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See detailHeilsgeschichte aus dem Osten
Ganschow, Inna UL; Majerus, Stephanie

Article for general public (2022)

Der russische Ultranationalismus kann als „politische Religion“ aufgefasst werden. Beeinflusst dieser ideologische Überbau russischsprachige Gemeinschaften in Luxemburg?

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See detailLëtzebuerger Mathematikerin bei der Weltraumagence ESA
Palmirotta, Guendalina UL

Speeches/Talks (2022)

Eng Lëtzebuerger Mathematikerin schafft zanter e puer Woche fir d'europäesch Weltraumagence zu Darmstadt. Déi jonk Fra entwéckelt Modeller fir d'Wieder am Weltall virauszesoen, fir esou d ... [more ▼]

Eng Lëtzebuerger Mathematikerin schafft zanter e puer Woche fir d'europäesch Weltraumagence zu Darmstadt. Déi jonk Fra entwéckelt Modeller fir d'Wieder am Weltall virauszesoen, fir esou d' Satellittesystemer viru Sonnestierm ze schützen. De Weltall passionéiert zanter, datt et d'Mënschheet gëtt. D’Dr. Guenda Palmirotta ass Mathematikerin a huet sech fréi fir dat interesséiert, wat ausserhalb vun der Äerd geschitt. Mam Job bei der Europäescher Weltraumagence geet en Dram an Erfëllung. Zu Darmstadt entwéckelt d'Lëtzebuergerin Modeller fir d’Weltraumwieder virauszesoen. Bestëmmt gëtt dëst vun der Sonn an de Sonnestierm, déi kennen entstoen, déi fir Satellittesystemer e Problem kënnen duerstellen. Ee vun den Ziler ass et, an den nächste Joren d’Previsioune méi präzis ze maachen, ma och méi wäit am viraus kënne viraussoen, wat geschitt. Konkret ginn d’Modeller elo schonn agesat, fir d’Astronauten op der Internationaler Weltraumstatioun ze schützen. Zu Darmstadt huet d'ESA een neien Iwwerwaachungszentral, wou nieft dem Weltraumwieder och aner Elementer vun der Weltraumsécherheet am A behale ginn. Esou zum Beispill de Weltraumschrott, mëttlerweil gëtt et vill Satellitten, déi net fonctionéieren a mat Aktive kéinte kollidéieren. Mat mathematesche Modeller sollen déi aktiv Satellitte gewarnt ginn a se esou hir Positioun fréizäiteg kënnen änneren. [less ▲]

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See detailDemand and Interference Aware Adaptive Resource Management for High Throughput GEO Satellite Systems
Abdu, Tedros Salih UL; Kisseleff, Steven UL; Lagunas, Eva UL et al

in IEEE Open Journal of the Communications Society (2022)

The scarce spectrum and power resources, the inter-beam interference, together with the high traffic demand, pose new major challenges for the next generation of Very High Throughput Satellite (VHTS ... [more ▼]

The scarce spectrum and power resources, the inter-beam interference, together with the high traffic demand, pose new major challenges for the next generation of Very High Throughput Satellite (VHTS) systems. Accordingly, future satellites are expected to employ advanced resource/interference management techniques to achieve high system spectrum efficiency and low power consumption while ensuring user demand satisfaction. This paper proposes a novel demand and interference aware adaptive resource management for geostationary (GEO) VHTS systems. For this, we formulate a multi-objective optimization problem to minimize the total transmit power consumption and system bandwidth usage while matching the offered capacity with the demand per beam. In this context, we consider resource management for a system with full-precoding, i.e. all beams are precoded; without precoding, i.e. no precoding is applied to any beam; and with partial precoding, i.e. only some beams are precoded. The nature of the problem is non-convex and we solve it by jointly using the Dinkelbach and Successive Convex Approximation (SCA) methods. The simulation results show that the proposed method outperforms the benchmark schemes. Specifically, we show that the proposed method requires low resource consumption, low computational time, and simultaneously achieves a high demand satisfaction. [less ▲]

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See detailCommission Delegated Regulation (EU) 2022/30 Supplementing Directive 2014/53/EU on Radio Equipment: Strengthening Cybersecurity, Privacy and Personal Data Protection of Wireless Devices
Chiara, Pier Giorgio UL

in European Data Protection Law Review (2022), 8(1), 103-107

This contribution highlights how the Delegated Regulation (EU) 2022/30 - activating the essential requirements of Article 3(3)(d), (e) and (f) of Directive 2014/53/EU on radio equipment (RED) - will ... [more ▼]

