Parsing and validation functions are crucial because they process untrusted data, e.g., user inputs. Due to their complexity, these functions are highly susceptible to bugs, making them a primary target for security audits. However, identifying such functions within a binary is time-intensive and challenging, given the numerous functions typically present and the lack of source code or supporting documentation. This paper presents an AI-based methodology for identifying functions with parser-like behavior and complex processing logic within a binary. Our methodology analyzes each binary by identifying its functions, extracting their Control Flow Graphs (CFGs), and enriching them with features derived from an embedding model that captures both structural and semantic aspects of their behavior. These annotated CFGs are the input to a Graph Neural Network trained to identify parsing functions. We implement this methodology in the tool ParserHunter, which allows users to train the model on labeled data, query the model with unseen binaries, and accommodate a symbolic execution phase on the processed binary through a user interface. Our experiments on ten real-world projects from GitHub show that our tool effectively identifies parsers in binaries.

Social media is a powerful tool also for discussing mental health. The conversations that take place in these spaces provide a unique insight into how users talk about the issue. This study uses fine-tuned pretrained transformer models (BERT and MentalBERT), to classify Reddit posts about anxiety, depression, bipolar disorder and borderline personality disorder (BPD) in specialised subreddits. By assessing how well subreddit conversations align with their intended mental health focus, the analysis ensures that these communities are effectively serving their purpose as support spaces. Our classification models achieve an average accuracy of 82%, with MentalBERT slightly outperforming BERT. To ensure transparency, we use Local Interpretable Model-agnostic Explanations (LIME) to identify key linguistic patterns that influence the model predictions. The outcome reveals distinct language use across conditions: as examples, discussions in bipolar disorder subreddits often refer to mood instability, while BPD communities emphasise challenges in emotional regulation. By integrating classification with explainability, this study offers insights into thematic patterns in online discourse that can support mental health professionals in identifying trends. While our models are not diagnostic tools, they function as subreddit-alignment classifiers, helping to uncover how different topics are discussed across communities. These insights may inform human-in-the-loop community management strategies and contribute to raising awareness and reducing stigma around mental health issues, ultimately fostering more supportive digital environments.

Decentralized Finance (DeFi) applications introduce novel financial instruments replicating and extending traditional ones through blockchain-based smart contracts. Among these applications, DeFi derivatives protocols enable the creation and trading of decentralized derivative instruments whose value depends on underlying cryptoassets, indices, or other reference variables. Despite their growing significance, however, they remain relatively understudied compared to other DeFi protocols, such as lending protocols and decentralized exchanges. This paper systematically analyzes DeFi derivatives protocols, categorized into perpetuals, options, and synthetics, with the aim of comparing their instrument structures, protocol mechanisms, operational dynamics, and economic agents. We provide a formal characterization of the main classes of decentralized derivative instruments and develop a protocol-agnostic framework that connects instrument-level specifications, market-state variables, and protocol-level mechanisms. We complement the analytical framework with numerical simulations that evaluate how derivative positions evolve under varying economic conditions, including changes in underlying asset prices, volatility, protocol-specific fees, and leverage. Overall, this study provides a structured analytical framework for understanding and comparing the design and functioning of decentralized finance derivatives protocols.

In this resource paper, we present two publicly available datasets of semantically enriched human trajectories, together with the pipeline to build them. The trajectories are publicly available GPS traces retrieved from OpenStreetMap. Each dataset includes contextual layers such as stops, moves, points of interest (POIs), inferred transportation modes, and weather data. A novel semantic feature is the inclusion of synthetic, realistic social media posts generated by Large Language Models (LLMs), enabling multimodal and semantic mobility analysis. The datasets are available in both tabular and Resource Description Framework (RDF) formats, supporting semantic reasoning and FAIR data practices. They cover two structurally distinct, large cities: Paris and New York. Our open source reproducible pipeline allows for dataset customization, while the datasets support research tasks such as behavior modeling, mobility prediction, knowledge graph construction, and LLM-based applications. To our knowledge, our resource is the first to combine real-world movement, structured semantic enrichment, LLM-generated text, and semantic web compatibility in a reusable framework.

Diffusion models have become widely used for generating text, image, video, and audio. In recent years, these models have also been introduced into the time series domain for tasks such as forecasting, imputation, and generation. An interesting application is conditional generation with respect to metadata, enabling the synthesis of data sequences that match specified conditions. In the present work, we focus on spatio-temporal data and more precisely on time series data that also exhibit a spatial nature—for instance, measurements from sensor networks, traffic flows, mobile network usage across different areas. However, current approaches for time series conditional generation often neglect spatial autocorrelation. In this work, we extend the well-known DIFFWAVE model to address this challenge by directly taking into account the spatial nature of the data in the denoising process of the diffusion model. We evaluate our approach on a large and complex real-world dataset from the NET-MOB 2023 data challenge, which collects mobile network usage of different mobile applications across urban areas. Our results demonstrate that, in addition to achieving competitive performance across all evaluated metrics, our approach is also able to correctly capture the spatial autocorrelation present in the real data.

