Publications

2025

• Approximate Hypothesis Testing
  
  • Le Gouic Nicolas
  • Graczyk Robert
  • Moser Stefan M
  , 2025. We establish the sample complexity of Approximate Hypothesis Testing (AHT) where—unlike in classical hypothesis testing—we need only approximate the hypothesis governing the observed samples rather than recover it exactly. We show that the AHT sample complexity scales inversely with the multivariate Bhatthacharyya distance evaluated on a “maximally confusable” subset of hypotheses that is characterized by the chosen distance measure and approximation accuracy. Index terms—hypothesis testing, sample complexity, learning, Bhattacharyya distance, Hellinger distance.
• Optimized Co-Design of Delta Sigma Modulators and Fir-DACs for High Speed Transmitters
  
  • Lima Evelyn
  • Schlegel Nicolas
  • Mathieu Yves
  • Jabbour Chadi
  , 2025, pp.246-250. Delta sigma modulators (DSMs) are a very attractive solution to build flexible high resolution transmitters needed for future massive multiple-input multiple-output (mMIMO) systems. The main challenge in this architecture is its out-of-band (OOB) noise which can be addressed by using FIRDACs. This paper proposes a joint design of the DSM and FIRDACs. The coefficients of the latter are chosen using a multiobjective optimization approach which takes into account the DAC and the DSM noise. The proposed solution is simulated for a 400 MHz 4-Channel signal. Compared to a classical design approach with similar complexity, it achieves 2.86 dB and 16.19 dB improvements for respectively the adjacent and the alternate channel power ratio and error vector magnitude (EVM) of 1.02. (10.1109/NewCAS64648.2025.11107152)
  DOI : 10.1109/NewCAS64648.2025.11107152
• Side-Channel Attack Detection using gem5 and HPCs
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025.
• A 24 GHz Rectifier and its Applications in Energy Harvesting and Wireless Power Transfer Systems
  
  • Wang Yibo
  • Niotaki Kyriaki
  • Lepage Anne Claire
  • Begaud Xavier
  , 2025. This paper presents the design and characterization of a millimeter wave 24 GHz rectifier. A prototype was fabricated on a Rogers 5880 substrate and was characterized over input power and frequency. The rectifier is able to collect power from the FR3 band reserved for Integrated Sensing and Communication. The rectifier exhibits a measured RF-to-dc power conversion efficiency of 21.5% for an input power of about 5 dBm and for a 1.5 kΩ load at 22.6 GHz, while its peak measured efficiency is 34% for an input power of 15 dBm. The potential of the rectifier in energy harvesting and wireless power transfer systems is analysed, showing that the proposed rectifier has the potential to drive low-power devices at a distance of a few meters from a base station or an access point.
• Circulator-Based RF Energy Harvester with Wide Input Power Dynamic Range
  
  • Kibiwott Albert
  • Mohellebi Reda
  • Niotaki Kyriaki
  • Jabbour Chadi
  , 2025, pp.440-444. Radio frequency energy harvesting (RFEH) is a promising solution for powering ultra-low-power (ULP) devices. However, capturing ambient energy is challenging due to low power densities and unpredictable RF power available in the surrounding environment. Moreover, conventional rectifiers exhibit limited power conversion efficiency (PCE) and a narrow input power dynamic range (PDR) due to impedance mismatch and power reflection losses. This paper presents a novel circulatorbased multi-branch RF energy harvester designed to efficiently recycle reflected RF power across multiple rectifier branches, significantly enhancing overall energy conversion efficiency and extending the operational power range. Simulation results at 900 MHz reveal that the proposed architecture achieves a PCE of 81% at an input power of 4 dBm and a PDR of 23 dB (spanning from −15 dBm to 8 dBm). These results demonstrate an 8 dB and 26% improvement in dynamic range and efficiency respectively compared to traditional single-rectifier designs, highlighting the effectiveness of the proposed solution. (10.1109/NewCAS64648.2025.11106982)
  DOI : 10.1109/NewCAS64648.2025.11106982
• Contributions to explainable anomaly detection using data depth
  