This contribution highlights how the Delegated Regulation (EU) 2022/30 - activating the essential requirements of Article 3(3)(d), (e) and (f) of Directive 2014/53/EU on radio equipment (RED) - will enhance and complement existing cybersecurity and privacy & data protection EU legal frameworks while strengthening the (cyber)security of wireless (IoT) devices. [less ▲]

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See detailAutonomy and the social dilemma of online manipulative behavior
Botes, Wilhelmina Maria UL

in AI and Ethics (2022)

Persuasive online technologies were initially designed and used to gain insights into the online behavior of individuals to personalize advertising campaigns in an effort to influence people and convince ... [more ▼]

Persuasive online technologies were initially designed and used to gain insights into the online behavior of individuals to personalize advertising campaigns in an effort to influence people and convince them to buy certain products. But recently, these technologies have blurred the lines and morphed into technologies that covertly and gradually manipulate people into attaining a goal that is predetermined by the algorithm and disregards the decision-making rights of the individual. This may lead to people exercising decisions that do not align with their personal values and beliefs, and rob them of their autonomy—an ethical principle, in the absence of which the application of these technologies may be unethical. However, not all technologies that are persuasive are necessarily manipulative which require the careful consideration of a couple of elements to determine whether or not technologies are manipulative and ultimately whether their application is ethical or not. In this article, we analyze the ethical principle of autonomy and unpack the underlying elements of this ethical principle which must be considered to determine whether the application of a technology is ethical or not in the context of it being persuasive or manipulative. [less ▲]

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See detailSpotlight on Young Researchers: Nature’s shapes as mathematical challenges
Palmirotta, Guendalina UL

Article for general public (2022)

In nature, we see hyperbolic forms in corals, flatworms, and many other species of reef organisms, such as sponges and kelps. The hyperbolic spaces are also of interest for mathematicians, who are looking ... [more ▼]

In nature, we see hyperbolic forms in corals, flatworms, and many other species of reef organisms, such as sponges and kelps. The hyperbolic spaces are also of interest for mathematicians, who are looking to prove the solvability of invariant systems of differential equations in unusual spaces such as these. [less ▲]

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See detailPRINS: Scalable Model Inference for Component-based System Logs
Shin, Donghwan UL; Bianculli, Domenico UL; Briand, Lionel UL

in Empirical Software Engineering (2022)

Behavioral software models play a key role in many software engineering tasks; unfortunately, these models either are not available during software development or, if available, quickly become outdated as ... [more ▼]

Behavioral software models play a key role in many software engineering tasks; unfortunately, these models either are not available during software development or, if available, quickly become outdated as implementations evolve. Model inference techniques have been proposed as a viable solution to extract finite state models from execution logs. However, existing techniques do not scale well when processing very large logs that can be commonly found in practice. In this paper, we address the scalability problem of inferring the model of a component-based system from large system logs, without requiring any extra information. Our model inference technique, called PRINS, follows a divide-and-conquer approach. The idea is to first infer a model of each system component from the corresponding logs; then, the individual component models are merged together taking into account the flow of events across components, as reflected in the logs. We evaluated PRINS in terms of scalability and accuracy, using nine datasets composed of logs extracted from publicly available benchmarks and a personal computer running desktop business applications. The results show that PRINS can process large logs much faster than a publicly available and well-known state-of-the-art tool, without significantly compromising the accuracy of inferred models. [less ▲]

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See detailSustainability and Circular Economy in Learning Factories – Case Studies
Weyand, Astrid; Thiede, Sebastian; Mangers, Jeff UL et al

in SSRN (2022, April 11)

Since the mitigation of climate change is one of the biggest challenges to face on a global scale, the topic has become more relevant also in industrial context. Learning factories have proven to be ... [more ▼]

