arXiv subjects

Francesco Greco

Publications and source records attributed to Francesco Greco.

At least 19 recordsLinked to original sources

arXiv2026

Cavity-mediated localization and collective electron correlation phases

Collective strong coupling of molecular ensembles to optical cavities opens a route to modifying matter through genuinely collective electronic correlations. Yet even in the absence of a cavity, Coulomb correlations are notoriously difficult to describe, and cavity coupling adds transverse correlation channels extending over the entire molecular ensemble. Here we show that this seemingly intractable problem admits a controlled description by spin glass theory, i.e., by the analytically solvable spherical Sherrington-Kirkpatrick model. Our results predict two collective correlation phases, a paracorrelated phase and a spin-glass correlation phase, beyond the conventional uncorrelated molecular regime. These phases reveal an entropy-driven localization-delocalization mechanism that transfers molecular electronic states into collectively degenerate cavity-dressed states. Analytic calculation suggest that this collective correlated state can either remain insulating or even turn metallic if the entropic occupations favor a fractional filling. Our work reveals cavity-mediated electron correlations as a microscopic mechanism for emergent phases in strongly coupled molecular ensembles.

quant-ph↗

arXiv2025

MORPHEUS: A Multidimensional Framework for Modeling, Measuring, and Mitigating Human Factors in Cybersecurity

Despite technical advancements, the human factor remains cybersecurity's most exploited vulnerability. Current research acknowledges this but remains fragmented, treating vulnerabilities as isolated, static traits. To address this, we introduce MORPHEUS, a holistic framework conceptualizing human-centric security as a dynamic, interconnected system. Grounded in the Cognition-Affect-Behavior (CAB) model and Attribution Theory, MORPHEUS consolidates 50 human factors influencing susceptibility to major cyberthreats (e.g., phishing, malware, password management, and misconfigurations). Beyond mere identification, the framework introduces a hierarchical Causal Pathway Architecture. Systematically mapping 302 empirical interactions (82.8% architecture-compliant), we reveal how cognitive, affective, and behavioral processes jointly shape security outcomes, distilling them into 12 recurring interaction mechanisms. MORPHEUS further links theory to practice through an inventory of 99 validated psychometric instruments for empirical assessment. We illustrate its applicability through in-depth operational scenarios for risk diagnosis and targeted interventions. Overall, MORPHEUS provides a comprehensive theoretical foundation for advancing human-centered cybersecurity.

cs.CR↗

arXiv2025

Improving Phishing Resilience with AI-Generated Training: Evidence on Prompting, Personalization, and Duration

Phishing remains a persistent cybersecurity threat; however, developing scalable and effective user training is labor-intensive and challenging to maintain. Generative Artificial Intelligence offers an interesting opportunity, but empirical evidence on its instructional efficacy remains scarce. This paper provides an experimental validation of Large Language Models (LLMs) as autonomous engines for generating phishing resilience training. Across two controlled studies (N=480), we demonstrate that AI-generated content yields significant pre-post learning gains regardless of the specific prompting strategy employed. Study 1 (N=80) compares four prompting techniques, finding that even a straightforward "direct-profile" strategy--simply embedding user traits into the prompt--produces effective training material. Study 2 (N=400) investigates the scalability of this approach by testing personalization and training duration. Results show that complex psychometric personalization offers no measurable advantage over well-designed generic content, while longer training duration provides a modest boost in accuracy. These findings suggest that organizations can leverage LLMs to generate high-quality, effective training at scale without the need for complex user profiling, relying instead on the inherent capabilities of the model.

cs.CR↗

arXiv2025

Bug Detective and Quality Coach: Developers' Mental Models of AI-Assisted IDE Tools

