Diabetic retinopathy (DR) screening requires artificial intelligence (AI) models that are not only highly accurate in grading five clinical stages but are also capable of generating quantitatively evaluated lesion-aware explanations to earn the trust of clinicians. We propose RobustDRNet , a hybrid ensemble model that combines local convolutional features from Residual Network-34 (ResNet-34) and ConvNeXt-Tiny with global transformer embeddings from Vision Transformer Base/16 (ViT-B16) via two-stage feature fusion and a disentangled multilayer perceptron (MLP), followed by a logistic regression stacking meta-learner for prediction aggregation. To address severe class imbalance, our training pipeline employs stratified sampling, contrast-limited adaptive histogram equalization (CLAHE) for contrast enhancement, strong data augmentation, and class-weighted focal loss. Evaluated on the Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset, RobustDRNet achieved 88.4% validation accuracy, a 0.967 macro-averaged area under the receiver operating characteristic curve (macro-AUC), and Cohen’s kappa of 0.823, outperforming individual backbones and simple voting ensembles. In addition to classification performance, we integrated six complementary explainable AI (XAI) techniques: Gradient-weighted Class Activation Mapping++ (Grad-CAM++), Integrated Gradients, attention rollout, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Testing with Concept Activation Vectors (TCAV). Each technique was quantitatively benchmarked against expert-annotated lesion maps from the Indian Diabetic Retinopathy Image Dataset (IDRiD). Saliency maps achieved mean Intersection over Union (IoU) scores of 0.06 for Grad-CAM++ and approximately 0.10 for Integrated Gradients; SHapley Additive exPlanations (SHAP) perturbations showed a deletion drop of 0.25 and an insertion gain of 0.22; and TCAV achieved complete classifier-level TCAV alignment (score = 1.0) with clinically coherent, grade-wise importance trajectories. By combining competitive grading performance with multi-perspective and quantitatively evaluated interpretability, RobustDRNet provides a promising DR screening framework whose decisions are supported by lesion-aware explanatory evidence.

Machine learning (ML) is essential in modern technology, driving complex data-driven decisions. By 2025, daily data generation will exceed 463 exabytes, increasing ML’s influence and ethical risks of data exploitation and discrimination. The European Union’s Artificial Intelligence Act highlights the need for ethical AI solutions.

The term metaverse refers to a persistent, virtual, three-dimensional environment where individuals may communicate, engage, and collaborate. One of the most multifaceted and challenging use cases of the metaverse is education, where educators and learners may require multiple technical, social, psychological, and interaction instruments to accomplish their learning objectives. While the characteristics of the metaverse might nicely fit the problem’s needs, our research points out a noticeable lack of knowledge into (1) the specific requirements that an educational metaverse should actually fulfill to let educators and learners successfully interact towards their objectives and (2) how to design an appropriate educational metaverse for both educators and learners. In this paper, we aim to bridge this knowledge gap by proposing SENEM, a novel software engineering-enabled educational metaverse. We first elicit a set of functional requirements that an educational metaverse should fulfill. In this respect, we conduct a literature survey to extract the currently available knowledge on the matter discussed by the research community, and afterward, we assess and complement such knowledge through semi-structured interviews with educators and learners. Upon completing the requirements elicitation stage, we then build our prototype implementation of SENEM, a metaverse that makes available to educators and learners the features identified in the previous stage. Finally, we evaluate the tool in terms of learnability, efficiency, and satisfaction through a Rapid Iterative Testing and Evaluation research approach, leading us to the iterative refinement of our prototype. Through our survey strategy, we extracted nine requirements that guided the tool development that the study participants positively evaluated. Our study reveals that the target audience appreciates the elicited design strategy. Our work has the potential to form a solid contribution that other researchers can use as a basis for further improvements.

Software engineering is a human-centered activity involving various stakeholders with different backgrounds that have to communicate and collaborate to reach shared objectives. The emergence of conflicts among stakeholders may lead to undesired effects on software maintainability, yet it is often unavoidable in the long run. Community smells, i.e., sub-optimal communication and collaboration practices, have been defined to map recurrent conflicts among developers. While some community smell detection tools have been proposed in the recent past, these can be mainly used for research purposes because of their limited level of usability and user engagement. To facilitate a wider use of community smell-related information by practitioners, we present CADOCS, a client-server conversational agent that builds on top of a previous community smell detection tool proposed by Almarini et al. to (1) make it usable within a well-established communication channel like Slack and (2) augment it by providing initial support to software analytics instruments useful to diagnose and refactor community smells. We describe the features of the tool and the preliminary evaluation conducted to assess and improve robustness and usability.