Publications

2025

• Finding Software Supply Chain Attack Paths with Logical Attack Graphs
  
  • Soeiro Luı́s
  • Robert Thomas
  • Zacchiroli Stefano
  , 2025. Cyberattacks are becoming increasingly frequent and sophisticated, often exploiting the software supply chain (SSC) as an attack vector. Attack graphs provide a detailed representation of the sequence of events and vulnerabilities that could lead to a successful security breach in a system. MulVal is a widely used open-source tool for logical attack graph generation in networked systems. However, its current lack of support for capturing and reasoning about SSC threat propagation makes it unsuitable for addressing modern SSC attacks, such as the XZ compromise or the 3CX double SSC attack. To address this limitation, we propose an extension to MulVal that integrates SSC threat propagation analysis with existing network-based threat analysis. This extension introduces a new set of predicates within the familiar MulVal syntax, enabling seamless integration. The new facts and interaction rules model SSC assets, their dependencies, interactions, compromises, additional security mechanisms, initial system states, and known threats. We explain how this integration operates in both directions and demonstrate the practical application of the extension.
• LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance
  
  • Kim Jae Myung
  • Alaniz Stephan
  • Schmid Cordelia
  • Akata Zeynep
  , 2025. Despite recent advances in text-to-image generation, using synthetically generated data seldom brings a significant boost in performance for supervised learning. Oftentimes, synthetic datasets do not faithfully recreate the data distribution of real data, i.e., they lack the fidelity or diversity needed for effective downstream model training. While previous work has employed few-shot guidance to address this issue, existing methods still fail to capture and generate features unique to specific real images. In this paper, we introduce a novel dataset generation framework named LoFT, LoRA-Fused Training-data Generation with Few-shot Guidance. Our method fine-tunes LoRA weights on individual real images and fuses them at inference time, producing synthetic images that combine the features of real images for improved diversity and fidelity of generated data. We evaluate the synthetic data produced by LoFT on 10 datasets, using 8 to 64 real images per class as guidance and scaling up to 1000 images per class. Our experiments show that training on LoFT-generated data consistently outperforms other synthetic dataset methods, significantly increasing accuracy as the dataset size increases. Additionally, our analysis demonstrates that LoFT generates datasets with high fidelity and sufficient diversity, which contribute to the performance improvement. The code is available at https://github.com/ExplainableML/LoFT.
• Equivariant Denoisers for Plug and Play Image Restoration
  
  • Renaud Marien
  • Guez Eliot
  • Leclaire Arthur
  • Papadakis Nicolas
  , 2025. One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Typical image distributions are invariant to some set of transformations, such as rotations or flips. However, most deep architectures are not designed to represent an invariant image distribution. Recent works have proposed to overcome this difficulty by including equivariance properties within a Plug-and-Play paradigm. In this work, we propose two unified frameworks named Equivariant Regularization by Denoising (ERED) and Equivariant Plug-and-Play (EPnP) based on equivariant denoisers and stochastic optimization. We analyze the convergence of the proposed algorithms and discuss their practical benefit.
• Consensus-Based Optimization Beyond Finite-Time Analysis
  
  • Bianchi Pascal
  • Dragomir Radu-Alexandru
  • Priser Victor
  , 2025. We analyze a zeroth-order particle algorithm for the global optimization of a non-convex function, focusing on a variant of Consensus-Based Optimization (CBO) with small but fixed noise intensity. Unlike most previous studies restricted to finite horizons, we investigate its long-time behavior with fixed parameters. In the mean-field limit, a quantitative Laplace principle shows exponential convergence to a neighborhood of the minimizer x * . For finitely many particles, a block-wise analysis yields explicit error bounds: individual particles achieve long-time consistency near x * , and the global best particle converge to x * . The proof technique combines a quantitative Laplace principle with block-wise control of Wasserstein distances, avoiding the exponential blow-up typical of Grönwall-based estimates.
• Extending Timed Automata with Clock Derivatives
  
