References of "Scientific congresses, symposiums and conference proceedings"
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See detailDouble-RIS Communication with DF Relaying for Coverage Extension: Is One Relay Enough?
Abdullah, Zaid UL; Kisseleff, Steven UL; Ntontin, Konstantinos UL et al

in IEEE International Conference on Communications, Seoul, May 2022 (2022)

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See detailA Graphical Proof Theory of Logical Time
Acclavio, Matteo UL; Horne, Ross James UL; Mauw, Sjouke UL et al

in Felty, Amy P. (Ed.) Proc. 7th International Conference on Formal Structures for Computation and Deduction (FSCD 2022) (2022)

Logical time is a partial order over events in distributed systems, constraining which events precede others. Special interest has been given to series-parallel orders since they correspond to formulas ... [more ▼]

Logical time is a partial order over events in distributed systems, constraining which events precede others. Special interest has been given to series-parallel orders since they correspond to formulas constructed via the two operations for "series" and "parallel" composition. For this reason, series-parallel orders have received attention from proof theory, leading to pomset logic, the logic BV, and their extensions. However, logical time does not always form a series-parallel order; indeed, ubiquitous structures in distributed systems are beyond current proof theoretic methods. In this paper, we explore how this restriction can be lifted. We design new logics that work directly on graphs instead of formulas, we develop their proof theory, and we show that our logics are conservative extensions of the logic BV. [less ▲]

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See detailIntJect: Vulnerability Intent Bug Seeding
PETIT, Benjamin; Khanfir, Ahmed UL; Soremekun, Ezekiel UL et al

in 22nd IEEE International Conference on Software Quality, Reliability, and Security (2022)

Studying and exposing software vulnerabilities is important to ensure software security, safety, and reliability. Software engineers often inject vulnerabilities into their programs to test the ... [more ▼]

Studying and exposing software vulnerabilities is important to ensure software security, safety, and reliability. Software engineers often inject vulnerabilities into their programs to test the reliability of their test suites, vulnerability detectors, and security measures. However, state-of-the-art vulnerability injection methods only capture code syntax/patterns, they do not learn the intent of the vulnerability and are limited to the syntax of the original dataset. To address this challenge, we propose the first intent-based vulnerability injection method that learns both the program syntax and vulnerability intent. Our approach applies a combination of NLP methods and semantic-preserving program mutations (at the bytecode level) to inject code vulnerabilities. Given a dataset of known vulnerabilities (containing benign and vulnerable code pairs), our approach proceeds by employing semantic-preserving program mutations to transform the existing dataset to semantically similar code. Then, it learns the intent of the vulnerability via neural machine translation (Seq2Seq) models. The key insight is to employ Seq2Seq to learn the intent (context) of the vulnerable code in a manner that is agnostic of the specific program instance. We evaluate the performance of our approach using 1275 vulnerabilities belonging to five (5) CWEs from the Juliet test suite. We examine the effectiveness of our approach in producing compilable and vulnerable code. Our results show that INTJECT is effective, almost all (99%) of the code produced by our approach is vulnerable and compilable. We also demonstrate that the vulnerable programs generated by INTJECT are semantically similar to the withheld original vulnerable code. Finally, we show that our mutation-based data transformation approach outperforms its alternatives, namely data obfuscation and using the original data. [less ▲]

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See detailPrinciples for setting single or multiline bus holding control based on network characteristics
Laskaris, Georgios; Cats, Oded; Jenelius, Erik et al

Scientific Conference (2022)

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See detailVision-Based Safety System for Barrierless Human-Robot Collaboration
Amaya-Mejía, Lina María; Duque-Suárez, Nicolás; Jaramillo-Ramírez, Daniel et al

in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022)

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See detailA Dialogue on Socio-Technical Systems and Situation Awareness
Botev, Jean UL; Bellman, Kirstie

in Proceedings of 12th IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) (2022)

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See detailContingent payments from two-party signing and verification for abelian groups
Bursuc, Sergiu UL; Mauw, Sjouke UL

in Conference Proceedings 2022 IEEE 35th Computer Security Foundations Symposium (CSF) (2022)

The fair exchange problem has faced for a long time the bottleneck of a required trusted third party. The recent development of blockchains introduces a new type of party to this problem, whose ... [more ▼]

The fair exchange problem has faced for a long time the bottleneck of a required trusted third party. The recent development of blockchains introduces a new type of party to this problem, whose trustworthiness relies on a public ledger and distributed computation. The challenge in this setting is to reconcile the minimalistic and public nature of blockchains with elaborate fair exchange requirements, from functionality to privacy. Zero-knowledge contingent payments (ZKCP) are a class of protocols that are promising in this direction, allowing the fair exchange of data for payment. We propose a new ZKCP protocol that, when compared to others, requires less computation from the blockchain and less interaction between parties. The protocol is based on two-party (weak) adaptor signatures, which we show how to instantiate from state of the art multiparty sign- ing protocols. We improve the symbolic definition of ZKCP security and, for automated verification with Tamarin, we propose a general security reduction from the theory of abelian groups to the theory of exclusive or. [less ▲]

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See detailFunctional scenario classification for Android applications using GNNs
Li, Guiyin; Zhu, Fengyi; Pang, Jun UL et al

in Proceedings of the 13th Asia-Pacific Symposium on Internetware (2022)

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See detailElysium: Context-Aware Bytecode-Level Patching to Automatically Heal Vulnerable Smart Contracts
Ferreira Torres, Christof UL; Jonker, Hugo; State, Radu UL

in International Symposium on Research in Attacks, Intrusions and Defenses, Limassol, Cyprus 26-28 October 2022 (2022)

Fixing bugs is easiest by patching source code. However, source code is not always available: only 0.3% of the ∼49M smart contracts that are currently deployed on Ethereum have their source code publicly ... [more ▼]

Fixing bugs is easiest by patching source code. However, source code is not always available: only 0.3% of the ∼49M smart contracts that are currently deployed on Ethereum have their source code publicly available. Moreover, since contracts may call functions from other contracts, security flaws in closed-source contracts may affect open-source contracts as well. However, current state-of-the-art approaches that operate on closed-source contracts (i.e., EVM bytecode), such as EVMPatch and SmartShield, make use of purely hard-coded templates that leverage fix patching patterns. As a result, they cannot dynamically adapt to the bytecode that is being patched, which severely limits their flexibility and scalability. For instance, when patching integer overflows using hard-coded templates, a particular patch template needs to be employed as the bounds to be checked are different for each integer size (i.e., one template for uint256, another template for uint64, etc.). In this paper, we propose Elysium, a scalable approach towards automatic smart contract repair at the bytecode level. Elysium combines template-based and semantic-based patching by inferring context information from bytecode. Elysium is currently able to patch 7 different types of vulnerabilities in smart contracts automatically and can easily be extended with new templates and new bug-finding tools. We evaluate its effectiveness and correctness using 3 different datasets by replaying more than 500K transactions on patched contracts. We find that Elysium outperforms existing tools by patching at least 30% more contracts correctly. Finally, we also compare the overhead of Elysium in terms of deployment and transaction cost. In comparison to other tools, we find that generally Elysium minimizes the runtime cost (i.e., transaction cost) up to a factor of 1.7, for only a marginally higher deployment cost, where deployment cost is a one-time cost as compared to the runtime cost. [less ▲]

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See detailOn Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

in The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI- 23) - SafeAI Workshop, Washington, D.C., Feb 13-14, 2023 (2022)

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See detailRecurrent Design of Probing Waveform for Sparse Bayesian Learning Based DOA Estimation
Wu, Linlong; Dai, Jisheng; Mysore Rama Rao, Bhavani Shankar UL et al

in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022)

Direction-of-arrival (DOA) estimation can be represented as a sparse signal recovery problem and effectively solved by sparse Bayesian learning (SBL). For the DOA estimation in active sensing, the SBL ... [more ▼]

Direction-of-arrival (DOA) estimation can be represented as a sparse signal recovery problem and effectively solved by sparse Bayesian learning (SBL). For the DOA estimation in active sensing, the SBL-based estimation error is related to the transmitted probing waveform. Therefore, it is expected to improve the estimation by waveform optimization. In this paper, we propose a recurrent scheme of waveform design by sequentially leveraging on the previous-round SBL estimates. Within this scheme, we formulate the waveform design problem as a minimization of the SBL estimation variance, which is non-convex and then solved by a majorization-minimization based algorithm. The simulations demonstrate the efficacy of the proposed design scheme in terms of avoiding incorrect detection and accelerating the DOA estimation convergence. Further, the results indicate that the waveform design is essentially a beampattern shaping methodology. [less ▲]

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See detailDeXTER: A post-authentic approach to heritage visualisation
Viola, Lorella UL

Scientific Conference (2022)

Cultural heritage institutions and academics are resorting more and more to visual representations of cultural heritage material as a way to enhance access to collections for users’ appreciation and ... [more ▼]