AI-assisted tools support developers in performing cognitively demanding tasks such as bug detection and code readability assessment. Despite the advancements in the technical characteristics of these tools, little is known about how developers mentally model them and how mismatches affect trust, control, and adoption. We conducted six co-design workshops with 58 developers to elicit their mental models about AI-assisted bug detection and readability features. It emerged that developers conceive bug detection tools as \textit{bug detectives}, which warn users only in case of critical issues, guaranteeing transparency, actionable feedback, and confidence cues. Readability assessment tools, on the other hand, are envisioned as \textit{quality coaches}, which provide contextual, personalized, and progressive guidance. Trust, in both tasks, depends on the clarity of explanations, timing, and user control. A set of design principles for Human-Centered AI in IDEs has been distilled, aiming to balance disruption with support, conciseness with depth, and automation with human agency.

cs.SE↗

arXiv2025

Can Large Language Models Automate Phishing Warning Explanations? A Controlled Experiment on Effectiveness and User Perception

Phishing has become a prominent risk in modern cybersecurity, often used to bypass technological defences by exploiting predictable human behaviour. Warning dialogues are a standard mitigation measure, but the lack of explanatory clarity and static content limits their effectiveness. In this paper, we report on our research to assess the capacity of Large Language Models (LLMs) to generate clear, concise, and scalable explanations for phishing warnings. We carried out a large-scale between-subjects user study (N = 750) to compare the influence of warning dialogues supplemented with manually generated explanations against those generated by two LLMs, Claude 3.5 Sonnet and Llama 3.3 70B. We investigated two explanatory styles (feature-based and counterfactual) for their effects on behavioural metrics (click-through rate) and perceptual outcomes (e.g., trust, risk, clarity). The results provide empirical evidence that LLM-generated explanations achieve a level of protection statistically comparable to expert-crafted messages, effectively automating a high-cost task. While Claude 3.5 Sonnet showed a trend towards reducing click-through rates compared to manual baselines, Llama 3.3, despite being perceived as clearer, did not yield the same behavioral benefits. Feature-based explanations were more effective for genuine phishing attempts, whereas counterfactual explanations diminished false-positive rates. Other variables, such as workload, gender, and prior familiarity with warning dialogues, significantly moderated the effectiveness of warnings. These results indicate that LLMs can be used to automatically build explanations for warning users against phishing, and that such solutions are scalable, adaptive, and consistent with human-centred values.

cs.CR↗

arXiv2025

Explanation User Interfaces: A Systematic Literature Review

Artificial Intelligence (AI) is one of the major technological advancements of this century, bearing incredible potential for users through AI-powered applications and tools in numerous domains. Being often black-box (i.e., its decision-making process is unintelligible), developers typically resort to eXplainable Artificial Intelligence (XAI) techniques to interpret the behaviour of AI models to produce systems that are transparent, fair, reliable, and trustworthy. However, presenting explanations to the user is not trivial and is often left as a secondary aspect of the system's design process, leading to AI systems that are not useful to end-users. This paper presents a Systematic Literature Review on Explanation User Interfaces (XUIs) to gain a deeper understanding of the solutions and design guidelines employed in the academic literature to effectively present explanations to users. To improve the contribution and real-world impact of this survey, we also present a platform to support Human-cEnteRed developMent of Explainable user interfaceS (HERMES) and guide practitioners and scholars in the design and evaluation of XUIs.

cs.HC↗

arXiv2025

Explanation-Driven Interventions for Artificial Intelligence Model Customization: Empowering End-Users to Tailor Black-Box AI in Rhinocytology

The integration of Artificial Intelligence (AI) in modern society is transforming how individuals perform tasks. In high-risk domains, ensuring human control over AI systems remains a key design challenge. This article presents a novel End-User Development (EUD) approach for black-box AI models, enabling users to edit explanations and influence future predictions through targeted interventions. By combining explainability, user control, and model adaptability, the proposed method advances Human-Centered AI (HCAI), promoting a symbiotic relationship between humans and adaptive, user-tailored AI systems.

cs.HC↗

arXiv2025

Understanding User Mental Models in AI-Driven Code Completion Tools: Insights from an Elicitation Study