  • Cortés David
  • Leneutre Jean
  • Malvone Vadim
  • Ortiz James
  • Schobbens Pierre-Yves
  , 2025, 16194, pp.99-119. <div><p>The increasing complexity of safety-critical systems in domains like aerospace, robotics, and industrial control demands precise modeling and verification methods. While Timed Automata (TA) and Distributed Timed Automata (DTA) are standard formalisms for realtime systems, they assume synchronized clocks or lack the expressiveness to capture clock drift and indirect timing dependencies. To overcome these limits, we propose Timed Automata with Clock Derivatives (idTA), extending TA with rate constraints to model independent clock evolution. We also introduce DLν , a temporal logic over Multi-Timed Labeled Transition Systems (MLTS), capturing properties of systems with unsynchronized clocks. We show that model checking for DLν is EXP-TIME-complete. Finally, we present MIMETIC, a model checking tool supporting idTA and DLν , providing a platform for analyzing clock interactions and verification of Distributed Real-time Systems (DRTS).</p></div> (10.1007/978-3-032-10794-7_6)
  DOI : 10.1007/978-3-032-10794-7_6
• A Maximum Length Sequence-Based Method for Robust Round-Trip Latency Estimation in online Digital Audio Workstations
  
  • Gil Panal J M
  • Richard Gaël
  • David Aurélien
  , 2025. Accurate estimation of latency when working with digital audio equipment is critical for the precise operation of certain applications. This is particularly true for Digital Audio Workstations (DAWs) and other tools used in the creation and editing of audio, especially music. These systems require exact synchronization or alignment of tracks, which is essential for the mixing process. Latency is an inherent phenomenon in audio capture and restitution. Although it may sometimes be minimal, it is always variable depending on the device, operating system, and audio configuration. The undesired effect introduced by latency—specifically referred to in this context as round-trip latency—manifests as a delay between the audio input and the corresponding output. The most effective way to address this issue is through prior measurement to enable proper compensation. Various methods exist for performing this measurement, generally based on the playback and recording of acoustic signals. This article presents an existing method applied in a novel way within the domain of audio and web browsers, based on the use of a Maximum Length Sequence (MLS) signal. This signal is commonly used in room impulse response characterization. To validate its effectiveness and identify the limitations of the proposed approach, multiple tests and experiments were conducted on different devices. Results were compared across various browsers and operating systems, and the proposed solution was benchmarked against the methods employed by existing online DAWs. The implementation of the proposed method is available as part of the Hi-Audio online platform—an open-source, browser-based DAW—providing a practical demonstration of its applicability and integration in real-world web audio environments.
• Huygens' Metasurfaces Analysis Through Electric and Magnetic Surface Impedance Modeling
  
  • Medrar Ghiles
  • Lepage Anne Claire
  • Begaud Xavier
  , 2025.
• Approximate solutions to games of ordered preference
  
  • de las Heras Molins Pau
  • Roy-Almonacid Eric
  • Lee Dong Ho
  • Peters Lasse
  • Fridovich-Keil David
  • Bakirtzis Georgios
  , 2025, pp.3378-3385. Autonomous vehicles must balance ranked objectives, such as minimizing travel time, ensuring safety, and coordinating with traffic. Games of ordered preference effectively model these interactions but become computationally intractable as the time horizon, number of players, or number of preference levels increase. While receding horizon frameworks mitigate long-horizon intractability by solving sequential shorter games, often warm-started, they do not resolve the complexity growth inherent in existing methods for solving games of ordered preference. This paper introduces a solution strategy that avoids excessive complexity growth by approximating solutions using lexicographic iterated best response (IBR) in receding horizon, termed “lexicographic IBR over time.” Lexicographic IBR over time uses past information to accelerate convergence. We demonstrate through simulated traffic scenarios that lexicographic IBR over time efficiently computes approximate-optimal solutions for receding horizon games of ordered preference, converging towards generalized Nash equilibria. (10.1109/ITSC60802.2025.11423775)
  DOI : 10.1109/ITSC60802.2025.11423775
• A Dual-Rate Hybrid DAC for High-Speed 6G Massive MIMO Transceivers
  