  • Valla Romain
  , 2025. Abnormal events are subjects of interest in various fields of application, such as industry, finance and medicine, when they provide information that differs from the general trend. We can see that the arrival of new interconnected sensors, more powerful and in greater numbers, is leading to an increase in the mass of data available for analysis, requiring innovative methods to solve modern challenges. On the one hand, the growing volume of samples requires faster algorithms, while potential data contamination calls for reliable and robust techniques. On the other hand, it is becoming complicated but necessary to interpret certain decisions made by these new tools, as their complexity has increased when coupled with a large quantity of data.The first part of this thesis focuses on the use of data depths, which measure the centrality of data, in the context of anomaly detection. The development of these methods in recent years, particularly their implementation via numerical approximations coupled with optimisation procedures, has made it possible to extend their use to large-scale datasets. We carry out several comparisons with recognised and widely applied methods in this field, while providing experimental (visual) tools to facilitate parameterisation and thus facilitate their use by practitioners.In the second part, we propose a new method for visualising multivariate data based on reducing the dimensionality of the input space with the aim of better visual separation of abnormal observations (=anomalies) in the sense of data depths. Abnormal Component Analysis (ACA) consists in searching for a new orthogonal basis in an unsupervised way to represent the data while providing interpretation opportunities on the input variables responsible for anomalies of interest. The detailed algorithm, together with certain details enabling users to use it, is followed by applications on real data sets to demonstrate the relevance of the technique.Finally, the last part extends the ACA reasoning to functional data in order to take advantage of the continuous nature of many observed phenomena (time series, frequency analysis, etc.). This extension makes use of dictionaries of functions with various properties to extract significant information from different types of anomalies, which are also described using real data sets. This extension proves to be effective for obtaining a "summary" visualisation of the functional data, complemented by an interpretation obtained using the functions employed.
• Exploiting Heterogeneous Labels in Deep Learning for Medical Image Analysis : Application to the Automated Diagnosis of Liver Diseases
  
  • Sarfati Emma
  , 2025. Liver cancer is one of the deadliest cancers worldwide, with a high mortality-to-incidence ratio and major health and economic consequences. Main risk factors include chronic hepatitis B and C infections, non-alcoholic steatohepatitis (NASH), alcohol-related liver disease, and exposure to hepatotoxic substances. Cirrhosis is the leading predisposing factor for hepatocellular carcinoma (HCC), present in the majority of cases. Cirrhosis and HCC diagnoses are typically based on liver biopsy, an invasive and costly procedure. Biomedical imaging is a less invasive alternative but suffers from high inter-radiologist variability. Radiological annotations, while more accessible, are considered weak labels compared to histological standards. This creates a common imbalance: many weakly annotated images and few strongly labeled ones. In this context, pretraining models using weak supervision can improve performance on rare but critical classification tasks. Contrastive learning, a self-supervised method that structures the representation space by pulling similar samples together, enables the use of unlabeled or partially labeled data. However, the integration of heterogeneous annotations remains underexplored. This thesis proposes strategies combining contrastive learning with supervised training via hybrid loss functions, as well as fully contrastive frameworks incorporating both discrete and continuous clinical metadata. Another approach leverages weak radiological predictors directly to guide the learning process. These methods were applied to cirrhosis classification and small HCC detection from CT scans. Results show improved diagnostic accuracy, reduced inter-expert variability, and potential for earlier and more reliable detection of liver diseases.
• A Secure and Cooperative Departure Protocol for Connected Automated Platoons
  