Integrated Development Environments increasingly implement AI-powered code completion tools (CCTs), which promise to enhance developer efficiency, accuracy, and productivity. However, interaction challenges with CCTs persist, mainly due to mismatches between developers' mental models and the unpredictable behavior of AI-generated suggestions, which is an aspect underexplored in the literature. We conducted an elicitation study with 56 developers using co-design workshops to elicit their mental models when interacting with CCTs. Different important findings that might drive the interaction design with CCTs emerged. For example, developers expressed diverse preferences on when and how code suggestions should be triggered (proactive, manual, hybrid), where and how they are displayed (inline, sidebar, popup, chatbot), as well as the level of detail. It also emerged that developers need to be supported by customization of activation timing, display modality, suggestion granularity, and explanation content, to better fit the CCT to their preferences. To demonstrate the feasibility of these and the other guidelines that emerged during the study, we developed ATHENA, a proof-of-concept CCT that dynamically adapts to developers' coding preferences and environments, ensuring seamless integration into diverse workflows.

cs.HC↗

arXiv2025

At low temperatures, glass-forming liquids relax in a simple way

Glass-forming liquids have only a modest tendency to crystallize and hence their dynamics can be studied even below the melting temperature. The relaxation dynamics of most of these liquids shows at a temperature $T_c$, somewhat above the glass-transition temperature $T_g$, a crossover, which indicates the conjunction of two different dynamical regimes. For temperatures slightly above $T_c$, experiments and computer simulations have extensively probed this dynamics on the particle level and identified several universal scaling laws that are often compatible with theoretical predictions. Using large scale computer simulations we extend these studies to temperatures below $T_c$ and find that the relaxation mechanism is qualitatively different from the one found at higher temperatures. We identify new scaling laws that allow to give a simple description of the relaxation dynamics at very low $T$s. Specifically we reveal that the cage-escape process is related to rare but large particle displacements that give rise to a distinctive sub-diffusive power-law in the time correlation functions. This insight helps to advance our understanding on the relaxation dynamics of glass-forming systems at temperatures that are close to the experimental glass transition.

cond-mat.soft↗

arXiv2024

PEtra: A Flexible and Open-Source PE Loop Tracer for Polymer Thin-Film Transducers

Accurate characterization of ferroelectric properties in polymer piezoelectrics is critical for optimizing the performance of flexible and wearable ultrasound transducers, such as screen-printed PVDF devices. Standard charge measurement techniques, like the Sawyer-Tower circuit, often fall short when applied to ferroelectric polymers due to low-frequency leakage. In this work, we present PEtra, an open-source and versatile piezoelectric loop tracer. PEtra employs a transimpedance amplifier (LMP7721, TI) to convert picoampere-level currents into measurable voltages, covering a frequency range of 0.1 Hz to 5 Hz for a gain setting of 10^7 V/A, and 0.1 Hz to 200 Hz for gain settings between 10^3 V/A to 10^6 V/A (10-fold increments). We demonstrate through simulations and experimental validations that PEtra achieves a sensitivity down to 2 pA, effectively addressing the limitations of traditional charge measurement methods. Compared to the Sawyer-Tower circuit, PEtra directly amplifies currents without the need for a reference capacitor. As a result, it is less susceptible to leakage and can operate at lower frequencies, improving measurement accuracy and reliability. PEtra's design is fully open source, offering researchers and engineers a versatile tool to drive advancements in flexible PVDF transducer technology.

eess.SY↗

arXiv2024

APOLLO: A GPT-based tool to detect phishing emails and generate explanations that warn users

Phishing is one of the most prolific cybercriminal activities, with attacks becoming increasingly sophisticated. It is, therefore, imperative to explore novel technologies to improve user protection across both technical and human dimensions. Large Language Models (LLMs) offer significant promise for text processing in various domains, but their use for defense against phishing attacks still remains scarcely explored. In this paper, we present APOLLO, a tool based on OpenAI's GPT-4o to detect phishing emails and generate explanation messages to users about why a specific email is dangerous, thus improving their decision-making capabilities. We have evaluated the performance of APOLLO in classifying phishing emails; the results show that the LLM models have exemplary capabilities in classifying phishing emails (97 percent accuracy in the case of GPT-4o) and that this performance can be further improved by integrating data from third-party services, resulting in a near-perfect classification rate (99 percent accuracy). To assess the perception of the explanations generated by this tool, we also conducted a study with 20 participants, comparing four different explanations presented as phishing warnings. We compared the LLM-generated explanations to four baselines: a manually crafted warning, and warnings from Chrome, Firefox, and Edge browsers. The results show that not only the LLM-generated explanations were perceived as high quality, but also that they can be more understandable, interesting, and trustworthy than the baselines. These findings suggest that using LLMs as a defense against phishing is a very promising approach, with APOLLO representing a proof of concept in this research direction.