Cultural heritage institutions and academics are resorting more and more to visual representations of cultural heritage material as a way to enhance access to collections for users’ appreciation and research purposes (Windhager et al., 2019). However, scholars have pointed out (Drucker, 2011; 2013; 2014; 2020; Windhager et al., 2019) how a critical approach to visualisation is still largely missing and how on the whole, user-interface (UI) design still shows a functional and task-driven approach, oriented towards satisfying a need for information rather than towards eliciting curiosity, engagement and reflection. With this presentation, I aim to contribute to the urgent need for the establishment of a critical data and visualisation literacy in current task-driven approaches to interface design which continue to see the user as a consumer and to operate within a problem solver model. Drawing on critical posthumanities (Braidotti, 2017; 2019), I here propose a critical framework to digital cultural heritage and digital cultural heritage visualisation. With this approach —which I labelled “postauthentic framework” (Viola, 2021) —I want to initiate a discussion and critique of the fetishization of empriricism and technical objectivity in digital knowledge creation. To exemplify how the post-authentic framework works in practice, I present the design choices for developing the tool DeXTER – DeepteXTminER, an interactive visualisation app to explore enriched cultural heritage material. The discussion will revolve around the challenges facing product design, with specific reference to visualising the ambiguities and uncertainties of network analysis (NA) and sentiment analysis (SA). DeXTER is currently loaded with Chroniclitaly 3.0 (Viola and Fiscarelli, 2021a), a digital heritage collection of Italian American newspapers published in the USA by Italian immigrants between 1897 and 1936. [less ▲]

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See detailRobust and Deployable Gesture Recognition for Smartwatches
Kunwar, Utkarsh; Borar, Sheetal; Berghofer, Moritz et al

in Proceedings of the ACM Conference on Intelligent User Interfaces (IUI) (2022)

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See detailMission to Mars Using Space-Sourced Propellant
Thoemel, Jan UL; Karakatsanis, Panagiotis; Ludwikowski, Mariusz et al

in Proceedings of the International Astronautical Congress, IAC (2022)

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See detailLGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity
Gubri, Martin UL; Cordy, Maxime UL; Papadakis, Mike UL et al

in Computer Vision -- ECCV 2022 (2022)

We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks. LGV starts from a pretrained surrogate model and collects ... [more ▼]

We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks. LGV starts from a pretrained surrogate model and collects multiple weight sets from a few additional training epochs with a constant and high learning rate. LGV exploits two geometric properties that we relate to transferability. First, models that belong to a wider weight optimum are better surrogates. Second, we identify a subspace able to generate an effective surrogate ensemble among this wider optimum. Through extensive experiments, we show that LGV alone outperforms all (combinations of) four established test-time transformations by 1.8 to 59.9\% points. Our findings shed new light on the importance of the geometry of the weight space to explain the transferability of adversarial examples. [less ▲]

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See detailChallenges of protecting confidentiality in social media data and their ethical import
Rossi, Arianna; Arenas Correa, Monica Patricia UL; Kocyigit, Emre UL et al

in 2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) (2022)

This article discussed the challenges of pseudonymizing unstructured, noisy social media data for cybersecurity research purposes and presents an open- source package developed to pseudonymize personal ... [more ▼]

This article discussed the challenges of pseudonymizing unstructured, noisy social media data for cybersecurity research purposes and presents an open- source package developed to pseudonymize personal and confidential information (i.e., personal names, companies, and locations) contained in such data. Its goal is to facilitate compliance with EU data protection obligations and the upholding of research ethics principles like the respect for the autonomy, privacy and dignity of research participants, the social responsibility of researchers, and scientific integrity. We discuss the limitations of the pseudonymizer package, their ethical import, and the additional security measures that should be adopted to protect the confidentiality of the data. [less ▲]

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See detailTime vs. Unit Cell Splitting for Autonomous Reconfigurable Intelligent Surfaces
Ntontin, Konstantinos UL; Boulogeorgos, Alexandros-Apostolos A.; Abdullah, Zaid UL et al

Scientific Conference (2022)

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See detailTransforming IoT Data Preprocessing: A Holistic, Normalized and Distributed Approach
Tawakuli, Amal UL; Kaiser, Daniel UL; Engel, Thomas UL

in The Fifth International Workshop on Data: Acquisition To Analysis (2022)

Data preprocessing is an integral part of Artificial Intelligence (AI) pipelines. It transforms raw data into input data that fulfill algorithmic criteria and improve prediction accuracy. As the adoption ... [more ▼]

Data preprocessing is an integral part of Artificial Intelligence (AI) pipelines. It transforms raw data into input data that fulfill algorithmic criteria and improve prediction accuracy. As the adoption of Internet of Things (IoT) gains more momentum, the data volume generated from the edge is exponentially increasing that far exceeds any expansion of infrastructure. Social responsibilities and regulations (e.g., GDPR) must also be adhered when handling IoT data. In addition, we are currently witnessing a shift towards distributing AI to the edge. The aforementioned reasons render the distribution of data preprocessing to the edge an urgent requirement. In this paper, we introduce a modern data preprocessing framework that consists of two main parts. Part1 is a design tool that reduces the complexity and costs of the data preprocessing phase for AI via generalization and normalization. The design tool is a standard template that maps specific techniques into abstract categories and highlights dependencies between them. In addition, it presents a holistic notion of data preprocessing that is not limited to data cleaning. The second part is an IoT tool that adopts the edge-cloud collaboration model to progressively improve the quality of the data. It includes a synchronization mechanism that ensures adaptation to changes in data characteristics and a coordination mechanism that ensures correct and complete execution of preprocessing plans between the cloud and the edge. The paper includes an empirical analysis of the framework using a developed prototype and an automotive use-case. Our results demonstrate reductions in resource consumption (e.g., energy, bandwidth) while maintaining the value and integrity of the data. [less ▲]

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See detailPhysics-Based Cognitive Radar Modeling and Parameter Estimation
Sedighi, Saeid UL; Mysore Rama Rao, Bhavani Shankar UL; Mishra, Kumar Vijay et al

in IEEE Radar Conference (2022)

We consider the problem of channel response estimation in cognitive fully adaptive radar (CoFAR). We show that this problem can be expressed as a constrained channel estimation problem exploiting the ... [more ▼]

We consider the problem of channel response estimation in cognitive fully adaptive radar (CoFAR). We show that this problem can be expressed as a constrained channel estimation problem exploiting the similarity between the channel impulse responses (CIRs) of the adjacent channels. We develop a constrained CIR estimation (CCIRE) algorithm enhancing estimation performance compared to the unconstrained CIR estimation where the similarity between the CIRs of the adjacent channels is not employed. Further, we we derive the Cram\'{e}r-Rao bound (CRB) for the CCIRE and show the optimality of the proposed CCIRE through comparing its performance with the derived CRB. [less ▲]

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See detailArtificial Intelligence in Energy Demand Response : A Taxonomy of Input Data Requirements
Michaelis, Anne; Halbrügge, Stephanie; Körner, Marc-Fabian et al

in Proceedings of the 17th International Conference on Wirtschaftsinformatik (WI) (2022)

The ongoing energy transition increases the share of renewable energy sources. To combat inherent intermittency of RES, increasing system flexibility forms a major opportunity. One way to provide ... [more ▼]

The ongoing energy transition increases the share of renewable energy sources. To combat inherent intermittency of RES, increasing system flexibility forms a major opportunity. One way to provide flexibility is demand response (DR). Research already reflects several approaches of artificial intelligence (AI) for DR. However, these approaches often lack considerations concerning their applicability, i.e., necessary input data. To help putting these algorithms into practice, the objective of this paper is to analyze, how input data requirements of AI approaches in the field of DR can be systematized from a practice-oriented information systems perspective. Therefore, we develop a taxonomy consisting of eight dimensions encompassing 30 characteristics. Our taxonomy contributes to research by illustrating how future AI approaches in the field of DR should represent their input data requirements. For practitioners, our developed taxonomy adds value as a structuring tool, e.g., to verify applicability with respect to input data requirements. [less ▲]

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See detailNon-Admissibility in abstract argumentation
Dvorak, Wolfgang; Rienstra, Tjitze; van der Torre, Leon UL et al

in Frontiers in Artificial Intelligence and Applications (2022), 353

In this paper, we give an overview of several recent proposals for non-Admissible non-naive semantics for abstract argumentation frameworks. We highlight the similarities and differences between weak ... [more ▼]

In this paper, we give an overview of several recent proposals for non-Admissible non-naive semantics for abstract argumentation frameworks. We highlight the similarities and differences between weak admissibility-based approaches and undecidedness-blocking approaches using examples and principles as well as a study of their computational complexity. We introduce a kind of strengthened undecidedness-blocking semantics combining some of the distinctive behaviours of weak admissibility-based semantics with the lower complexity of undecidedness-blocking approaches. We call it loop semantics, because in our new semantics, an argument can only be undecided if it is part of a loop of undecided arguments. Our paper shows how a principle-based approach and a complexity-based approach can be used in tandem to further develop the foundations of formal argumentation. [less ▲]

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See detailDigitizing Inclusion in a Changing World: Comparing insights into four European countries for matching new global realities
Andersen, Katja Natalie UL; Vogt, Michaela; Bagger, Anette et al

Scientific Conference (2022)

Inclusion and digitalization are among the driving forces in global discourses on education and learning. These global realities cause extensive transformations at local, national, and international ... [more ▼]

