Pixel-level feature attributions are an important tool in eXplainable AI for Computer Vision (XCV), providing visual insights into how image features influence model predictions. The Owen formula for hierarchical Shapley values has been widely used to interpret machine learning (ML) models and their learned representations. However, existing hierarchical Shapley approaches do not exploit the multiscale structure of image data, leading to slow convergence and weak alignment with the actual morphological features. Moreover, no prior Shapley method has leveraged data-aware hierarchies for Computer Vision tasks, leaving a gap in model interpretability of structured visual data. To address this, this paper introduces ShapBPT, a novel data-aware XCV method based on the hierarchical Shapley formula. ShapBPT assigns Shapley coefficients to a multiscale hierarchical structure tailored for images, the Binary Partition Tree (BPT). By using this data-aware hierarchical partitioning, ShapBPT ensures that feature attributions align with intrinsic image morphology, effectively prioritizing relevant regions while reducing computational overhead. This advancement connects hierarchical Shapley methods with image data, providing a more efficient and semantically meaningful approach to visual interpretability. Experimental results confirm ShapBPT’s effectiveness, demonstrating superior alignment with image structures and improved efficiency over existing XCV methods, and a 20-subject user study confirming that ShapBPT explanations are preferred by humans.

Data augmentation represents an opportunity for Artificial Intelligence (AI) applications, as it aims at creating new synthetic data based on an existing baseline. In this paper, we present a new evaluation framework for Generative Adversarial Networks (GANs), a data augmentation technique, in multivariate data classification contexts. The goal is not limited to assessing the performance variations obtained through GANs, but also to inspect results with explainable AI (XAI) tools, understanding how GANs work and, finally, exploiting them to discover new knowledge. To this aim, we adopt the Logic Learning Machine (LLM) for performance assessment and rule extraction, and introduce a new measure of rule similarity to compare different artificial datasets. We apply the methodology on two case studies , activity recognition and physical fatigue detection, confirming that GANs can help in overcoming limitations of original datasets and lead to new discoveries.

We investigate the use of a stratified sampling approach for LIME Image, a popular model-agnostic explainable AI method for computer vision tasks, in order to reduce the artifacts generated by typical Monte Carlo sampling. Such artifacts are due to the undersampling of the dependent variable in the synthetic neighborhood around the image being explained, which may result in inadequate explanations due to the impossibility of fitting a linear regressor on the sampled data. We then highlight a connection with the Shapley theory, where similar arguments about undersampling and sample relevance were suggested in the past. We derive all the formulas and adjustment factors required for an unbiased stratified sampling estimator. Experiments show the efficacy of the proposed approach.

The Internet of Things is promising as it drives the datafication of our everyday life and thus, leverages synergies between originally considered “dead” things and enables them to proactively serve humans. IoT5.0, an Artificial Intelligence assisted Internet of Things, could even more benefit society, as the devices could even learn how to provide more value. But the ubiquitous connectivity comes at a cost. Security levels have to rise tremendously to ensure a network stays secure and safe for humans. This additional effort often is a burden for small and medium sized enterprises as the complexity and security demands of such systems rise faster than available resources. Consequently., RESILIENT TRUST focuses on end-to-end security of IoT processing chains with a focus on strong exploitation for SMEs. Moreover, RESILIENT TRUST will address and significantly mitigate the major risks to enable IoT 5.0. That way., this project will be a driver for sustainable development and the generation of convenience and wealth. A solution is proposed to ensure end-to-end security by boosting RESILIENCE and TRUST along different key supply chains of IoT device.

Data augmentation is a widespread innovative technique in Artificial Intelligence: it aims at creating new synthetic data given an existing real baseline, thus allowing to overcome the issues arising from the lack of labelled data for proper training of classification algorithms. Our paper focuses on how a common data augmentation methodology, the Generative Adversarial Networks (GANs), which is widespread for images and timeseries data, can be also applied to generate multivariate data. We propose a novel scheme for GANs evaluation, based on the performance of an explainable AI (XAI) algorithm and an innovative definition of rule similarity. In particular, we will consider an application dealing with the augmentation of Inertial Movement Units (IMU) data for physical fatigue monitoring in two age subgroups (under and over 40 years old) of the original data. We will show how our innovative rule similarity metric can drive the selection of the best fake dataset among a set of different candidates, corresponding to different GAN training runs.

Manufacturers of automated systems and their components have been allocating an enormous amount of time and effort in R&D activities. This effort translates into an overhead on the V&V (verification and validation) process making it time-consuming and costly. In this paper, we present an ECSEL JU project (VALU3S) that aims to evaluate the state-of-the-art V&V methods and tools, and design a multi-domain framework to create a clear structure around the components and elements needed to conduct the V&V process. The main expected benefit of the framework is to reduce time and cost needed to verify and validate automated systems with respect to safety, cyber-security, and privacy requirements. This is done through identification and classification of evaluation methods, tools, environments and concepts for V&V of automated systems with respect to the mentioned requirements. To this end, VALU3S brings together a consortium with partners from 10 different countries, amounting to a mix of 25 industrial partners, 6 leading research institutes, and 10 universities to reach the project goal.

The objective of the FRACTAL project is to create a new approach to reliable edge computing. The computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes). The cognitive skill will be given by an internal and external architecture that allows forecasting its internal performance and the state of the surrounding world. The node will have the capability of learning how to improve its performance against the uncertainty of the environment. New industrial functions will flourish through the created space of the cognitive system. Cognitive advantages are brought to a resilient edge and a computing paradigm that lay down between the physical world and the cloud.

