The design and analysis of pallet setups are essential for ensuring safety of packages transportation. With rising demands in the logistics sector, the development of automated systems utilizing advanced technologies has become increasingly crucial. Moreover, the widespread use of plastic wrapping has motivated researchers to investigate eco-friendly alternatives that still adhere to safety standards. We present a fully controllable and accurate physical simulation system capable of replicating the behavior of moving pallets. It features a 3D graphics-based virtual environment that supports a wide range of configurations, including variable package layouts, different wrapping materials, and diverse dynamic conditions. This innovative approach reduces the need for physical testing, cutting costs and environmental impact while improving measurement accuracy for analyzing pallet dynamics. Additionally, we train a deep neural network to evaluate the rendered videos generated by our simulator, as a crash-test predictor for pallet configurations, further enhancing the system's utility in safety analysis.

This paper presents a complete explainable system that interprets a set of data, abstracts the underlying features and describes them in a natural language of choice. The system relies on two crucial stages: (i) identifying emerging properties from data and transforming them into abstract concepts, and (ii) converting these concepts into natural language. Despite the impressive natural language generation capabilities demonstrated by Large Language Models, their statistical nature and the intricacy of their internal mechanism still force us to employ these techniques as black boxes, forgoing trustworthiness. Developing an explainable pipeline for data interpretation would allow facilitating its use in safety-critical environments like processing medical information and allowing non-experts and visually impaired people to access narrated information. To this end, we believe that the fields of knowledge representation and automated reasoning research could present a valid alternative. Expanding on prior research that tackled the first stage (i), we focus on the second stage, named Concept2Text. Being explainable, data translation is easily modeled through logic-based rules, once again emphasizing the role of declarative programming in achieving AI explainability. This paper explores a Prolog/CLP-based rewriting system to interpret concepts-articulated in terms of classes and relations, plus common knowledge-derived from a generic ontology, generating natural language text. Its main features include hierarchical tree rewritings, modular multilingual generation, support for equivalent variants across semantic, grammar, and lexical levels, and a transparent rule-based system. We outline the architecture and demonstrate its flexibility through some examples capable of generating numerous diverse and equivalent rewritings based on the input concept.

We present a novel methodology for extracting and interpreting feature interactions from tree-based machine learning models using logic programming. While traditional machine learning techniques often act as black boxes, offering limited insight into the role of individual features in classification outcomes, our framework leverages the inherently explainable nature of Answer Set Programming (ASP) to investigate the dependencies among features encoded in decision structures. Rather than extracting explicit rules, we employ an arc-consistency-like reasoning mechanism to constrain feature values in a way that explains the classification in a symbolic and interpretable form. Our approach preserves the original predictive accuracy, while significantly enhancing transparency. We demonstrate the effectiveness of the method on the task of classifying malicious Portable Executable (PE) files, a challenging domain for explainability due to the opaque and low-level nature of its input features. Experimental results highlight the potential of our ASP-based framework to advance explainable AI in cybersecurity contexts.

Background Head and Neck Squamous Cell Carcinoma (HNSCC) presents a significant challenge in oncology due to its inherent heterogeneity. Traditional staging systems, such as TNM (Tumor, Node, Metastasis), provide limited information regarding patient outcomes and treatment responses. There is a need for a more robust system to improve patient stratification. Method In this study, we utilized advanced statistical techniques to explore patient stratification beyond the limitations of TNM staging. A comprehensive dataset, including clinical, radiomic, genomic, and pathological data, was analyzed. The methodology involved correlation analysis of variable pairs and triples, followed by clustering techniques. Results The analysis revealed that HNSCC subpopulations exhibit distinct characteristics, which challenge the conventional one-size-fits-all approach. Conclusion This study underscores the potential for personalized treatment strategies based on comprehensive patient profiling, offering a pathway towards more individualized therapeutic interventions.

In recent years there has been a renewed burst of interest in systems able to textually summarize data, producing natural language text as a description of input data series. Many of the recently proposed approaches to solve the data-to-text task are based on Machine Learning (ML) and ultimately rely on Deep Learning (DL) techniques. This technological choice often prevents the system from enjoying explainability properties. In this paper we outline our ongoing research and present a framework that is ML/DL free and is conceived to be compliant with xAI requirements. In particular we design ASP/Python programs that enable explicit control of the abstraction process, descriptions’ accuracy and relevance handling, and amount of synthesis. We provide a critical analysis of the xAI features that should be implemented and a working proof of concept that addresses crucial aspects in the abstraction of data. In particular we discuss: how to model and output the abstraction accuracy of a concept w.r.t. data; how to identify what to say with controlled synthesis level: i.e., the key descriptive elements to be addressed in the data; how to represent abstracted information by means of visual annotation to charts. The main advantages of such approach are a trustworthy and reliable description, a transparent methodology, logically provable output, and measured accuracy that can control natural language modulation of descriptions.