  • Braiteh Farah-Emma
  • Tse Davy
  • Yhia Ounas
  • Bassi Francesca
  • Khatoun Rida
  , 2025. Cooperative and automated vehicular platoons enhance road safety and reduce traffic congestion by enabling vehicles to travel closely together and maneuver in a synchronized manner. This synchronization relies on vehicle-to-vehicle (V2V) communications, which, while beneficial, also introduce vulnerabilities to potential cyberattacks. In this paper, we introduce a new cooperative and secure protocol for platoon departures, focusing specifically on the departure phase. We demonstrate that, without security measures, a vehicle attempting to leave the platoon could exploit the leave messages of the platoon protocol and introduce attacks that may disrupt the formation of the platoon or even jeopardize its stability. To mitigate this risk, we propose data consistency measures that protect both the stability and integrity of the platoon. Simulations conducted using Plexe simulator validate the security of the proposed protocol through rigorous security assessments.
• Primal-Dual Coordinate Descent for Nonconvex-Nonconcave Saddle Point Problems Under the Weak MVI Assumption
  
  • Walwil Iyad
  • Fercoq Olivier
  , 2025. <div><p>We introduce two novel primal-dual algorithms for addressing nonconvex, nonconcave, and nonsmooth saddle point problems characterized by the weak Minty Variational Inequality (MVI). The first algorithm, Nonconvex-Nonconcave Primal-Dual Hybrid Gradient (NC-PDHG), extends the well-known Primal-Dual Hybrid Gradient (PDHG) method to this challenging problem class. The second algorithm, Nonconvex-Nonconcave Stochastic Primal-Dual Hybrid Gradient (NC-SPDHG), incorporates a randomly extrapolated primal-dual coordinate descent approach, extending the Stochastic Primal-Dual Hybrid Gradient (SPDHG) algorithm.</p><p>To our knowledge, designing a coordinate-based algorithm to solve nonconvexnonconcave saddle point problems is unprecedented, and proving its convergence posed significant difficulties. This challenge motivated us to utilize PEPit, a Pythonbased tool for computer-assisted worst-case analysis of first-order optimization methods. By integrating PEPit with automated Lyapunov function techniques, we successfully derived the NC-SPDHG algorithm.</p><p>Both methods are effective under a mild condition on the weak MVI parameter, achieving convergence with constant step sizes that adapt to the structure of the problem. Numerical experiments on logistic regression with squared loss and perceptronregression problems validate our theoretical findings and show their efficiency compared to existing state-of-the-art algorithms, where linear convergence is observed. Additionally, we conduct a convex-concave least-squares experiment to show that NC-SPDHG performs competitively with SAGA, a leading algorithm in the smooth convex setting.</p></div>
• Evaluating KASLR Break on RISC-V using gem5: Microarchitectural Side-Channel Analysis of Page-Table Walks
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025, 2500, pp.229–235. <div><p>This paper leverages the gem5 simulator to analyze a microarchitectural KASLR break on RISC-V systems. Previous research [2] demonstrated the feasibility of KASLR breaks on RISC-V hardware platforms (C906 and U74). Our paper aims to provide insights that are not easily attainable through traditional hardware experiments. By employing gem5, we gain access to fine-grained metrics such as cycle counts, cache behavior, branch prediction statistics, and TLB accesses, among others. These detailed insights give a deeper analysis of the KASLR bypass and help understand the attack mechanics better.</p></div> (10.1007/978-3-031-94855-8_15)
  DOI : 10.1007/978-3-031-94855-8_15
• Traffic Prediction Improvement in 5G and beyond: AI and Self-Controlled Components
  
  • N’kouka Thierry Isaac
  • Aubonnet Tatiana
  • Lemoine Frédéric
  • Kellil Mounir
  • Simoni Noëmie
  , 2025, pp.213-216. The advent of 5G and Beyond 5G (B5G) networks requires novel network management strategies to mitigate potential congestion. Traditional reactive approaches are inadequate as they address issues only post-occurrence, whereas proactive Artificial Intelligence (AI) powered methods can predict and optimize resource allocation. This paper leverages AI on 5G emulated datasets to forecast network traffic, facilitating proactive resource allocation. The experimental results however indicate suboptimal model performance due to the high variability, irregular patterns, sudden traffic bursts, noise, and inconsistent data distributions in the datasets. Our analysis revealed that these issues arise from uncoordinated background traffic, system operations, and random traffic-consuming activities, leading to underperforming model outcomes. Given these challenges, we have proposed a Self-Controlled Component (SCC)-based approach to ensure that high-quality data is fed into the selected AI models, thereby improving prediction accuracy and enhancing performance. (10.1109/NTMS65597.2025.11076981)
  DOI : 10.1109/NTMS65597.2025.11076981
• Automatic Classification of Software Repositories: a Systematic Mapping Study
  
