Datasets may contain observations with multiple labels. If the labels are not mutually exclusive, and if the labels vary greatly in frequency, obtaining a sample that includes sufficient observations with scarcer labels to make inferences about those labels, and which deviates from the population frequencies in a known manner, creates challenges. In this paper, we consider a multivariate Bernoulli distribution as our underlying distribution of a multi-label problem. We present a novel sampling algorithm that takes label dependencies into account. It uses observed label frequencies to estimate multivariate Bernoulli distribution parameters and calculate weights for each label combination. This approach ensures the weighted sampling acquires target distribution characteristics while accounting for label dependencies. We applied this approach to a sample of research articles from Web of Science labeled with 64 biomedical topic categories. We aimed to preserve category frequency order, reduce frequency differences between most and least common categories, and account for category dependencies. This approach produced a more balanced sub-sample, enhancing the representation of minority categories.

Abstract--The accurate labeling of datasets is often both costly and time-consuming. Given an unlabeled dataset, programma tic weak supervision obtains probabilistic predictions for th e labels by leveraging multiple weak labeling functions (LFs) that p ro-vide rough guesses for labels. Weak LFs commonly provide guesses with assorted types and unknown interdependences that can result in unreliable predictions. This paper presents a methodology for programma tic weak supervision that can provide confidence intervals for l abel probabilities and obtain more reliable predictions. In par ticular, the methods proposed use uncertainty sets of distributions that encapsulate the information provided by LFs with unrestric ted behavior and typology. Experiments on multiple benchmark datasets show the improvement of the presented methods over the state-of-the-art and the practicality of the confidence intervals presented. OR many machine learning applications, the accurate labeling of datasets is both costly and time-consuming [1]-[4]. Given an unlabeled dataset, methods for programmatic weak supervision aim to leverage multiple wea k labeling functions (LFs) to provide accurate labels [5], [6 ]. Since common LFs only provide rough guesses for labels, programmatic weak supervision methods use the outputs of multiple LFs to obtain probabilistic predictions for the la bel of each instance [7]-[13]. These predictions can then be use d to create a fully supervised dataset composed by the instanc es corresponding to high-confidence predictions, e.g., a labe l with a large enough predicted probability is regarded as the actu al Manuscript received September 30, 2024; accepted August 4, 2025.

--The Artificial Intelligence field has focused on developing optimisation methods to solve multiple problems, specifically problems that we thought to be only solvable through cognition. The obtained results have been outstanding, being able to even surpass the T uring T est. However, we have found that these optimisation methods share some fundamental flaws that impede them to become a true artificial cognition. Specifically, the field have identified catastrophic forgetting as a fundamental problem to develop such cognition. This paper formally proves that this problem is inherent to optimisation methods, and as such it will always limit approaches that try to solve the Artificial General Intelligence problem as an optimisation problem. Additionally, it addresses the problem of overfitting and discuss about other smaller problems that optimisation methods pose. Finally, it empirically shows how world-modelling methods avoid suffering from either problem. As a conclusion, the field of Artificial Intelligence needs to look outside the machine learning field to find methods capable of developing an artificial cognition. HERE is a common goal in the Artificial Intelligence field: approaching the achievement of an artificial cognition by producing results similar to those produced by a natural cognition (i.e. a human). That is, the efforts in such field have been focused on mimicking the effects of cognition. This approach has produced a plethora of optimisation methods that try to solve problems that are considered solvable only by humans. The underlying assumption was that, if some algorithm is able to solve these problems, it will be due to the emergence of cognition (or at least some kind of cognition-like reasoning).

Imbalanced data poses a significant obstacle in machine learning, as an unequal distribution of class labels often results in skewed predictions and diminished model accuracy. To mitigate this problem, various resampling strategies have been developed, encompassing both oversampling and undersampling techniques aimed at modifying class proportions. Conventional oversampling approaches like SMOTE enhance the representation of the minority class, whereas undersampling methods focus on trimming down the majority class. Advances in deep learning have facilitated the creation of more complex solutions, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which are capable of producing high-quality synthetic examples. This paper reviews a broad spectrum of data balancing methods, classifying them into categories including synthetic oversampling, adaptive techniques, generative models, ensemble-based strategies, hybrid approaches, undersampling, and neighbor-based methods. Furthermore, it highlights current developments in resampling techniques and discusses practical implementations and case studies that validate their effectiveness. The paper concludes by offering perspectives on potential directions for future exploration in this domain.

As neural networks become dominant in essential systems, Explainable Artificial Intelligence (XAI) plays a crucial role in fostering trust and detecting potential misbehavior of opaque models. LIME (Local Interpretable Model-agnostic Explanations) is among the most prominent model-agnostic approaches, generating explanations by approximating the behavior of black-box models around specific instances. Despite its popularity, LIME faces challenges related to fidelity, stability, and applicability to domain-specific problems. Numerous adaptations and enhancements have been proposed to address these issues, but the growing number of developments can be overwhelming, complicating efforts to navigate LIME-related research. To the best of our knowledge, this is the first survey to comprehensively explore and collect LIME's foundational concepts and known limitations. We categorize and compare its various enhancements, offering a structured taxonomy based on intermediate steps and key issues. Our analysis provides a holistic overview of advancements in LIME, guiding future research and helping practitioners identify suitable approaches. Additionally, we provide a continuously updated interactive website (https://patrick-knab.github.io/which-lime-to-trust/), offering a concise and accessible overview of the survey.