Inclusion and digitalization are among the driving forces in global discourses on education and learning. These global realities cause extensive transformations at local, national, and international levels. At the same time, the goal of inclusion is directly interwoven with local conditions, understandings, and historically grown valuations. The concept oscillates between universally shared norms and highly divergent local implementations. Regarding the focus of the symposium it can be assumed that especially in the course of digitalization as one main reaction of the corona pandemic, learning materials are not only expected to be inclusion oriented but also need to be combinable with digital demands to address alls kinds of inequalities amongst students appropriately. Learning materials thus are a central momentum to expatiate on transformations of a changing world. In correspondence with the congress theme, the symposium will therefore discuss how a reflection on centrifugal forces of interdependence and individuality can be located in the context of the immediate everyday life in schools and educational practice. In order to be able to discuss these tensions and current changes within educational practices starting from learning materials, an anchor point is needed in order to open up the international comparison of local specifics. The anchor point for this symposium is a criteria catalog which enables the international comparison of learning materials in their cultural contexts and provides a visualization of local particularities in the field of learning materials (Vogt et al. 2021). If these results are put into relation with the inclusive landscapes in different countries, further insights of relevance for inclusion as a global topic can be generated. The data basis springs from the ITM project (2018-2021, funded by Erasmus+) and other international collections of material. Speakers with expertise in regard to the triangulation of learning materials, inclusion and digitalization from four European universities (Ostrava, Czech Republic; Bielefeld, Germany; Luxemburg and Örebro, Sweden) present their findings in the symposium, more in-depth talks about national perspectives as well as a concluding wrap up. The following research questions are a guideline for each section of the symposium: 1. What are the local specifics and constellations as well as understandings of inclusion as a global reality that are being mirrored in teaching material? 2. How can the international comparison of teaching material impact the advancement of inclusion on a local level? 3. How can international comparative educational research become a driving force in the productive negotiation of globally relevant tensions? The contributions are methodologically based on group discussions and expert interviews about selected learning materials, their quality and criteria for evaluating them. These exchanges have been evaluated with the qualitative content analysis by Mayring (2015). Discourse analysis and context analysis were applied. Furthermore, source corpora of learning material were developed and materials were examined using a qualitative content analysis. This refers to the ITM project from Luxembourg, Örebro and Bielefeld University (Erasmus funding line, 2018-2021) as well as the contribution by Ostrava University. The material mainly comes from primary school education and takes into account various subjects, e.g. mathematics and natural sciences. By looking at the results of the interviews and the analysis of teaching materials as well as on the findings about the cultural and local conditions the presenters were able to develop a deeper understanding about how learning materials are set up for inclusive learning processes and about the underpinning understanding of inclusion in different cultural settings. This is the needed foundation to contextualize everyday practices of teaching and learning with the demands of education within the digital condition. [less ▲]

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See detailEspaces (in)sensibles : l'appropriation en didactique comparée
Thiltges, Sébastian UL; Raus, Tonia

in Thiltges, Sébastian; Raus, Tonia (Eds.) spaces (in)sensibles : l'appropriation en didactique comparée (2022)

La nouvelle d'anticipation « Une voix dans le silence » (trad.) de Yorick Schmit sensibilise les lecteurs aux bouleversements environnementaux, psychologiques et affectifs engendrés par une catastrophe ... [more ▼]

La nouvelle d'anticipation « Une voix dans le silence » (trad.) de Yorick Schmit sensibilise les lecteurs aux bouleversements environnementaux, psychologiques et affectifs engendrés par une catastrophe nucléaire. Partant d'une lecture écopoétique, nous imaginons l'étude de ce récit en langue luxembourgeoise en cours de français, afin d'exploiter la tension ainsi créée par le dispositif translinguistique et interculturel. Dans une double perspective de didactique des langues et de la littérature, nous interrogeons dès lors le rôle du sensible dans le développement du plurilinguisme dans l'enseignement-apprentissage de la littérature. Pour ce faire, notre approche de didactique comparée se fonde sur le paradigme de l'appropriation en tant que concept à la fois linguistique et littéraire : le lecteur devient sensible en faisant sien le récit lu, dans un corps à corps avec la matérialité du texte écrit dans une langue familière en tant que langue véhiculaire mais étrangère en tant que langue littéraire, favorisant ainsi une prise de conscience des sens de la langue et du texte. [less ▲]

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See detailDeveloping a distributed and fractionated system of 10 grams satellites for planetary observation
Borgue, Olivia UL; Kanavouras, Konstantinos; Laur, Johannes UL et al

in Proceedings of the International Astronautical Congress, IAC2 (2022)

The miniaturization of electronic components enables major reduction of spacecraft size and mass, as is the caseof CubeSats, PocketQubes, and Femtosats, which offer shorter development time and costs, in ... [more ▼]

The miniaturization of electronic components enables major reduction of spacecraft size and mass, as is the caseof CubeSats, PocketQubes, and Femtosats, which offer shorter development time and costs, in comparison withtraditionally larger satellites. However, these miniaturized satellites still require a considerable effort in terms of timeand investment (several years of development, and costs around 105-106 EUR for CubeSats). More recently, an evensmaller class of spacecraft, the Chipsats has been introduced. Chipsats are microchip-shaped spacecraft with massesranging from a few grams to 10s of grams. At the lower end of the mass range (1-10 grams), they belong to the classof Attosats. Due to their small size, Attosats enable unprecedented low costs, agile development and potential forswarm missions of distributed and fractioned systems for applications such as planetary observation. However,despite their benefits there have not been many initiatives to develop Attosat systems. In this article, the developmentof a satellite system of three 10-grams satellites is presented. The three satellites work together to achieve thecommon goal of studying Earth’s atmospheric environment, conforming a distributed system. Moreover, as thedifferent satellites are designed to perform different functions, they conform a fractionated system as well: onesatellite acts as a communication node transmitting data to ground stations, while the two other satellites haveenvironment sampling capabilities. Visual intersatellite communication capabilities ensure data transmission amongthe satellites. The presented system is meant as a technology demonstration project for future distributed andfragmented satellite swarm systems for atmospheric planetary exploration. The Attosats are scheduled for launch onOctober 2023 on board of a Momentus spacecraft [less ▲]

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See detailDer 50cm lange Gliedermaßstab
Foyen, Andy UL; Perucca, Antonella UL

in Beiträge zum Mathematikunterricht 2022 (Tagungsband GDM) (2022)

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See detailWhat if data protection embraced foresight and speculative design?
Rossi, Arianna UL; Chatellier, Regis; Leucci, Stefano et al

in DRS2022: Bilbao (2022)

Due to rapid technological advancements and the growing “datafication” of our societies, individuals’ privacy constitutes an increasingly explored speculative space for regulators, researchers ... [more ▼]

Due to rapid technological advancements and the growing “datafication” of our societies, individuals’ privacy constitutes an increasingly explored speculative space for regulators, researchers, practitioners, designers and artists. This article reports two experiences at a national and an international data protection authority (i.e., the Commission Nationale de l'Informatique et des Libertés - CNIL - and the European Data Protection Supervisor - EDPS - respectively), where foresight methods and speculative design are employed in policy-making with the goal of anticipating technological trends, their implications for society and their impact on regulations, as well as the effects of existing and upcoming laws on emerging technologies. Such initiatives can enhance strategic proactive abilities, raise public awareness of privacy issues and engender a participatory approach to the design of policies. They can also inspire the research, education and practice of legal design. [less ▲]

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See detailAutomated Question Answering for Improved Understanding of Compliance Requirements: A Multi-Document Study
Abualhaija, Sallam UL; Arora, Chetan; Sleimi, Amin et al

in In Proceedings of the 30th IEEE International Requirements Engineering Conference (RE'22), Melbourne, Australia 15-19 August 2022 (2022)

Software systems are increasingly subject to regulatory compliance. Extracting compliance requirements from regulations is challenging. Ideally, locating compliance-related information in a regulation ... [more ▼]

Software systems are increasingly subject to regulatory compliance. Extracting compliance requirements from regulations is challenging. Ideally, locating compliance-related information in a regulation requires a joint effort from requirements engineers and legal experts, whose availability is limited. However, regulations are typically long documents spanning hundreds of pages, containing legal jargon, applying complicated natural language structures, and including cross-references, thus making their analysis effort-intensive. In this paper, we propose an automated question-answering (QA) approach that assists requirements engineers in finding the legal text passages relevant to compliance requirements. Our approach utilizes large-scale language models fine-tuned for QA, including BERT and three variants. We evaluate our approach on 107 question-answer pairs, manually curated by subject-matter experts, for four different European regulatory documents. Among these documents is the general data protection regulation (GDPR) – a major source for privacy-related requirements. Our empirical results show that, in ~94% of the cases, our approach finds the text passage containing the answer to a given question among the top five passages that our approach marks as most relevant. Further, our approach successfully demarcates, in the selected passage, the right answer with an average accuracy of ~ 91%. [less ▲]

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See detailWhich companies go public via SPACs? The role of venture capital
Hansen, Christopher UL

Scientific Conference (2022)

This study investigates the characteristics and stock market performance of companies going public via special-purpose acquisition companies (SPACs). Using a sample of 798 companies that went public in ... [more ▼]

