References of "2021"
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See detailThe Impact of the Asset Purchase Programme on Systemic Risk in the Euro Area: Is There a Threat?
Chavarro Sanchez, Leidy; Nadal De Simone, Francisco; Lehnert, Thorsten UL

E-print/Working paper (2021)

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See detailCompétences en littératie numérique et pensée computationnelle des élèves de huitième année – Principales conclusions d’ICILS 2018
Boualam, Rachid UL; Lomos, Catalina; Fischbach, Antoine UL

in LUCET; SCRIPT (Eds.) Rapport national sur l’éducation au Luxembourg 2021 (2021)

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See detailLe droit à la sauce piquante n°25 - Décembre 2021
Hiez, David UL; Laurent, Rémi

in Le droit à la sauce piquante (2021), 25

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See detailMassive Superpoly Recovery with Nested Monomial Predictions
Hu, Kai; Sun, Siwei; Todo, Yosuke et al

in Advances in Cryptology - ASIACRYPT 2021 - 27th International Conference on the Theory and Application of Cryptology and Information Security Singapore, December 6-10, 2021, Proceedings, Part I (2021, December)

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See detailEine Frage der Haltung – Spannungsfelder des Unterrichtens in einer digitalen Welt
Pause, Johannes UL; Harion, Dominic UL

in Nationaler Bildungsbericht Luxemburg 2021 (2021)

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See detailRésultats du monitoring scolaire national ÉpStan dans le contexte de la pandémie de COVID-19 (Matériels supplémentaires)
Fischbach, Antoine UL; Colling, Joanne UL; Levy, Jessica UL et al

in LUCET; SCRIPT (Eds.) Rapport National sur l´Éducation au Luxembourg 2021 (2021)

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See detailHow enriching sensory awareness develops and affects well-being throughout childhood
Linzarini, Adriano; Cebotari, Victor UL; Richardson, Dominic et al

E-print/Working paper (2021)

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See detailInklusion in Luxemburg: Definitionen, Ansichten und Bereitschaft zur inklusiven Bildung
Pit-Ten Cate, Ineke UL; Powell, Justin J W UL; Krischler, Mireille UL

in LUCET; SCRIPT (Eds.) Nationaler Bildungsbericht Luxemburg 2021 (2021)

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See detailTOrPEDO: Witnessing Model Correctness with Topological Proofs
Menghi, Claudio UL; Rizzi, Alessandro Maria; Bernasconi, Anna et al

in Formal Aspects of Computing (2021), 33(6), 1039-1066

Model design is not a linear, one-shot process. It proceeds through refinements and revisions. To effectively support developers in generating model refinements and revisions, it is desirable to have some ... [more ▼]

Model design is not a linear, one-shot process. It proceeds through refinements and revisions. To effectively support developers in generating model refinements and revisions, it is desirable to have some automated-support to verify evolvable models. To address this problem, we recently proposed to adopt topological proofs, which are slices of the original model that witness property satisfaction. We implemented TOrPEDO, a framework that provides automated support for using topological proofs during model design. Our results showed that topological proofs are significantly smaller than the original models, and that, in most of the cases, they allow the property to be re-verified by relying only on a simple syntactic check. However, our results also show that the procedure that computes topological proofs, which requires extracting unsatisfiable cores of LTL formulae, is computationally expensive. For this reason, TOrPEDO currently handles models with a small dimension. With the intent of providing practical and efficient support for flexible model design and wider adoption of our framework, in this paper, we propose an enhanced – re-engineered – version of TOrPEDO. The new version of TOrPEDO relies on a novel procedure to extract topological proofs, which has so far represented the bottleneck of TOrPEDO performances. We implemented our procedure within TOrPEDO by considering Partial Kripke Structures (PKSs) and Linear-time Temporal Logic (LTL): two widely used formalisms to express models with uncertain parts and their properties. To extract topological proofs, the new version of TOrPEDO converts the LTL formulae into an SMT instance and reuses an existing SMT solver (e.g., Microsoft Z3) to compute an unsatisfiable core. Then, the unsatisfiable core returned by the SMT solver is automatically processed to generate the topological proof. We evaluated TOrPEDO by assessing (i) how does the size of the proofs generated by TOrPEDO compares to the size of the models being analyzed; and (ii) how frequently the use of the topological proof returned by TOrPEDO avoids re-executing the model checker. Our results show that TOrPEDO provides proofs that are smaller (≈60%) than their respective initial models effectively supporting designers in creating model revisions. In a significant number of cases (≈79%), the topological proofs returned by TOrPEDO enable assessing the property satisfaction without re-running the model checker. We evaluated our new version of TOrPEDO by assessing (i) how it compares to the previous one; and (ii) how useful it is in supporting the evaluation of alternative design choices of (small) model instances in applied domains. The results show that the new version of TOrPEDO is significantly more efficient than the previous one and can compute topological proofs for models with less than 40 states within two hours. The topological proofs and counterexamples provided by TOrPEDO are useful to support the development of alternative design choices of (small) model instances in applied domains. [less ▲]

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See detailCybersecurity is Gaining Momentum – NIS 2.0 Is on its Way
Schmitz, Sandra UL

in European Data Protection Law Review (2021), (4), 580-585

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See detailGender Differences and Extreme Events
Lehnert, Thorsten UL