Since the mitigation of climate change is one of the biggest challenges to face on a global scale, the topic has become more relevant also in industrial context. Learning factories have proven to be suitable environments to address and convey competencies to tackle industrial challenges in an interactive way. Hence, several learning factories are already dealing with sustainability topics in various use cases. This paper strives to present a state of the art of sustainability and circular economy in learning factories. Therefore, a classification framework is developed based on the state of the art of several learning factories and existing literature regarding the topic. This framework is then used to systematically describe the different activities regarding sustainability and circular economy that are currently ongoing in learning factories worldwide. This can be used to get an idea about the different aspects of the topic and how to address them, but furthermore also offers assistance to identify “blind spots” which could and should be addressed in learning factories in the future. [less ▲]

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See detailUnsichtbares sichtbar machen
Scuto, Denis UL; Harnoncourt, Julia UL

Article for general public (2022)

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See detailCreating positive learning experiences with technology: A field study on the effects of user experience for digital concept mapping
Rohles, Björn UL; Backes, Susanne UL; Fischbach, Antoine UL et al

in Heliyon (2022), 8(4),

Learning and assessment are increasingly mediated by digital technologies. Thus, learners’ experiences with these digital technologies are growing in importance, as they might affect learning and ... [more ▼]

Learning and assessment are increasingly mediated by digital technologies. Thus, learners’ experiences with these digital technologies are growing in importance, as they might affect learning and assessment. The present paper explores the impact of user experience on digital concept mapping. It builds on user experience theory to explain variance in the intention to use digital concept mapping tools and in concept map-based assessment scores. Furthermore, it identifies fulfillment of psychological needs as an important driver of positive experiences. In a field study in three schools and a university (N = 71), we tested two concept mapping prototypes on computers and tablets. We found that user experience is a significant factor explaining variance in intention to use. User experience also explained variance in three out of four concept mapping scores on tablets, potentially related to the lower pragmatic quality of the tablet prototypes. Fulfillment of psychological needs strongly affected perceptions of different qualities of user experience with digital concept mapping. These results indicate that user experience needs to be considered in digital concept mapping to provide a positive and successful environment for learning and assessment. Finally, we discuss implications for designers of digital learning and assessment tools. [less ▲]

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See detailHistoricising online virality
Schafer, Valerie UL

Presentation (2022, April 08)

This presentation aims to questions the methodologies and challenges related to an historical study of European online virality.

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See detailThe Instrumental Turn of Nationality: An Investment Law Perspective
Garcia Olmedo, Javier UL

Speeches/Talks (2022)

This workshop takes a holistic approach to examine the role and future of nationality in a globalised world, taking citizenship by investment (CBI) schemes as a point of departure. It explores the ... [more ▼]

This workshop takes a holistic approach to examine the role and future of nationality in a globalised world, taking citizenship by investment (CBI) schemes as a point of departure. It explores the implications and impacts of CBI schemes, followed by a discussion on the instrumental turn of nationality in different areas, including EU law, private international law, human rights law, immigration law, diplomatic protection and international investment law. [less ▲]

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See detailReplicating the Kinora: 3D Modelling and Printing as Heuristics in Digital Media History
Wolf, Claude UL; van der Heijden, Tim

Computer development (2022)

The Kinora replica 3D model was produced in the context of the project "Doing Experimental Media Archaeology: Practice and Theory (DEMA)", funded by the Fonds National de la Recherche, Luxembourg (FNR ... [more ▼]

The Kinora replica 3D model was produced in the context of the project "Doing Experimental Media Archaeology: Practice and Theory (DEMA)", funded by the Fonds National de la Recherche, Luxembourg (FNR) (C18/SC/12703137): https://dema.uni.lu/. The Kinora replica project was the result of a collaboration between the Luxembourg Centre for Contemporary and Digital History (C2DH) and the Department of Engineering (DoE) of the University of Luxembourg. See for more information the article "Replicating the Kinora: 3D modelling and printing as heuristics in digital media history", published in the Journal of Digital History: https://journalofdigitalhistory.org/en/article/33pRxE2dtUHP [less ▲]

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See detailSEMKIS: A CONTRIBUTION TO SOFTWARE ENGINEERING METHODOLOGIES FOR NEURAL NETWORK DEVELOPMENT
Jahic, Benjamin UL

Doctoral thesis (2022)

Today, there is a high demand for neural network-based software systems supporting humans during their daily activities. Neural networks are computer programs that simulate the behaviour of simplified ... [more ▼]

