275 pubblicazioni classificate nel seguente modo:

Token Reduction in Vision Transformers via Discrete Wavelet Decomposition
Proc. of the International Conferenze on Pattern Recognition (ICPR'26)
Autore/i: Buratti, C.; Marchetti, M.; Parlapiano, F.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/354992

A Hybrid FinBERT-GAT-Based Framework for Predicting the ESG Score of a Company
Atti del Trentaquattresimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'26)
Autore/i: Baiocco, S.; Bonifazi, G.; Buratti, C.; Parlapiano, F.; Quaglieri, G.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/355652

Efficient Path-Coherent Retrieval via Branch Pruning in KG-RAG
CEUR Workshop Proceedings
Autore/i: Buratti, C.; Marchetti, M.; Parlapiano, F.; Quaglieri, G.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/356735

Generating Masks from Similarity-based Graphs for Vision Transformer Explainability
NEURAL COMPUTING & APPLICATIONS
Autore/i: Marchetti, M.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/360212

Leveraging Adversarial Attacks to Generate Multiple-Choice Quizzes Robust Against Large Language Models
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
Autore/i: Bonifazi, G.; Buratti, C.; Marchetti, M.; Parlapiano, F.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/354212

PIF-MN: a framework for investigating perturbations and their effects in multilayer networks
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Di Gaetano, A.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/352112

Graph-Enhanced Retrieval Augmented Generation Through Cross-Attention and Contrastive Learning
Proc. of the IEEE World Congress on Computational Intelligence (WCCI'26)
Autore/i: Bonifazi, G.; Buratti, C.; Marchetti, M.; Parlapiano, F.; Quaglieri, G.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/354532

An Ego Network-Based Approach to Fine-Tune Large Language Models Using Knowledge Graphs
INFORMATION SCIENCES
Autore/i: Amelio, A.; Buratti, C.; Marchetti, M.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/359412

Exploiting Knowledge Graph Communities to Fine-Tune Large Language Models
EXPERT SYSTEMS WITH APPLICATIONS
Autore/i: Amelio, A.; Buratti, C.; Marchetti, M.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: Since the introduction of GPT-2, Large Language Models (LLMs) have proven to be able to handle various tasks with impressive performance. However, they sometimes generate incorrect output or even hallucinations. To overcome this problem, many researchers have investigated the possibility of integrating external factual knowledge, such as that encoded in Knowledge Graphs (KGs), into LLMs. Although there are many approaches in the existing literature that integrate KGs and LLMs in different ways, few of them use KGs to fine-tune LLMs, and none of them systematically use KG substructures. In this paper, we propose CoFine (Community-Based Fine-Tuner), an approach to fine-tune an LLM using the communities of a KG. CoFine works as follows: it first divides the KG into communities, each of which contains a homogeneous portion of the knowledge expressed by the KG. It then uses these communities to fine-tune the LLM. This way of proceeding allows LLM fine-tuning to focus on specific homogeneous information contained in the KG expressed by each community. CoFine allows the LLM to achieve a very high accuracy in knowledge completion tasks. This is evidenced by comparisons between CoFine and a baseline LLM fine-tuning approach, which showed that our approach achieves better results for all metrics considered with several KG.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/347512

A Complex Network-Based Approach for Detecting and Characterizing Power Neurons in Drosophila
NEUROINFORMATICS
Autore/i: Corradini, E.; Parlapiano, F.; Terracina, G.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Abstract: Connectome analysis investigates the connections in the brain to understand how brain regions communicate with each other and how brain structure relates to its function. In recent years, researchers have reconstructed the structural connectome of several organisms, the most complex being Drosophila melanogaster. Two research groups have reconstructed the larval and adult connectomes of this organism and have applied network analysis to learn more about the Drosophila brain and its behavior. In this paper, we aim to continue the work of these two research groups at the larval and adult stages. Specifically, we construct several derived network representations and define a set of techniques that use the main concepts and measures of complex network analysis to extract new knowledge about Drosophila connectomes at the larval and adult stages. First, we conduct an Exploratory Data Analysis on the larval and adult connectomes to detect similarities and differences between them. Then, we define the concept of power neurons and illustrate an approach to detect them. Next, we demonstrate that power neurons represent a limited set of highly interconnected neurons that form a backbone and that, given their peculiar connectivity properties, may play a strategic role in brain functions. Finally, we extract a set of connectome motifs that allow us to learn about various features characterizing power neurons. We demonstrate that complex network analysis can allow the extraction of relevant knowledge about connectomes. Furthermore, we show that a very small number of power neurons can strongly influence all other neurons in the Drosophila brain.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/352332

Reinforcement Learning Meets Logic Programming : Towards Explainable AI
Logics in Artificial Intelligence (JELIA 2025)
Autore/i: Caroprese, L.; Zumpano, E.; Ursino, D.
Editore: Springer
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper introduces a neuro-symbolic framework designed to predict and explain subsequent facts from current observations. Facts are generated through causal relationships, which can be modeled by a set of propositional logic rules representing the domain knowledge. However, these rules remain unknown to the agent. By observing the facts, the agent constructs an approximation of them, which is then used to predict and explain new facts. The proposed framework can learn and adapt to different environments modeled by various forms of logic programs, also handling negation and recursion. Most notably, it can handle dynamic environments whose structure evolves over time. In these scenarios, the agent modifies its understanding of the environment to capture new observations, guaranteeing that its model of the domain knowledge remains up-to-date. To achieve this goal, our approach leverages the A2C (Advantage Actor-Critic) reinforcement learning algorithm. This choice allows us to integrate reinforcement learning principles into our logic framework. Through this research, we aspire to contribute to the development of explainable neuro-symbolic Artificial Intelligence systems in dynamic environments.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/347172

A Multiclass Graph Neural Network-Based Framework for Predicting Post Engagement in Social Media
JOURNAL OF INFORMATION SCIENCE
Autore/i: Arazzi, M.; Cotogni, M.; Nocera, A.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In this article, we present PostGage, a new framework that identifies the most suitable publishers of target content on social media, based on the level of user engagement they are expected to generate. PostGage uses a network-based data model that considers the characteristics of publishers, as well as the degree to which the semantic content and lifespan of the posts to be published overlap with those of the posts already published. Leveraging this data model, PostGage builds a multinomial classifier based on a graph neural network (GNN). The alignment between the graph-based data model and the GNN technology allows a precise classification of publishers based on their potential to generate user engagement for target content. This feature enables a more precise definition of publishing strategies on social media, as it allows the selection of the most promising publishers for a given post. The network-based model, the post ageing mechanism and the adoption of the GNN as a multiclass classifier represent PostGage’s key contributions. We also present a series of tests performed on X that demonstrate the high quality of PostGage’s results (e.g. 92% precision in predicting high-engagement posts), establishing it as an invaluable tool for various applications, including marketing and social media management.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/349512

Behavioral Engagement in VR-Based Sign Language Learning: Visual Attention as a Predictor of Performance and Temporal Dynamics
MULTIMODAL TECHNOLOGIES AND INTERACTION
Autore/i: Traini, D.; Alcalde-Llergo, J. M.; Buenestado-Fernández, M.; Ursino, D.; Yeguas-Bolívar, E.
Classificazione: 1 Contributo su Rivista
Abstract: Understanding how learners engage with immersive sign language training environments is essential for advancing virtual reality-based education and inclusion. This study analyzes behavioral engagement in SONAR, a virtual reality application designed for sign language training and validation. We focus on three automatically derived engagement indicators (Visual Attention (VA), Video Replay Frequency (VRF), and Post-Playback Viewing Time (PPVT)) and examine their relationship with learning performance in a sample of 117 university students. Participants completed a self-paced Training phase with 12 sign language instructional videos, followed by a Validation quiz assessing retention. We employed Pearson correlation analysis to examine the relationships between engagement indicators and quiz performance, followed by binomial Generalized Linear Model (GLM) regression to assess their joint predictive contributions. Additionally, we conducted temporal analysis by aggregating moment-to-moment VA traces across all learners to characterize engagement dynamics during the learning session. Results show that VA exhibits a strong positive correlation with quiz performance (r = 0.76), followed by PPVT (r = 0.66), whereas VRF shows no meaningful association. A binomial GLM confirms that VA and PPVT are significant predictors of learning success, jointly explaining a substantial proportion of performance variance (pseudo-R2 = 0.83). Going beyond outcome-oriented analysis, we characterize temporal engagement patterns by aggregating moment-to-moment VA traces across all learners. The temporal profile reveals distinct attention peaks aligned with informationally dense segments of both training and validation videos, as well as phase-specific engagement dynamics, including initial acclimatization, oscillatory attention cycles during learning, and pronounced attentional peaks during assessment. Together, these findings highlight the central role of sustained and strategically allocated visual attention in VR-based sign language learning and demonstrate the value of behavioral trace data for understanding and predicting learner engagement in immersive environments.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/353772

A Multilayer Network-Based Framework for Handling and Comparing User Histories in X
JOURNAL OF INFORMATION SCIENCE
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In this article, we propose a framework that uses a multimodal multilayer network to build, manage and compare User Histories in X. A User History not only considers the contents of interest to the user, as is generally the case with a User Profile. In fact, it also records the set of interactions she has made with contents, the timestamps when they happened, and, ultimately, the history of her actions and behaviour. This provides a more comprehensive view of the user, her preferences and needs and paves the way to a number of additional applications. Most of them need to compare two User Histories to calculate their similarity degree. Our framework proposes two approaches, one naive and one refined, to perform this task. We also mention an application that, along with others already proposed in the literature, can greatly benefit from User Histories with the aim of fostering people inclusiveness. Finally, we describe a set of experiments we conducted to evaluate the goodness of the proposed framework
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/333812