  • Balla Stefano
  • Degueule Thomas
  • Robbes Romain
  • Falleri Jean-Rémy
  • Zacchiroli Stefano
  , 2025. The rapid growth of software repositories on development platforms such as GitHub, as well as archives like Software Heritage, prompts the need for better repository classification. Machine learning is increasingly used to automate this classification, but there are no secondary studies analyzing this research landscape. We present a systematic mapping study of 43 primary sources published between 2002 and 2023, where we examine the goals, inputs, outputs, training, and evaluation processes involved in automatic repository classification. Our findings reveal a growing interest in automatic classification, particularly to enhance the discoverability and recommendation of relevant repositories. Other applications, such as classification for mining studies, were surprisingly underrepresented. We also observe that a lack of standardized datasets, classification tasks, and evaluation metrics makes it difficult to compare the performance of different techniques.
• CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation
  
  • Hou Abe Bohan
  • Weller Orion
  • Qin Guanghui
  • Yang Eugene
  • Lawrie Dawn
  • Holzenberger Nils
  • Blair-Stanek Andrew
  • van Durme Benjamin
  , 2025. Legal professionals need to write analyses that rely on citations to relevant precedents, i.e., previous case decisions. Intelligent systems assisting legal professionals in writing such documents provide great benefits but are challenging to design. Such systems need to help locate, summarize, and reason over salient precedents in order to be useful. To enable systems for such tasks, we work with legal professionals to transform a large open-source legal corpus into a dataset supporting two important backbone tasks: information retrieval (IR) and retrieval-augmented generation (RAG). This dataset CLERC (Case Law Evaluation Retrieval Corpus), is constructed for training and evaluating models on their ability to (1) find corresponding citations for a given piece of legal analysis and to (2) compile the text of these citations (as well as previous context) into a cogent analysis that supports a reasoning goal. We benchmark state-of-the-art models on CLERC, showing that current approaches still struggle: GPT-4o generates analyses with the highest ROUGE F-scores but hallucinates the most, while zero-shot IR models only achieve 48.3% recall@1000.
• Modeling Musical Genre Trajectories through Pathlet Learning
  
  • Marey Lilian
  • Laclau Charlotte
  • Sguerra Bruno
  • Viard Tiphaine
  • Moussallam Manuel
  , 2025, pp.202-210. <div><p>The increasing availability of user data on music streaming platforms opens up new possibilities for analyzing music consumption. However, understanding the evolution of user preferences remains a complex challenge, particularly as their musical tastes change over time. This paper uses the dictionary learning paradigm to model user trajectories across different musical genres. We define a new framework that captures recurring patterns in genre trajectories, called pathlets, enabling the creation of comprehensible trajectory embeddings. We show that pathlet learning reveals relevant listening patterns that can be analyzed both qualitatively and quantitatively. This work improves our understanding of users' interactions with music and opens up avenues of research into user behavior and fostering diversity in recommender systems. A dataset of 2000 user histories tagged by genre over 17 months, supplied by Deezer (a leading music streaming company), is also released with the code.</p></div> (10.1145/3708319.3733695)
  DOI : 10.1145/3708319.3733695
• Can AI expose tax loopholes? Towards a new generation of legal policy assistants
  