cs.HC↗

arXiv2023

Rheological signatures of a glass-glass transition in an aging colloidal clay

The occurrence of non-equilibrium transitions between arrested states has recently emerged as an intriguing issue in the field of soft glassy materials. The existence of one such transition has been suggested for aging colloidal clays (Laponite$^{\circledR}$ suspensions) at weight concentration 3.0%, although further experimental evidences are necessary to validate this scenario. Here, we test the occurrence of this transition for spontaneously aged (non-rejuvenated) samples, by exploiting the rheological tools of Dynamical Mechanical Analysis. On imposing consecutive compression cycles to differently aged clay suspensions, we find that a quite abrupt change of rheological parameters occurs for ages around three days. For the Young and elastic moduli, the change with the waiting time is essentially independent from the deformation rate, whereas other "fluid-like" properties, such as the loss modulus, do clearly display some rate dependence. We also show that the crossover identified by rheology coincides with deviations of the relaxation time (obtained through X-Ray Photon Correlation Spectroscopy) from its expected monotonic increase with aging. Thus, our results robustly support the existence of a glass-glass transition in aging colloidal clays, highlighting characteristic features of of their viscoelastic behaviour.

cond-mat.soft↗

arXiv2023

Towards a Novel Ultrasound System Based on Low-Frequency Feature Extraction From a Fully-Printed Flexible Transducer

Ultrasound is a key technology in healthcare, and it is being explored for non-invasive, wearable, continuous monitoring of vital signs. However, its widespread adoption in this scenario is still hindered by the size, complexity, and power consumption of current devices. Moreover, such an application demands adaptability to human anatomy, which is hard to achieve with current transducer technology. This paper presents a novel ultrasound system prototype based on a fully printed, lead-free, and flexible polymer ultrasound transducer, whose bending radius promises good adaptability to the human anatomy. Our application scenario focuses on continuous blood flow monitoring. We implemented a hardware envelope filter to efficiently transpose high-frequency ultrasound signals to a lower-frequency spectrum. This reduces computational and power demands with little to no degradation in the task proposed for this work. We validated our method on a setup that mimics human blood flow by using a flow phantom and a peristaltic pump simulating 3 different heartbeat rhythms: 60, 90 and 120 beats per minute. Our ultrasound setup reconstructs peristaltic pump frequencies with errors of less than 0.05 Hz (3 bpm) from the set pump frequency, both for the raw echo and the enveloped echo. The analog pre-processing showed a promising reduction of signal bandwidth of more than 6x: pulse-echo signals of transducers excited at 12.5 MHz were reduced to about 2 MHz. Thus, allowing consumer MCUs to acquire and elaborate signals within mW-power range in an inexpensive fashion.

physics.med-ph↗

arXiv2023

Topology optimization of flexoelectric metamaterials with apparent piezoelectricity

The flexoelectric effect, coupling polarization and strain gradient as well as strain and electric field gradients, is universal to dielectrics, but, as compared to piezoelectricity, it is more difficult to harness as it requires field gradients and it is a small-scale effect. These drawbacks can be overcome by suitably designing metamaterials made of a non-piezoelectric base material but exhibiting apparent piezoelectricity. We develop a theoretical and computational framework to perform topology optimization of the representative volume element of such metamaterials by accurately modeling the governing equations of flexoelectricity using a Cartesian B-spline method, describing geometry with a level set, and resorting to genetic algorithms for optimization. We consider a multi-objective optimization problem where area fraction competes with four fundamental piezoelectric functionalities (stress/strain sensor/ actuator). We computationally obtain Pareto fronts, and discuss the different geometries depending on the apparent piezoelectric coefficient being optimized. In general, we find competitive estimations of apparent piezoelectricity as compared to reference materials such as quartz and PZT ceramics. This opens the possibility to design devices for sensing, actuation and energy harvesting from a much wider, cheaper and effective class of materials.