Analyzing the dynamics of user influence in Threads
CEUR Workshop Proceedings
Autore/i: Bonifazi, G.; Buratti, C.; Corradini, E.; Marchetti, M.; Parlapiano, F.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: One of the most common analyses in social networks concerns power users (also called influencers, lead users, influential users, etc.), i.e. users who play a crucial role in the dissemination of information in a social platform. In this paper, we want to make a double contribution to this line of research by proposing a new definition of power users that takes into account the four main centralities of Social Network Analysis and then applying it to Threads, a social platform that is still little studied by social network analysts because of its young age.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/342714

A Novel Framework for Evaluating Polarization in Online Social Networks
BIG DATA AND COGNITIVE COMPUTING
Autore/i: Buratti, C.; Marchetti, M.; Parlapiano, F.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In online communities, polarization refers to the phenomenon in which individuals become more divided and extreme in their opinions due to their exposure to specific content. In this paper, we present a network-based framework for evaluating polarization levels in Online Social Networks (OSNs). Starting from a dataset of comments, our framework creates a network of user interactions and leverages the Louvain algorithm, the Rao’s Quadratic Entropy, and ego networks to assess the polarization level of communities and the most influential users. To test our framework, we leveraged a dataset of tweets about climate change. After performing Extraction, Transformation and Loading activities on the dataset, we evaluated its labels, identified communities, and analyzed their polarization level and that of the most influential users. We also analyzed the ego networks of believers and deniers and the aggressiveness of the corresponding tweets. Our analysis revealed the existence of polarized communities and homophily among the most influential users. It also showed that the type of communication used to disseminate information influences the polarization level of both communities and individual users. These results demonstrate our framework’s ability to support the polarization analysis in OSNs.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/346933

Are Large Language Models Better Peer-Reviewers Than Humans? An Early Investigation on OpenReview
Proc. of the Italian Conference on Big Data and Data Science (ITADATA'25)
Autore/i: Bonifazi, G.; Buratti, C.; Marchetti, M.; Parlapiano, F.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In recent years, Large Language Models (LLMs) have often been used by paper reviewers, despite this practice being generally prohibited. This has raised, and continues to raise, issues concerning ethics, review reliability, and the risk of review manipulation. Indeed, several arXiv preprints were recently discovered to contain invisible, LLM-targeted instructions designed to persuade an AI reviewer to yield a positive review. In this paper, we propose a systematic analysis of LLMs’ review capabilities in this complex and evolving scenario. In particular, we want to address two research questions: (i) How can LLM ratings be compared with human ratings?, and (ii) Can hidden positive prompts injected in a manuscript alter an LLM’s generated review? To address these questions, we created a dataset of 400 papers from OpenReview. For each paper, this dataset contains human reviews and scores already present in OpenReview, as well as reviews performed by three state-of-the-art LLMs, added by us. Our results show that human reviewers assign higher and more widely dispersed scores that clearly distinguish accepted and rejected papers. In contrast, LLM ratings cluster close to their mean value, blurring the distinction between accepted and rejected papers. Furthermore, a negative prompt given by the reviewer makes the LLM lower its scores, while a hidden positive prompt injected by the author often fails to raise scores, and sometimes triggers even lower scores, if detected by the LLM. These results reveal both the potential and fragility of delegating peer review tasks to LLMs.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/346932

An IoE-based Framework Supporting Human-Centric Industry
2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)
Autore/i: Arazzi, M.; Belli, A.; Cusano, C.; Esposito, M.; Facchinetti, T.; Ferretti, M.; Galimberti, G.; Sciarroni, M. M.; Napoletano, P.; Nocera, A.; Pierleoni, P.; Storti, E.; Ursino, D.
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Industry 5.0 envisions manufacturing systems that are human-centric, sustainable, and resilient. In this context, the Internet of Everything (IoE) enables integration of devices, people, and processes into a unified digital ecosystem. This paper presents a modular, semantically enriched framework that supports this transition by managing heterogeneous data sources—such as IoT sensors, wearable devices, and smart objects—through a layered architecture. The platform enables real-time data stream processing, semantic interoperability, and secure, context-aware access. Anomaly detection is enabled through a privacy-preserving mechanism based on behavioral fingerprinting and federated learning. The platform supports immersive human-machine interaction via gesture recognition, empowering workers to control and interact with industrial systems. Use cases demonstrate the system’s ability to support gesture-based control and intelligent monitoring, highlighting its potential to enhance adaptability, security, and worker empowerment in Industry 5.0 environments.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/353632

Efficient Token Pruning in Vision Transformers Using an Attention-Based Multilayer Network
EXPERT SYSTEMS WITH APPLICATIONS
Autore/i: Marchetti, M.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: Vision Transformers (ViTs), although very successful, have a major limitation to overcome, namely the need for significant computational resources to use them. Several approaches have been proposed to limit the resources required to work with ViTs, aiming at pruning the data provided in input to them. In this paper, we propose Token Reduction via an Attention-based Multilayer network (TRAM), the first approach that achieves this goal using a multilayer network-based representation of the attention matrices. TRAM can work with most ViTs without the need for fine-tuning. It makes several contributions to the literature in this research area; in particular, it is characterized by: (i) a new representation of ViTs based on a multilayer network; (ii) a new approach to evaluate the relevance of tokens based on a new centrality measure computed on the multilayer network; and (iii) an approach to reduce the number of tokens based on this centrality measure. We have validated TRAM by comparing it with several state-of-the-art approaches during an extensive experimental campaign carried out on different image datasets. The results obtained demonstrate not only the efficiency but also the effectiveness of TRAM in reducing the computational load of ViTs while still allowing them to provide accurate results
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/342374

Explaining Vision Transformers Through Similarity-based Graphs
Proceedings of the International Joint Conference on Neural Networks
Autore/i: Marchetti, M.; Traini, D.; Ursino, D.; Virgili, L.
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Vision Transformers (ViTs) have gained recognition in computer vision due to their outstanding performance. Despite their success, the explainability of ViT outputs is still a challenging issue. To address it, we propose a novel explainability method that leverages image patch embeddings from each attention layer of a ViT to construct similarity graphs. The latter are used to generate binary masks by exploring paths starting from specific patches. The masks from all layers are then aggregated into a comprehensive heatmap using the coverage bias formula. We tested our method on two Vision Transformer architectures (ViT-Base and DeiT-Base) and a subset of the ImageNet validation set. Using Insertion and Deletion metrics, we demonstrate the effectiveness of our proposed method compared to similar ones in the literature. Finally, we include a qualitative analysis that shows the capabilities of our method to make ViTs more interpretable.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/342492

Integrating Gradient and Mask-based Approaches for Vision Transformer Explainability
Proceedings of the International Joint Conference on Neural Networks
Autore/i: Marchetti, M.; Traini, D.; Ursino, D.; Virgili, L.
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Vision Transformers (ViTs) have demonstrated outstanding performance across different computer vision tasks thanks to their self-attention mechanism that captures long-range dependencies effectively. However, the inherent complexity of ViTs presents significant challenges in explaining their outputs, which is fundamental in safety-critical domains. To tackle the challenge of explaining ViT outputs, this paper presents GradMask, a novel method that integrates gradients into the mask generation process to create explanation heatmaps. GradMask uses the query, key, and value matrices from each attention layer and computes their gradients with respect to a target class. Afterward, it uses these gradients to generate binary masks, which are then weighted by the corresponding ViT's confidence scores. Finally, it combines the weighted masks to generate the resulting heatmap. Experimental evaluations on an ImageNet subset with ViT and DeiT (Data-efficient Image Transformer) architectures show that GradMask achieves competitive performance according to standard explainability metrics, such as Insertion, Deletion, and Pointing Game. A hyperparameter analysis confirms the high computational efficiency of GradMask, while an ablation study highlights the importance of combining gradients and masks for the generation of the explanation heatmap. Finally, a qualitative analysis shows the improved explainability of GradMask compared to existing methods, making it a promising approach for understanding ViTs.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/342493

A Framework for Investigating Discording Communities on Social Platforms
ELECTRONICS
Autore/i: Cauteruccio, F.; Corradini, E.; Marchetti, M.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In recent years, polarization on social media has risen significantly. Social platforms often feature a range of topics that give rise to communities of users with diametrically opposed views, who tend to avoid engaging with others having different perspectives. We call these types of communities “diverging communities”. Examples include communities of supporters and skeptics of climate change or COVID-19 vaccines. In this paper, we aim to investigate this phenomenon. To do so, we first propose a formal definition of discording communities. We then present a framework for investigating the behavior of users of discording communities on a social platform. Our framework is general in that it can be adapted to any social platform where users discuss a topic that polarizes them into communities with diametrically opposed viewpoints rejecting confrontation. Our framework considers not only the structure of communities but also the content of the messages posted by their users. Finally, it can also handle the temporal evolution of the polarization level of both communities and their users. In addition to proposing a formal definition of diverging communities and presenting our framework, we illustrate the results of an extensive experimental campaign carried out on two case studies involving Reddit and X and show how our framework is able to identify a number of features that distinguish the users of one diverging community from the users of the other
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/340078

A Linguistics-Based Approach to Refining Automatic Intent Detection in Conversational Agent Design
INFORMATION SCIENCES
Autore/i: Ferrera, Alessandra; Mezzotero, Giulio; Ursino, Domenico
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, we propose Automatic Intent Detector (AID), a framework for automatic intent detection to facilitate the creation of a conversational agent. AID follows an eight-step process incorporating best practices from the current literature and introducing innovative approaches in certain steps. The most notable innovation within AID is the automatic labeling of clusters, which is based on detailed and sophisticated rules derived from linguistics. These rules focus on morphosyntactic analysis, while also taking into account an aspect of semantic role theory. Furthermore, as for the overall validation of the results obtained, it provides an approach based on the concepts of semantic coherence, variability, and label appropriateness. After describing AID at the technical level, we illustrate the experiments we conducted both on a dataset widely used as benchmark in the literature and on a real corporate dataset. Finally, we present a critical discussion on the results obtained
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/334633

