Query Performance Prediction (QPP) estimates the effectiveness of retrieval systems for a given query, offering valuable insights for search effectiveness and query processing. Despite extensive research, a critical gap remains in understanding how well QPPs generalize across diverse retrieval paradigms and collections, a question of robustness that has significant implications for their practical utility. This article provides the first comprehensive cross-paradigm evaluation of QPP robustness and generalization capabilities, examining state-of-the-art QPPs including NQC, WIG, LETOR-based features, and newly explored dense-based predictors MQPPF and BERT-QPP. We systematically assess their performance across diverse sparse (BM25, DFree with and without query expansion), hybrid (SPLADE), and dense (ColBERT, TCT-ColBERT) rankers on four benchmark collections: TREC Robust, GOV2, WT10G, and MS-MARCO. The results reveal fundamental robustness challenges: predictors exhibit significant variability in accuracy, with collection being the dominant factor, followed by ranker type. Some sparse predictors perform adequately on specific collections such as TREC Robust and GOV2, but critically fail to generalize to other collections like WT10G and MS-MARCO. Dense-based predictors, while showing promise in specific scenarios with dense rankers, similarly lack generalization to sparse contexts. We demonstrate that these generalization failures severely limit practical applications: QPP-driven selective query processing achieves only marginal gains ( \(\approx\) 4% NDCG improvement), with reliability varying dramatically across settings. Our findings underscore that current QPP methods lack the robustness necessary for real-world deployment and highlight the urgent need for predictors that generalize reliably across diverse collections, align with modern dense retrieval architectures, and provide consistent utility for downstream applications. We publicly release our data and code to facilitate future research on robust QPP methods ( https://github.com/adrianchifu/UncoveringTheLimitationsofQPP/ ).

Mixup has become a popular augmentation strategy for image classification, yet its naive pixel-wise interpolation often produces unrealistic images that can hinder learning, particularly in high-stakes medical applications. We propose GeMix, a two-stage framework that replaces heuristic blending with a learned, label-aware interpolation powered by class-conditional GANs. First, a StyleGAN2-ADA generator is trained on the target dataset. During augmentation, we sample two label vectors from Dirichlet priors biased toward different classes and blend them via a Beta-distributed coefficient. Then, we condition the generator on this soft label to synthesize visually coherent images that lie along a continuous class manifold. We benchmark GeMix on the large-scale COVIDx-CT-3 dataset using three backbones (ResNet-50, ResNet-101, EfficientNet-B0). When combined with real data, our method increases macro-F1 over traditional mixup for all backbones, reducing the false negative rate for COVID-19 detection. GeMix is thus a drop-in replacement for pixel-space mixup, delivering stronger regularization and greater semantic fidelity, without disrupting existing training pipelines. We publicly release our code at https://github.com/hugocarlesso/GeMix to foster reproducibility and further research.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by symptoms that affect social interaction, communication, and behavior, the diagnosis being complicated by significant individual variability and the absence of definitive biomarkers.Current artificial intelligence methods have improved diagnostic accuracy, but their reliance on subjective assessments or single-modal data, coupled with their "blackbox" nature, limits consistency and clinical applicability.Addressing current limitations, this paper introduces a multimodal ASD detection framework using deep neural networks (DNN) with explainable AI (xAI) to enhance model transparency.Our model achieves a mean 5-fold crossvalidation accuracy of 98.64% (± 0.86%), surpassing existing methods and demonstrating potential for clinical dependability of ASD diagnoses.The source code is available at : Multimodal-Explainable-Diagnosis-of-ASD.git

Hyperparameters tuning is a fundamental, yet computationally expensive, step in optimizing machine learning models. Beyond optimization, understanding the relative importance and interaction of hyperparameters is critical to efficient model development. In this paper, we introduce MetaSHAP, a scalable semi-automated eXplainable AI (XAI) method, that uses meta-learning and Shapley values analysis to provide actionable and datasetaware tuning insights. MetaSHAP operates over a vast benchmark of over 09 millions evaluated machine learning pipelines, allowing it to produce interpretable importance scores and actionable tuning insights that reveal how much each hyperparameter matters, how it interacts with others and in which value ranges its influence is concentrated. For a given algorithm and dataset, MetaSHAP learns a surrogate performance model from historical configurations, computes hyperparameters interactions using SHAP-based analysis, and derives interpretable tuning ranges from the most influential hyperparameters. This allows practitioners not only to prioritize which hyperparameters to tune, but also to understand their directionality and interactions. We empirically validate MetaSHAP on a diverse benchmark of 164 classification datasets and 14 classifiers, demonstrating that it produces reliable importance rankings and competitive performance when used to guide Bayesian optimization.

This paper presents our investigation into dense retrieval models for Amharic, a low-resource language spoken by more than 120 million people.We constructed training datasets tailored to dense retrieval models and evaluated model performance by comparing dense and sparse retrieval approaches on Amharic information retrieval.The study also highlights the challenges and efforts involved in advancing retrieval systems for low-resource languages.

The emergence of Industry 4.0 has led a significant shift towards the widespread integration of Cyber Physical Systems(CPSs) across diverse industrial domains. Yet, the intricate design and implementation of these systems necessitate adept knowledge and expertise, posing challenges for researchers and engineers. In response, this paper introduces IoT-AID, a Cyber Physical Recommendation System aimed at alleviating these challenges. Leveraging the capabilities of large language models (LLMs) as decision support systems, IoT-AID relies on state-of-the-art techniques such as BERT and Sentence Transformers for semantic understanding and context-aware recommendations. Through a comprehensive exploration and evaluation, this study sheds light on the efficacy and potential of LLM-driven recommendation systems within the realm of CPSs, offering insights crucial for navigating the complexities of Industry 4.0 integration.

The rapid evolution of machine learning (ML) has led to the widespread adoption of complex “black box” models, such as deep neural networks and ensemble methods. These models exhibit exceptional predictive performance, making them invaluable for critical decision-making across diverse domains within society. However, their inherently opaque nature raises concerns about transparency and interpretability, making them untrustworthy decision support systems. To alleviate such a barrier to high-stakes adoption, research community focus has been on developing methods to explain black box models as a means to address the challenges they pose. Efforts are focused on explaining these models instead of developing ones that are inherently interpretable. Designing inherently interpretable models from the outset, however, can pave the path towards responsible and beneficial applications in the field of ML. In this position paper, we clarify the chasm between explaining black boxes and adopting inherently interpretable models. We emphasize the imperative need for model interpretability and, following the purpose of attaining better (i.e., more effective or efficient w.r.t. predictive performance) and trustworthy predictors, provide an experimental evaluation of latest hybrid learning methods that integrates symbolic knowledge into neural network predictors. We demonstrate how interpretable hybrid models could potentially supplant black box ones in different domains.