Today, there is a high demand for neural network-based software systems supporting humans during their daily activities. Neural networks are computer programs that simulate the behaviour of simplified human brains. These neural networks can be deployed on various devices e.g. cars, phones, medical devices...) in many domains (e.g. automotive industry, medicine...). To meet the high demand, software engineers require methods and tools to engineer these software systems for their customers. Neural networks acquire their recognition skills e.g. recognising voice, image content...) from large datasets during a training process. Therefore, neural network engineering (NNE) shall not be only about designing and implementing neural network models, but also about dataset engineering (DSE). In the literature, there are no software engineering methodologies supporting DSE with precise dataset selection criteria for improving neural networks. Most traditional approaches focus only on improving the neural network’s architecture or follow crafted approaches based on augmenting datasets with randomly gathered data. Moreover, they do not consider a comparative evaluation of the neural network’s recognition skills and customer’s requirements for building appropriate datasets. In this thesis, we introduce a software engineering methodology (called SEMKIS) supported by a tool for engineering datasets with precise data selection criteria to improve neural networks. Our method considers mainly the improvement of neural networks through augmenting datasets with synthetic data. SEMKIS has been designed as a rigorous iterative process for guiding software engineers during their neural network-based projects. The SEMKIS process is composed of many activities covering different development phases: requirements’ specification; dataset and neural network engineering; recognition skills specification; dataset augmentation with synthetized data. We introduce the notion of key-properties, used all along the process in cooperation with a customer, to describe the recognition skills. We define a domain-specific language (called SEMKIS-DSL) for the specification of the requirements and recognition skills. The SEMKIS-DSL grammar has been designed to support a comparative evaluation of the customer’s requirements with the key-properties. We define a method for interpreting the specification and defining a dataset augmentation. Lastly, we apply the SEMKIS process to a complete case study on the recognition of a meter counter. Our experiment shows a successful application of our process in a concrete example. [less ▲]

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See detailCybermemorials: Remembrance and Places of Memory in the Digital Age
Camarda, Sandra UL

in Noiret, Serge; Tebeau, Mark; Zaagsma, Gerben (Eds.) Handbook of Digital Public History (2022)

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See detailLeveraging Graph Machine Learning for Social Network Analysis
Zhong, Zhiqiang UL

Postdoctoral thesis (2022)

As a ubiquitous complex system in quotidian life around everyone, online social networks (OSNs) provide a rich source of information about billions of users worldwide. To some extent, OSNs have mirrored ... [more ▼]

As a ubiquitous complex system in quotidian life around everyone, online social networks (OSNs) provide a rich source of information about billions of users worldwide. To some extent, OSNs have mirrored our real society: people perform a multitude of different activities in OSNs as they do in the offline world, such as establishing social relations, sharing life moments, and expressing opinions about various topics. Therefore, understanding OSNs is of immense importance. One key characteristic of human social behaviour in OSNs is their inter-relational nature, which can be represented as graphs. Due to sparsity and complex structure, analysing these graphs is quite challenging and expensive. Over the past several decades, many expert-designed approaches to graphs have been proposed with elegant theoretical properties and successfully addressed numerous practical problems. Nevertheless, most of them are either not data-driven or do not benefit from the rapidly growing scale of data. Recently, in the light of remarkable achievements of artificial intelligence, especially deep neural networks techniques, graph machine learning (GML) has emerged to provide us with novel perspectives to understanding and analysing graphs. However, the current efforts of GML are relatively immature and lack attention to specific scenarios and characteristics of OSNs. Based on the pros and cons of GML, this thesis discusses several aspects of how to build advanced approaches to better simplify and ameliorate OSN analytic tasks. Specifically: 1) Overcoming flat message-passing graph neural networks. One of the most widely pursued branches in GML research, graph neural networks (GNNs), follows a similar flat message-passing principle for representation learning. Precisely, information is iteratively passed between adjacent nodes along observed edges via non-linear transformation and aggregation functions. Its effectiveness has been widely proved; however, two limitations need to be tackled: (i) they are costly in encoding long-range information spanning the graph structure; (ii) they are failing to encode features in the high-order neighbourhood in the graphs as they only perform information aggregation across the observed edges in the original graph. To fill up the gap, we propose a novel hierarchical message-passing framework to facilitate the existing GNN mechanism. Following this idea, we design two practical implementations, i.e., HC-GNN and AdamGNN, to demonstrate the framework's superiority. 2) Extending graph machine learning to heterophilous graphs. The existing GML approaches implicitly hold a homophily assumption that nodes of the same class tend to be connected. However, previous expert studies have shown the enormous importance of addressing the heterophily scenario, where ``opposites attract'', is essential for network analysis and fairness study. We demonstrate the possibility of extending GML to heterophilous graphs by simplifying supervised node classification models on heterophilous graphs (CLP) and designing an unsupervised heterophilous graph representation learning model (Selene). 3) Online social network analysis with graph machine learning. As GML approaches have demonstrated significant effectiveness over general graph analytic tasks, we perform two practical OSN analysis projects to illustrate the possibility of employing GML in practice. Specifically, we propose a semantic image graph embedding (SiGraph) to improve OSN image recognition task with the associated hashtags semantics and a simple GNN-based neural link prediction framework (NeuLP) to boost the performance with tiny change. Keywords: Graph machine learning, Social network analysis, Graph neural networks, Hierarchical structure, Homophily/Heterophily graphs, Link prediction, Online image content understanding. [less ▲]