Scientific Conference (2021, December)

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See detailCompte rendu de Dirk Richtus, Naar de hel met Hitler
Brüll, Christoph UL

in Contemporanea (2021), (4),

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See detailLes enseignant(e)s, acteurs essentiels dans la mise en œuvre des technologies de l’information et de la communication (TIC) dans l’enseignement et l’apprentissage – Principales conclusions d’ICILS 2018
Lomos, Catalina; Luyten, Hans J.W.; Boualam, Rachid UL et al

in LUCET; SCRIPT (Eds.) Rapport national sur l’éducation au Luxembourg 2021 (2021)

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See detailBefunde aus dem nationalen Bildungsmonitoring ÉpStan vor dem Hintergrund der COVID-19 Pandemie (Supplement)
Fischbach, Antoine UL; Colling, Joanne UL; Levy, Jessica UL et al

in LUCET; SCRIPT (Eds.) Nationaler Bildungsbericht Luxemburg 2021 (2021)

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See detailWhat's in a Cyber Threat Intelligence sharing platform?: A mixed-methods user experience investigation of MISP
Stojkovski, Borce UL; Lenzini, Gabriele UL; Koenig, Vincent UL et al

in Annual Computer Security Applications Conference (ACSAC ’21) (2021, December)

The ever-increasing scale and complexity of cyber attacks and cyber-criminal activities necessitate secure and effective sharing of cyber threat intelligence (CTI) among a diverse set of stakeholders and ... [more ▼]

The ever-increasing scale and complexity of cyber attacks and cyber-criminal activities necessitate secure and effective sharing of cyber threat intelligence (CTI) among a diverse set of stakeholders and communities. CTI sharing platforms are becoming indispensable tools for cooperative and collaborative cybersecurity. Nevertheless, despite the growing research in this area, the emphasis is often placed on the technical aspects, incentives, or implications associated with CTI sharing, as opposed to investigating challenges encountered by users of such platforms. To date, user experience (UX) aspects remain largely unexplored. This paper offers a unique contribution towards understanding the constraining and enabling factors of security information sharing within one of the leading platforms. MISP is an open source CTI sharing platform used by more than 6,000 organizations worldwide. As a technically-advanced CTI sharing platform it aims to cater for a diverse set of security information workers with distinct needs and objectives. In this respect, MISP has to pay an equal amount of attention to the UX in order to maximize and optimize the quantity and quality of threat information that is contributed and consumed. Using mixed methods we shed light on the strengths and weaknesses of MISP from an end-users’ perspective and discuss the role UX could play in effective CTI sharing. We conclude with an outline of future work and open challenges worth further exploring in this nascent, yet highly important socio-technical context. [less ▲]

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See detailChicken and Egg: Reporting from a Datathon Exploring Datasets of the COVID-19 Special Collections
Aasman, Susan; Bingham, Nicola; Brügger, Niels et al

Report (2021)

This report is the first in a short series of WARCnet papers which aim to provide feedback on an internal datathon conducted by Working Group 2 of the WARCnet project. It explores the creation of ... [more ▼]

This report is the first in a short series of WARCnet papers which aim to provide feedback on an internal datathon conducted by Working Group 2 of the WARCnet project. It explores the creation of transnational merged datasets and corpora, based on seed lists, derived data and metadata provided by several web archiving institutions. The report highlights our first explorations of specially curated COVID web archives, in order to prepare an in-depth exploration of the issues, challenges, limitations and opportunities afforded by these heterogeneous datasets. [less ▲]

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See detailLearning-Assisted User Clustering in Cell-Free Massive MIMO-NOMA Networks
Le, Quang Nhat; Nguyen, van Dinh UL; Dobre, Octavia A. et al

in IEEE Transactions on Vehicular Technology (2021), 70(12), 12872-12887

The superior spectral efficiency (SE) and user fairness feature of non-orthogonal multiple access (NOMA) systems are achieved by exploiting user clustering (UC) more efficiently. However, a random UC ... [more ▼]

The superior spectral efficiency (SE) and user fairness feature of non-orthogonal multiple access (NOMA) systems are achieved by exploiting user clustering (UC) more efficiently. However, a random UC certainly results in a suboptimal solution while an exhaustive search method comes at the cost of high complexity, especially for systems of medium-to-large size. To address this problem, we develop two efficient unsupervised machine learning based UC algorithms, namely k-means++ and improved k-means++, to effectively cluster users into disjoint clusters in cell-free massive multiple-input multiple-output (CFmMIMO) system. Adopting full-pilot zero-forcing at access points (APs) to comprehensively assess the system performance, we formulate the sum SE optimization problem taking into account power constraints at APs, necessary conditions for implementing successive interference cancellation, and required SE constraints at user equipments. The formulated optimization problem is highly non-convex, and thus, it is difficult to obtain the global optimal solution. Therefore, we develop a simple yet efficient iterative algorithm for its solution. In addition, the performance of collocated massive MIMO-NOMA (COmMIMO-NOMA) system is also characterized. Numerical results are provided to show the superior performance of the proposed UC algorithms compared to baseline schemes. The effectiveness of applying NOMA in CFmMIMO and COmMIMO systems is also validated. [less ▲]

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