Multiplex Network-Based Representation of Vision Transformers for Visual Explainability
NEURAL COMPUTING & APPLICATIONS
Autore/i: Marchetti, M.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: The enormous growth of artificial intelligence (AI), and deep learning (DL) in particular, has led to the widespread use of these systems in a variety of contexts. One DL model capable of addressing complex computer vision tasks is the vision transformer (ViT). Despite its huge success, the reasoning behind the inferences it makes is often unclear, which poses significant challenges in critical scenarios. In this paper, we propose a new approach called MUltiplex Transformer EXplainer (MUTEX), which aims to explain the inferences made by ViTs. MUTEX combines multiplex network-based representations of attention matrices and mask perturbation approaches to provide insight into the inference process of ViTs. By mapping the attention layers of a ViT into a multiplex network, MUTEX is able to analyze the relationships between different parts of the input image and identify the image patches that most influence the inference process. We tested MUTEX on a subset of ImageNet and on BloodMNIST and compared its performance with that of existing visual explainability approaches. In addition, to assess the robustness and adaptability of MUTEX, we conducted a qualitative analysis, along with a hyperparameter and ablation study, which allowed us to further appreciate its potential in visual explainability of ViT
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/346672

A fine-grained approach for Visual Interpretability of Convolutional Neural Networks
APPLIED SOFT COMPUTING
Autore/i: Amelio, A.; Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, we propose Multilayer network-based Visual Interpreter (MuVI), a framework for visual interpretability of Convolutional Neural Networks (CNNs) based on their mapping into multilayer networks. The peculiarity of MuVI is that it constructs a pixel-level heatmap of the salient parts of an image processed by a CNN, where the importance of each pixel depends on all layers of the CNN and not only on the final ones, as in the existing approaches in the literature. MuVI first maps the CNN into a multilayer network. It then uses this representation to identify the parts of the CNN that most influence the prediction results by extracting those paths within the multilayer network whose nodes correspond to the most active areas of the feature maps. The weight of the paths is given by the sum of the weights of the arcs corresponding to the activations across all feature maps of the CNN; this characteristic allows MuVI to consider all layers of the CNN, not just the last ones. Finally, MuVI constructs the visual interpretability heatmap by selecting the paths with the highest weights. The experimental tests performed show that MuVI is able to achieve very satisfactory results in terms of AUC insertion (0.25), AUC deletion (0.11), % Increase in Confidence (12.32), Average Drop % (51.22), Pointing Game Accuracy (0.28) and Computation time (26.226s). These results, taking all these measures together, are better than those obtained by the classical approaches already proposed in the literature, such as SmoothGrad, Grad-CAM, Grad-CAM++, and RISE. They are also comparable to state-of-the-art approaches in the literature, such as Score-CAM and HSIC
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/338512

Adaptive Patch Selection to Improve Vision Transformers through Reinforcement Learning
APPLIED INTELLIGENCE
Autore/i: Cauteruccio, F.; Marchetti, M.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In recent years, Transformers have revolutionized the management of Natural Language Processing tasks, and Vision Transformers (ViTs) promise to do the same for Computer Vision ones. However, the adoption of ViTs is hampered by their computational cost. Indeed, given an image divided into patches, it is necessary to compute for each layer the attention of each patch with respect to all the others. Researchers have proposed many solutions to reduce the computational cost of attention layers by adopting techniques such as quantization, knowledge distillation and manipulation of input images. In this paper, we aim to contribute to the solution of this problem. In particular, we propose a new framework, called AgentViT, which uses Reinforcement Learning to train an agent that selects the most important patches to improve the learning of a ViT. The goal of AgentViT is to reduce the number of patches processed by a ViT, and thus its computational load, while still maintaining competitive performance. We tested AgentViT on CIFAR10, FashionMNIST, and Imagenette+ (which is a subset of ImageNet) in the image classification task and obtained promising performance when compared to baseline ViTs and other related approaches available in the literature
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/342373

Defining, Detecting, and Characterizing Power Users in Threads
BIG DATA AND COGNITIVE COMPUTING
Autore/i: Bonifazi, G.; Buratti, C.; Corradini, E.; Marchetti, M.; Parlapiano, F.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: Threads is a new social network that was launched by Meta in July 2023 and conceived as a direct alternative to X. It is a unique case study in the social network landscape, as it is content-based like X, but has an Instagram-based growth model, which makes it significantly different from X. As it was launched recently, studies on Threads are still scarce. One of the most common investigations in social networks regards power users (also called influencers, lead users, influential users, etc.), i.e., those users who can significantly influence information dissemination, user behavior, and ultimately the current dynamics and future development of a social network. In this paper, we want to contribute to the knowledge of Threads by showing that there are indeed power users in this social network and then attempt to understand the main features that characterize them. The definition of power users that we adopt here is novel and leverages the four classical centrality measures of Social Network Analysis. This ensures that our study of power users can benefit from the enormous knowledge on centrality measures that has accumulated in the literature over the years. In order to conduct our analysis, we had to build a Threads dataset, as none existed in the literature that contained the information necessary for our studies. Once we built such a dataset, we decided to make it open and thus available to all researchers who want to perform analyses on Threads. This dataset, the new definition of power users, and the characterization of Threads power users are the main contributions of this paper
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/341954

A Complex Network-Based Approach to Detect and Investigate Connectome Motifs in the Larval Drosophila
COMPUTERS IN BIOLOGY AND MEDICINE
Autore/i: Corradini, E.; Parlapiano, F.; Ronci, A.; Terracina, G.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Abstract: Analyzing the connectome of an organism allows us to understand how different areas of its brain communicate with each other and how the structure of the brain is related to its function. Thanks to new technological advances, the connectome of increasingly complex organisms has been reconstructed in recent years. Drosophila melanogaster is currently the most complex organism whose complete connectome is known, both structurally and functionally. In this paper, we aim to contribute to the study of the Drosophila structural connectome by proposing an ad hoc approach for the discovery of network motifs that may be present in it. Unlike previous approaches, which focused on parts of the connectome of complex organisms or the entire connectome of very simple organisms, our approach operates at the whole-brain scale for the most complex organism whose complete connectome is currently known. Furthermore, while previous works have focused on extending existing motif extraction approaches to the connectome case, our approach proposes a motif concept specifically designed for the connectome of an organism. This allows us to find very complex motifs while abstracting them into a few simple types that take into account the brain regions to which the neurons involved belong
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/342494

Extraction and Investigation of Power Neurons in the Caenorhabditis elegans Connectome
CEUR Workshop Proceedings
Autore/i: Buratti, C.; Marchetti, M.; Parlapiano, F.; Terracina, G.; Ursino, D.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Connectome analysis is the study of the brain's neurons and their connections (or synapses) to understand how brain regions communicate with each other and how the brain's structure affects its function. In recent years, researchers have reconstructed the complete connectomes of some organisms and have begun to analyze them in depth. One of the most studied connectomes is that of the nematode Caenorhabditis elegans, whose analysis has led to numerous discoveries. In this paper, we would like to make a contribution in this direction. In particular, by applying concepts and techniques of Complex Network Analysis, we want to test whether there are power neurons in this organism's connectome, i.e., neurons that are particularly important compared to the others. If so, we want to determine their characteristics and whether they form a backbone or not.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/342713

Evaluating status and value assortativity in Threads
Proceedings of the 32nd Symposium on Advanced Database Systems
Autore/i: Bonifazi, G.; Corradini, E.; Ursino, D.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The concept of assortativity in complex networks indicates the preference of a node to relate to other nodes that are somewhat similar. It is possible to think of different forms of similarity between nodes that can give rise to different forms of assortativity. In this paper, along the lines of homophily (of which assortativity can be seen as a special case), we define two categories of assortativity, namely status assortativity and value assortativity. We then show that all definitions of assortativity introduced in the past belong to one of the two categories. Afterwards, we define and evaluate two forms of status assortativity and one form of value assortativity in Threads. Since this social network is relatively new, we could not use existing datasets related to it, and therefore had to build one from scratch, which we now make available to all interested researchers
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/328952

Speeding up Vision Transformers Through Reinforcement Learning
Proceedings of the 32nd Symposium on Advanced Database Systems
Autore/i: Cauteruccio, F.; Marchetti, M.; Traini, D.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In recent years, Transformers have led a revolution in Natural Language Processing, and Vision Transformers (ViTs) promise to do the same in Computer Vision. The main obstacle to the widespread use of ViTs is their computational cost. Indeed, given an image divided into a list of patches, ViTs compute, for each layer, the attention of each patch with respect to all others. In the literature, many solutions try to reduce the computational cost of attention layers using quantization, knowledge distillation, and input perturbation. In this paper, we aim to make a contribution in this setting. In particular, we propose AgentViT, a framework that uses Reinforcement Learning to train an agent whose task is to identify the least important patches during the training of a ViT. Once such patches are identified, AgentViT removes them, thus reducing the number of patches processed by the ViT. Our goal is to reduce the training time of the ViT while maintaining competitive performance
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/328951

