Abstract
Predictive Maintenance (PdM) aims to prevent failures through early detection, yet lacks explainability to support decision-making. Current PdM models often identify failures, but fail to explain their root causes, especially in real-world scenarios, with complex and limited labeled data. This study proposes an interpretable framework that combines LSTM-based Anomaly Detection with a dual-layered Root Cause Analysis (RCA) based on SHAP attributions. Applied to a real-world dataset, the method detects degradation transitions, tracks failure patterns over time, and provides interpretable information without explicit root cause labels.

Abstract

Abstract
The goal of providing legal identity to all individuals remains a global challenge. Current digital identity models depend on the existence of authoritative issuers, which are not always available, particularly in Low-Income Countries (LICs) and Lower-Middle-Income Countries (LMICs). This limitation severely restricts the coverage of identity systems. In response, new approaches such as the non-authoritative identity model have emerged, enabling entities with local reputation to serve as primary sources of information about individuals. However, existing literature does not yet offer a comprehensive solution for implementing such systems. In this paper, we present Nexus, a data fusion module that enables reputable entities to serve as primary providers of identity information, facilitating the design and implementation of digital identity systems that complement the State or other centralized authorities. A central element of the proposed data fusion component is a novel scheme combining truth discovery algorithms and reputation tracking. The feasibility, behavior, and performance of the proposed approach are demonstrated through experiments conducted on multiple datasets. In addition, Nexus was evaluated using a dataset from a health line service in a LIC, thereby demonstrating its applicability in real-world settings. This work represents an essential step toward eliminating barriers to pursuing a comprehensive legal identity.

Abstract
Automated seizure quantification and classification are needed for semiology-based epileptic seizure diagnosis support. To the best of our knowledge, the 5-class (Hypermotor, Automotor, Complex Motor, Psychogenic Non-Epileptic Seizures, and Generalized Tonic-Clonic Seizures) seizure video dataset (198 seizures from 74 patients) studied in this paper is the largest 5-class dataset ever curated, composed of monocular RGB videos from two university hospital epilepsy monitoring units. 2D skeletons were estimated using ViTPose, a vision transformer deep learning (DL) architecture, and lifted to 3D space using MotionBERT, a multimodal motion transformer architecture. The movements were quantified based on the estimated 3D skeleton sequences. Two approaches were evaluated for seizure classification: (1) classical machine learning methods (Random Forest (RF) and XGBoost) applied to quantified movement parameters, and (2) 2D skeleton-based DL using MotionBERT action, an action recognition DL model, to which we perform transfer-learning. The best model achieved a promising, above literature, 5-fold cross-validated macro average F1-score of 0.84 +/- 0.09 (RF) for 5-class classification. The binary case (Automotor vs Hypermotor) resulted in 0.80 +/- 0.18 (MotionBERT action), and adding a 3rd class (Complex motor) lowered to 0.65 +/- 0.14 (RF). This novel multi-stage classification ensures that the included movement features are traceable, allowing interpretable AI exploration of this novel approach supporting future clinical diagnosis.

Abstract

Abstract
Autonomous warehouse drones require detection that is both accurate and spatially selective, detecting only the labels directly in front of the drone and ignoring those on neighbouring racks. Standard YOLOv8n has no built-in awareness of absolute pixel position, and architectural fixes (CoordConv, attention modules) are precluded by the ModalAI VOXL2 TFLite GPU runtime that constrains the deployment platform. We propose Geometric Channel Hijacking (GCH), a data-level technique that collapses the colour camera image to a single greyscale channel and replaces the two remaining input channels with horizontal and vertical positional gradients. We then run a controlled multi-factor ablation across three training regimes (INESC TEC's Autonomous Systems Laboratory (LSA), Volkswagen Autoeuropa (AE) production, and the LSA + AE combined multi-domain set), six training set sizes from n=50 to full, and six independent random seeds per cell, totalling 200 retrained models (150 across the data-size sweep and 50 at full N), on top of the original seed-0 runs, evaluated on three equalised 42-image test sets. At full data, GCH and an architecturally identical Target-Only Annotation (TOA) ablation are empirically equivalent across every metric we tested, showing that target-only annotation alone is sufficient to induce spatial selectivity in unmodified YOLOv8n. At low data, the multi-seed analysis reveals no GCH-favouring difference in mean spatial precision at p<0.05 in any of the 18 (regime, N) cells tested (paired t-test; two cells exhibit a small TOA-favouring gap in the AE regime), but TOA training in the single-domain LSA regime at N <= 100 exhibits a stochastic collapse failure mode that affects similar to 1 in 6 seeds (cross-seed std of similar to 33 percentage points on SpP vs. similar to 5.6 pp for GCH), which we also reproduce in AE at n=100 . GCH eliminates this collapse mode in the LSA regime and provides a similar to 6 & times; variance reduction at n=50 . An ablation with patience = 999 additionally shows that the collapse is recoverable in principle with roughly 2.4 & times; the standard training budget. A quantitative cross-camera test on a 1920 & times; 1080 Arducam 64 MP USB module (a completely different sensor and lens to the IMX412 used during training, no retraining performed, 115 manually annotated frames) reveals a second regime in which GCH beats TOA: cross-camera target recall is 82.5% for GCH against 64.7% for TOA at a matching spatial precision, a 17.8 percentage-point lead that confirms that the explicit positional prior provides additional robustness when the input visual statistics shift away from the training distribution. The cross-position split additionally confirms that spatial selectivity is not a memorised centre bias. We deploy the full pipeline on the VOXL2 at 22-25 FPS within the DRIVOLUTION project.