The dataset includes the results from the Online Survey activity in the framework of the project's "D1.1 Trustworthy Efficient AI for the Cloud-edge Deep Dive". read more of MANOLO_OnlineSurveyResults_Dataset4_20240731_v1

The dataset includes the questions used to collect feedback from the general public through the Online Survey activity in the framework of the project's "D1.1 Trustworthy Efficient AI for the Cloud-edge Deep Dive". read more of MANOLO_OnlineSurveyQuestionnaire_Dataset3_20240731_v1

The dataset includes the input collected from MANOLO partners through the Desk Research activities in the framework of the project's "D1.1 Trustworthy Efficient AI for the Cloud-edge Deep Dive". read more of MANOLO_DeskResearchResults_Dataset2_20240731_v1

The dataset includes the questions used to collect feedback from the MANOLO consortium regarding the project's "D1.1 Trustworthy Efficient AI for the Cloud-edge Deep Dive". read more of MANOLO_DeskResearchQuestionnaire_Dataset1_20240731_v1

Dataset of EEG data acquired during our experiment. Participants had to decide with support from an AI model what is shown in a series of noisy images, either with or without AI explanations. From the EEG data, we can infer how difficult the decision was ... read more of Decision Difficulty EEG Dataset

This recurrent neural network model exploits historical data measured from machine sensors to perform inference on future usage and detect possible future faults in the machine itself. Explainability metrics targets sensor groups and are powered by the SH... read more of GRU + SHAP - Explainable Predictive Maintenance for Irregular Multivariate Time Series

This regression model exploits historical data measured from machine sensors to perform inference on future usage and detect possible future faults in the machine itself. Explainability metrics targets sensor groups and are powered by the SHAP library. read more of Facebook Prophet + SHAP - Explainable Predictive Maintenance for Regular Multivariate Time Series

This app identifies whether an audio file is synthetic or real and uses GradCAM saliency maps to visualize the key audio features that influenced its classification. read more of Audio Deepfake Detection with GradCam Saliency Map Explainability

To boost interpretability with concept vectors, a reverse engineering approach automates concept identification by analyzing the latent space of deep neural networks using Singular Value Decomposition. This framework combines factorization, latent space c... read more of Unique Concept Vectors through Latent Space Decomposition

Learning Visual Explanations for DCNN-Based Image Classifiers Using an Attention Mechanism read more of L-CAM

EU-funded XMANAI project deals with bringing explainable AI to the Industry, and this asset is an example of the models developed during the project. read more of XMANAI Hybrid model

This library lists the outcomes of our research on video summarization and explainable AI-based summarization. read more of Explainable Video Summarization