A Network Analysis-Based Framework to Understand the Representation Dynamics of Graph Neural Networks
NEURAL COMPUTING & APPLICATIONS
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, we propose a framework that uses the theory and techniques of (Social) Network Analysis to investigate the learned representations of a Graph Neural Network (GNN, for short). Our framework receives a graph as input and passes it to the GNN to be investigated, which returns suitable node embeddings. These are used to derive insights on the behavior of the GNN through the application of (Social) Network Analysis theory and techniques. The insights thus obtained are employed to define a new training loss function, which takes into account the differences between the graph received as input by the GNN and the one reconstructed from the node embeddings returned by it. This measure is finally used to improve the performance of the GNN. In addition to describe the framework in detail and compare it with related literature, we present an extensive experimental campaign that we conducted to validate the quality of the results obtained
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/323291

A model-agnostic, network theory-based framework for supporting XAI on classifiers
EXPERT SYSTEMS WITH APPLICATIONS
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In recent years, the enormous development of Machine Learning, especially Deep Learning, has led to the widespread adoption of Artificial Intelligence (AI) systems in a large variety of contexts. Many of these systems provide excellent results but act as black-boxes. This can be accepted in various contexts, but there are others (e.g., medical ones) where a result returned by a system cannot be accepted without an explanation on how it was obtained. Explainable AI (XAI) is an area of AI well suited to explain the behavior of AI systems that act as black-boxes. In this paper, we propose a model-agnostic XAI framework to explain the behavior of classifiers. Our framework is based on network theory; thus, it is able to make use of the enormous amount of results that researchers in this area have discovered over time. Being network-based, our framework is completely different from the other model-agnostic XAI approaches. Furthermore, it is parameter-free and is able to handle heterogeneous features that may not even be independent of each other. Finally, it introduces the notion of dyscrasia that allows us to detect not only which features are important in a particular task but also how they interact with each other.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/324431

Definition of status and value assortativity in complex networks and their evaluation in Threads
SOCIAL NETWORK ANALYSIS AND MINING
Autore/i: Bonifazi, G.; Corradini, E.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Abstract: The concept of “assortativity” was introduced to denote the preference of a network node to relate to other nodes similar in some way. This concept is closely related to that of homophily, which is used in Social Network Analysis to indicate that users tend to connect with others who have similar characteristics. It is possible to distinguish between two types of homophily, namely status homophily and value homophily. We believe that this distinction can be extended to assortativity. Therefore, in this paper we propose a novel conceptualization of assortativity by introducing status and value assortativity. This dual categorization provides a comprehensive understanding of both structural and content-based dimensions of network interactions. We apply these new assortativity concepts to Threads, a new social platform launched by Meta, and demonstrate their relevance and versatility in the analysis of emerging online platforms. Assortativity has been extensively studied in major social networks and various complex networks, but not in Threads, as this social platform is new. Introducing two types of assortativity and computing them for Threads are two important contributions of this paper. Actually, we provide a third. In fact, in the past literature, we did not find any dataset on Threads that could enable our assortativity analysis. To address this issue, we built a dataset and make it available to the scientific community for future investigations and analyses on Threads.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/337092

Representation, detection and usage of the content semantics of comments in a social platform
JOURNAL OF INFORMATION SCIENCE
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: The analysis of people’s comments in social platforms is a widely investigated topic because comments are the place where people show their spontaneity most clearly. In this article, we present a network-based data structure and a related approach to represent and manage the underlying semantics of a set of comments. Our approach is based on the extraction of text patterns that take into account not only the frequency, but also the utility of the analysed comments. Our data structure and approach are ‘multidimensional’ and ‘holistic’, in the sense that they can simultaneously handle content semantics from multiple perspectives. They are also easily extensible, because additional content semantics perspectives can be easily added to them. Furthermore, our approach is able to evaluate the semantic similarity of two sets of comments. In this article, we also illustrate the results of several tests we conducted on Reddit comments, even if our approach can be applied to any social platform. Finally, we provide an overview of some possible applications of this research.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/296426

A multilayer network-based framework for investigating the evolution and resilience of multimodal social networks
SOCIAL NETWORK ANALYSIS AND MINING
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Giannelli, E.; Marchetti, M.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In recent literature, multilayer networks are increasingly being used to model and manage complex scenarios. One of them well suited to be modeled and managed using multilayer networks is represented by multimodal social networks, in which nodes and edges can be of different types. In fact, in this case, each layer of the multilayer network can be used to model one type of nodes in the multimodal social network while the edges of the latter can be represented by intra-layer edges of the former. In this paper, we want to demonstrate the feasibility of this idea by proposing a framework for analyzing multimodal social networks through the analysis of a corresponding multilayer network. In particular, we use our framework to address two challenging issues, namely the evolution of multilayer networks and their resilience to intra- and inter-layer perturbations. After introducing the model and describing the technical details of our framework, we present several experiments that demonstrate the effectiveness of our proposal.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/324271

Performing Wash Trading on NFTs: is the Game Worth the Candle?
BIG DATA AND COGNITIVE COMPUTING
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Montella, D.; Scarponi, S.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: Wash trading is considered a highly inopportune and illegal behavior in regulated markets. Instead, it is practiced in unregulated markets, such as cryptocurrency or NFT (Non-Fungible Tokens) markets. Regarding the latter, in the past many researchers have been interested in this phenomenon from an “ex-ante” perspective, aiming to identify and classify wash trading activities before or at the exact time they happen. In this paper, we want to investigate the phenomenon of wash trading in the NFT market from a completely different perspective, namely “ex-post”. Our ultimate goal is to analyze wash trading activities in the past to understand whether the game is worth the candle, i.e., whether these illicit activities actually lead to a significant profit for their perpetrators. To the best of our knowledge, this is the first paper in the literature that attempts to answer this question in a “structured” way. The efforts to answer this question have enabled us to make some additional contributions to the literature in this research area. They are: (i) a framework to support future “ex-post” analyses of the NFT wash trading phenomenon; (ii) a new dataset on wash trading transactions involving NFTs that can support further future investigations of this phenomenon; (iii) a set of insights of the NFT wash trading phenomenon extracted at the end of an experimental campaign.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/311627

Applying Social Network Analysis to Model and Handle a Cross-Blockchain Ecosystem
ELECTRONICS
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In recent years, the huge growth in the number and variety of blockchains has prompted researchers to investigate the cross-blockchain scenario. In this setting, multiple blockchains coexist, and wallets can exchange data and money from one blockchain to another. The effective and efficient management of a cross-blockchain ecosystem is an open problem. This paper aims to address it by exploiting the potential of Social Network Analysis. This general objective is declined into a set of activities. First, a social network-based model is proposed to represent such a scenario. Then, a multi-dimensional and multi-view framework is presented, which uses such a model to handle a cross-blockchain scenario. Such a framework allows all the results found in the past research on Social Network Analysis to be applied to the cross-blockchain ecosystem. Afterwards, this framework is used to extract insights and knowledge patterns concerning the behavior of several categories of wallets in a cross-blockchain scenario. To verify the goodness of the proposed framework, it is applied on a real dataset derived from Multichain, in order to identify various user categories and their “modus operandi”. Finally, a new centrality measure is proposed, which identifies the most significant wallets in the ecosytem. This measure considers several viewpoints, each of which addresses a specific aspect that may make a wallet more or less central in the cross-blockchain scenario.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/311707

A framework for investigating the dynamics of user and community sentiments in a social platform
DATA & KNOWLEDGE ENGINEERING
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: Social platforms are the preferred medium for many people to express their opinions on many topics. This has led many professionals from various fields (marketing, politics, research and development, etc.) to demand increasingly advanced approaches capable of analyzing the evolution of user or community sentiments on particular topics. In this paper, we want to make a contribution to addressing this issue. Specifically, we propose a model and a framework to analyze the dynamics of user and community sentiments in a social platform. In particular, our framework currently focuses on three activities, namely: (i) finding users capable of creating and maintaining a community that reflects their sentiment on a topic; (ii) studying how a user or community sentiment on a topic evolves over time; and (iii) investigating the cross-contamination between a user community and its neighborhood. We tested our framework by means of an extensive experimental campaign that we describe in the paper. Our framework is extremely scalable, and further activities can be easily implemented in it in the near future.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/314828

Representation and Compression of Residual Neural Networks through a Multilayer Network Based Approach
EXPERT SYSTEMS WITH APPLICATIONS
Autore/i: Amelio, A.; Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In recent years different types of Residual Neural Networks (ResNets, for short) have been introduced to improve the performance of deep Convolutional Neural Networks. To cope with the possible redundancy of the layer structure of ResNets and to use them on devices with limited computational capabilities, several tools for exploring and compressing such networks have been proposed. In this paper, we provide a contribution in this setting. In particular, we propose an approach for the representation and compression of a ResNet based on the use of a multilayer network. This is a structure sufficiently powerful to represent and manipulate a ResNet, as well as other families of deep neural networks. Our compression approach uses a multilayer network to represent a ResNet and to identify the possible redundant convolutional layers belonging to it. Once such layers are identified, it prunes them and some related ones obtaining a new compressed ResNet. Experimental results demonstrate the suitability and effectiveness of the proposed approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/308781

NT4XAI: a framework exploiting network theory to support XAI on classifiers
Atti del Trentunesimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'23)
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/315230

An ex-post analysis of the phenomenon of wash trading on NFTs
Atti del Trentunesimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'23)
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/315232

A Social Network Analysis based approach to investigate user behaviour during a cryptocurrency speculative bubble
JOURNAL OF INFORMATION SCIENCE
Autore/i: Bonifazi, G.; Corradini, E.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In this article, we present a Social Network Analysis–based approach to investigate user behaviour during a cryptocurrency speculative bubble in order to extract knowledge patterns about it. Our approach is general and can be applied to any past, present and future cryptocurrency speculative bubble. To verify its potential, we apply it to investigate the Ethereum speculative bubble happened in the years 2017 and 2018. We also describe several interesting knowledge patterns about the behaviour of specific categories of users that we obtained from this investigation. Furthermore, we describe how our approach can support the construction of an identikit of the speculators who maneuvered behind the Ethereum bubble analysed. Finally, we show that this capability of supporting the hunting for speculators is intrinsic of our approach and can cover past, present and future bubbles.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/291445