This study investigates the characteristics and stock market performance of companies going public via special-purpose acquisition companies (SPACs). Using a sample of 798 companies that went public in 2020 and 2021, we compare those companies that went public via SPAC to companies that conducted a traditional IPO. Moreover, we examine the potential impact of venture capital funding on the mode of going public and the post-listing performance of the respective companies. Our results show that companies going public via SPAC in 2020 and 2021 did not systematically differ in their firm characteristics from traditional IPO companies. However, they perform nevertheless constantly and significantly worse in terms of stock return over a period of six months after becoming publicly listed. Venture capital funding has not per se an impact on the mode of going public, but companies with more prestigious VCs invested prefer to chose a traditional IPO over a SPAC to go public. [less ▲]

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See detailBelElect: A New Dataset for Bias Research from a ”Dark” Platform
Hoehn, Sviatlana UL; Mauw, Sjouke UL; Asher, Nicholas

in Vol. 16 (2022): Proceedings of the Sixteenth International AAAI Conference on Web and Social Media (2022)

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See detailThe Value of non-formal education form the perspective of young people
Biewers, Sandra UL; Weis, Daniel UL; Latz, Anita UL

in Carmo, Mafalda (Ed.) Education and New Developments 2022 - book of abstracts (2022)

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See detailKryvos: Publicly Tally-Hiding Verifiable E-Voting
Huber, Nicolas; Kuesters, Ralf; Krips, Toomas et al

in 2022 ACM SIGSAC Conference on Computer and Communications Security (2022)

Elections are an important corner stone of democratic processes. In addition to publishing the final result (e.g., the overall winner), elections typically publish the full tally consisting of all ... [more ▼]

Elections are an important corner stone of democratic processes. In addition to publishing the final result (e.g., the overall winner), elections typically publish the full tally consisting of all (aggregated) individual votes. This causes several issues, including loss of privacy for both voters and election candidates as well as so-called Italian attacks that allow for easily coercing voters. Several e-voting systems have been proposed to address these issues by hiding (parts of) the tally. This property is called tally-hiding. Existing tally-hiding e-voting systems in the literature aim at hiding (part of) the tally from everyone, including voting authorities, while at the same time offering verifiability, an important and standard feature of modern e-voting systems which allows voters and external observers to check that the published election result indeed corresponds to how voters actually voted. In contrast, real elections often follow a different common practice for hiding the tally: the voting authorities internally compute (and learn) the full tally but publish only the final result (e.g., the winner). This practice, which we coin publicly tally-hiding, indeed solves the aforementioned issues for the public, but currently has to sacrifice verifiability due to a lack of practical systems. In this paper, we close this gap. We formalize the common notion of publicly tally-hiding and propose the first provably secure verifiable e-voting system, called Kryvos, which directly targets publicly tally-hiding elections. We instantiate our system for a wide range of both simple and complex voting methods and various result functions. We provide an extensive evaluation which shows that Kryvos is practical and able to handle a large number of candidates, complex voting methods and result functions. Altogether, Kryvos shows that the concept of publicly tally-hiding offers a new trade-off between privacy and efficiency that is different from all previous tally-hiding systems and which allows for a radically new protocol design resulting in a practical e-voting system. [less ▲]

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See detailNonlinear Least Squares Estimation for Breathing Monitoring Using FMCW Radars
Tedgue Beltrao, Gabriel UL; Alaeekerahroodi, Mohammad UL; Schroeder, Udo et al

in 2021 18th Eur. Radar Conf. (2022)

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See detailArtificial Intelligence as a Call for Retail Banking : Applying Digital Options Thinking to Artificial Intelligence Adoption
Fridgen, Gilbert UL; Hartwich, Eduard UL; Rägo, Vadim et al

in Proceedings of the Thirtieth European Conference on Information Systems (ECIS 2022), Timisoara, Romania: Artificial Intelligence as a Call for Retail Banking : Applying Digital Options Thinking to Artificial Intelligence Adoption (2022)

Technology-driven challenges, both existing and emerging, require banks to invest in IT capabilities, especially in artificial intelligence (AI). Digital options theory presents a valuable guide rail for ... [more ▼]

Technology-driven challenges, both existing and emerging, require banks to invest in IT capabilities, especially in artificial intelligence (AI). Digital options theory presents a valuable guide rail for these investments. However, the nature of AI as a moving frontier of computing requires certain extensions to established digital option thinking. Based on interviews with 23 experts in the retail banking industry, we highlight the importance of thinking broadly when laying the foundation for AI options and being mindful of the dynamic effects of contextual factors. Drawing from digital options theory and the Technology-Organization-Environment framework as dual lens, our study adds a structured approach to consciously balance resources and AI-related capability investments with a broader consideration of the banking industry’s complex environment. In this way, our study complements recent research on the interplay between incumbents’ resources and digital opportunities. [less ▲]

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See detailJOINT CARRIER ALLOCATION AND PRECODING OPTIMIZATION FOR INTERFERENCE-LIMITED GEO SATELLITE
Abdu, Tedros Salih UL; Kisseleff, Steven UL; Lagunas, Eva UL et al

Scientific Conference (2022)

The rise of flexible payloads on satellites opens a door for controlling satellite resources according to the user demand, user location, and satellite position. In addition to resource management ... [more ▼]

The rise of flexible payloads on satellites opens a door for controlling satellite resources according to the user demand, user location, and satellite position. In addition to resource management, applying precoding on flexible payloads is essential to obtain high spectral efficiency. However, these cannot be achieved using a conventional resource allocation algorithm that does not consider the user demand. In this paper, we propose a demand-aware algorithm based on multiobjective optimization to jointly design the carrier allocation and precoding for better spectral efficiency and demand matching with proper management of the satellite resources. The optimization problem is non-convex, and we solve it using convex relaxation and successive convex approximation. Then, we evaluate the performance of the proposed algorithm through numerical results. It is shown that the proposed method outperforms the benchmark schemes in terms of resource utilization and demand satisfaction. [less ▲]

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See detailDerogations Analysis of European Legislation Through Hybrid AI Approach
Palmirani, Monica; Liga, Davide UL

in Electronic Government and the Information Systems Perspective: 11th International Conference, EGOVIS 2022 (2022)

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See detailDual-Function Radar-Communication Systems with Constant-Modulus and Similarity Constraints
Tsinos, Christos UL; Arora, Aakash UL; Chatzinotas, Symeon UL et al

in IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM) (2022)

In this paper, the problem of joint transmit waveform and receive filter design for dual-function radar-communication (DFRC) systems is studied. A multiple antenna base station (BS) serving multiple ... [more ▼]

In this paper, the problem of joint transmit waveform and receive filter design for dual-function radar-communication (DFRC) systems is studied. A multiple antenna base station (BS) serving multiple single antenna users on the downlink is assumed. Furthermore, the BS simultaneously accommodates sensing capabilities in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. The core objective of the present paper is to design the optimal waveform and radar receive filter such that the derived waveform satisfies constant-modulus and similarity to known waveform constraints that are of particular importance to the radar's part operation. The proposed designs are derived through the solution to difficult non-convex optimization problems. To that end, novel algorithmic solutions with guaranteed convergence are developed for the solution of the aforementioned problems. The effectiveness of the proposed solutions is verified via simulations. [less ▲]

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See detailIntegral Attacks on Pyjamask-96 and Round-Reduced Pyjamask-128
Cui, Jiamin; Hu, Kai; Wang, Qingju UL et al

in Topics in Cryptology - CT-RSA 2022 - The Cryptographers' Track at the RSA Conference 2022, San Francisco, CA, USA, February 7-10, 2022, Proceedings (2022)

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See detailTAPHSIR: Towards AnaPHoric Ambiguity Detection and ReSolution In Requirements
Ezzini, Saad UL; Abualhaija, Sallam UL; Arora, Chetan et al

in ACM SIGSOFT CONFERENCE ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (2022)

We introduce TAPHSIR -- a tool for anaphoric ambiguity detection and anaphora resolution in requirements. TAPHSIR facilities reviewing the use of pronouns in a requirements specification and revising ... [more ▼]

We introduce TAPHSIR -- a tool for anaphoric ambiguity detection and anaphora resolution in requirements. TAPHSIR facilities reviewing the use of pronouns in a requirements specification and revising those pronouns that can lead to misunderstandings during the development process. To this end, TAPHSIR detects the requirements which have potential anaphoric ambiguity and further attempts interpreting anaphora occurrences automatically. TAPHSIR employs a hybrid solution composed of an ambiguity detection solution based on machine learning and an anaphora resolution solution based on a variant of the BERT language model. Given a requirements specification, TAPHSIR decides for each pronoun occurrence in the specification whether the pronoun is ambiguous or unambiguous, and further provides an automatic interpretation for the pronoun. The output generated by TAPHSIR can be easily reviewed and validated by requirements engineers. TAPHSIR is publicly available on Zenodo (https://doi.org/10.5281/zenodo.5902117). [less ▲]

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See detailAdaptive Resource Allocation for Satellite Illumination Pattern Design
Chen, Lin UL; Lagunas, Eva UL; Lei, Lei et al

in IEEE 96st Vehicular Technology Conference, London-Beijing, Sept. 2022 (2022)

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See detailCubeSat-CDT: A Cross-Domain Dataset for 6-DoF Trajectory Estimation of a Symmetric Spacecraft
Mohamed Ali, Mohamed Adel UL; Rathinam, Arunkumar UL; Gaudilliere, Vincent UL et al

in Proceedings of the 17th European Conference on Computer Vision Workshops (ECCVW 2022) (2022)

