Designing semantically meaningful and operationally effective intrusion detection signatures remains a labor-intensive and expertise-driven task, particularly within the Zeek network monitoring framework. In this paper, we introduce a formalized and modular system for automating Zeek signature generation using Large Language Models (LLMs). Our pipeline begins with static analysis of binary artifacts, extracts salient behavioral features, and transforms them into structured prompts for an LLM tasked with synthesizing Zeek scripts. We provide a rigorous formal framework that defines each stage of this transformation, along with theoretical models for prompt distortion, injection resilience, and sanitization. Furthermore, we explore the adversarial surface exposed by LLMs—introducing a taxonomy of injection attacks, prompt inversion risks, and behavioral feedback loops—and propose mitigations grounded in filtering and robust prompt engineering. Our approach not only accelerates signature creation but also enhances interpretability and adaptability in evolving threat environments. The framework lays the groundwork for future extensions involving dynamic analysis and automated post-validation of generated signatures.

Smart contracts are increasingly being adopted in modern supply chain (SC) systems, offering a transformative shift from traditional centralized models to decentralized, automated, and trustless processes. However, vulnerabilities in the chain of smart contract represent a critical threat to the reliability and security of supply chain ecosystems, often arising from intricate logical flaws, unintended inter-contract interactions, or improper handling of user input—issues that remain difficult to uncover through conventional testing or manual auditing. This paper introduces an innovative deep learning–based methodology that transforms smart contract bytecode into image representations, enabling precise and efficient classification of diverse vulnerability patterns. Unlike existing approaches that rely on source code availability or dynamic execution, the proposed framework operates independently of source code and circumvents the limitations inherent to dynamic analysis, thereby offering a versatile and system-agnostic solution. Experimental evaluations conducted on a newly curated dataset collected from multiple publicly available repositories demonstrate the robustness of the proposed method, achieving 92.24% accuracy and an 89.06% F1-score. Beyond its strong empirical performance, the framework ensures reproducibility, data transparency, and adaptability across heterogeneous blockchain environments. Collectively, these contributions establish a comprehensive and accessible foundation for enhancing the detection, mitigation, and overall resilience of blockchain smart contracts.

This study addresses the increasing threat to Psychological Well-Being (PWB) posed by Depression, Anxiety, and Stress conditions. Machine learning methods have shown promising results for several psychological conditions. However, the lack of transparency in existing models impedes practical application. The study aims to develop explainable machine learning models for depression, anxiety and stress prediction, focusing on features extracted from tasks involving handwriting and drawing. Two hundred patients completed the Depression, Anxiety, and Stress Scale (DASS-21) and performed seven tasks related to handwriting and drawing. Extracted features, encompassing pressure, stroke pattern, time, space, and pen inclination, were used to train the explainable-by-design Entropy-based Logic Explained Network (e-LEN) model, employing first-order logic rules for explanation. Performance comparison was performed with XGBoost, enhanced by the SHAP explanation method. The trained models achieved notable accuracy in predicting depression (0.749 ±0.089), anxiety (0.721 ±0.088), and stress (0.761 ±0.086) through 10-fold cross-validation (repeated 20 times). The e-LEN model’s logic rules facilitated clinical validation, uncovering correlations with existing clinical literature. While performance remained consistent for depression and anxiety on an independent test dataset, a slight degradation was observed for stress prediction in the test task.

Detecting malware across diverse architectures and evasion techniques has become a critical challenge as modern malware increasingly targets non-traditional platforms such as IoT devices. Traditional signature-based approaches, which rely on architecture-specific bytecode patterns, often fail when malware is recompiled for different platforms or obfuscated to evade detection. In this paper, we propose a novel framework for cross-architecture, signature-based malware detection. Our approach leverages Intermediate Representation (IR) to identify malicious behaviors in a platform-independent manner. By matching higher-level patterns in the IR, our framework generates signatures capable of detecting malware across multiple architectures and resisting common obfuscation techniques. The proposed framework adopts the YARA syntax, a widely used tool for malware detection, while introducing custom high-level primitives that abstract complex IR constructs. These primitives simplify the rule-writing process, enabling more efficient and precise signature creation. Additionally, we discuss the limitations of current approaches and demonstrate how our framework advances the state of the art in signature-based malware detection.