A Multilayer Network-Based Approach to Represent, Explore and Handle Convolutional Neural Networks
COGNITIVE COMPUTATION
Autore/i: Amelio, Alessia; Bonifazi, Gianluca; Corradini, Enrico; Ursino, Domenico; Virgili, Luca
Classificazione: 1 Contributo su Rivista
Abstract: Deep learning techniques and tools have experienced enormous growth and widespread diffusion in recent years. Among the areas where deep learning has become more widespread there are computational biology and cognitive neuroscience. At the same time, the need for tools able to explore, understand, and possibly manipulate, a deep learning model has strongly emerged. We propose an approach to map a deep learning model into a multilayer network. Our approach is tailored to Convolutional Neural Networks (CNN), but can be easily extended to other architectures. In order to show how our mapping approach enables the exploration and management of deep learning networks, we illustrate a technique for compressing a CNN. It detects whether there are convolutional layers that can be pruned without losing too much information and, in the affirmative case, returns a new CNN obtained from the original one by pruning such layers. We prove the effectiveness of the multilayer mapping approach and the corresponding compression algorithm on the VGG16 network and two benchmark datasets, namely MNIST, and CALTECH-101. In the former case, we obtain a 0.56% increase in accuracy, precision, and recall, and a 21.43% decrease in mean epoch time. In the latter case, we obtain an 11.09% increase in accuracy, 22.27% increase in precision, 38.66% increase in recall, and 47.22% decrease in mean epoch time. Finally, we compare our multilayer mapping approach with a similar one based on single layers and show the effectiveness of the former. We show that a multilayer network-based approach is able to capture and represent the complexity of a CNN. Furthermore, it allows several manipulations on it. An extensive experimental analysis described in the paper demonstrates the suitability of our approach and the goodness of its performance.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/308281

Modeling, Evaluating, and Applying the eWoM Power of Reddit Posts
BIG DATA AND COGNITIVE COMPUTING
Autore/i: Bonifazi, G.; Corradini, E.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: Electronic Word of Mouth (eWoM) has been largely studied for social platforms, such as Yelp and TripAdvisor, which are highly investigated in the context of digital marketing. However, it can also have interesting applications in other contexts. Therefore, it can be challenging to investigate this phenomenon on generic social platforms, such as Facebook, Twitter, and Reddit. In the past literature, many authors analyzed eWoM on Facebook and Twitter, whereas it was little considered in Reddit. In this paper, we focused exactly on this last platform. In particular, we first propose a model for representing and evaluating the eWoM Power of Reddit posts. Then, we illustrate two possible applications, namely the definition of lifespan templates and the construction of profiles for Reddit posts. Lifespan templates and profiles are ultimately orthogonal to each other and can be jointly employed in several applications.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/312271

DLE4FC: a deep learning ensemble to identify fabric colors
Atti del Trentunesimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'23)
Autore/i: Amelio, A.; Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/315231

An approach to detect backbones of information diffusers among different communities of a social platform
DATA & KNOWLEDGE ENGINEERING
Autore/i: Bonifazi, Gianluca; Cauteruccio, Francesco; Corradini, Enrico; Marchetti, Michele; Pierini, Alberto; Terracina, Giorgio; Ursino, Domenico; Virgili, Luca
Classificazione: 1 Contributo su Rivista
Abstract: Information diffusion in social networks is a classic and, at the same time, very current problem. In fact, information diffusers are always looking for new techniques to disseminate information of their interest by creating backbones among them. In this paper, we focus on a specific, but very current and relevant, scenario regarding this way of proceeding. In fact, we propose an approach for the detection of possible backbones of information diffusers among different communities of a social network. Our approach is based on a new centrality measure that we call disseminator centrality. It is specifically designed to detect the so-called disseminator bridges, i.e., users belonging to multiple communities of a single social network, who want to disseminate information of their interest from one community to another by supporting each other. This paper describes the proposed approach, presents the disseminator centrality, illustrates the differences with respect to the related literature and presents the results of the experiments carried out to evaluate its performance.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/304320

Extraction and analysis of text patterns from NSFW adult content in Reddit
DATA & KNOWLEDGE ENGINEERING
Autore/i: Cauteruccio, F.; Corradini, E.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: Reddit is one of the few social networks that handles Not Safe For Work (NSFW) content in an explicit and well-structured way. Despite this, in the past literature on Reddit, there are very few researches concerning this topic. In particular, a study on the text of NSFW comments and posts published in this social medium is missing. In this paper, we aim at contributing to fill this gap by proposing an approach for extracting and analyzing text patterns from NSFW adult content in Reddit. Some peculiarities of our approach are the following: (i) text patterns are extracted based not only on frequency but also, and mostly, on several utility measures; (ii) extracted patterns contribute to the definition of social networks whose analysis allows us to extract several useful information about the users publishing and/or accessing NSFW content and the language adopted by them; (iii) our approach is not only descriptive but also predictive, because, in addition to identifying already existing user communities, it is able to propose new ones; these are made up of users who do not yet know each other but share the same interests and the same language.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/294442

Mapping and compressing a Convolutional Neural Network through a multilayer network
Atti del Trentesimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'22)
Autore/i: Amelio, Alessia; Bonifazi, Gianluca; Corradini, Enrico; Marchetti, Michele; Ursino, Domenico; Virgili, Luca
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/299402

A network-based model and a related approach to represent and handle the semantics of comments in a social network
Atti del Trentesimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'22)
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Pierini, A.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/299401

Defining a deep neural network ensemble for identifying fabric colors
APPLIED SOFT COMPUTING
Autore/i: Amelio, A.; Bonifazi, G.; Corradini, E.; Di Saverio, S.; Marchetti, M.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: Colors characterize each object around us. For this reason, the study of colors has played a key role in Artificial Intelligence (think, for instance, of image classification, object recognition and segmentation). However, there are some topics about colors still little explored. One of them concerns fabric colors. This is a particular topic since fabrics have some characteristics, such as specific textures, that are not found in other contexts. In this paper, we want to propose a new Convolutional Neural Network (CNN) based model for identifying fabric colors. After introducing this model, we consider three different versions of it and create an ensemble of the corresponding CNNs to get better results. Finally, through a series of experiments, we show that our ensemble is able to improve the state-of-the-art on the identification of fabric colors.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/306601

A Machine Learning based Sentient Multimedia Framework to increase safety at work
MULTIMEDIA TOOLS AND APPLICATIONS
Autore/i: Bonifazi, G.; Corradini, E.; Ursino, D.; Virgili, L.; Anceschi, E.; De Donato, M. C.
Classificazione: 1 Contributo su Rivista
Abstract: In the last few decades, we have witnessed an increasing focus on safety in the workplace. ICT has always played a leading role in this context. One ICT sector that is increasingly important in ensuring safety at work is the Internet of Things and, in particular, the new architectures referring to it, such as SIoT, MIoT and Sentient Multimedia Systems. All these architectures handle huge amounts of data to extract predictive and prescriptive information. For this purpose, they often make use of Machine Learning. In this paper, we propose a framework that uses both Sentient Multimedia Systems and Machine Learning to support safety in the workplace. After the general presentation of the framework, we describe its specialization to a particular case, i.e., fall detection. As for this application scenario, we describe a Machine Learning based wearable device for fall detection that we designed, built and tested. Moreover, we illustrate a safety coordination platform for monitoring the work environment, activating alarms in case of falls, and sending appropriate advices to help workers involved in falls.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/289927

Defining user spectra to classify Ethereum users based on their behavior
JOURNAL OF BIG DATA
Autore/i: Bonifazi, G.; Corradini, E.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: Purpose: In this paper, we define the concept of user spectrum and adopt it to classify Ethereum users based on their behavior. Design/methodology/approach: Given a time period, our approach associates each user with a spectrum showing the trend of some behavioral features obtained from a social network-based representation of Ethereum. Each class of users has its own spectrum, obtained by averaging the spectra of its users. In order to evaluate the similarity between the spectrum of a class and the one of a user, we propose a tailored similarity measure obtained by adapting to this context some general measures provided in the past. Finally, we test our approach on a dataset of Ethereum transactions. Findings: We define a social network-based model to represent Ethereum. We also define a spectrum for a user and a class of users (i.e., token contract, exchange, bancor and uniswap), consisting of suitable multivariate time series. Furthermore, we propose an approach to classify new users. The core of this approach is a metric capable of measuring the similarity degree between the spectrum of a user and the one of a class of users. This metric is obtained by adapting the Eros distance (i.e., Extended Frobenius Norm) to this scenario. Originality/value: This paper introduces the concept of spectrum of a user and a class of users, which is new for blockchains. Differently from past models, which represented user behavior by means of univariate time series, the user spectrum here proposed exploits multivariate time series. Moreover, this paper shows that the original Eros distance does not return satisfactory results when applied to user and class spectra, and proposes a modified version of it, tailored to the reference scenario, which reaches a very high accuracy. Finally, it adopts spectra and the modified Eros distance to classify Ethereum users based on their past behavior. Currently, no multi-class automatic classification approach tailored to Ethereum exists yet, albeit some single-class ones have been recently proposed. Therefore, the only way to classify users in Ethereum are online services (e.g., Etherscan), where users are classified after a request from them. However, the fraction of users thus classified is low. To address this issue, we present an automatic approach for a multi-class classification of Ethereum users based on their past behavior.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/297701