  • Akbari Akram
  • Wantiez Eric
  • Perez Cristobal
  • Jabbour Chadi
  , 2025, pp.1-4. This paper presents the design, modeling, and simulation of a dual-rate hybrid digital-to-analog converter (DAC) architecture optimized for 6G massive multiple-input multiple-output (MIMO) transmitters. The proposed structure splits the digital input into a low-resolution Nyquist-rate path for the most significant bits (MSBs) and a high-speed delta-sigma modulator (DSM) path for the least significant bits (LSBs). This configuration enables a favorable trade-off between bandwidth, linearity, and power efficiency, while addressing the limitations of conventional Nyquist and DSM-only DACs. We demonstrate how the architecture improves robustness without dynamic element matching (DEM), and analyze the spectral behavior including out-of-band (OOB) noise suppression. Simulation results using sinusoidal and 256-QAM modulated inputs confirm that the hybrid DAC achieves spectral cleanliness compared to pure DSM DACs, even in the presence of 1% mismatch. These findings position the hybrid DAC as a (10.1109/ICECS66544.2025.11270801)
  DOI : 10.1109/ICECS66544.2025.11270801
• Generation of frequency entanglement with an effective quantum dot-waveguidetwo-photon quadratic interaction
  
  • Meguebel Mohamed
  • Federico Maxime
  • Felicetti Simone
  • Belabas Nadia
  • Fabre Nicolas
  Optica Quantum, Optica publishig group, 2025, 3 (6), pp.617-633. Light-matter interactions with quantum dots have been extensively studied to harness key quantum properties of photons, such as indistinguishability and entanglement. In this theoretical work, we exploit the atomic-like four-level structure of a quantum dot coupled to a waveguide to model a shaping frequency entangling gate (FrEnGATE) for single photons. Our approach is based on the identification of input frequencies and an atomic level structure for which frequencydependent one-photon transitions are adiabatically eliminated, while frequency-dependent twophoton transitions are resonantly enhanced. The frequency entanglement performance of the gate is analyzed using a Schmidt decomposition for continuous variables, revealing a trade-off between entanglement generation efficiency and entanglement quality. We further demonstrate the use of the FrEnGATE for the generation of entangled frequency qudit states. (10.1364/OPTICAQ.571592)
  DOI : 10.1364/OPTICAQ.571592
• Altered Histories in Version Control System Repositories: Evidence from the Trenches
  
  • Rapaport Solal
  • Pautet Laurent
  • Tardieu Samuel
  • Zacchiroli Stefano
  , 2025. Version Control Systems (VCS) like Git allow developers to locally rewrite recorded history, e.g., to reorder and suppress commits or specific data in them. These alterations have legitimate use cases, but become problematic when performed on public branches that have downstream users: they break push/pull workflows, challenge the integrity and reproducibility of repositories, and create opportunities for supply chain attackers to sneak into them nefarious changes. We conduct the first large-scale investigation of Git history alterations in public code repositories. We analyze 111 M (millions) repositories archived by Software Heritage, which preserves VCS histories even across alterations. We find history alterations in 1.22 M repositories, for a total of 8.7 M rewritten histories. We categorize changes by where they happen (which repositories, which branches) and what is changed in them (files or commit metadata). Conducting two targeted case studies we show that altered histories recurrently change licenses retroactively, or are used to remove "secrets" (e.g., private keys) committed by mistake. As these behaviors correspond to bad practices-in terms of project governance or security management, respectively-that software recipients might want to avoid, we introduce GitHistorian, an automated tool, that developers can use to spot and describe history alterations in public Git repositories.
• Temporal change of outdoor RF-EMF levels in four European countries: a microenvironmental measurement study
  