In this study, we investigate the use of a large language model to assist in the evaluation of the reliability of the vast number of existing online news publishers, addressing the impracticality of relying solely on human expert annotators for this task. In the context of the Italian news media market, we first task the model with evaluating expert-designed reliability criteria using a representative sample of news articles. We then compare the model's answers with those of human experts. The dataset consists of 340 news articles, each annotated by two human experts and the LLM. Six criteria are taken into account, for a total of 6,120 annotations. We observe good agreement between LLM and human annotators in three of the six evaluated criteria, including the critical ability to detect instances where a text negatively targets an entity or individual. For two additional criteria, such as the detection of sensational language and the recognition of bias in news content, LLMs generate fair annotations, albeit with certain trade-offs. Furthermore, we show that the LLM is able to help resolve disagreements among human experts, especially in tasks such as identifying cases of negative targeting.

Blockchain technology is revolutionizing digital asset exchange by eliminating the need for central authority control. However, the decentralized nature of blockchain attracts malicious actors, leading to the proliferation of financial scams, with Ponzi schemes being particularly prevalent. Consequently, there is a growing need to develop automatic detection mechanisms for such scams. So far, the problem has been tackled by considering only classifier performances and with limited focus on the explanation and interpretation of the results. However, interpretability and explainability are crucial when classifier decisions may have economic consequences. This paper introduces X-SPIDE (XAI Smart Ponzi Identification and Detection), an explainable machine learning pipeline for Ponzi scheme detection within the Ethereum blockchain that aims to find the trade-off between performance and explainability. X-SPIDE allows for comparing the results of different classifiers, identifying a small set of features that offer strong performance, and understanding how these features contribute to classification — highlighting specific characteristics of malicious contracts. Moreover, we introduce and make publicly available a new comprehensive dataset comprising 7446 smart contracts, incorporating features derived from transaction history, creation, and deployment bytecodes to train and test our pipeline.

Social media is a powerful tool also for discussing mental health. The conversations that take place in these spaces provide a unique insight into how users talk about the issue. This study uses state-of-the-art transformer models, BERT and MentalBERT, to classify Reddit posts about anxiety, depression, bipolar disorder and borderline personality disorder (BPD) in specialised subreddits. By assessing how well subreddit conversations align with their intended mental health focus, the analysis ensures that these communities are effectively serving their purpose as support spaces.Our classification models achieves an average accuracy of 82%, with MentalBERT slightly outperforming BERT. To ensure transparency, we use Local Interpretable Model-agnostic Explanations (LIME) to identify key linguistic patterns that influence the model predictions. The outcome reveals distinct language use across conditions: as examples, discussions in bipolar disorder subreddits often refer to mood instability, while BPD communities emphasise challenges in emotional regulation. By integrating classification with explainability, this study provides insights that can help mental health professionals understand trends in online discourses and support platform moderation efforts to promote more effective and supportive digital environments. This, in turn, can help to raise awareness and reduce stigma around mental health issues.

This study provides a comparative review of how cloud integration is reshaping cybersecurity in the maritime sector, focusing on the Baltic and Mediterranean basins. It aims to identify how regional threat landscapes, governance frameworks, and industrial ecosystems influence both vulnerabilities and resilience in a sector increasingly reliant on digital infrastructures. Methodologically, the paper adopts a qualitative and interpretive approach, combining policy and regulatory analysis, technical assessment of OT/IT and cloud convergence, and case studies such as the NotPetya incident and the cybersecurity architecture of Fincantieri and its subsidiary E-phors. The paper contributes to the literature by (1) systematising regional approaches to maritime cyber resilience, (2) mapping transferable security patterns between two contrasting maritime ecosystems, and (3) integrating an industrial case study into a comparative governance framework. Theoretically, it extends maritime cybersecurity research by situating cloud adoption within regional security ecologies; practically, it highlights the managerial value of security-by-design practices, Zero Trust adoption, and cross-regional learning. Policy recommendations include the creation of EU-wide federated identity registries, regional threat intelligence hubs, and lifecycle-integrated cybersecurity frameworks across the maritime supply chain.

With the advent of advanced 4G/5G mobile networks, mobile phone data collected by operators now includes detailed, service-specific traffic information with high spatio-temporal resolution. In this paper, we leverage this type of data to explore its potential for generating high-quality representations of urban regions. To achieve this, we present a methodology for creating urban region embeddings from service-specific mobile traffic data, employing a temporal convolutional network-based autoencoder, transformers, and learnable weighted sum models to capture key urban features. In the extensive experimental evaluation conducted using a real-world dataset, we demonstrate that the embeddings generated by our methodology effectively capture urban characteristics. Specifically, our embeddings are compared against those of a state-of-the-art competitor across two downstream tasks. Additionally, through clustering techniques, we investigate how well the embeddings produced by our methodology capture the temporal dynamics and characteristics of the underlying urban regions. Overall, this work highlights the potential of service-specific mobile traffic data for urban research and emphasizes the importance of making such data accessible to support public innovation.