This paper introduces a new cross-domain dataset, CubeSat- CDT, that includes 21 trajectories of a real CubeSat acquired in a labora- tory setup, combined with 65 trajectories generated using two ... [more ▼]

This paper introduces a new cross-domain dataset, CubeSat- CDT, that includes 21 trajectories of a real CubeSat acquired in a labora- tory setup, combined with 65 trajectories generated using two rendering engines – i.e. Unity and Blender. The three data sources incorporate the same 1U CubeSat and share the same camera intrinsic parameters. In ad- dition, we conduct experiments to show the characteristics of the dataset using a novel and efficient spacecraft trajectory estimation method, that leverages the information provided from the three data domains. Given a video input of a target spacecraft, the proposed end-to-end approach re- lies on a Temporal Convolutional Network that enforces the inter-frame coherence of the estimated 6-Degree-of-Freedom spacecraft poses. The pipeline is decomposed into two stages; first, spatial features are ex- tracted from each frame in parallel; second, these features are lifted to the space of camera poses while preserving temporal information. Our re- sults highlight the importance of addressing the domain gap problem to propose reliable solutions for close-range autonomous relative navigation between spacecrafts. Since the nature of the data used during training impacts directly the performance of the final solution, the CubeSat-CDT dataset is provided to advance research into this direction. [less ▲]

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See detailGeodetic climate research in the Austrian Alps
Ullrich, Christian; Francis, Olivier UL; Tabibi, Sajad UL et al

Scientific Conference (2022)

The Federal Office of Metrology and Surveying (BEV) in Austria is responsible for the geodetic reference system like gravity and height reference frame. Some of these gravity reference stations are ... [more ▼]

The Federal Office of Metrology and Surveying (BEV) in Austria is responsible for the geodetic reference system like gravity and height reference frame. Some of these gravity reference stations are monitored regularly by different geodetic terrestrial techniques. The gravity data on some stations show variations and/or changes in gravity. In this presentation, the alpine geodetic reference stations Obergurgl and Franz-Josefs- Höhe in the Austrian eastern Alps will be presented. Both stations are investigated with different geodetic terrestrial techniques in a cooperation of the University of Luxemburg with BEV. Global warming and associated climate change during the last century and recent decades are among the main reasons for glacier retreat in the Alps. Absolute gravity measurements have been regularly performed in the Austrian Eastern Alps since 1987 in the Ötztal Valley at Obergurgl. In addition, absolute gravity has been regularly observed at Obergurgl from 1987 to 2009 with the absolute gravimeter JILAg6. From 2010, the absolute gravity measurements were continued with the highest accurate absolute gravimeters FG5 from BEV and FG5x from University of Luxemburg. The newest gravity data show again a small increase of gravity. Additionally, a permanent GNSS station was established in 2019 to record information about the assumed vertical uplift at this station. A second alpine research station was established near the Pasterze Glacier at Großglockner Mountain in 2019. The Pasterze Glacier is one of the largest glaciers in the eastern Alps and is in the vicinity of the highest mountain of Austria, the Großglockner. The station is monitored by repeated absolute gravity measurements and is equipped with a permanent GNSS station. In addition, precise leveling measurements were also tied to this station. In this contribution, initial results of the geodetic research like the gravity results, precise leveling and GNSS measurements will be presented. In the future, gravity data will be quantitively compared to ice mass balance information derived from glacier inventories. A Geodetic Global Navigation Satellite System reflectometry (GNSS-R) antenna will also be installed to study glacier-ice change. A third station at an altitude of 3300 m is planned and will be checked for operating absolute gravity measurements there. The geodynamical processes like vertical uplift and postglacial deformation will be investigated together with glacier retreat on these stations. [less ▲]

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See detailA Flash(bot) in the Pan: Measuring Maximal Extractable Value in Private Pools
Weintraub, Ben; Ferreira Torres, Christof UL; Nita-Rotaru, Cristina et al

in ACM Internet Measurement Conference, Nice, France 25-27 October 2022 (2022)

The rise of Ethereum has lead to a flourishing decentralized marketplace that has, unfortunately, fallen victim to frontrunning and Maximal Extractable Value (MEV) activities, where savvy participants ... [more ▼]

The rise of Ethereum has lead to a flourishing decentralized marketplace that has, unfortunately, fallen victim to frontrunning and Maximal Extractable Value (MEV) activities, where savvy participants game transaction orderings within a block for profit. One popular solution to address such behavior is Flashbots, a private pool with infrastructure and design goals aimed at eliminating the negative externalities associated with MEV. While Flashbots has established laudable goals to address MEV behavior, no evidence has been provided to show that these goals are achieved in practice. In this paper, we measure the popularity of Flashbots and evaluate if it is meeting its chartered goals. We find that (1) Flashbots miners account for over 99.9% of the hashing power in the Ethereum network, (2) powerful miners are making more than 2x what they were making prior to using Flashbots, while non-miners' slice of the pie has shrunk commensurately, (3) mining is just as centralized as it was prior to Flashbots with more than 90% of Flashbots blocks coming from just two miners, and (4) while more than 80% of MEV extraction in Ethereum is happening through Flashbots, 13.2% is coming from other private pools. [less ▲]

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See detailVerifiable Decryption in the Head
Gjosteen, Kristian; Haines, Thomas; Mueller, Johannes UL et al

in ACISP 2022 (2022)

In this work we present a new approach to verifiable decryption which converts a 2-party passively secure distributed decryption protocol into a 1-party proof of correct decryption. This leads to an ... [more ▼]

In this work we present a new approach to verifiable decryption which converts a 2-party passively secure distributed decryption protocol into a 1-party proof of correct decryption. This leads to an efficient and simple verifiable decryption scheme for lattice-based cryptography, especially for large sets of ciphertexts; it has small size and lightweight computations as we reduce the need of zero-knowledge proofs for each ciphertext. We believe the flexibility of the general technique is interesting and provides attractive trade-offs between complexity and security, in particular for the interactive variant with smaller soundness. Finally, the protocol requires only very simple operations, making it easy to correctly and securely implement in practice. We suggest concrete parameters for our protocol and give a proof of concept implementation, showing that it is highly practical. [less ▲]

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See detailAdversarial Robustness in Multi-Task Learning: Promises and Illusions
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

in Proceedings of the thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) (2022)

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets, very little research ... [more ▼]

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets, very little research has considered complex multi-task models that are common in real applications. In this paper, we evaluate the design choices that impact the robustness of multi-task deep learning networks. We provide evidence that blindly adding auxiliary tasks, or weighing the tasks provides a false sense of robustness. Thereby, we tone down the claim made by previous research and study the different factors which may affect robustness. In particular, we show that the choice of the task to incorporate in the loss function are important factors that can be leveraged to yield more robust models. [less ▲]

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See detailTranslational Challenges of Biomedical Machine Learning Solutions in Clinical and Laboratory Settings
Vega Moreno, Carlos Gonzalo UL; Kratochvil, Miroslav UL; Satagopam, Venkata UL et al

in Bioinformatics and Biomedical Engineering (2022)

The ever increasing use of artificial intelligence (AI) methods in biomedical sciences calls for closer inter-disciplinary collaborations that transfer the domain knowledge from life scientists to ... [more ▼]

The ever increasing use of artificial intelligence (AI) methods in biomedical sciences calls for closer inter-disciplinary collaborations that transfer the domain knowledge from life scientists to computer science researchers and vice-versa. We highlight two general areas where the use of AI-based solutions designed for clinical and laboratory settings has proven problematic. These are used to demonstrate common sources of translational challenges that often stem from the differences in data interpretation between the clinical and research view, and the unmatched expectations and requirements on the result quality metrics. We outline how explicit interpretable inference reporting might be used as a guide to overcome such translational challenges. We conclude with several recommendations for safer translation of machine learning solutions into real-world settings. [less ▲]

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See detailPrivacy-preserving Copy Number Variation Analysis with Homomorphic Encryption
Demirci, Huseyin UL; Lenzini, Gabriele UL

Scientific Conference (2022)

Innovative pharma-genomics and personalized medicine services are now possible thanks to the availability for processing and analysis of a large amount of genomic data. Operating on such databases, is ... [more ▼]

Innovative pharma-genomics and personalized medicine services are now possible thanks to the availability for processing and analysis of a large amount of genomic data. Operating on such databases, is possible to test for predisposition to diseases by searching for genomic variants on whole genomes as well as on exomes, which are collections of protein coding regions called exons. Genomic data are therefore shared amongst research institutes, public/private operators, and third parties, creating issues of privacy, ethics, and data protection because genome data are strictly personal and identifying. To prevent damages that could follow a data breach—a likely threat nowadays—and to be compliant with current data protection regulations, genomic data files should be encrypted, and the data processing algorithms should be privacy-preserving. Such a migration is not always feasible: not all operations can be implemented straightforwardly to be privacypreserving; a privacy-preserving version of an algorithm may not be as accurate for the purpose of biomedical analysis as the original; or the privacy-preserving version may not scale up when applied to genomic data processing because of inefficiency in computation time. In this work, we demonstrate that at least for a wellknown genomic data procedure for the analysis of copy number variants called copy number variations (CNV) a privacy-preserving analysis is possible and feasible. Our algorithm relies on Homomorphic Encryption, a cryptographic technique to perform calculations directly on the encrypted data. We test our implementation for performance and reliability, giving evidence that it is practical to study copy number variations and preserve genomic data privacy. Our proof-of-concept application successfully and efficiently searches for a patient’s somatic copy number variation changes by comparing the patient gene coverage in the whole exome with a healthy control exome coverage. Since all the genomics data are securely encrypted, the data remain protected even if they are transmitted or shared via an insecure environment like a public cloud. Being this the first study for privacy-preserving copy number variation analysis, we demonstrate the potential of recent Homomorphic Encryption tools in genomic applications. [less ▲]