  • Fratrič Peter
  • Holzenberger Nils
  • Amariles David Restrepo
  , 2025. The legislative process is the backbone of a state built on solid institutions. Yet, due to the complexity of laws -- particularly tax law -- policies may lead to inequality and social tensions. In this study, we introduce a novel prototype system designed to address the issues of tax loopholes and tax avoidance. Our hybrid solution integrates a natural language interface with a domain-specific language tailored for planning. We demonstrate on a case study how tax loopholes and avoidance schemes can be exposed. We conclude that our prototype can help enhance social welfare by systematically identifying and addressing tax gaps stemming from loopholes.
• A Quantitative Approach to the GDPR’s Anonymization and Pseudonymization Tests
  
  • Holzenberger Nils
  • Maxwell Winston
  , 2025. This article examines two tests from the European General Data Protection Regulation (GDPR): (1) the test for full anonymisation (the "anonymisation test"), and (2) the test for applying "appropriate technical measures" to protect personal data when full anonymisation is not achieved (the "pseudonymisation test"). Both tests depend on vague legal standards and have given rise to legal disputes and differing interpretations among data protection authorities and courts, including in the context of machine learning. Under the anonymisation test, data are sufficiently anonymised when they are immune from re-identification by an attacker using "all means reasonably likely to be used". Under the pseudonymisation test, technical measures to protect personal data that are not anonymised must be "appropriate" with regard to the risks of data loss. Here, we use methods from law and economics to transform these qualitative tests into quantitative tests: we take a risk-management approach and put forward a mathematical formalization of the GDPR's criteria, to supplement existing qualitative approaches. We chart different attack efforts and re-identification probabilities, and propose this as a methodology to help stakeholders discuss whether data are sufficiently anonymised to satisfy the GDPR anonymisation test, or alternatively, whether pseudonymisation efforts are "appropriate" under the GDPR. The resulting graphs can help stakeholders decide whether the anonymisation test is fulfilled, and discuss the use of Privacy-Enhancing Technologies necessary to pass the pseudonymisation test. We apply our proposed framework to several scenarios, applying the anonymisation test to a Large Language Model, and the pseudonymisation test to a database protected with differential privacy. (10.2139/ssrn.5162461)
  DOI : 10.2139/ssrn.5162461
• A Transductive and Inductive GNNs for Physical Moving Objects Detection in Surface Scenes for Digital Twins
  
  • Prummel Wieke
  • Giraldo Jhony
  • Subudhi Badri
  • Zakharova Anastasia
  • Bouwmans Thierry
  , 2025 (1), pp.133-149. Computer vision applications using static or moving cameras are often required in digital twins generation. More specifically, the detection of moving objects is essential to provide a virtual representation of an environment in order to reflect physical moving objects accurately. To this end, background subtraction (BGS) is then applied to separate the background (BG) and the foreground (FG) from videos. Numerous publications employ mathematical, machine learning, and signal processing models to be more robust to the open challenges presented in videos. Recently, many methods using graph neural networks for BGS have been reported, with very promising outcomes. This chapter provides a survey of transductive and inductive Graph Neural Networks (GNNs) for moving objects detection (MOD) comparing their architectures. After analysis of their strategies and limitations, a comparative evaluation of the large-scale CDnet2014 dataset is provided. Finally, we conclude with some potential future research directions. (10.1201/9781003582489-10)
  DOI : 10.1201/9781003582489-10
• From information leakage to rank statistics in side-channel attacks
  
  • Béguinot Julien
  • Rioul Olivier
  , 2025. In practical side-channel analysis, evaluators can estimate some key hypothesis rank statistics used as security indicators—e.g., arithmetic or geo- metric mean, median, α-marginal guesswork, or enumeration success rate. Yet, a direct estimation becomes time-consuming as security levels increase. We provide new bounds on these figures of merit in terms of the mutual in- formation between the secret and its side-channel leakages. These bounds provide theoretical insights on the evolution of the figures of merit in terms of noise level, computational complexity, and data complexity. Our results enable fast shortcut formulas for the certification laboratories, potentially enabling them to speed up the security evaluation process.
• Categorical semantics of compositional reinforcement learning
  
