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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 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 ▲]

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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 detailExecutive functions in birds
Bobrowicz, Katarzyna UL; Greiff, Samuel UL

in Birds (2022)

Executive functions comprise of top-down cognitive processes that exert control over information processing, from acquiring information to issuing a behavioral response. These cogni- tive processes of ... [more ▼]

Executive functions comprise of top-down cognitive processes that exert control over information processing, from acquiring information to issuing a behavioral response. These cogni- tive processes of inhibition, working memory, and cognitive flexibility underpin complex cognitive skills, such as episodic memory and planning, which have been repeatedly investigated in several bird species in recent decades. Until recently, avian executive functions were studied in relatively few bird species but have gained traction in comparative cognitive research following MacLean and colleagues’ large-scale study from 2014. Therefore, in this review paper, the relevant previous findings are collected and organized to facilitate further investigations of these core cognitive processes in birds. This review can assist in integrating findings from avian and mammalian cognitive research and further the current understanding of executive functions’ significance and evolution. [less ▲]

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See detailGeneral Principles of Procedural Justice
Demkova, Simona UL; Hofmann, Herwig UL

in Ziegler, Katja S.; Jennings, Sir Robert; Neuvonen, Päivi J. (Eds.) et al Research Handbook on General Principles in EU Law: Constructing Legal Orders in Europe (2022)

This chapter addresses general principles of EU law concerning procedural justice. It does so using two sets of procedural rights: first, the right to good administration, and, second, the right to an ... [more ▼]

This chapter addresses general principles of EU law concerning procedural justice. It does so using two sets of procedural rights: first, the right to good administration, and, second, the right to an effective remedy. These procedural rights are central to ensuring the rule of law in the EU legal system and the accountability of the exercise of public functions in the EU. General principles of EU law within this chapter are understood as principles of constitutional character applicable throughout Union policies. In addition, many specific EU policies are governed by principles, which although general in nature, are applicable only to a specific policy sector. The working definition within this chapter is that only the former are general principles of EU law and not the latter, which are merely ‘principles’. This chapter traces the development and role of procedural principles and rights in the EU in very broad strokes. It does so in three steps: the chapter starts by briefly outlining the scope of application of the procedural general principles of EU law to sketch the depth and breadth of rights protected in this context (Section II). It then undertakes a quantitative assessment of references to procedural rights in the case law of the Court of Justice of the EU (CJEU) (Section III). The chapter finally concludes by discussing some possible explanations for the rise of procedural rights in EU law as general principles of procedural justice. We discuss how some of the difficulties arising from the existing situation can be remedied by the introduction of an EU regulation on administrative procedures (Section IV). [less ▲]

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See detailStudent profiles of self-concept and interest in four domains: A latent transition analysis
Franzen, Patrick UL; Arens, A. Katrin; Greiff, Samuel UL et al

in Learning and Individual Differences (2022), 95

Dimensional comparisons lead to contrast effects between academic self-concepts, implying that students view themselves as a math-type or a verbal-type. This study examined the short-term stability of ... [more ▼]

Dimensional comparisons lead to contrast effects between academic self-concepts, implying that students view themselves as a math-type or a verbal-type. This study examined the short-term stability of these types and their generalizability to academic interest. N = 382 students completed questionnaires on self-concept and interest in math, physics, German, and English at two measurement waves over five weeks. Latent transition analyses were conducted with self-concepts and interests as indicators, revealing four profiles for both constructs. For self-concept a math + high profile, verbal + high profile, verbal + low profile and generally-moderate profile were found. For interest a math profile, verbal profile, generally-high profile, and generally-low profile were found. These profiles indicated that the formation of domain-specific self-concept and interest differs between groups of students. The profiles were stable across measurement waves. Relations to school grades and gender matched theoretical assumptions. [less ▲]

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See detailAlgorithmic Decision Making with Python Resources: From multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs
Bisdorff, Raymond UL

Book published by Springer - 1 (2022)

This book describes Python3 programming resources for implementing decision aiding algorithms in the context of a bipolar-valued outranking approach. These computing resources, made available under the ... [more ▼]