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See detailDeep Mining Covid-19 Literature
Sirajzade, Joshgun UL; Bouvry, Pascal UL; Schommer, Christoph UL

in Applied Informatics, 5th International Conference, ICAI 2022, Arequipa, Peru, October 27–29, 2022, Proceedings (2022)

In this paper we investigate how scientific and medical papers about Covid-19 can be effectively mined. For this purpose we use the CORD19 dataset which is a huge collection of all papers published about ... [more ▼]

In this paper we investigate how scientific and medical papers about Covid-19 can be effectively mined. For this purpose we use the CORD19 dataset which is a huge collection of all papers published about and around the SARS-CoV2 virus and the pandemic it caused. We discuss how classical text mining algorithms like Latent Semantic Analysis (LSA) or its modern version Latent Drichlet Allocation (LDA) can be used for this purpose and also touch more modern variant of these algorithms like word2vec which came with deep learning wave and show their advantages and disadvantages each. We finish the paper with showing some topic examples from the corpus and answer questions such as which topics are the most prominent for the corpus or how many percentage of the corpus is dedicated to them. We also give a discussion of how topics around RNA research in connection with Covid-19 can be examined. [less ▲]

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See detailWith or Without Blockchain? Towards a Decentralized, SSI-based eRoaming Architecture
Höß, Alexandra UL; Roth, Tamara UL; Sedlmeir, Johannes UL et al

in Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS) (2022)

Fragmentation and limited accessibility of charging infrastructure impede the adoption of electric vehicles. To improve the availability of charging infrastructure independent of providers, eRoaming ... [more ▼]

Fragmentation and limited accessibility of charging infrastructure impede the adoption of electric vehicles. To improve the availability of charging infrastructure independent of providers, eRoaming offers a promising solution. Yet, current eRoaming systems are typically centralized, which raises concerns of market power concentration. While the use of blockchain technology can obviate such concerns, it comes with significant privacy challenges. To address these challenges, we explore a combination of blockchain with self-sovereign identity. Specifically, we apply a design science research approach, which helps us to identify requirements, derive a conceptual architecture, and deduce design principles for decentralized eRoaming and beyond. We find that blockchain may best leverage its benefits when it takes a backseat as a public registry for legal entities. Moreover, we find that the use of self-sovereign identities could improve compliance with privacy regulations, but they should not be overused. [less ▲]

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See detailIs this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
Tian, Haoye UL; Tang, Xunzhu UL; Habib, Andrew UL et al

in Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness (2022)

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See detailTransfer Learning for Deontic Rule Classification: the Case Study of the GDPR
Liga, Davide UL; Palmirani, Monica

in INTERNATIONAL CONFERENCE ON LEGAL KNOWLEDGE AND INFORMATION SYSTEMS, Saarbrücken 14-16 December 2022 (2022)

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See detailIs Eve nearby? Analysing protocols under the distant-attacker assumption
Gil Pons, Reynaldo UL; Horne, Ross James UL; Mauw, Sjouke UL et al

in IEEE Computer Security Foundations Symposium, August 7 - 10, 2022, Haifa, Israel (2022)

Detailed reference viewed: 62 (12 UL)
See detailStochastic porous media, towards a digital meniscus
Obeidat, Anas UL; Zilian, Andreas UL

in Bulletin of the American Physical Society (2022)

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See detailTarget Control of Boolean Networks with Permanent Edgetic Perturbations
Zeyen, Olivier Georges Rémy UL; Pang, Jun UL

in Proceedings of the 61st International Conference on Decision and Control (CDC 2022) (2022)

Boolean network is a popular and well-established modelling framework for gene regulatory networks. The steady-state behaviour of Boolean networks can be described as attractors, which are hypothesised to ... [more ▼]

Boolean network is a popular and well-established modelling framework for gene regulatory networks. The steady-state behaviour of Boolean networks can be described as attractors, which are hypothesised to characterise cellular phenotypes. In this work, we study the target control problem of Boolean networks, which has important applications for cellular reprogramming. More specifically, we want to reduce the total number of attractors of a Boolean network to a single target attractor. Different from existing approaches to solving control problems of Boolean networks with node perturbations, we aim to develop an approach utilising edgetic perturbations. Namely, our objective is to modify the update functions of a Boolean network such that there remains only one attractor. The design of our approach is inspired by Thomas’ first rule, and we primarily focus on the removal of cycles in the interaction graph of a Boolean network. We further use results in the literature to only remove positive cycles which are responsible for the appearance of multiple attractors. We apply our solution to a number of real-life biological networks modelled as Boolean networks, and the experimental results demonstrate its efficacy and efficiency. [less ▲]

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See detailIs this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
Tian, Haoye UL; Tang, Xunzhu UL; Habib, Andrew UL et al

in Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness (2022)

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See detailAndorra: eine Einherrschaft? Monarch: ein Einzelorgan? Der Verfall des Begriffsmerkmals „monos“ in Europas sogenannten „Monarchien“
Heuschling, Luc UL

in Bussjäger, Peter; Gamper, Anna (Eds.) 100 Jahre Liechtensteinische Verfassung (2022)

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See detailCase-Based Reasoning via Comparing the Strength Order of Features
Yu, Liuwen UL; Gabbay, Dov M. UL

in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2022), 13283 LNAI

Case-based reasoning (CBR) is broadly speaking a method of giving a verdict/decision on a new case query by comparing it with verdicts/decisions of known similar cases. Similarity of cases is determined ... [more ▼]

Case-based reasoning (CBR) is broadly speaking a method of giving a verdict/decision on a new case query by comparing it with verdicts/decisions of known similar cases. Similarity of cases is determined either by best distance of the query case from the known cases and recently also using argumentation. The approach of this paper is not to rely on similarity or argumentation, but to use the entire set of known cases and their known verdicts to define the relative strength and importance of all the features involved in these cases. We then decide the verdict for the new case based on the strength of the features appearing in it. [less ▲]

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See detailRhythmic Stimuli Effects on Subjective Time Perception in Immersive Virtual Environments
Picard, Stéven UL; Botev, Jean UL

in Proceedings of the 13th ACM Multimedia Systems Conference (MMSys) (2022)

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See detailAutonomous Reconfigurable Intelligent Surfaces Through Wireless Energy Harvesting
Ntontin, Konstantinos UL; Boulogeorgos, Apostolos-Alexandros A.; Björnson, Emil et al

Scientific Conference (2022)

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See detailTalent turnover in High-Tech industry: Is there a contagion effect?
Usanova, Ksenia UL; Geraudel, Mickaël UL

Scientific Conference (2022)

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See detailMULTI LABEL IMAGE CLASSIFICATION USING ADAPTIVE GRAPH CONVOLUTIONAL NETWORKS (ML-AGCN)
Singh, Inder Pal UL; Ghorbel, Enjie UL; Oyedotun, Oyebade UL et al

in IEEE International Conference on Image Processing (2022)

In this paper, a novel graph-based approach for multi-label image classification called Multi-Label Adaptive Graph Convolutional Network (ML-AGCN) is introduced. Graph-based methods have shown great ... [more ▼]

In this paper, a novel graph-based approach for multi-label image classification called Multi-Label Adaptive Graph Convolutional Network (ML-AGCN) is introduced. Graph-based methods have shown great potential in the field of multi-label classification. However, these approaches heuristically fix the graph topology for modeling label dependencies, which might be not optimal. To handle that, we propose to learn the topology in an end-to-end manner. Specifically, we incorporate an attention-based mechanism for estimating the pairwise importance between graph nodes and a similarity-based mechanism for conserving the feature similarity between different nodes. This offers a more flexible way for adaptively modeling the graph. Experimental results are reported on two well-known datasets, namely, MS-COCO and VG-500. Results show that ML-AGCN outperforms state-of-the-art methods while reducing the number of model parameters. [less ▲]

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See detailTowards an evaluation of incentives and nudges for smart charging
Marxen, Hanna UL; Chemudupaty, Raviteja UL; Graf-Drasch, Valerie et al

in Proceedings of the 30th European Conference on Information Systems (ECIS 2022) (2022)

Electric vehicles (EVs) are an important cornerstone to achieve transport decarbonization. Still, simultaneous charging of EVs when home charging increases peak demand, especially during evenings. Smart ... [more ▼]

Electric vehicles (EVs) are an important cornerstone to achieve transport decarbonization. Still, simultaneous charging of EVs when home charging increases peak demand, especially during evenings. Smart charging allows optimal distribution of load, thus preventing peak loads. Nevertheless, this incorporates certain risks for the EV user, e.g., unavailability of EVs for unplanned events. This might lead to a lack of user acceptance. This paper focuses on specific incentives and nudges, motivating users to adopt smart charging. We conducted an integrative literature review, bringing together literature from different areas. Possible incentives and nudges are monetary incentives, feedback, gamification, or smart charging as a default-setting. We conducted three focus groups with 13 EV users in Luxembourg to get first insights into which of those incentives and nudges they prefer. Preliminary results indicate that incentives and nudges should be individualized. In the future, we would use these first insights to develop a large-scale survey. [less ▲]