  • Beláčková Lea
  • Veludo Adriana Fernandes
  • Aminzadeh Reza
  • van Bladel Han
  • Griffon Vincent
  • Cardis Elisabeth
  • Dongus Stefan
  • Eeftens Marloes
  • Guxens Mònica
  • Joseph Wout
  • de Llobet Patricia
  • Mazet Paul
  • van Torre Patrick
  • Thielens Arno
  • Vermeulen Roel
  • Wiart Joe
  • Röösli Martin
  • Huss Anke
  Environmental Research, Elsevier, 2025, 285, pp.122315 (1-8). Introduction Over the past two decades, the amount of transmitted mobile data has increased rapidly. It is unknown whether the implementation of the new technologies enabling this has resulted in changes of outdoor radio-frequency electromagnetic fields (RF-EMF) exposure. Therefore, microenvironmental measurements were used to investigate temporal trends in RF-EMF exposure between 2016 and 2023, in the Netherlands, Switzerland, Belgium and Spain, following a similar protocol across campaigns. Microenvironmental measurements refer to exposure measurements performed at predefined small areas that have been differentiated with a specific function in that particular area. This allowed us to compare exposure trends between countries and years. Methods The data was collected as part of the ACCEDERA (2016–2018), ETAIN (2023), and GOLIAT (2023) projects, walking repeatedly the same routes with RF-EMF exposimeters. Identical microenvironments were identified in each country and measurements of the exposure from mobile base stations, mobile phones and the total exposure were compared across years. Results Comparing measurements between 6 and 14 unique microenvironments in each country, our data did not suggest significant changes in the exposure from the mobile base station origin (total downlink exposure) between baseline measurements in 2016 to follow up and 2023 for the four countries. Across all countries and years the median values of the mobile base station exposure ranged from 0.11 mW/m2 (Switzerland, 2023) to 0.62 mW/m2 (Netherlands, 2018). There was no consistent trend in the individual microenvironments across the countries. Conclusions Our measurements of RF-EMF outdoor exposure levels across included microenvironment groups do not indicate change in exposure levels between 2016 and 2023 despite an increase in mobile data traffic by a factor of 8 in Western Europe1. (10.1016/j.envres.2025.122315)
  DOI : 10.1016/j.envres.2025.122315
• Comprehensive Measurement‐Based Assessment of Downlink RF‐EMF Exposure in Urban Environments: Multi‐Method Analysis and Intercomparison
  
  • Wang Shanshan
  • Zhang Yarui
  • Liu Yukun
  • Liu Jiang
  • Conil Emmanuelle
  • Jawad Ourouk
  • Samaras Theodoros
  • Ourak Lamine
  • Wiart Joe
  Bioelectromagnetics, Wiley, 2025, 46 (8), pp.e70033 (1-12). ABSTRACT This paper presents a comprehensive measurement‐based assessment of radio‐frequency (RF) electromagnetic field (EMF) exposure level in a French city. Three types of assessment methods are used to collect measurement data: drive test (DT), spot measurements, and sensor networks. The DT measurements were conducted by a portable spectrum analyzer, i.e., Tektronix RSA 306B, connected to a 3‐axis antenna mounted on the roof of the vehicle. DT system continuously recorded frequency‐dependent electric field (E‐field) values on a pre‐defined outdoor route. The spot measurements were done in the same region, covered by DT, with both broadband and frequency‐selective systems. Additionally, 19 sensors were installed on streetlamps in the same part of the city to measure the broadband E‐field level. The overall statistical analysis on raw data shows good agreement on RF‐EMF exposure level from three types of measurements. Then a distance‐based moving average method was carried out to remove the random noise in the DT data, where the optimized window size is explored using Kolmogorov‐Smirnov test. The smoothed DT data show a good correlation with nearby spot measurement values, as well as with base station antenna (BSA) density. Specific fifth‐generation (5G) spot measurements, performed with and without traffic‐attracting downloads, demonstrate the impact of beamforming on exposure levels in 5G new radio (NR) bands. Then spot measurements were used to build the exposure map using the kriging method, where the kriging prediction from the trained model is further compared with DT. Furthermore, the temporal variations observed in the sensor network were analyzed in relation to distance from the nearest BSA, revealing an inverse proportional relationship between E‐field level and proximity to the nearest BSA. This study shows good reliability in assessing the RF‐EMF exposure level using different systems. The advantages and limitations of different systems are also demonstrated by performing the intercomparison between data sets. (10.1002/bem.70033)
  DOI : 10.1002/bem.70033
• Potentially Problematic Word Usages and How to Detect Them: A Survey
  
  • Garí Soler Aina
  • Labeau Matthieu
  • Clavel Chloé
  , 2025, pp.1-24. We introduce and frame the concept of potentially problematic word usages (PPWUs): word occurrences that are likely to cause communication breakdowns of a semantic nature. While much research has been devoted to lexical complexity, ambiguity, vagueness and related issues, no work has attempted to fully capture the intricate nature of PPWUs. We review linguistic factors, datasets and metrics that can be helpful for PPWU detection. We also discuss challenges to their study, such as their complexity and subjectivity, and highlight the need for future work on this phenomenon. (10.18653/v1/2025.starsem-1.35)
  DOI : 10.18653/v1/2025.starsem-1.35
• Extrapolation of quantum measurement data
  