The biological target identification process, a pivotal phase in the drug discovery workflow, becomes particularly challenging when mutations affect proteins' mechanisms of action. COVID-19 Spike glycoprotein mutations are known to modify the affinity toward the human angiotensin-converting enzyme ACE2 and several antibodies, compromising their neutralizing effect. Predicting new possible mutations would be an efficient way to develop specific and efficacious drugs, vaccines, and antibodies. In this work, we developed and applied a computational procedure, combining constrained logic programming and careful structural analysis based on the Structural Activity Relationship (SAR) approach, to predict and determine the structure and behavior of new future mutants. "Mutations rules" that would track statistical and functional types of substitutions for each residue or combination of residues were extracted from the GISAID database and used to define constraints for our software, having control of the process step by step. A careful molecular dynamics analysis of the predicted mutated structures was carried out after an energy evaluation of the intermolecular and intramolecular interactions using the HINT (Hydrophatic INTeraction) force field. Our approach successfully predicted, among others, known Spike mutants.

Pallets are critical components in the logistics of food and beverage products transportation, and their stability is essential to ensure reliability of the handling system as well as safety during road and rail freight. This work aims to develop an innovative solution for evaluating pallet stability very early, ideally during the design phase of the pallet schema and the wrapping format. Differently from other investigations, the goal of our work is to analyse the dynamics of pallets wrapped in an envelope of paper material instead of plastic. By collecting raw video data from an acceleration test bench, and using computer vision and machine learning techniques, we develop a physically realistic multi-body simulation. The simulation is completely virtual and capable to evaluate the stability of the pallet under different configurations and loading conditions.

There is a need to persuade public and private entities to share their currently unexposed bio-data banks by preserving ownership and secrecy. The reason is to make available results that can be obtained by massively exploiting the content of such data by modern machine learning approaches. Digital catalogues of data collections are being provided. However, they are not developed to protect private content that may be shared according to privileges assigned by the owners. Here, we present BIOCHAIN, a data-sharing module which will be the basis for a computational platform aimed at performing federated data analysis. The platform is intended to be used by a consortium of private and public institutions in the field of microbiology. BIOCHAIN makes use of blockchain technology to guarantee fairness among entities of the consortium by allowing them to securely share their data.

This paper presents and discuss an overview of an AI pipeline to analyze the effects of substituting plastic film with Kraft paper in the tertiary packaging, i.e., in the external envelope of a pallet. Since there is no prior knowledge about paper wrapping yet, the goal is to understand the physics of the load unit—wrapped in paper—when subject to horizontal accelerations. This permits to study and analyze its rigidity and robustness to permanent deformations and/or excessive shifting during road or rail freight, to avoid damages and ripping of the envelope. The idea behind our AI pipeline is to virtually simulate such a situation, to precisely identify critical use cases, and eventually suggest a correction in the wrapping format. The first gain in using such an approach is to drastically reduce the number of physical tests needed to build a solid base knowledge about the behavior of Kraft paper enveloping the pallet during motion. The proposed pipeline consists of three phases: (i) data collection from real tests, (ii) modeling of the simulation, fitting relevant parameters between the actual test and the simulated one, and (iii) performing of virtual experiments on different settings, to suggest the best format. Computer vision and machine learning techniques are employed to accomplish these tasks, and preliminary results show encouraging performances of the proposed idea.

The generation of natural language text from data series gained renewed interest among AI research goals.Not surprisingly, the few proposals in the state of the art are based on training some system, in order to produce a text that describes and that is coherent to the data provided as input.Main challenges of such approaches are the proper identification of what to say (the key descriptive elements to be addressed in the data) and how to say: the correspondence and accuracy between data and text, the presence of contradictions/redundancy in the text, the control of the amount of synthesis.This paper presents a framework that is compliant with xAI requirements.In particular we model ASP/Python programs that enable an explicit control of accuracy errors and amount of synthesis, with proven optimal solutions.The text description is hierarchically organized, in a top-down structure where text is enriched with further details, according to logic rules.The generation of natural language descriptions' structure is also managed by logic rules.

This paper provides an overview of the use of Prolog and its derivatives to sustain research and development in the fields of bioinformatics and computational biology. A number of applications in this domain have been enabled by the declarative nature of Prolog and the combinatorial nature of the underlying problems. The paper provides a summary of some relevant applications as well as potential directions that the Prolog community can continue to pursue in this important domain. The presentation is organized in two parts: “small,” which explores studies in biological components and systems, and “large,” that discusses the use of Prolog to handle biomedical knowledge and data. A concrete encoding example is presented and the effective implementation in Prolog of a widely used approximated search technique, large neighborhood search, is presented.