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See detailDigital Simulator of the Human Arteries
Torres Gómez, Jorge; Gonzalez Rios, Jorge Luis UL; Dressler, Falko

in European Wireless (EW 2022) (2022)

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See detailSimulation technology and application of safe collaborative operations in human-robot interaction
Miro, Michael; Glogowski, Paul; Lemmerz, Kai et al

in Simulation Technology and Application of Safe Collaborative Operations in Human-Robot Interaction (2022)

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See detailUnwinding a Legal and Ethical Ariadne’s Thread Out of the Twitter Scraping Maze
Rossi, Arianna UL; Kumari, Archana; Lenzini, Gabriele UL

in Schiffner, Stefan; Ziegler, Sebastien; Quesada Rodriguez, Adrian (Eds.) Data Protection Law International Convergence and Compliance with Innovative Technologies (DPLICIT) (2022)

Social media data is a gold mine for research scientists, but such type of data carries unique legal and ethical implications while there is no checklist that can be followed to effortlessly comply with ... [more ▼]

Social media data is a gold mine for research scientists, but such type of data carries unique legal and ethical implications while there is no checklist that can be followed to effortlessly comply with all the applicable rules and principles. On the contrary, academic researchers need to find their way in a maze of regulations, sectoral and institutional codes of conduct, interpretations and techniques of compliance. Taking an autoethnographic approach combined with desk research, we describe the path we have paved to find the answers to questions such as: what counts as personal data on Twitter and can it be anonymized? How may we inform Twitter users of an ongoing data collection? Is their informed consent necessary? This article reports practical insights on ethical, legal, and technical measures that we have adopted to scrape Twitter data and discusses some solutions that should be envisaged to make the task of compliance less daunting for academic researchers. The subject matter is relevant for any social computing research activity and, more in general, for all those that intend to gather data of EU social media users. [less ▲]

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See detailAn Evaluation of Methodologies for Legal Formalization
Libal, Tomer UL; Novotná, Tereza

in Lecture Notes in Computer Science 13283, Springer 2022, (2022)

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See detailSharing Big Data in Health Care: Focus on the Role Played by Co-regulatory Instruments in Light of the New European Health Data Space
Vigna, Francesco UL

in Tom, Goffin; Tom, Balthazar (Eds.) Book of Proceedings: Eighth European Conference on Health Law (2022)

Big data and AI are revolutionizing data processing in health care. The new paradigm of data-driven decision making will lead primary activities, such as the provision of care services, as well as ... [more ▼]

Big data and AI are revolutionizing data processing in health care. The new paradigm of data-driven decision making will lead primary activities, such as the provision of care services, as well as secondary health care purposes, like clini-cal research. However, the extraction of knowledge from big data in health care depends on the possibility for stakeholders to share and connect datasets in compliance with data protection law requirements. In this regard, the EU Data Strategy seeks to foster data sharing for public benefit. The EU Commission, in the preparatory works for the European Health Data Space, has stressed that issues related to GDPR compliance could hinder the sharing and the re-use of data in health care. Namely, two order of problems may be highlighted in this perspective: 1) the special regime for health data pro-cessing in GDPR, which allows MS to add even stricter data protection rules; 2) margins of choice for data controllers and processors about how to implement technical and organisational measures for reaching GDPR compliance, according to the risk-based approach. According to the EU Commission, legal uncertainty might be mitigated by adopting co-regulatory measures, such as a Code of Conduct for the sharing of health data. A Code of Conduct (articles 40 and 41 GDPR) could establish common ways to interpret and comply with loose GDPR requirements and prin-ciples, therefore creating harmonization. However, given the legal fragmentation typical of the health domain among MS, the elaboration of an EU-wide Code of Conduct for personal data processing could not bring the desired effects. More-over, some doubts concern the lack of legal effects produced by a Code of Conduct on the burden of proof for data controllers. Finally, also costs and complexities linked to the elaboration of GDPR co-regulatory instruments may hamper the diffusion of these mechanisms. The paper discusses opportunities and limits of GDPR co-regulatory mecha-nisms, considering the forthcoming European Health Data Space. It is argued that despite legal and governance reasoning are theoretically correct about the role of these instruments, their spread is limited once the reasoning is linked to market and economic aspects. [less ▲]

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See detailGeometrie der römischen Mosaiken
Perucca, Antonella UL

Scientific Conference (2022)

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See detailGiving talents a voice: Talent management n the aerospace industry
Usanova, Ksenia UL; Geraudel, Mickaël UL

Scientific Conference (2022)

Although Talent Management (TM) has been studied for the last two decades, the empirical knowledge represents highly managerial view on the phenomenon. Those important studies that investigated TM from ... [more ▼]

Although Talent Management (TM) has been studied for the last two decades, the empirical knowledge represents highly managerial view on the phenomenon. Those important studies that investigated TM from both managers’ and talents’ perspectives are scarce and quite fragmented since they focus only on one or two components of TM. In this work, we try to examine TM holistically by focusing on five main TM components together – talent definition, TM goals, attraction, development and retention of talents – through the perspectives of managers and talented individuals. To that end, we interviewed 40 talents and their managers in the aerospace industry in Luxembourg. We identified shared, exclusively managers’ and exclusively talents’ views of TM and named them “functional”, “pragmatic” and “romanticized” respectively. By combining the shared view and expected views, we propose an ideal TM for talent and an ideal TM for managers. [less ▲]

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See detailBreaking and Fixing Vote Privacy of the Estonian E-Voting Protocol IVXV
Mueller, Johannes UL

in Workshop on Advances in Secure Electronic Voting 2022 (2022)

We revisit the e-voting protocol IVXV that is used for legally-binding political elections in Estonia from a privacy perspective. We demonstrate that IVXV is vulnerable to attacks against vote privacy in ... [more ▼]

We revisit the e-voting protocol IVXV that is used for legally-binding political elections in Estonia from a privacy perspective. We demonstrate that IVXV is vulnerable to attacks against vote privacy in those threat scenarios that were considered for IVXV originally. We explain how to improve IVXV so that it protects against the privacy issues we discovered. [less ▲]

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See detailDynamic Deontic Logic for Permitted Announcements
Li, Xu UL; Gabbay, Dov M. UL; Markovich, Réka UL

in 19th International Conference on Principles of Knowledge Representation and Reasoning, KR 2022 (2022)

In this paper, we introduce and study a dynamic deontic logic for permitted announcements. In our logic framework, it is permitted to announce something if announcing it would not lead to forbidden ... [more ▼]

In this paper, we introduce and study a dynamic deontic logic for permitted announcements. In our logic framework, it is permitted to announce something if announcing it would not lead to forbidden knowledge. It is shown that the logic is not compact, and we propose a sound and weakly complete Hilbert-style axiomatisation. We also study the computational complexity of the model checking problem and the decidability of the satisfiability problem. Finally, we introduce a neighbourhood semantics with a strongly complete axiomatisation. [less ▲]

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See detailStatistical Performance Analysis of Radar-Based Vital-Sign Processing Techniques
Tedgue Beltrao, Gabriel UL; Alaeekerahroodi, Mohammad UL; Schroeder, Udo et al

Scientific Conference (2022)

Radar-based vital-sign monitoring provides several advantages over standard methodologies. Despite the huge amount of recent work, the preference for particular technique(s) is in debt, due to lack of a ... [more ▼]

Radar-based vital-sign monitoring provides several advantages over standard methodologies. Despite the huge amount of recent work, the preference for particular technique(s) is in debt, due to lack of a formal comparison between them. In addition, collection of real data is a time-consuming process and therefore most of the proposed solutions are only evaluated under very limited scenarios. In this paper we present a simulation framework and a selection of results which allow easy performance comparison between radar-based vital-sign processing techniques. The proposed simulation tool scans over multiple breathing and heartbeat frequencies, and the combined effects along the entire signal processing chain can be analyzed, for different combinations of scenarios and techniques. The results have shown specific limitations for each method, thus indicating a need for proper selection based on operating conditions. In addition, while breathing estimation performance is only limited by noise, heartbeat estimation is limited by the presence of breathing harmonics and, despite promising results at specific breathing/heartbeat frequencies, the presented methods fail to fully mitigate this type of interference in all scenarios. [less ▲]

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See detailQoE-Oriented Resource Allocation Design Coping with Time-Varying Demands in Wireless Communication Networks
Kebedew, Teweldebrhan Mezgebo UL; Ha, Vu Nguyen UL; Lagunas, Eva UL et al

in IEEE 96st Vehicular Technology Conference, London-Beijing, Sept. 2022 (2022)

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See detailUnlimited Sampling for FMCW Radars: A Proof of Concept
Feuillen, Thomas; Alaeekerahroodi, Mohammad UL; Bhandari, Ayush et al

in 2022 IEEE Radar Conference (RadarConf22) (2022)

High-resolution FMCW radar systems are becoming an integral aspect of applications ranging from automotive safety and autonomous driving to health monitoring of infants and the elderly. This integration ... [more ▼]