A two-tier blockchain framework to increase protection and autonomy of smart objects in the IoT
COMPUTER COMMUNICATIONS
Autore/i: Corradini, E.; Nicolazzo, S.; Nocera, A.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In recent years, the Internet of Things paradigm has become pervasive in everyday life attracting the interest of the research community. Two of the most important challenges to be addressed concern the protection of smart objects and the need to guarantee them a great autonomy. For this purpose, the definition of trust and reputation mechanisms appears crucial. At the same time, several researchers have started to adopt a common distributed ledger, such as a Blockchain, for building advanced solutions in the IoT. However, due to the high dimensionality of this problem, enabling a trust and reputation mechanism by leveraging a Blockchain-based technology could give rise to several performance issues in the IoT. In this paper, we propose a two-tier Blockchain framework to increase the security and autonomy of smart objects in the IoT by implementing a trust-based protection mechanism. In this framework, smart objects are suitably grouped into communities. To reduce the complexity of the solution, the first-tier Blockchain is local and is used only to record probing transactions performed to evaluate the trust of an object in another one of the same community or of a different community. Periodically, after a time window, these transactions are aggregated and the obtained values are stored in the second-tier Blockchain. Specifically, stored values are the reputation of each object inside its community and the trust of each community in the other ones of the framework. In this paper, we describe in detail our framework, its behavior, the security model associated with it and the tests carried out to evaluate its correctness and performance. © 2021 Elsevier B.V.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/292801

Investigating Reddit to detect subreddit and author stereotypes and to evaluate author assortativity
JOURNAL OF INFORMATION SCIENCE
Autore/i: Cauteruccio, F.; Corradini, E.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In recent years, Reddit has attracted the interest of many researchers due to its popularity all over the world. In this article, we aim at providing a contribution to the knowledge of this social network by investigating three of its aspects, interesting from the scientific viewpoint, and, at the same time, by analysing a large number of applications. In particular, we first propose a definition and an analysis of several stereotypes of both subreddits and authors. This analysis is coupled with the definition of three possible orthogonal taxonomies that help us to classify stereotypes in an appropriate way. Then, we investigate the possible existence of author assortativity in this social medium; specifically, we focus on co-posters, that is, authors who submitted posts on the same subreddit.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/285343

Fine-tuning SalGAN and PathGAN for extending saliency map and gaze path prediction from natural images to websites
EXPERT SYSTEMS WITH APPLICATIONS
Autore/i: Corradini, E.; Porcino, G.; Scopelliti, A.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In recent years, researches dealing with the study of visual attention have become very popular thanks to the enormous increase of Artificial Intelligence. Machine Learning and, in particular, Deep Learning allowed researchers to propose new predictive models operating on natural images. In the meantime, an increasing number of websites has been made available on the Internet. However, few approaches, aiming at extending the results obtained on natural images to web pages, have been proposed. In this paper, we provide a contribution in this setting by applying fine-tuning and other refinements to two existing GAN-based approaches (i.e., SalGAN and PathGAN) originally proposed to predict the saliency maps and gaze paths on natural images. Our ultimate goal is defining some variants of them able to deal with websites. In particular, our SalGAN variant represents one of the first attempts to employ GANs for saliency map prediction on web pages, whereas our PathGAN variant is the first attempt to adopt GANs for gaze path prediction on websites Here, we present our proposals, highlight their main novelties, describe the tests done and the results obtained. We also highlight two further contributions of this paper, namely: (i) a new dataset, more complete than the existing ones, supporting the analysis of visual attention on websites, and (ii) a tool supporting a web page designer in her attempt to increase the visitor interest and curiosity
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/293342

A Space-Time Framework for Sentiment Scope Analysis in Social Media
BIG DATA AND COGNITIVE COMPUTING
Autore/i: Bonifazi, G.; Cauteruccio, F.; Corradini, E.; Marchetti, M.; Sciarretta, L.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: The concept of scope was introduced in Social Network Analysis to assess the authoritativeness and convincing ability of a user toward other users on one or more social platforms. It has been studied in the past in some specific contexts, for example to assess the ability of a user to spread information on Twitter. In this paper, we propose a new investigation on scope, as we want to assess the scope of the sentiment of a user on a topic. We also propose a multi-dimensional definition of scope. In fact, besides the traditional spatial scope, we introduce the temporal one, which has never been addressed in the literature, and propose a model that allows the concept of scope to be extended to further dimensions in the future. Furthermore, we propose an approach and a related set of parameters for measuring the scope of the sentiment of a user on a topic in a social network. Finally, we illustrate the results of an experimental campaign we conducted to evaluate the proposed framework on a dataset derived from Reddit. The main novelties of this paper are: (i) a multi-dimensional view of scope; (ii) the introduction of the concept of sentiment scope; (iii) the definition of a general framework capable of analyzing the sentiment scope related to any subject on any social network.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/307501

New Approaches to Extract Information from Posts on COVID-19 Published on Reddit
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Autore/i: Bonifazi, G.; Corradini, E.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In the last two years, we have seen a huge number of debates and discussions on COVID-19 in social media. Many authors have analyzed these debates on Facebook and Twitter, while very few ones have considered Reddit. In this paper, we focus on this social network and propose three approaches to extract information from posts on COVID-19 published in it. The first performs a semi-automatic and dynamic classification of Reddit posts. The second automatically constructs virtual subreddits, each characterized by homogeneous themes. The third automatically identifies virtual communities of users with homogeneous themes. The three approaches represent an advance over the past literature. In fact, the latter lacks studies regarding classification algorithms capable of outlining the differences among the thousands of posts on COVID-19 in Reddit. Analogously, it lacks approaches able to build virtual subreddits with homogeneous topics or virtual communities of users with common interests. © 2022 World Scientific Publishing Company.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/297921

Investigating community evolutions in TikTok dangerous and non-dangerous challenges
JOURNAL OF INFORMATION SCIENCE
Autore/i: Bonifazi, G.; S., Cecchini; Corradini, E.; Giuliani, L.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In just few years, TikTok has become a major player in the social media environment, especially with regard to teenagers. One of the key factors of this success is the idea of challenges, that is, video competitions/emulations on a certain topic, which a user can launch and other ones can join. Most of the challenges are fun and harmless. However, there are also users who launch challenges that are dangerous, or at least suitable only for an adult audience (and TikTok is the most popular social network for teenagers). This article focuses primarily on this kind of challenge. In particular, it investigates an aspect not yet studied in the literature, which is the different characteristics and evolutionary dynamics of the communities of users participating in non-dangerous and dangerous challenges. Its final goal is the identification of evolutionary patterns that distinguish the communities of users participating in the two types of challenges. The knowledge of these patterns could be a first step in implementing an approach to the early detection of dangerous challenges in TikTok. © The Author(s) 2022.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/304481

Extracting time patterns from the lifespans of TikTok challenges to characterize non-dangerous and dangerous ones
SOCIAL NETWORK ANALYSIS AND MINING
Autore/i: Bonifazi, Gianluca; Cecchini, Silvia; Corradini, Enrico; Giuliani, Lorenzo; Ursino∗, Domenico; Virgili, Luca
Classificazione: 1 Contributo su Rivista
Abstract: One of the key aspects that distinguish TikTok from other social media is the presence of challenges. A challenge is a kind of competition that starts when a user posts a video with certain actions and a certain hashtag and invites other users to replicate the same video in their own way. Most challenges are fun and harmless, but sometimes dangerous challenges are launched as well. The authors of these challenges use various tricks to bypass TikTok’s controls. In this paper, we analyze the lifespans of some TikTok challenges and show how they are very different for non-dangerous and dangerous ones. Then, we deepen our analysis by identifying some time patterns that characterize the two types of challenges. Finally, we test the accuracy of the results obtained on a large set of challenges different from those used during the detection of time patterns. The focus of this paper is the detection of time patterns allowing the classification of challenges in dangerous and non-dangerous ones. This could represent a first step towards an approach for the early detection of dangerous challenges in TikTok.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/300663

Integrative bioinformatics and omics data source interoperability in the next-generation sequencing era-Editorial
BRIEFINGS IN BIOINFORMATICS
Autore/i: Rombo, S. E.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/288019

SaveMeNow.AI: a Machine Learning based wearable device for fall detection in a workplace
Enabling AI applications in Data Science
Autore/i: Anceschi, E.; Bonifazi, G.; Callisto de Donato, M.; Corradini, E.; Ursino, D.; Virgili, L.
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/277594

Applying Generative Adversarial Networks to perform gaze path prediction in websites
Atti del Ventinovesimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'21)
Autore/i: Bonifazi, G.; Corradini, E.; Porcino, G.; Scopelliti, A.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290548

Increasing protection and autonomy in the IoT through a two-tier blockchain framework
Atti del Ventinovesimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'21)
Autore/i: Corradini, E.; Nicolazzo, S.; Nocera, A.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290551

Investigating negative reviews and detecting negative influencers in Yelp through a multi-dimensional social network based model
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
Autore/i: Corradini, E.; Nocera, A.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, we propose an investigation of negative reviews and define the profile of negative influencers in Yelp. The methodology adopted to achieve this goal consists of two phases. The first one is theoretical and aims at defining a multi-dimensional social network based model of Yelp, three stereotypes of Yelp users, and a network based model to represent negative reviewers and their relationships. The second phase is experimental and consists in the definition of five hypotheses on negative reviews and reviewers in Yelp and their verification through an extensive data analysis campaign. This was performed on Yelp data represented by means of the models introduced during the first phase. Its most important result is the construction of the profile of negative influencers in Yelp. The main novelties of this paper are: (i) the definition of the two social network based models of Yelp and its users; (ii) the definition of three stereotypes of Yelp users and their characteristics; (iii) the construction of the profile of negative influencers in Yelp.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290580