Over the last few years, Artificial Intelligence (AI) has pervaded our lives. As a result, automated tools that “reason” on different scenarios have become more and more common. As this trend continues to grow, it has become necessary to ensure that newly developed tools and technologies can be safely adopted, as demonstrated by the numerous EU regulations. This is especially true when the concept of AI is intertwined with the field of medicine, where every decision may be critical. That is why, in this work, we decided to tackle the problem of automated interpretation of Computed Tomography (CT) scans using an explainable approach. In fact, while several methods based on Machine Learning (ML) are currently available, these are still outperformed by medical doctors and provide answers that cannot be traced back to a logical deduction. This paper presents CARING, a new methodology based on Answer Set Programming (ASP), which returns reliable, easy-to-program and explainable interpretations of CT scans. In particular, CARING makes use of transparent technologies in order to handle medical knowledge provided either by experts or by verified ontologies. This proof of concept shows that Logic Programming is a mature technology that can match the newest challenges in the xAI field.

Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.

Automated segmentation of CT scans is the first step in the pipeline for the interpretation and identification of potential pathologies in human organs. Several methods based on Machine Learning are currently available, even if their precision is still outperformed by medical doctors. In this field there are some intrinsic limitations to ML approaches, such as the cost and time to acquire high quality annotated scans for training; a considerably high variability of organs morphology due to age, health conditions, genetics; acquisition noise. This paper outlines a new methodology based on Answer Set Programming, which returns reliable, easy-to-program and explainable interpretations. In particular, we focus on the CT scan analysis and retrieval of tree-like structure, corresponding to main blood vessels (arteries) arrangement. The structure is compared to the knowledge base of vessels contained in anatomy text-books. The mapping of vessels names is computed by an ASP program. This preliminary step produces a robust input to a reasoner for the multi-organ labeling and localization problem.

The paper presents a new model for single channel images low-level interpretation. The image is decomposed into a graph which captures a complete set of structural features. The description allows to accurately identify every edge location and its correct connectivity. The key features of the method are: vector description of the edges, subpixel precision, and parallelism of the underlying algorithm. The methodology outperforms classical and state of the art edge detectors at both conceptual and experimental levels. It also enables graph based algorithms for higher-level feature extraction. Any image processing pipeline can benefit from such results: e.g., controlled denoising, edge preserving filtering, upsampling, compression, vector and graph based pattern matching, neural network training.

This article presents a multi-GPU implementation of a Finite-Volume solver on a multi-resolution grid. The implementation completely offloads the computation to the GPUs and communications between different GPUs are implemented by means of the Message Passing Interface (MPI) API. Different domain decomposition techniques have been considered and the one based on the Hilbert Space Filling Curves (HSFC) showed optimal scalability. Several optimizations are introduced: One-to-one MPI communications among MPI ranks are completely masked by GPU computations on internal cells and a novel dynamic load balancing algorithm is introduced to minimize the waiting times at global MPI synchronization barriers. Such algorithm adapts the computational load of ranks in response to dynamical changes in the execution time of blocks and in network performances; Its capability to converge to a balanced computation has been empirically shown by numerical experiments. Tests exploit up to 64 GPUs and 83M cells and achieve an efficiency of 90 percent in weak scalability and 85 percent for strong scalability. The framework is general and the results of the article can be ported to a wide range of explicit 2D Partial Differential Equations solvers.

An epistemic logic program is a set of rules written in the language of Epistemic Specifications, an extension of the language of answer set programming that provides for more powerful introspective reasoning through the use of modal operators K and M. We propose adding a new construct to Epistemic Specifications called a world view constraint that provides a universal device for expressing global constraints in the various versions of the language. We further propose the use of subjective literals (literals preceded by K or M) in rule heads as syntactic sugar for world view constraints. Additionally, we provide an algorithm for finding the world views of such programs. 2012 ACM Subject Classification Software and its engineering → Constraints

This paper presents a novel methodology for estimating the unknown discharge hydrograph at the entrance of a river reach when no information is available. The methodology couples an optimization procedure based on the Bayesian geostatistical approach (BGA) with a forward self-developed 2-D hydraulic model. In order to accurately describe the flow propagation in real rivers characterized by large floodable areas, the forward model solves the 2-D shallow water equations (SWEs) by means of a finite volume explicit shock-capturing algorithm. The two-dimensional SWE code exploits the computational power of graphics processing units (GPUs), achieving a ratio of physical to computational time of up to 1000. With the aim of enhancing the computational efficiency of the inverse estimation, the Bayesian technique is parallelized, developing a procedure based on the Secure Shell (SSH) protocol that allows one to take advantage of remote high-performance computing clusters (including those available on the Cloud) equipped with GPUs. The capability of the methodology is assessed by estimating irregular and synthetic inflow hydrographs in real river reaches, also taking into account the presence of downstream corrupted observations. Finally, the procedure is applied to reconstruct a real flood wave in a river reach located in northern Italy.