As predictive machine learning models become increasingly adopted and advanced, their role has evolved from merely predicting outcomes to actively shaping them. This evolution has underscored the importance of Trustworthy AI, highlighting the necessity to extend our focus beyond mere accuracy and toward a comprehensive understanding of these models' behaviors within the specific contexts of their applications. To further progress in explainability, we introduce poem, Prefetched Offline Explanation Model, a model-agnostic, local explainability algorithm for image data. The algorithm generates exemplars, counter exemplars and saliency maps to provide quick and effective explanations suitable for time-sensitive scenarios. Leveraging an existing local algorithm, poem infers factual and counterfactual rules from data to create illustrative examples and opposite scenarios with an enhanced stability by design. A novel mechanism then matches incoming test points with an explanation base and produces diverse exemplars, informative saliency maps and believable counterexemplars. Experimental results indicate that poem outperforms its predecessor abele in speed and ability to generate more nuanced and varied exemplars alongside more insightful saliency maps and valuable counterexemplars.

Blockchain technology has been successfully exploited for deploying new economic applications. However, it has started arousing the interest of malicious actors who deliver scams to deceive honest users and to gain economic advantages. Ponzi schemes are one of the most common scams. Here, we present a classifier for detecting smart Ponzi contracts on Ethereum, which can be used as the backbone for developing detection tools. First, we release a labelled data set with 4422 unique real-world smart contracts to address the problem of the unavailability of labelled data. Then, we show that our classifier outperforms the ones proposed in the literature when considering the AUC as a metric. Finally, we identify a small and effective set of features that ensures a good classification quality and investigate their impacts on the classification using eXplainable AI techniques.

With the increasing pervasiveness of Information and Communication Technology (ICT) in the fabric of economic activities, the corporate digital divide has become a crucial issue for the assessment of Information Technology (IT) competencies and the digital gap between firms and territories. With little granular data available to measure the phenomenon, most studies have used survey data. To address this empirical gap, we scanned the homepages of 182,705 Italian companies and extracted ten characteristics related to their digital footprint to develop a new index for the corporate digital assessment. Our results show a significant digital divide between Italian companies according to size, sector and geographical location, opening new perspectives for monitoring and data-driven analysis.

Mobility data analysis provides insights into human movement patterns, traffic flows, and urban planning strategies. Human dynamics analysis focuses on tracking people to investigate how individuals and groups behave, interact, and evolve. Various mobility data sources, such as GPS, mobile phone records, social media, and transportation logs, are often semantically enriched and used for these analyses. This results in the generation of new, complex datasets that require effective summarization methods to reduce data volume while preserving relevant information. In this work, we aim to demonstrate the effective use of summarized semantic trajectories in analyzing human mobility behaviours. We offer empirical evidence from a case study, showing how this type of trajectory helps in understanding human mobility, especially in distinguishing between routine and non-routine behaviours. Experimental results show that the analysis results are comparable with the results obtained in the original (non summarized) dataset.

The proliferation of low-quality online information in today's era has underscored the need for robust and automatic mechanisms to evaluate the trustworthiness of online news publishers. In this paper, we analyse the trustworthiness of online news media outlets by leveraging a dataset of 4033 news stories from 40 different sources. We aim to infer the trustworthiness level of the source based on the classification of individual articles' content. The trust labels are obtained from NewsGuard, a journalistic organization that evaluates news sources using well-established editorial and publishing criteria. The results indicate that the classification model is highly effective in classifying the trustworthiness levels of the news articles. This research has practical applications in alerting readers to potentially untrustworthy news sources, assisting journalistic organizations in evaluating new or unfamiliar media outlets and supporting the selection of articles for their trustworthiness assessment.

The aim of the REoCAS Colloquium in honor of the 70th birthday of Rocco De Nicola was to acknowledge his scientific impact on the field of collective adaptive systems and, more in general, his much broader legacy in concurrency theory, distributed systems, and formal methods, exploring Rocco’s more recent contributions to cybersecurity. Numerous colleagues have contributed to this task. With this paper, we wish to provide an overview of Rocco’s efforts in shaping computer science research within IMT School for Advanced Studies Lucca, which Rocco has fundamentally shaped since his appointment until his serving as Rector for 2021–2024.

The proliferation of tracking sensors in today's devices has led to the generation of high-frequency, high-volume streams of mobility data capturing the movements of various objects. These movement data can be enriched with semantic contextual information, such as activities, events, user preferences, and more, generating semantically enriched trajectories. Creating and managing these types of trajectories presents challenges due to the massive data volume and the heterogeneous, complex semantic dimensions. To address these issues, we introduce a novel approach, MAT-Sum, which uses a location-centric enrichment perspective to summarize massive volumes of mobility data while preserving essential semantic information. Our approach enriches geographical areas with semantic aspects to provide the underlying context for trajectories, enabling effective data reduction through trajectory summarization. In the experimental evaluation, we show that MAT-Sum effectively minimizes trajectory volume while retaining a good level of semantic quality, thus presenting a viable solution to the relevant issue of managing massive mobility data.