physics.app-ph↗

arXiv2022

Supercooled liquids are Fickian yet non-Gaussian

Reply to "Comment on 'Fickian non-Gaussian diffusion in glass-forming liquids' ". In [ArXiv:2210.07119v1], Berthier et al. questioned the findings of our letter [Phys. Rev. Lett. 128, 168001 (2022)], concerning the existence and the features of Fickian non-Gaussian diffusion in glass-forming liquids. Here we demonstrate that their arguments are either wrong, or not meaningful to our scope. Thus, we fully confirm the validity and novelty of our results.

cond-mat.stat-mech↗

arXiv2021

SENSATION: An Authoring Tool to Support Event-State Paradigm in End-User Development

In this paper, we present the design and the evaluation of an authoring tool for End-User Development, which supports the definition of Trigger-Actions rules that combines events and states in the triggers. The possibility of using either states or events in triggers has already been discussed in the literature. However, it is recognized that the state/event distinction is difficult to manage for users. In this paper, we propose an authoring tool that provides explicit support for managing this distinction. We compare it with a state-of-the-art authoring tool that implements the classical event-event paradigm.

cs.HC↗

arXiv2020

Toward the use of temporary tattoo electrodes for impedancemetric respiration monitoring and other electrophysiological recordings

Development of dry, ultra-conformable and unperceivable temporary tattoo electrodes (TTEs), based on the ink-jet printing of PEDOT:PSS on top of commercially available temporary tattoo paper, has gained increasing attention as a new and promising technology for electrophysiological recordings on skin. In this work we present a TTEs epidermal sensor for real time monitoring of respiration through transthoracic impedance measurements, exploiting a new design based on the application of soft screen printed Ag ink and magnetic interlink, that guarantees a repositionable, long term stable and robust interconnection of TTEs with external docking devices. The efficiency of the TTE and the proposed interconnection strategy under stretching (up to 10%) and over time (up to 96 hours) has been verified on a dedicated experimental setup and on humans, fulfilling the proposed specific application of transthoracic impedance measurements. The proposed approach makes this technology suitable for large-scale production and suitable not only for the specific use case presented, but also for real time monitoring of different bio-electric signals, as demonstrated through specific proof of concept demonstrators.

cond-mat.mtrl-sci↗

arXiv2019

Concentrated suspensions of Brownian beads in water: dynamic heterogeneities trough a simple experimental technique

Concentrated suspensions of Brownian hard-spheres in water are an epitome for understanding the glassy dynamics of both soft materials and supercooled molecular liquids. From an experimental point of view, such systems are especially suited to perform particle tracking easily, and, therefore, are a benchmark for novel optical techniques, applicable when primary particles cannot be resolved. Differential Variance Analysis (DVA) is one such novel technique that simplifies significantly the characterization of structural relaxation processes of soft glassy materials, since it is directly applicable to digital image sequences of the sample. DVA succeeds in monitoring not only the average dynamics, but also its spatio-temporal fluctuations, known as dynamic heterogeneities. In this work, we study the dynamics of dense suspensions of Brownian beads in water, imaged through digital video-microscopy, by using both DVA and single-particle tracking. We focus on two commonly used signatures of dynamic heterogeneities: the dynamic susceptibility, $\chi_4$, and the non-Gaussian parameter, $\alpha_2$. By direct comparison of these two quantities, we are able to highlight similarities and differences. We do confirm that $\chi_4$ and $\alpha_2$ provide qualitatively similar information, but we find quantitative discrepancies in the scalings of characteristic time and length scale on approaching the glass transition.

cond-mat.soft↗