in the trees of a random forest of iSOUP-Trees. By utilizing iSOUP-Trees, which can address multiple structured output prediction tasks on data streams, iSOUP-SymRF promises feature ranking across a variety of online structured output prediction tasks. We examine the robustness of iSOUP-SymRF and the feature rankings it produces in terms of the methods’ parameters: the size of the ensemble and the number of selected features. Furthermore, to show the utility of iSOUP-SymRF and its rankings we use them in conjunction with two state-of-the-art online multi-target regression methods, iSOUP-Tree and AMRules, and analyze the impact of adding features according to the rankings.

We propose AiTLAS—an open-source, state-of-the-art toolbox for exploratory and predictive analysis of satellite imagery. It implements a range of deep-learning architectures and models tailored for the EO tasks illustrated in this case. The versatility and applicability of the toolbox are showcased in a variety of EO tasks, including image scene classification, semantic image segmentation, object detection, and crop type prediction. These use cases demonstrate the potential of the toolbox to support the complete data analysis pipeline starting from data preparation and understanding, through learning novel models or fine-tuning existing ones, using models for making predictions on unseen images, and up to analysis and understanding of the predictions and the predictive performance yielded by the models. AiTLAS brings the AI and EO communities together by facilitating the use of EO data in the AI community and accelerating the uptake of (advanced) machine-learning methods and approaches by EO experts. It achieves this by providing: (1) user-friendly, accessible, and interoperable resources for data analysis through easily configurable and readily usable pipelines; (2) standardized, verifiable, and reusable data handling, wrangling, and pre-processing approaches for constructing AI-ready data; (3) modular and configurable modeling approaches and (pre-trained) models; and (4) standardized and reproducible benchmark protocols including data and models.

Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use different data models and formats, which drastically complicates the identification of relevant datasets, their interpretation, and their interoperability. Therefore, a semantically rich, ontology-based, machine-readable data model that can be used by different platforms is highly desirable. In this article, we report on the development of such an ontology, which we call OPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides the vocabulary needed for semantic annotation of the core entities involved in the benchmarking process, such as algorithms, problems, and evaluation measures. It also provides means for automatic data integration, improved interoperability, and powerful querying capabilities, thereby increasing the value of the benchmarking data. We demonstrate the utility of OPTION, by annotating and querying a corpus of benchmark performance data from the BBOB collection of the COCO framework and yet another black-box optimization benchmark (YABBOB) family of the Nevergrad environment. In addition, we integrate features of the BBOB functional performance landscape into the OPTION knowledge base (KB) using publicly available datasets with exploratory landscape analysis. Finally, we integrate the OPTION KB into the IOHprofiler environment and provide users with the ability to perform a meta-analysis of performance data.

This data set contains telemetry data of the Mars Express Spacecraft (MEX), a spacecraft orbiting Mars andoperated by the European Space Agency. The data, in terms of context data and thermal power consumption measurements, capture the status of the spacecraft over four Martian years (from 22.8.2008 to 1.3.2016) sampled at 60 minute resolution.

We present six datasets containing telemetry data of the Mars Express Spacecraft (MEX), a spacecraft orbiting Mars operated by the European Space Agency. The data consisting of context data and thermal power consumption measurements, capture the status of the spacecraft over three Martian years, sampled at six different time resolutions that range from 1 min to 60 min. From a data analysis point-of-view, these data are challenging even for the more sophisticated state-of-the-art artificial intelligence methods. In particular, given the heterogeneity, complexity, and magnitude of the data, they can be employed in a variety of scenarios and analyzed through the prism of different machine learning tasks, such as multi-target regression, learning from data streams, anomaly detection, clustering, etc. Analyzing MEX's telemetry data is critical for aiding very important decisions regarding the spacecraft's status and operation, extracting novel knowledge, and monitoring the spacecraft's health, but the data can also be used to benchmark artificial intelligence methods designed for a variety of tasks.

INTEGRAL (INTErnational Gamma-Ray Astrophysics Laboratory) is an astronomical observatory of the European Space Agency, responsible for many significant scientific discoveries in the last few decades. It orbits Earth since 2002 in a highly elliptical orbit, passing through the Van Allen belts – areas with high-energy ionized particles that can damage the spacecraft's on-board equipment. An essential part of mission planning and operation of INTEGRAL is thus the prediction of its radiation belts entry and exit times. We propose a novel compact representation of the data and evaluate its potential using several machine learning methods. The experimental validation identifies gradient boosted trees with quantile loss as the best performing method. By using our approach, INTEGRAL can perform 2 additional hours (on average) of scientific measurements per orbit (with adjustment for uncertainty at the 95th percentile). This approach protects INTEGRAL from damages and improves its scientific return at the same time. It can be easily extended and applied to other spacecraft with similar orbits.

Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. MLC is increasingly gaining interest in different application domains such as text mining, computer vision, and bioinformatics. Several MLC algorithms have been proposed in the literature, resulting in a meta-optimization problem that the user needs to address: which MLC approach to select for a given dataset? To address this algorithm selection problem, we investigate in this work the quality of an automated approach that uses characteristics of the datasets - so-called features - and a trained algorithm selector to choose which algorithm to apply for a given task. For our empirical evaluation, we use a portfolio of 38 datasets. We consider eight MLC algorithms, whose quality we evaluate using six different performance metrics. We show that our automated algorithm selector outperforms any of the single MLC algorithms, and this is for all evaluated performance measures. Our selection approach is explainable, a characteristic that we exploit to investigate which meta-features have the largest influence on the decisions made by the algorithm selector. Finally, we also quantify the importance of the most significant meta-features for various domains.