Investigating the phenomenon of NSFW posts in Reddit
INFORMATION SCIENCES
Autore/i: Corradini, E.; Nocera, A.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, we study the characteristics of NSFW (Not Safe For Work) posts in Reddit, highlighting their differences from SFW (Safe For Work) posts, which have been much more studied in the past literature. In our investigation, we studied all Reddit posts from 2019. Through both descriptive analytics techniques and social network analysis techniques, we extract three findings on the main differences between NSFW and SFW posts in Reddit. Thanks to these findings, we are able to better understand the dynamics (authors, subreddits, readers) behind NSFW posts. In particular, it becomes clear that this is a niche world where authors are strongly cohesive. However, at the same time, the most popular ones show a clear opening to new authors, whom they are willing to collaborate with, from the beginning
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/287167

A framework for anomaly detection and classification in Multiple IoT scenarios
FUTURE GENERATION COMPUTER SYSTEMS
Autore/i: Cauteruccio, F.; Cinelli, L.; Corradini, E.; Terracina, G.; Ursino, D.; Virgili, L.; Fortino, G.; Liotta, A.; Savaglio, C.
Classificazione: 1 Contributo su Rivista
Abstract: The investigation of anomalies is an important element in many scientific research fields. In recent years, this activity has been also extended to social networking and social internetworking, where different networks interact with each other. In these research fields, we have recently witnessed an important evolution because, beside networks of people, networks of things are becoming increasingly common. IoT and Multiple IoT scenarios are thus more and more studied. This paper represents a first attempt to investigate anomalies in a Multiple IoT scenario (MIoT). First, we propose a new methodological framework that can make future investigations in this research field easier, coherent, and uniform. Then, in the context of anomaly detection in an MIoT, we define the so-called “forward problem” and “inverse problem”. The definition of these problems allows the investigation of how anomalies depend on inter-node distances, the size of IoT networks, and the degree centrality and closeness centrality of anomalous nodes. The approach proposed herein is applied to a smart city scenario, which is a typical MIoT. Here, data coming from sensors and social networks can boost smart lighting in order to provide citizens with a smart and safe environment.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/283531

Querying the IoT Using Multiresolution Contexts
IEEE INTERNET OF THINGS JOURNAL
Autore/i: Diamantini, C.; Nocera, A.; Potena, D.; Storti, E.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Abstract: People’s daily life is increasingly intertwined with smart devices, which are more and more used in dynamic contexts. Therefore, searching and exploiting the wealth of information produced by the Internet of Things (IoT) requires novel models including a representation of the actual context of use. The definition of context is inherently difficult, due to the variety of application scenarios and user needs. In this paper, we propose a general model for devices’ contexts representing context components at different resolutions (or levels of granularity). This enables the definition of a multi-resolution context-based algorithm for querying the IoT, according to given preferences and contexts that can be tightened or relaxed depending on the given application goal. Experimental results show how the proposed approach outperforms traditional solutions by increasing the retrieval of relevant results while keeping precision under control.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284563

Anonymous Access Monitoring of Indoor Areas
IEEE ACCESS
Autore/i: Nicolazzo, S.; Nocera, A.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Abstract: Video surveillance of public spaces is a feature of modern society that has expanded quite quickly and in a pervasive way during the last decades becoming a fundamental need for both individual and collective security. But, as the sophistication of this type of systems increases, the concern about threat to individuals' right of privacy raises as well. Indeed, the video surveillance systems could breach personal privacy because location is clearly one of the most sensitive people information. Hence, preserving location privacy while achieving utility from it, is a challenging problem demanding the investigation of researchers. This paper tackles this non-trivial issue by designing a novel privacy-preserving architecture able to anonymously monitoring people access at the entrance of critical areas in an indoor space. At the same time our approach is able to provide full accountability in case of an accident or a legal requirement. Interestingly, our protocol is robust to server-side attacks and is efficient enough to be applied indoors through a set of IoT (Internet of Things) smart camera devices.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/289690

An approach to extracting topic-guided views from the sources of a data lake
INFORMATION SYSTEMS FRONTIERS
Autore/i: Diamantini, C.; Lo Giudice, P.; Potena, D.; Storti, E.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Abstract: In the last years, data lakes are emerging as an effective and an efficient support for information and knowledge extraction from a huge amount of highly heterogeneous and quickly changing data sources. Data lake management requires the definition of new techniques, very different from the ones adopted for data warehouses in the past. In this scenario, one of the most challenging issues to address consists in the extraction of topic-guided (i.e., thematic) views from the (very heterogeneous and often unstructured) sources of a data lake. In this paper, we propose a new network-based model to uniformly represent structured, semi-structured and unstructured sources of a data lake. Then, we present a new approach to, at least partially, “structuring” unstructured data. Finally, we define a technique to extract topic-guided views from the sources of a data lake, based on similarity and other semantic relationships among source metadata.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/275540

An investigation on Not Safe For Work adult content in Reddit.
Atti del Ventinovesimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'21)
Autore/i: Cauteruccio, F.; Corradini, E.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290550

An Approach to Compute the Scope of a Social Object in a Multi-IoT Scenario
PERVASIVE AND MOBILE COMPUTING
Autore/i: Cauteruccio, F.; Cinelli, L.; Fortino, G.; Savaglio, C.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In the last few years, classical social networking is turning into the more complex social internetworking and is extending from human users to objects. Indeed, objects are becoming increasingly complex, smart and social so that several authors have recently started to investigate the Social Internet of Things (SIoT) and the Multiple IoT (MIoT) paradigms. SIoT is more oriented to the technological issues to be faced in presence of multiple IoT interacting with each other. Instead, MIoT addresses data-driven and semantics-based aspects because it considers the contents exchanged by smart objects during their transactions. In such a research context, the concept of scope in a Multi-IoT scenario can play an important role. In this paper, we investigate this issue. In particular, first we define the concept of scope in a Multi-IoT scenario. Then, we propose two formalizations of this concept allowing the computation of its values. Afterwards, we present two possible applications of scope. Finally, we describe a set of experiments performed for its evaluation; the last of them compares scope with diffusion degree and influence degree, two parameters already proposed in past literature.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/283337

Humanizing IoT: defining the profile and the reliability of a thing in a Multi-IoT scenario
Towards Social Internet of Things: Enabling Technologies, Architectures and Applications
Autore/i: Ursino, D.; Virgili, L.
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266831

Co-posting Author Assortativity in Reddit
Atti del Ventottesimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'20)
Autore/i: Cauteruccio, F.; Corradini, E.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/277593

A General Approach to Uniformly Handle Different String Metrics Based on Hterogeneous Alphabets
IEEE ACCESS
Autore/i: Cauteruccio, F.; Cucchiarelli, A.; Morbidoni, C.; Terracina, G.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Abstract: In the last few years, we have assisted in a great increase of the usage of strings in the most disparate areas. In the meantime, the development of the Internet has brought the necessity of managing strings from very different contexts and possibly using different alphabets. This issue is not addressed by the numerous string comparison metrics previously proposed in the literature. In this paper, we aim at providing a contribution in this context. In fact, first we propose an approach to measure the similarity of strings based on different alphabets. Then we show that our approach can be specifically adapted to several classic string comparison metrics and that each specialization can lead to addressing completely different issues.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/275066

Generalizing identity-based string comparison metrics: Framework and Techniques
KNOWLEDGE-BASED SYSTEMS
Autore/i: Cauteruccio, F.; Terracina, G.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, we propose a framework that aims at handling metrics among strings defined over heterogeneous alphabets. Furthermore, we illustrate in detail its application to generalize one of the most important string metrics, namely the edit distance. This last activity leads us to define the Multi-Parameterized Edit Distance (MPED). As for this last metric, we investigate its computational properties and solution algorithms, and we present several experiments for its evaluation. As a final contribution, we provide several notes about some possible applications of MPED and other generalized metrics in different scenarios.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/267661

Defining and detecting k-bridges in a social network: the Yelp case, and more
KNOWLEDGE-BASED SYSTEMS
Autore/i: Corradini, E.; Nocera, A.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, we introduce the concept of k-bridge (i.e., a user who connects k sub-networks of the same network or k networks of a multi-network scenario) and propose an algorithm for extracting k-bridges from a social network. Then, we analyze the specialization of this concept and algorithm in Yelp and we extract several knowledge patterns about Yelp k-bridges. In particular, we investigate how some basic characteristics of Yelp k-bridges vary against k (i.e., against the number of macro-categories which the businesses reviewed by them belong to). Then, we verify if there exists an influence exerted by k-bridges on their friends and/or on their co-reviewers. We also analyze the relationship between k-bridges and power users. In addition, we investigate the relationship between k-bridges and the main centrality measures in the macro-categories of Yelp. We also propose two further specializations of k-bridges, regarding Reddit and the network of patent inventors, to prove that the knowledge on k-bridges we initially found in Yelp is not limited to this social network. Finally, we present two use cases that can highly benefit from the knowledge on k-bridges detected through our approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/275067

A Privacy-Preserving Approach to Prevent Feature Disclosure in an IoT Scenario
FUTURE GENERATION COMPUTER SYSTEMS
Autore/i: Nicolazzo, S.; Nocera, A.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, we propose a privacy-preserving approach to prevent feature disclosure in a multiple IoT scenario, i.e., a scenario where objects can be organized in (partially overlapped) networks interacting with each other. Our approach is based on two notions derived from database theory, namely k-anonymity and t-closeness. They are applied to cluster the involved objects in order to provide a unitary view of them and of their features. Indeed, the use of k-anonymity and t-closeness makes derived groups robust from a privacy perspective. In this way, not only information disclosure, but also feature disclosure, is prevented. This is an important strength of our approach because the malicious analysis of objects’ features can have disruptive effects on the privacy (and, ultimately, on the life) of people.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272513