  • Manos Konstantinos
  • Weilenmann Mirjam
  • Navascués Miguel
  , 2025. We consider the problem of predicting future expectation values of a collection of quantum observables, given their noisy expectation values at past times. The measured observables, the initial state of the physical system and even the Hilbert space are unknown; we nonetheless assume a promise on the energy distribution of the state. Investigating to what extent extrapolation is possible in this framework, we discover highly problematic datasets that allow full predictability at any future time $τ$, but only when past averages are known up to precision superexponential in $τ$. We also find families of "self-testing datasets", which allow practical predictability under reasonable noise levels and whose approximate realization singles out specific Hamiltonians, states and measurement operators. We identify "aha! datasets", which drastically increase the predictability of the future statistics of an unrelated measurement, as well as "fog banks": fairly simple datasets that exhibit complete unpredictability at some future time $τ$, but full predictability for a later time $τ'&gt;τ$. Finally, we prove that the extrapolation problem is efficiently solvable up to arbitrary precision through hierarchies of semidefinite programming relaxations.
• Correlation Self-Testing of Quantum Theory against Generalised Probabilistic Theories with Restricted Relabelling Symmetry
  
  • Sengupta Kuntal
  • Weilenmann Mirjam
  • Colbeck Roger
  , 2025. Correlation self-testing of quantum theory involves identifying a task or set of tasks whose optimal performance can be achieved only by theories that can realise the same set of correlations as quantum theory in every causal structure. Following this approach, previous work has ruled out various classes of generalised probabilistic theories whose joint state spaces have a certain regularity in the sense of a (discrete) rotation symmetry of the bipartite state spaces. Here we consider theories whose bipartite state spaces lack this regularity. We form them by taking the convex hull of all the local states and a finite number of non-local states. We show that a criterion of compositional consistency is needed in such theories: for a measurement effect to be valid, there must exist at least one measurement that it is part of. This goes beyond previous consistency criteria and corresponds to a strengthening of the no-restriction hypothesis. We show that quantum theory outperforms these theories in a task called the adaptive CHSH game, which shows that they can be ruled out experimentally. We further show a connection between compositional consistency and Tsirelson's bound.
• Proximal gradient descent on the smoothed duality gap to solve saddle point problems
  
  • Fercoq Olivier
  , 2025. In this paper, we minimize the self-centered smoothed gap, a recently introduced optimality measure, in order to solve convex-concave saddle point problems. The self-centered smoothed gap can be computed as the sum of a convex, possibly nonsmooth function and a smooth weakly convex function. Although it is not convex, we propose an algorithm that minimizes this quantity, effectively reducing convex-concave saddle point problems to a minimization problem. Its worst case complexity is comparable to the one of the restarted and averaged primal dual hybrid gradient method, and the algorithm enjoys linear convergence in favorable cases.
• Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation
  
  • Garí Soler Aina
  • Labeau Matthieu
  • Clavel Chloé
  , 2025, pp.24580–24596. Word Meaning Negotiations (WMN) are sequences in conversation where speakers collectively discuss and shape word meaning. These exchanges can provide insight into conversational dynamics and word-related misunderstandings, but they are hard to find in corpora. In order to facilitate data collection and speed up the WMN annotation process, we introduce the task of detecting WMN indicators - utterances where a speaker signals the need to clarify or challenge word meaning. We train a wide range of models and reveal the difficulty of the task. Our models have better precision than previous regular-expression based approaches and show some generalization abilities, but have moderate recall. However, this constitutes a promising first step toward an iterative process for obtaining more data. (10.18653/v1/2025.findings-emnlp.1337)
  DOI : 10.18653/v1/2025.findings-emnlp.1337
• Mm, Wat?" Detecting Other-initiated Repair Requests in Dialogue
  
  • Ngo Anh
  • Rollet Nicolas
  • Pelachaud Catherine
  • Clavel Chloé
  , 2025. <div><p>Maintaining mutual understanding is a key component in human-human conversation to avoid conversation breakdowns, in which repair, particularly Other-Initiated Repair (OIR, when one speaker signals trouble and prompts the other to resolve), plays a vital role. However, Conversational Agents (CAs) still fail to recognize user repair initiation, leading to breakdowns or disengagement. This work proposes a multimodal model to automatically detect repair initiation in Dutch dialogues by integrating linguistic and prosodic features grounded in Conversation Analysis. The results show that prosodic cues complement linguistic features and significantly improve the results of pretrained text and audio embeddings, offering insights into how different features interact. Future directions include incorporating visual cues, exploring multilingual and cross-context corpora to assess the robustness and generalizability.</p></div>
• iKnow-audio: Integrating Knowledge Graphs with Audio-Language Models
  