BACKGROUND: Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer was evaluated using a set of eight publicly available gene expression databases for cancer diagnosis. LLM accuracy was assessed by summary ROC curve (sROC) analysis and estimated by the area under an sROC curve (sAUC). Its performance was compared in cross validation with that of standard supervised methods, namely: decision tree, artificial neural network, support vector machine (SVM) and k-nearest neighbor classifier. RESULTS: LLM showed an excellent accuracy (sAUC = 0.99, 95%CI: 0.98-1.0) and outperformed any other method except SVM. CONCLUSIONS: LLM is a new powerful tool for the analysis of gene expression data for cancer diagnosis. Simple rules generated by LLM could contribute to a better understanding of cancer biology, potentially addressing therapeutic approaches.

The Safe Cooperating Cyber-Physical Systems using Wireless Communication (SafeCop) project addresses safety-related issues in cooperating cyber-physical systems. These systems, characterised by wireless communications, multiple stakeholders, and variable operating environments, are called Cooperative Open Cyber-Physical Systems (CO-CPS). CO-CPSs can successfully address several societal challenges -- cooperative vehicles have been shown to reduce fuel consumption as well as the number of accidents. A vehicle-to-infrastructure (V2I) cooperation for the traffic management scenario is therefore considered as a key use cases of SafeCop. In this paper, we outline the V2I traffic management scenario, assess the research goals that arise from it, and provide an overview of the architecture of the demonstrator, as well as a roadmap for its development and evaluation.

BACKGROUND: Tumour markers are standard tools for the differential diagnosis of cancer. However, the occurrence of nonspecific symptoms and different malignancies involving the same cancer site may lead to a high proportion of misclassifications. Classification accuracy can be improved by combining information from different markers using standard data mining techniques, like Decision Tree (DT), Artificial Neural Network (ANN), and k-Nearest Neighbour (KNN) classifier. Unfortunately, each method suffers from some unavoidable limitations. DT, in general, tends to show a low classification performance, whereas ANN and KNN produce a "black-box" classification that does not provide biological information useful for clinical purposes. METHODS: Logic Learning Machine (LLM) is an innovative method of supervised data analysis capable of building classifiers described by a set of intelligible rules including simple conditions in their antecedent part. It is essentially an efficient implementation of the Switching Neural Network model and reaches excellent classification accuracy while keeping low the computational demand. LLM was applied to data from a consecutive cohort of 169 patients admitted for diagnosis to two pulmonary departments in Northern Italy from 2009 to 2011. Patients included 52 malignant pleural mesotheliomas (MPM), 62 pleural metastases (MTX) from other tumours and 55 benign diseases (BD) associated with pleurisies. Concentration of three tumour markers (CEA, CYFRA 21-1 and SMRP) was measured in the pleural fluid of each patient and a cytological examination was also carried out. The performance of LLM and that of three competing methods (DT, KNN and ANN) was assessed by leave-one-out cross-validation. RESULTS: LLM outperformed all other considered methods. Global accuracy was 77.5% for LLM, 72.8% for DT, 54.4% for KNN, and 63.9% for ANN, respectively. In more details, LLM correctly classified 79% of MPM, 66% of MTX and 89% of BD. The corresponding figures for DT were: MPM = 83%, MTX = 55% and BD = 84%; for KNN: MPM = 58%, MTX = 45%, BD = 62%; for ANN: MPM = 71%, MTX = 47%, BD = 76%. Finally, LLM provided classification rules in a very good agreement with a priori knowledge about the biological role of the considered tumour markers. CONCLUSIONS: LLM is a new flexible tool potentially useful for the differential diagnosis of pleural mesothelioma.

The problem of reconstructing the and-or expression of a partially defined positive Boolean function (pdpBf) is solved by adopting a novel algorithm, denoted by LSC, which combines the advantages of two efficient techniques, Logical Analysis of Data (LAD) and Shadow Clustering (SC). The kernel of the approach followed by LAD consists in a breadth-first enumeration of all the prime implicants whose degree is not greater than a fixed maximum d. In contrast, SC adopts an effective heuristic procedure for retrieving the most promising logical products to be included in the resulting and-or expression. Since the computational cost required by LAD prevents its application even for relatively small dimensions of the input domain, LSC employs a depth-first approach, with asymptotically linear memory occupation, to analyze the prime implicants having degree not greater than d. In addition, the theoretical analysis proves that LSC presents almost the same asymptotic time complexity as LAD. Extensive simulations on artificial benchmarks validate the good behavior of the computational cost exhibited by LSC, in agreement with the theoretical analysis. Furthermore, the pdpBf retrieved by LSC always shows a better performance, in terms of complexity and accuracy, with respect to those obtained by LAD.

Discretization is a fundamental phase for many classification algorithms: it aims at finding a proper set of cutoffs that subdivide a continuous domain into homogeneous intervals; the points in each interval should have a high probability of belonging to the same class. This paper proposes two different approaches for discretization: the first one consists in retrieving the optimal set of separation points through the solution of a proper linear programming problem. Since the optimal solution may require an excessive computational burden, an alternative technique, based on the iterative addition of separation points, is described. The greedy algorithm is evaluated on some artificial datasets and compared with other well-known discretization techniques such as EntMDL. The results of the simulations show the good performances of the novel algorithm in terms both of accuracy of the solution and of computational effort required for its generation.