  • Bakirtzis Georgios
  • Savvas Michail
  • Topcu Ufuk
  Journal of Machine Learning Research, Microtome Publishing, 2025, 26 (130), pp.1-37. Compositional knowledge representations in reinforcement learning (RL) facilitate modular, interpretable, and safe task specifications. However, generating compositional models requires the characterization of minimal assumptions for the robustness of the compositionality feature, especially in the case of functional decompositions. Using a categorical point of view, we develop a knowledge representation framework for a compositional theory of RL. Our approach relies on the theoretical study of the category MDP, whose objects are Markov decision processes (MDPs) acting as models of tasks. The categorical semantics models the compositionality of tasks through the application of pushout operations akin to combining puzzle pieces. As a practical application of these pushout operations, we introduce zig-zag diagrams that rely on the compositional guarantees engendered by the category MDP. We further prove that properties of the category MDP unify concepts, such as enforcing safety requirements and exploiting symmetries, generalizing previous abstraction theories for RL.
• Masked Vector Sampling for HQC
  
  • Spyropoulos Maxime
  • Vigilant David
  • Perion Fabrice
  • Pacalet Renaud
  • Sauvage Laurent
  , 2025, 1, pp.750-758. Anticipating the advent of large quantum computers, NIST started a worldwide competition in 2016 aiming to define the next cryptographic standards. HQC is one of these post-quantum schemes selected for standardization. In 2022, Guo et al. introduced a timing attack that exploited a weakness in HQC rejection sampling function to recover its secret key in 866,000 calls to an oracle. The authors of HQC updated its specification by applying an algorithm to sample vectors in constant time. A masked implementation of this function was later proposed for BIKE but it is not directly applicable to HQC. In this paper we propose a specification-compliant masked version of the HQC vector sampling which relies, to our knowledge, on the first masked implementation of the Barrett reduction. (10.5220/0013637400003979)
  DOI : 10.5220/0013637400003979
• From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
  
  • Bouniot Quentin
  • Redko Ievgen
  • Mallasto Anton
  • Laclau Charlotte
  • Struckmeier Oliver
  • Arndt Karol
  • Heinonen Markus
  • Kyrki Ville
  • Kaski Samuel
  , 2025. In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explaining the upward trend of their performance, however, remains difficult as different DNN architectures of comparable depth and width -common factors associated with their expressive power -may exhibit a drastically different performance even when trained on the same dataset. In this paper, we introduce the concept of the non-linearity signature of DNN, the first theoretically sound solution for approximately measuring the non-linearity of deep neural networks. Built upon a score derived from closed-form optimal transport mappings, this signature provides a better understanding of the inner workings of a wide range of DNN architectures and learning paradigms, with a particular emphasis on the computer vision task. We provide extensive experimental results that highlight the practical usefulness of the proposed non-linearity signature and its potential for long-reaching implications. The code for our work is available at https: //github.com/qbouniot/AffScoreDeep.
• Assessing Security RISC: Analyzing Flush+Fault Attack on RISC-V using gem5 Simulator
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025, Assessing Security RISC: Analyzing Flush+Fault Attack on RISC-V using gem5 Simulator. <div><p>Microarchitectural side-channel attacks exploit vulnerabilities such as cache behavior to leak sensitive data. These attacks have been extensively studied on x86 architectures but they remain less explored on RISC-V systems. A recent paper (Gerlach et al., 2023) demonstrated existing and novel microarchitectural attacks on RISC-V hardware platforms (C906, U74, C910, C908). This hardware-based analysis, while realistic, lacks the flexibility and detailed behavioral insights needed to fully understand these attacks. Simulation environments like gem5 (Lowe-Power, 2024) provide fine-grained control and diverse metrics to overcome this limitation and observe the attack in detail. In this paper, gem5 is used to explore Flush+Fault (Gerlach et al., 2023) side-channel attack on RISC-V architecture which was originally tested on RISC-V hardware. Through gem5, we analyze detailed insights of attack such as cache patterns, and timing behaviors. Our results demonstrate the gem5's potential for advancing the understanding of RISC-V microarchitectural vulnerabilities and eventually for developing effective countermeasures.</p></div>
• Assessing the Vulnerabilities of RISC-V
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025. <div><p>Emerging RISC-V processors require rigorous security evaluation to address microarchitectural vulnerabilities inherent in their rapidly evolving ecosystem. A recent paper [1] implemented both known and novel side-channel attacks targeting commercial RISC-V CPUs (U74 and C906). While this hardware-based research confirmed vulnerabilities, it could not provide detailed insights into attack dynamics. We bridge this gap using the gem5 simulation framework to systematically analyze side-channel attacks on RISC-V architectures. Our paper focuses on the access-retired attack, which exploits the unprivileged rdinstret instruction to infer protected filesystem data. By tracking retired instruction counts, attackers detect microarchitectural state differences caused by directory access checks. We utilize the gem5 simulator in full-system (FS) mode to capture kernel-level behaviors, allowing us to analyze critical performance metrics including instruction retirement, cache performance, and branch prediction statistics. This detailed simulation-based analysis is essential for understanding the behavior of the attack and for developing effective countermeasures. Advancing RISC-V security research with simulation tools like gem5 is thus a promising direction for mitigating future side-channel vulnerabilities.</p></div>
• Navigating the sociotechnical labyrinth: Dynamic certification for responsible embodied AI
  