High-resolution FMCW radar systems are becoming an integral aspect of applications ranging from automotive safety and autonomous driving to health monitoring of infants and the elderly. This integration provides challenging scenarios that require radars with extremely high dynamic range (HDR) ADCs; these ADCs need to avoid saturation while offering high-performance and high-fidelity data-acquisition. The recent concept of Unlimited Sensing allows one to achieve high dynamic range (HDR) acquisition by recording low dynamic range, modulo samples. Interestingly, oversampling of these folded measurements, with a sampling rate independent of the modulo threshold, is sufficient to guarantee their perfect reconstruction for band-limited signals. This contrasts with the traditional methodology of increasing the dynamic range by adding a programmable-gain amplifier or operating multiple ADCs in parallel. This paper demonstrates an FMCW radar prototype that utilises the unlimited sampling strategy. Our hardware experiments show that even with the use of a modulo measurements of lower precision, the US reconstruction is able to match the performances of the conventional acquisition. Furthermore, our real-time processing capability demonstrates that our “proof-of-concept” approach is a viable solution for HDR FMCW radar signal processing, thus opening a pathway for future hardware-software optimization and integration of this technology with other mainstream systems. [less ▲]

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See detail5G Space Communications Lab: Reaching New Heights
Kodheli, Oltjon UL; Querol, Jorge UL; Astro, Abdelrahman et al

in Proceedings of the 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (2022)

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See detailWikiDoMiner: Wikipedia Domain-Specific Miner
Ezzini, Saad UL; Abualhaija, Sallam UL; Sabetzadeh, Mehrdad

in ACM SIGSOFT CONFERENCE ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (2022)

We introduce WikiDoMiner -- a tool for automatically generating domain-specific corpora by crawling Wikipedia. WikiDoMiner helps requirements engineers create an external knowledge resource that is ... [more ▼]

We introduce WikiDoMiner -- a tool for automatically generating domain-specific corpora by crawling Wikipedia. WikiDoMiner helps requirements engineers create an external knowledge resource that is specific to the underlying domain of a given requirements specification (RS). Being able to build such a resource is important since domain-specific datasets are scarce. WikiDoMiner generates a corpus by first extracting a set of domain-specific keywords from a given RS, and then querying Wikipedia for these keywords. The output of WikiDoMiner is a set of Wikipedia articles relevant to the domain of the input RS. Mining Wikipedia for domain-specific knowledge can be beneficial for multiple requirements engineering tasks, e.g., ambiguity handling, requirements classification, and question answering. WikiDoMiner is publicly available on Zenodo under an open-source license (https://doi.org/10.5281/zenodo.6672682) [less ▲]

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See detailNon-Orthogonal Multicast and Unicast Robust Beamforming in Integrated Terrestrial-Satellite Networks
Peng, Deyi; Domouchtsidis, Stavros UL; Chatzinotas, Symeon UL et al

in IEEE Global Communications Conference (2022)

This paper studies the non-orthogonal multicast and unicast coordinated beamforming design for integrated terrestrial and satellite networks (ITSN), when the channel state information at the transmitter ... [more ▼]

This paper studies the non-orthogonal multicast and unicast coordinated beamforming design for integrated terrestrial and satellite networks (ITSN), when the channel state information at the transmitter (CSIT) is imperfect. In order to mitigate the interference induced by simultaneous multicast and unicast links along with the spectrum coexisting mechanism for integrated terrestrial and satellite transmissions, we consider a two-layer layered division multiplexing (LDM) structure where the mul ticast and unicast services are provided in different layers. We formulate a coordinated beamforming problem with the objective to minimize the transmit power under individual quality of service (QoS) constraints. With regard to the unknown convexity of the transmit power minimization problem, we transform the original infeasible optimization into a deterministic optimization form with linear matrix inequality (LMI) by utilizing S-procedure and semi-definite relaxation (SDR) methods. Then, we introduce a penalty function and propose an iterative algorithm with guaranteed convergence to obtain optimal solutions. Simulation results demonstrate the superiority of the proposed coordinated beamforming scheme, especially for the case of imperfect CSIT, while our LDM based coordinated beamforming scheme signifi cantly outperforms the conventional ones in terms of sum rate. Index Terms—Integrated terrestrial and satellite networks (IT SN), beamforming, robust, layered division multiplexing (LDM) [less ▲]

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See detailConversational Agents Trust Calibration: A User-Centred Perspective to Design
Dubiel, Mateusz UL; Daronnat, Sylvain; Leiva, Luis A. UL

in Conversational Agents Trust Calibration: A User-Centred Perspective to Design (2022)

Previous work identified trust as one of the key requirements for adoption and continued use of conversational agents (CAs). Given recent advances in natural language processing and deep learning, it is ... [more ▼]

Previous work identified trust as one of the key requirements for adoption and continued use of conversational agents (CAs). Given recent advances in natural language processing and deep learning, it is currently possible to execute simple goal-oriented tasks by using voice. As CAs start to provide a gateway for purchasing products and booking services online, the question of trust and its impact on users’ reliance and agency becomes ever-more pertinent. This paper collates trust-related literature and proposes four design suggestions that are illustrated through example conversations. Our goal is to encourage discussion on ethical design practices to develop CAs that are capable of employing trust-calibration techniques that should, when relevant, reduce the user’s trust in the agent. We hope that our reflections, based on the synthesis of insights from the fields of human-agent interaction, explainable ai, and information retrieval, can serve as a reminder of the dangers of excessive trust in automation and contribute to more user-centred CA design. [less ▲]

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See detailJoint Communications and Sensing for Hybrid Massive MIMO LEO Satellite Systems With Beam Squint
Qiang, Xiaoyu; You, Li; Tsinos, Christos UL et al

in 2022 IEEE International Conference on Communications Workshops (ICC Workshops) (2022)

To improve the efficient utilization of spectral and hardware resources, joint communications and sensing (JCS) has drawn extensive attention. Most existing JCS works focus on terrestrial networks and can ... [more ▼]

To improve the efficient utilization of spectral and hardware resources, joint communications and sensing (JCS) has drawn extensive attention. Most existing JCS works focus on terrestrial networks and can not be straightforwardly applied in satellite systems due to the significantly different electromagnetic wave propagation properties. In this work, we investigate the application of JCS in massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems. We first charac-terize the statistical wave propagation properties by considering beam squint effects. Based on this analysis, we propose a beam squint-aware JCS technique for hybrid analog/digital massive MIMO LEO satellite systems exploiting statistical channel s-tate information. Simulation results demonstrate that both the wireless communications and target sensing can be operated simultaneously with satisfactory performance, and the beam squint effects can be efficiently mitigated with the proposed method in typical LEO satellite systems. [less ▲]

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See detailMulticast MMSE-based Precoded Satellite Systems: User Scheduling and Equivalent Channel Impact
Lagunas, Eva UL; Ha, Vu Nguyen UL; Chien, Trinh-Van et al

in IEEE 96st Vehicular Technology Conference, London-Beijing, Sept. 2022 (2022)

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See detailEfficient and Transferable Adversarial Examples from Bayesian Neural Networks
Gubri, Martin UL; Cordy, Maxime UL; Papadakis, Mike UL et al

in The 38th Conference on Uncertainty in Artificial Intelligence (2022)

An established way to improve the transferability of black-box evasion attacks is to craft the adversarial examples on an ensemble-based surrogate to increase diversity. We argue that transferability is ... [more ▼]

An established way to improve the transferability of black-box evasion attacks is to craft the adversarial examples on an ensemble-based surrogate to increase diversity. We argue that transferability is fundamentally related to uncertainty. Based on a state-of-the-art Bayesian Deep Learning technique, we propose a new method to efficiently build a surrogate by sampling approximately from the posterior distribution of neural network weights, which represents the belief about the value of each parameter. Our extensive experiments on ImageNet, CIFAR-10 and MNIST show that our approach improves the success rates of four state-of-the-art attacks significantly (up to 83.2 percentage points), in both intra-architecture and inter-architecture transferability. On ImageNet, our approach can reach 94% of success rate while reducing training computations from 11.6 to 2.4 exaflops, compared to an ensemble of independently trained DNNs. Our vanilla surrogate achieves 87.5% of the time higher transferability than three test-time techniques designed for this purpose. Our work demonstrates that the way to train a surrogate has been overlooked, although it is an important element of transfer-based attacks. We are, therefore, the first to review the effectiveness of several training methods in increasing transferability. We provide new directions to better understand the transferability phenomenon and offer a simple but strong baseline for future work. [less ▲]

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See detailThe LegAi Editor: A Tool for the Construction of Legal Knowledge Bases
Libal, Tomer UL

in Legal Knowledge and Information Systems - JURIX 2022: The Thirty-fifth Annual Conference, Saarbrücken, Germany, 14-16 December 2022. Frontiers in Artificial Intelligence and Applications 362, IOS Press 2022 (2022)

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See detailIntelligent Human-input-based Blockchain Oracle (IHiBO)
Yu, Liuwen; Zichichi, Mirko; Markovich, Réka UL et al

in Proceedings of the 14th International Conference on Agents and Artificial Intelligence, ICAART 2022, Volume 1 (2022)

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See detailHomomorphic encryption for privacy-friendly augmented democracy
Brabant, Matthieu; Pereira, Olivier; Meaux, Pierrick UL

in 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) (2022)

Detailed reference viewed: 26 (0 UL)