  • Olvera Michel
  • Wang Changhong
  • Stamatiadis Paraskevas
  • Richard Gaël
  • Essid Slim
  , 2025. Contrastive Language–Audio Pretraining (CLAP) models learn by aligning audio and text in a shared embedding space, enabling powerful zero-shot recognition. However, their performance is highly sensitive to prompt formulation and language nuances, and they often inherit semantic ambiguities and spurious correlations from noisy pretraining data. While prior work has explored prompt engineering, adapters, and prefix tuning to address these limitations, the use of structured prior knowledge remains largely unexplored. We present iKnow-audio, a framework that integrates knowledge graphs with audio-language models to provide robust semantic grounding. iKnow-audio builds on the Audio-centric Knowledge Graph (AKG), which encodes ontological relations comprising semantic, causal, and taxonomic connections reflective of everyday sound scenes and events. By training knowlege graph embedding models on the AKG and refining CLAP predictions through this structured knowledge, iKnow-audio improves disambiguation of acoustically similar sounds and reduces reliance on prompt engineering. Comprehensive zero-shot evaluations across six benchmark datasets demonstrate consistent gains over baseline CLAP, supported by embedding-space analyses that highlight improved relational grounding. Resources are publicly available at https://github.com/michelolzam/iknow-audio.
• Formalizing an Iterated Morphological Erosion for the Discovery of Musical Patterns and Their Variations
  
  • Lascabettes Paul
  • Quaetaert Nils
  • Andreatta Moreno
  • Bloch Isabelle
  , 2026, 16296, pp.501-513. The discovery of patterns in a point-set representation of music consists in identifying repeating subsets of points. In this task, musical symbolic data is modeled as a discrete set of points, usually in R n , where each point represents a musical note and its coordinates represent the characteristics of the note, such as its onset or its pitch value. While numerous algorithms have been developed to discover all exact repetitions and extract musically relevant patterns, recent research has turned toward the discovery of patterns that repeat with some variations. Because the morphological erosion of a pattern provides its occurrences, we propose an adaptation of this operation to also obtain its variations with respect to a given approximation. This approach not only reveals certain variations of the pattern, but also enables to associate specific points to the pattern despite the fact that they were not initially present due to the constraints of strict repetition. We demonstrate that the proposed formalism satisfies certain fundamental properties for the musical pattern discovery task, such as the fact that iterating erosion produces cycles of patterns and its translation values. Finally, we apply these operations to the corpus of fugues from Bach's Well-Tempered Clavier, highlighting the usefulness of the proposed approach. (10.1007/978-3-032-09544-2_36)
  DOI : 10.1007/978-3-032-09544-2_36
• Bridging Educational Theories of Cognitive Load to Visualization Design and Evaluation
  
  • Cabouat Anne-Flore
  • Ciccione Lorenzo
  • Huron Samuel
  • Isenberg Tobias
  • Isenberg Petra
  , 2025, pp.33-64. We explore the validity and applicability of educational and cognitive science theoretical frameworks for designing and evaluating data visualizations. Specifically, we are interested in using well-known frameworks from other domains to learn about how the subjective readability of a visualization relates to the perceived cognitive load required to acquire knowledge from it. To that end, we conducted an online randomized study in which each participant performed learning tasks on two different data visualizations. One was presented in three successive parts, following the segmenting principle from the Cognitive Theory of Multimedia Learning, and the other was presented as a single image. Although most learners preferred the segmented style, this treatment did not significantly affect the overall mental effort they reported. Subjective measures of extraneous cognitive load, however, significantly and negatively correlated with visualizations' perceived readability measures. In other words, if a learner found a visualization more readable, they felt it required less mental effort to parse relevant information from it for learning. In addition to a qualitative analysis of learners' preferences, we also contribute an interdisciplinary perspective on cognitive processing of visualizations and a discussion of implications for designing and evaluating data visualizations beyond educational contexts. (10.1109/EduVIS69391.2025.00009)
  DOI : 10.1109/EduVIS69391.2025.00009
• FLORA: Unsupervised Knowledge Graph Alignment by Fuzzy Logic
  