  • Bakirtzis Georgios
  • Aler Tubella Andrea
  • Theodorou Andreas
  • Danks David
  • Topcu Ufuk
  , 2025. Sociotechnical requirements shape the governance of artificially intelligent (AI) systems. In an era where embodied AI technologies are rapidly reshaping various facets of contemporary society, their inherent dynamic adaptability presents a unique blend of opportunities and challenges. Traditional regulatory mechanisms, often designed for static---or slower-paced---technologies, find themselves at a crossroads when faced with the fluid and evolving nature of AI systems. Moreover, typical problems in AI, for example, the frequent opacity and unpredictability of the behaviour of the systems, add additional sociotechnical challenges. To address these interconnected issues, we introduce the concept of dynamic certification, an adaptive regulatory framework specifically crafted to keep pace with the continuous evolution of AI systems. The complexity of these challenges requires common progress in multiple domains: technical, socio-governmental, and regulatory. Our proposed transdisciplinary approach is designed to ensure the safe, ethical, and practical deployment of AI systems, aligning them bidirectionally with the real-world contexts in which they operate. By doing so, we aim to bridge the gap between rapid technological advancement and effective regulatory oversight, ensuring that AI systems not only achieve their intended goals but also adhere to ethical standards and societal values. (10.1016/B978-0-44-340553-2.00019-8)
  DOI : 10.1016/B978-0-44-340553-2.00019-8
• Cheat-proof random numbers generated from quantum entanglement
  
  • Brown Peter
  Nature, Nature Publishing Group, 2025, 642 (8069), pp.875-876. A quantum random-number generator has been developed that uses classical cryptography to certify that its output was produced by a quantum process. NEWS AND VIEWS 11 June 2025 Cheat-proof random numbers generated from quantum entanglement A quantum random-number generator has been developed that uses classical cryptography to certify that its output was produced by a quantum process. By Peter Brown Twitter Facebook Email Random-number generators are used to pick juries, select samples for financial audits and assign participants in clinical trials to treatment or placebo groups. The unpredictability of random numbers ensures that such selections are not biased. However, for these systems to be secure and fair, we need a way to verify that a random-number generator has not been tampered with. Writing in Nature, Kavuri et al.1 report a random-number generator based on the quantum phenomenon of entanglement and underpinned by classical cryptography. Their protocol marks an important step forward, because it doesn’t require users to take the randomness of the output on trust: every part of the protocol is publicly recorded, and the randomness of the numbers produced can be verified by anyone. (10.1038/d41586-025-01451-y)
  DOI : 10.1038/d41586-025-01451-y