In recent years, there has been a growing interest in explainable AI methods. We want not only to make accurate predictions using sophisticated neural networks but also to understand what the model's decision is based on. One of the fundamental levels of interpretability is to provide counterfactual examples explaining the rationale behind the decision and identifying which features, and to what extent, must be modified to alter the model's outcome. To address these requirements, we introduce HyConEx, a classification model based on deep hypernetworks specifically designed for tabular data. Owing to its unique architecture, HyConEx not only provides class predictions but also delivers local interpretations for individual data samples in the form of counterfactual examples that steer a given sample toward an alternative class. While many explainable methods generated counterfactuals for external models, there have been no interpretable classifiers simultaneously producing counterfactual samples so far. HyConEx achieves competitive performance on several metrics assessing classification accuracy and fulfilling the criteria of a proper counterfactual attack. This makes HyConEx a distinctive deep learning model, which combines predictions and explainers as an all-in-one neural network. The code is available at https://github.com/gmum/HyConEx.

Automated Machine Learning (AutoML) gained popularity due to the increased demand for Machine Learning (ML) specialists, allowing them to apply ML techniques effortlessly and quickly. AutoML implementations use optimisation methods to identify the most effective ML solution for a given dataset, aiming to improve one or more predefined metrics. However, most implementations focus on model selection and hyperparameter tuning. Despite being an important factor in obtaining high-performance ML systems, data quality is usually an overlooked part of AutoML and continues to be a manual and time-consuming task. This work presents EDCA, an Evolutionary Data Centric AutoML framework. In addition to the traditional tasks such as selecting the best models and hyperparameters, EDCA enhances the given data by optimising data processing tasks such as data reduction and cleaning according to the problems' needs. All these steps create an ML pipeline that is optimised by an evolutionary algorithm. To assess its effectiveness, EDCA was compared to FLAML and TPOT, two frameworks at the top of the AutoML benchmarks. The frameworks were evaluated in the same conditions using datasets from AMLB classification benchmarks. EDCA achieved statistically similar results in performance to FLAML and TPOT but used significantly less data to train the final solutions. Moreover, EDCA experimental results reveal that a good performance can be achieved using less data and efficient ML algorithm aspects that align with Green AutoML guidelines

Accordingly, education researchers and practitioners have increasingly turned to AI literacy as an important learning objective. However, the definition of AI literacy remains vague. Researchers have used the term to describe learning interventions that differ by in school contexts, learning objectives, and types of AI technologies they use. Furthermore, the research of AI literacy is shifting significantly in the wake of generative AI. Thus, it is crucial to review the field and develop a conceptual framework that captures the diverse conceptualizations of AI literacy. The concept of AI literacy and recognition of its potential significance are well-established [75, 127]. One of the pioneering works by Touretzky et al. in 2019 laid out "five big ideas" for the AI4K12 initiative: "computers perceive the world using sensors", "agents maintain models/representations of the world and use them for reasoning", "computers can learn from data", "making agents interact with humans is a substantial challenge for AI developers", and "AI applications can impact society in both positive and negative ways" [127]. This paper had a major influence on subsequent AI literacy curriculum design. The next year, another prominent work by Long and Magerko defined AI literacy as "a set

--In the realm of target tracking, performance evaluation plays a pivotal role in the design, comparison, and analytics of trackers. Compared with the traditional trajectory composed of a set of point-estimates obtained by a tracker in the measurement time-series, the trajectory that our series of studies including this paper pursued is given by a curve function of time (FoT). The trajectory FoT provides complete information of the movement of the target over time and can be used to infer the state corresponding to arbitrary time, not only at the measurement time. However, there are no metrics available for comparing and evaluating the trajectory FoT . T o address this lacuna, we propose a metric denominated as the spatiotemporal-aligned trajectory integral distance (Star-ID). The Star-ID associates and aligns the estimated and actual trajectories in the spatio-temporal domain and distinguishes between the time-aligned and unaligned segments in calculating the spatial divergence including false alarm, miss-detection and localization errors. The effectiveness of the proposed distance metric and the time-averaged version is validated through theoretical analysis and numerical examples of a single target or multiple targets. UL TI-target tracking (MTT) is an intricate process that entails the sequential estimation of both the cardinality (number of targets) and the kinematic states of multiple targets, where both parameters are potentially time-variant [1], [2], [3]. It has been a key technology in the applications of autonomous driving, guidance and defense systems, traffic control, and robotics.

The rapid advancement of digital technologies and recent global pandemic scenarios have led to a growing focus on how these technologies can enhance healthcare service delivery and workflow to address crises. Action plans that consolidate existing digital transformation programs are being reviewed to establish core infrastructure and foundations for sustainable healthcare solutions. Reforming health and social care to personalize home care, for example, can help avoid treatment in overcrowded acute hospital settings and improve the experiences and outcomes for both healthcare professionals and service users. In this information-intensive domain, addressing the interoperability challenge through standards-based roadmaps is crucial for enabling effective connections between health and social care services. This approach facilitates safe and trustworthy data workflows between different healthcare system providers. In this paper, we present a methodology for extracting, transforming, and loading data through a semi-automated process using a Common Semantic Standardized Data Model (CSSDM) to create personalized healthcare knowledge graph (KG). The CSSDM is grounded in the formal ontology of ISO 13940 ContSys and incorporates FHIR-based specifications to support structural attributes for generating KGs. We propose that the CSSDM facilitates data harmonization and linking, offering an alternative approach to interoperability. This approach promotes a novel form of collaboration between companies developing health information systems and cloud-enabled health services. Consequently, it provides multiple stakeholders with access to high-quality data and information sharing.