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See detailSocial Media: Snapshots in Public History
Armaselu, Florentina UL

in Zaagsma, Gerben; Noiret, Serge; Tebeau, Mark (Eds.) Handbook of Digital Public History (2022)

This chapter provides an overview of how social media foster the application of public history and communication with the public, and what types of institutions, projects, and communities are involved in ... [more ▼]

This chapter provides an overview of how social media foster the application of public history and communication with the public, and what types of institutions, projects, and communities are involved in the process. Without claiming to be exhaustive or referring to a representative selection, the study is based on an exploration of seventy-three public history websites and the methods they use to engage audiences via social media [less ▲]

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See detailARGUMENT MINING AND ITS APPLICATIONS IN POLITICAL DEBATES
Haddadan, Shohreh UL

Doctoral thesis (2022)

Presidential debates are significant moments in the history of presidential campaigns. In these debates, candidates are challenged to discuss the main contemporary and historical issues in the country and ... [more ▼]

Presidential debates are significant moments in the history of presidential campaigns. In these debates, candidates are challenged to discuss the main contemporary and historical issues in the country and attempt to persuade the voters to their benefit. These debates offer a legitimate ground for argumentative analysis to investigate political discourse argument structure and strategy. The recent advances in machine learning and Natural Language Processing (NLP) algorithms with the rise of deep learning have revolutionized many natural language applications, and argument analysis from textual resources is no exception. This dissertation targets argument mining from political debates data, a platform rifled with the arguments put forward by politicians to convince a general public in voting for them and discourage them from being appealed by the other candidates. The main contributions of the thesis are: i) Creation, release and reliability assessment of a valuable resource for argumentation research. ii) Implementation of a complete argument mining pipeline applying cutting-edge technologies in NLP research. iii) Launching of a demo tool for argumentative analysis of political debates. The original dataset is composed of the transcripts of 41 presidential election debates in the U.S. from 1960 to 2016. Beside argument extraction from political debates, this research also aims at investigating the practical applications of argument structure extraction, such as fallacious argument classification and argument retrieval. In order to apply supervised machine learning and NLP methods to the data, an excessive annotation study has been conducted on the data and led to the creation of a unique dataset with argument structures composed of argument components (i.e., claim and premise) and argument relations (i.e., support and attack). This dataset includes also another annotation layer with six fallacious argument categories and 14 sub-categories annotated on the debates. The final dataset is annotated with 32,296 argument components (i.e., 16,982 claims and 15,314 premises) and 25,012 relations (i.e., 3,723 attacks and 21,289 supports), and 1628 fallacious arguments. As the methodological approach, a complete argument mining pipeline is designed and implemented, composed of the two main stages of argument component detection and argument relation prediction. Each stage takes advantage of various NLP models outperforming standard baselines in the area, with an average F-score of 0.63 for argument components classification and 0.68 for argument relation classification. Additionally, DISPUTool, an argumentative analysis online tool, is developed as proof-of-concept. DISPUTool incorporates two main functionalities. Firstly, it provides the possibility of exploring the arguments which exist in the dataset. And secondly, it allows for extracting arguments from text segments inserted by the user leveraging the embedded trained model. [less ▲]

Detailed reference viewed: 59 (4 UL)