Schizophrenia is a psychiatric disorder that presents significant diagnostic challenges due to its complex neurophysiological characteristics. This paper investigates the potential of Large Language Models (LLMs), such as OpenAI’s GPT-4 and GPT-o1, in detecting schizophrenia through electroencephalography (EEG) analysis. Using the LMSU public ScZ EEG dataset, we conducted a series of experiments involving different types of input data, including raw EEG signals, frequency band summaries, and graphical representations of brain activity. Our findings demonstrate that LLMs can accurately classify schizophrenic and healthy individuals while offering interpretable, clinically relevant insights aligned with established EEG markers. By integrating these models into the diagnostic workflow, we explore the concept of Symbiotic AI, where LLMs act as cognitive collaborators, enhancing clinicians’ ability to analyze complex data efficiently and transparently. This approach not only improves diagnostic accuracy but also facilitates real-time decision-making, paving the way for earlier and more precise detection of schizophrenia in clinical settings.

Despite the widespread deployment of 5G technologies, there exists a critical gap in security testing for 5G Standalone (SA) devices. Existing methods, largely manual and labor-intensive, are ill-equipped to fully uncover the state of security in the implementations of 5G SA protocols and standards on devices, severely limiting the ability to conduct comprehensive evaluations. To address this issue, in this work, we introduce a novel, open-source framework that automates the security testing process for 5G SA devices. By leveraging enhanced functionalities of 5G SA core and Radio Access Network (RAN) software, our framework offers a streamlined approach to generating, executing, and evaluating test cases, specifically focusing on the Non-Access Stratum layer. Our application of this framework across multiple 5G SA devices provides in-depth security insights, significantly improving testing efficiency and breadth.

Subgraph-enhanced graph neural networks (SGNN) can increase the expressive power of the standard message-passing framework.This model family represents each graph as a collection of subgraphs, generally extracted by random sampling or with hand-crafted heuristics.Our key observation is that by selecting "meaningful" subgraphs, besides improving the expressivity of a GNN, it is also possible to obtain interpretable results.For this purpose, we introduce a novel framework that jointly predicts the class of the graph and a set of explanatory sparse subgraphs, which can be analyzed to understand the decision process of the classifier.The subgraphs produced by our framework allow to achieve comparable performance in terms of accuracy, with the additional benefit of providing explanations.

The increasing diffusion of mobile devices and their integration with sophisticated hardware and software components has promoted the development of numerous applications in which developers find new ingenious ways to exploit the possibilities offered by the access to resources such as cameras, biometric sensors, and GPS receivers. As a result, we are increasingly used to seeing applications that make extensive use of sensitive resources, potentially dangerous for our privacy. To address this problem, the latest approach to support user awareness in terms of privacy is represented by the Privacy Indicators (PI), a software solution implemented by the operating system to provide a visual stimulus to inform users whenever a dangerous resource is exploited by the app. However, the effectiveness of this approach has not been assessed yet. In this article, we present the result of a study on the effectiveness of using the PI to inform the user every time an app accesses the mobile device camera or microphone. We have chosen these two resources as the PI are currently implemented only for a very limited number of permissions. The controlled experiment involved 122 Android users who were asked to complete a series of tasks on their smartphone through prototypes using the involved resources in an explicit and latent way. Although the PI mechanism is very similar between Android and iOS, we have decided to focus on the former due to its greater diffusion. The results show no significant correlation between the use of PI and the detection of the resource being used by the app, suggesting that the effectiveness of PI in improving sensitive-related resources usage awareness, as currently implemented, is still unsatisfactory. In order to understand if the problem was due to the specific implementation of the PI, we implemented an enhanced version and compared it with the standard one. The results confirmed that an implementation that makes the indicators more visible and that is clearer in highlighting the fact that the app is accessing a resource improves resources usage awareness.

The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose the adoption of formal methods to discriminate between different users by exploiting a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we consider a feature vector able to capture the typing style that is specific to each given user. By considering this feature model, in detail we propose to consider model checking with logic temporal properties to discriminate between different users using a set of keystroke features.

Automatically generating test cases plays an important role to reduce the time spent by developers during the testing phase. In last years, several approaches have been proposed to tackle such a problem: amongst others, search-based techniques have been shown to be particularly promising. In this paper we describe Ocelot, a search-based tool for the automatic generation of test cases in C. Ocelot allows practitioners to write skeletons of test cases for their programs and researchers to easily implement and experiment new approaches for automatic test-data generation. We show that Ocelot achieves a higher coverage compared to a competitive tool in 81% of the cases. Ocelot is publicly available to support both researchers and practitioners.