  • Peng Yiwen
  • Bonald Thomas
  • Suchanek Fabian M.
  , 2025, pp.196–215. Knowledge graph alignment is the task of matching equivalent entities (that is, instances and classes) and relations across two knowledge graphs. Most existing methods focus on pure entity-level alignment, computing the similarity of entities in some embedding space. They lack interpretable reasoning and need training data to work. In this paper, we propose FLORA, a simple yet effective method that (1) is unsupervised, i.e., does not require training data, (2) provides a holistic alignment for entities and relations iteratively, (3) is based on fuzzy logic and thus delivers interpretable results, (4) provably converges, (5) allows dangling entities, i.e., entities without a counterpart in the other KG, and (6) achieves state-of-the-art results on major benchmarks. (10.1007/978-3-032-09527-5_11)
  DOI : 10.1007/978-3-032-09527-5_11
• TEP-ones: A simple yet effective approach for transferability estimation of pruned backbones
  
  • Spadaro Gabriele
  • Bragagnolo Andrea
  • Renzulli Riccardo
  • Grangetto Marco
  • Giraldo Jhony
  • Fiandrotti Attilio
  • Tartaglione Enzo
  Neurocomputing, Elsevier, 2025, 668, pp.132209:01-132209-13. In deep learning, the conventional transfer learning paradigm involves fine-tuning a model pre-trained on a complex source task to adapt it to a simpler target task, capitalizing on abundant training data. Concurrently, the paradigm of neural network pruning has emerged as a powerful strategy for enhancing model efficiency, reducing complexity, and optimizing resource utilization. This paper focuses on pruned model transferability estimation for resource-constraint scenarios, where the goal is to rank the performance of pruned pre-trained models on a downstream task without fine-tuning. To this end, from a formal analysis of the intra-class mutual information between samples belonging to the same target class, we observe that, as pruning increases, a sweet phase naturally arises, where the model benefits from better features at the encoder’s output. From this, we derive a Transferability Estimation for Pruned Backbones (TEP-ones) that eases the choice of which pruned model (without the need to train the classifier) is the best candidate for transfer learning. We publicly released the code and pre-trained pruned models at https://github.com/EIDOSLAB/TEP-ones. (10.1016/j.neucom.2025.132209)
  DOI : 10.1016/j.neucom.2025.132209
• MR-CoCo: an Open Mixed Reality Testbed for Co-located Couple Product Configuration and Decision-Making – A Sailboat Case Study
  
  • Vangi Fabio
  • Medeiros Daniel
  • Dastan Mine
  • Fiorentino Michele
  IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2025, 31 (11), pp.9636-9644. The literature has demonstrated the advantages of Mixed Reality (MR) for product configuration by providing a more engaging and effective end-user experience. While collaborative and remote design tools in MR have been widely explored in previous studies, a noticeable gap remains in the exploration of co-located product configuration for couples. This gap is noteworthy since in many industries, couples (e.g., friends, partners) often make purchasing decisions together in physical retail environments. In this paper, we introduce MR-CoCo, an open MR testbed designed to explore collaborative configurations by co-located couples, both in the role of customers. The testbed is developed in Unity and features: (i) a shared MR space with virtual product 3D model anchoring, (ii) shared visualization of the current configuration, (iii) a versatile UI for selecting configuration areas, (iv) hand gestures for 3D drag and drop of colors and materials from 3D catalog to the product. A case study of the personalization of a sailboat is provided as proof of concept. The user study involved 24 couples (48 participants in total), simulating a purchasing experience and the related configuration using MR-CoCo. We assessed usability through post-experience evaluations, with the System Usability Scale (SUS) and the Co-Presence Configuration Questionnaire (CCQ) to measure collaboration and decision-making. The results demonstrated a high level of usability and perceived quality of collaboration. We also explore guidelines that can be used for remote collaboration applications, enabling configuration across a wide range of industries (e.g., automotive and clothing). (10.1109/TVCG.2025.3616734)
  DOI : 10.1109/TVCG.2025.3616734