This research involved 88 young adults aged between 20 years and 35 years from two different countries, Spain and Italy. This work aims to explore preferences of the two groups toward synthetic voices, created for the experiment with variations in gender and quality for each language. The Spanish group was asked to evaluate the two high-quality voices of Elena and Pablo and the two low-quality voices of Maria and Juan while the Italian group was asked to assess the high-quality voices of Giulia and Antonio and the low-quality voices of Clara and Edoardo. The shortened and digitized version of the Virtual Agent Voice Acceptance Questionnaire (VAVAQ) was administered, respectively, in the Spanish or Italian version on the basis of the referring group to collect participants’ preferences. Due to the pandemic situation, participants were mainly contacted via email. Each participant was provided with a specific link. Outcomes revealed that Spanish and Italian young adults showed a greater appreciation toward the high-quality female voice compared to the other proposed voices. Regarding participants’ cross-cultural differences, Italian participants seem to judge the voices as more emotionally engaging than the Spanish participants whereas Spanish participants consider the audited voices as more natural and expressive than the Italian participants.

This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original online approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders ([Formula: see text]), and patients with a clinical diagnosis of depression ([Formula: see text]). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features’ categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation.

The growth of data-driven approaches typical of Machine Learning leads to an ever-increasing need for large quantities of labeled data. Unfortunately, these attributions are often made automatically and/or crudely, thus destroying the very concept of “ground truth” they are supposed to represent. To address this problem, we introduce HUM-CARD, a dataset of human trajectories in crowded contexts manually annotated by nine experts in engineering and psychology, totaling approximately 5000 hours. Our multidisciplinary labeling process has enabled the creation of a well-structured ontology, accounting for both individual and contextual factors influencing human movement dynamics in shared environments. Preliminary and descriptive analyzes are presented, highlighting the potential benefits of this dataset and its methodology in various research challenges.

The growing presence of robots in our daily life brings out the need to develop systems that are ever more user-friendly, considering users' needs and preferences. This is necessary in particular when robots are developed to be introduced into welfare settings. For this reason, a study is proposed with the aim to investigate differently aged (young, middle-aged, and seniors) potential users' assessment of male android robots as opposed to male virtual agents, in order to compare interactive systems characterized by different levels of embodiment. 180 participants joined the experiment, which consisted of watching video clips depicting android robots and virtual agents, and subsequently fulfilling the RAQ (Robot Acceptance Questionnaire) and the VAAQ (Virtual Agent Acceptance Questionnaire). Results highlighted substantial differences in robots and agents' assessment, differences which seem to be affected by participants' age, as well.

The easy solution of the selection tasks with social contract rules, compared to the poor results of the original formulation of the tasks with descriptive rules, has been interpreted, in the framework of massive modularity hypothesis, as the evidence that humans are adaptively skilled to reason about particular deontic domains. Nevertheless, the two versions of the tasks are incomparable because they differ not only for the content of the rule, but also in terms of structural features that make their solution based on different types of reasoning -- about and from a rule. In this study we disentangled these two aspects by testing type of reasoning (about vs. from) and content of the rule (descriptive vs. social contract) separately in order to establish their relative importance in human reasoning. In addition to these factors, we examined the putative effect of pragmatic expectation (neutral vs. disconfirming the status of the rule) on the participants' performance. Four hundred undergraduates participated in the study, with a 2x2x2 between-subjects design. Results showed that "reasoning from" tasks were better performed than "reasoning about" tasks, regardless of the content of the rule and the type of expectation.