An approach to evaluate trust and reputation of things in a Multi-IoTs scenario
COMPUTING
Autore/i: Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: In the past research, trust and reputation have been investigated for communities of people, for organizations and for multi-agent systems. However, in the last few years, things are becoming increasingly relevant in the Internet scenario and, at the same time, increasingly complex. As a matter of fact, the term “Internet of Things” (hereafter, IoT) is becoming more and more common in both the scientific and the technological contexts. But, if a thing can have a profile and a behavior like a human, it is not out of place to extend the concept of trust and reputation to things and to define ad hoc approaches for their computation. In this paper, we investigate trust and reputation of a thing in a Multiple IoTs scenario and we propose a context-aware approach to evaluate them. This task is not immediate because it should consider all the peculiarities of a thing compared to a human and all the specificities of a Multiple IoTs scenario compared to a community of people.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/277182

A lightweight approach to extract interschema properties from structured, semi-structured and unstructured sources in a big data scenario
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Autore/i: Cauteruccio, F.; Lo Giudice, P.; Musarella, L.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Abstract: The knowledge of interschema properties (e.g., synonymies, homonymies, hyponymies and subschema similarities) plays a key role for allowing decision-making in sources characterized by disparate formats. In the past, wide amount and variety of approaches to derive interschema properties from structured and semi-structured data have been proposed. However, currently, it is esteemed that more than 80% of data sources are unstructured. Furthermore, the number of sources generally involved in an interaction is much higher than in the past. As a consequence, the necessity arises of new approaches to address the interschema property derivation issue in this new scenario. In this paper, we aim at providing a contribution in this setting by proposing an approach capable of uniformly extracting interschema properties from a huge number of structured, semi-structured and unstructured sources. © 2020 World Scientific Publishing Company.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/276193

A network analysis based approach to characterizing Periodic Sharp Wave Complexes in electroencephalograms of patients with sporadic CJD
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
Autore/i: Lo Giudice, P.; Ursino, D.; Mammone, N.; Morabito, F. C.; Aguglia, U.; Cianci, V.; Ferlazzo, E.; Gasparini, S.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261820

A "big data oriented'' and "complex network based'' model supporting the uniform investigation of heterogeneous personalized medicine data
Proc. of the International Conference on Bioinformatics and Biomedicine (BIBM'18)
Autore/i: Lo Giudice, P.; Ursino, D.; Virgili, Luca
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/262041

The MIoT paradigm: main features and an “ad-hoc” crawler
FUTURE GENERATION COMPUTER SYSTEMS
Autore/i: Baldassarre, G.; Lo Giudice, P.; Musarella, L.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259660

From Artificial Intelligence and Databases to Cognitive Computing: Past and Future Computer Engineering Research at UNIVPM
The First Outstanding 50 Years of “Università Politecnica delle Marche”
Autore/i: Cucchiarelli, Alessandro; Diamantini, Claudia; Dragoni, Aldo Franco; Francescangeli, Fabrizio; Frontoni, Emanuele; Mancini, Adriano; Marinelli, Fabrizio; Morbidoni, Christian; Pezzella, Ferdinando; Pisacane, Ornella; Potena, Domenico; Ribighini, Giuseppa; Spalazzi, Luca; Storti, Emanuele; Ursino, Domenico; Vici, Francesco; Zingaretti, Primo
Editore: Springer, Cham
Classificazione: 2 Contributo in Volume
Abstract: In the last decades, Computer Engineering has shown an impressive development and has become a pervasive protagonist in daily life and scientific research. Databases and Artificial Intelligence represent two of the major players in this development. Today, they are quickly converging towards a new, much more sophisticated and inclusive, paradigm, namely Cognitive Computing. This paradigm leverages Big Data and Artificial Intelligence to design approaches and build systems capable of (at least partially) reproducing human brain behavior. In this paradigm, an important role can be also played by Mathematical Programming. Cognitive systems are able to autonomously learn, reason, understand and process a huge amount of extremely varied data. Their ultimate goal is the capability of interacting naturally with their users. In the last 50 years, UNIVPM has played a leading role in scientific research in Databases and Artificial Intelligence, and, thanks to the acquired expertise, is going to play a key role in Cognitive Computing research in the future.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272654

Multi-Dimensional Contexts for Querying IoT Networks
Proceedings of the 27th Italian Symposium on Advanced Database Systems
Autore/i: Diamantini, C.; Antonino, Nocera; Potena, D.; Storti, E.; Ursino, D.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/265546

Investigating the scope of a thing in a multiple Internet of Things scenario
Atti del Ventisettesimo Convegno Nazionale su Sistemi Evoluti per Basi di Dati (SEBD'19)
Autore/i: Cauteruccio, F.; Cinelli, L.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/265545

A new network-based approach to investigating neurological disorders
INTERNATIONAL JOURNAL OF DATA MINING, MODELLING AND MANAGEMENT
Autore/i: Cauteruccio, F.; Lo Giudice, P.; Terracina, G.; Ursino, Domenico; Mammone, N.; Morabito, F. C.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261819

A well-tailored centrality measure for evaluating patents and their citations
JOURNAL OF DOCUMENTATION
Autore/i: Donato, Claudia; Lo Giudice, Paolo; Marretta, Roberta; Ursino, Domenico; Virgili, Luca
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/263903

An approach to extracting complex knowledge patterns among concepts belonging to structured, semi-structured and unstructured sources in a data lake
INFORMATION SCIENCES
Autore/i: Lo Giudice, Paolo; Musarella, Lorenzo; Sofo, Giuseppe; Ursino, Domenico
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/262226

Leveraging Network Analysis to support experts in their analyses of subjects with MCI and AD
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Autore/i: Lo Giudice, P.; Mammone, N.; Morabito, F. C.; Pizzimenti, R. G.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/265025

Algorithms for graph and network analysis: Traversing/Searching/Sampling graphs
Encyclopedia of Bioinformatics and Computational Biology
Autore/i: Lo Giudice, P.; Ursino, D.
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/253379

Applying network analysis for extracting knowledge about environment changes from heterogeneous sensor data streams
Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies
Autore/i: Cauteruccio, F.; Lo Giudice, P.; Terracina, G.; Ursino, D.
Editore: Smart Innovation, Systems and Technologies. Springer
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/254021

Algorithms for graph and network analysis: Graph indexes/descriptors
Encyclopedia of Bioinformatics and Computational Biology
Autore/i: Lo Giudice, P.; Ursino, D.
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/253376

Algorithms for strings and sequences: Searching motifs
Encyclopedia of Bioinformatics and Computational Biology
Autore/i: Cauteruccio, F.; Terracina, G.; Ursino, D.
Editore: Elservier
Luogo di pubblicazione: Amsterdam
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/253367

Building Topic-Driven Virtual IoTs in a Multiple IoTs Scenario
SENSORS
Autore/i: Lo Giudice, P.; Nocera, A.; Ursino, D.; Virgili, L.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/267662

Find the Right Peers: Building and Querying Multi-IoT Networks Based on Contexts
Flexible Query Answering Systems
Autore/i: Diamantini, C.; Nocera, A.; Potena, D.; Storti, E.; Ursino, D.
Editore: Springer, Cham
Classificazione: 2 Contributo in Volume
Abstract: With the evolution of the features smart devices are equipped with, the IoT realm is becoming more and more intertwined with people daily-life activities. This has, of course, impacts in the way objects are used, causing a strong increase in both the dynamism of their contexts and the diversification of their objectives. This results in an evolution of the IoT towards a more complex environment composed of multiple overlapping networks, called Multi-IoTs (MIoT). The low applicability of classical cooperation mechanisms among objects leads to the necessity of developing more complex and refined strategies that take the peculiarity of such a new environment into consideration. In this paper, we address this problem by proposing a new model for devices and their contexts following a knowledge representation approach. It borrows ideas from OLAP systems and leverages a multidimensional perspective by defining dimension hierarchies. In this way, it enables roll-up and drill-down operations on the values of the considered dimensions. This allows for the design of more compact object networks and the definition of new strategies for the retrieval of relevant devices.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266832

Redefining Betweenness Centrality in a Multiple IoT Scenario
Proc. of the International Workshop on Artificial Intelligence & Internet of Things (AI&IOT'19)
Autore/i: Cauteruccio, F.; Terracina, G.; Ursino, D.; Virgili, L.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271329

Leveraging linked entities to estimate focus time of short texts
Proc. of the International Database Engineering & Applications Symposium (IDEAS 2018)
Autore/i: Morbidoni, C.; Cucchiarelli, A.; Ursino, D.
Editore: Association for Computing Machinery
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Time is a useful dimension to explore in text databases especially when historical and factual information is concerned. As documents generally refer to different events and time periods, understanding the focus time of key sentences, defined as the time the content refers to, is a crucial task to temporally annotate a document. In this paper, we leverage a bag of linked entities representation of sentences and temporal information from Wikipedia and DBpedia to implement a novel approach to focus time estimation. We evaluate our approach on sample datasets and compare it with a state of the art method, measuring improvements in MRR.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/258613

A new Social Network Analysis-based approach to extracting knowledge patterns about research activities and hubs in a set of countries
INTERNATIONAL JOURNAL OF BUSINESS INNOVATION AND RESEARCH
Autore/i: Lo Giudice, P.; Russo, P.; Ursino, D.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/253300

A paradigm for the cooperation of objects belonging to different IOTs
Proc. of the International Database Engineering & Applications Symposium (IDEAS 2018)
Autore/i: Baldassarre, G.; Lo Giudice, P.; Musarella, L.; Ursino, D.
Editore: ACM
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/258612