This book describes Python3 programming resources for implementing decision aiding algorithms in the context of a bipolar-valued outranking approach. These computing resources, made available under the name Digraph3, are useful in the field of Algorithmic Decision Theory and more specifically in outranking-based Multiple-Criteria Decision Aiding (MCDA). The first part of the book presents a set of tutorials introducing the Digraph3 collection of Python3 modules and its main objects such as bipolar-valued digraphs and outranking digraphs. The second part illustrates in eight methodological chapters multiple-criteria evaluation models and decision algorithms. These chapters are mostly problem oriented and show how to edit a new multiple-criteria performance tableau, how to build a best choice recommendation, how to compute the winner of an election and how to rank or rate with incommensurable criteria. The book's third part presents three real decision case studies, while the fourth part addresses more advanced topics, such as computing ordinal correlations between bipolar-valued outranking digraphs, computing kernels in bipolar-valued digraphs, testing for confidence or stability of outranking statements when facing uncertain or solely ordinal criteria significance weights, and tempering plurality tyranny effects in social choice problems. The last part is more specifically focused on working with undirected graphs, tree graphs and forests. The closing chapter explores comparability, split, interval and permutation graphs. The book is primarily intended for graduate students in management sciences, computational statistics and operations research. Chapters presenting algorithms for ranking multicriteria performance records will be of computational interest for designers of web recommender systems. Similarly, the relative and absolute quantiles-rating algorithms, discussed and illustrated in several chapters, will be of practical interest for public or private performance auditors. [less ▲]

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See detailDigital Public History in the United States
Cauvin, Thomas UL

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

Digital history goes, by definition, beyond national frontiers, but can one decipher national specificities in its practices and projects? This chapter explores the birth, development, and ... [more ▼]

Digital history goes, by definition, beyond national frontiers, but can one decipher national specificities in its practices and projects? This chapter explores the birth, development, and institutionalization of digital public history in the United States. Issued from a strong network of digital history practitioners, the success of digital public history in the United States stemmed from its connection with pre-existing public history academic centers and projects. Through projects like the Valley of the Shadow or, later, the 9/11 Digital Archives, digital historians re-imagined the concept of authority and relations with the public. The Center for History and New Media was created by Roy Rosenzweig in 1994 and rapidly became one of the main actors in the move from digital to digital public history. Finally, the chapter explores the future of digital public history in the United States, its institutionalization as a discipline, and its increased focus on user-generated projects. [less ▲]

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See detailDigital Personal Memories: The Archiving of the Self and Public History
Schafer, Valerie UL

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

By providing a facilitated access to data storage, digital technologies seem to make expression and preservation of the self more straightforward. They reconfigure the means and forms of access to data ... [more ▼]

By providing a facilitated access to data storage, digital technologies seem to make expression and preservation of the self more straightforward. They reconfigure the means and forms of access to data, thus also affecting the relationships and participation of individuals in heritagization and history, and potentially impacting historians. This renews questions that scholars already know well, such as the place of memories in the making of history, and that of self-narratives. Examining how “ordinary voices” can/could archive digital/digitized data and documents, this chapter aims at investigating this increased interest in preserving the self and memories, the heritagization of these data, and finally the role played by user-generated contributions in Digital Public History projects and in historical research in general. [less ▲]

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See detailAnalyse des Séries Temporelles
Bourbonnais, Régis; Terraza, Virginie UL

Book published by Dunod (2022)

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See detailLocational Choice and Secondary Movements from the Perspective of Forced Migrants: A Comparison of the Destinations Luxembourg and Germany
Glorius, Birgit; Nienaber, Birte UL

in Comparative Population Studies (2022), 47

In 2015 and 2016, the enormous increase in asylum seekers travelling along the Balkan Route confronted the Member States of the European Union with an exceptional pressure on national asylum systems ... [more ▼]

In 2015 and 2016, the enormous increase in asylum seekers travelling along the Balkan Route confronted the Member States of the European Union with an exceptional pressure on national asylum systems. Since then academic literature has revealed a reappraisal of the Common European Asylum System at regulative and policy implementation level, notably regarding the fair distribution of asylum seekers across Member States and regions. Yet we know very little about the locational choices of forced migrants or how those choices evolved and transformed during their journey. In this paper, we aim to shed light on those decision-making processes and (individual, subjective) locational choices based on the aspiration-ability model, drawing from a series of qualitative interviews with migrants held in Luxembourg and Germany in the context of the H2020 project CEASEVAL. We focus on the migrants’ journeys to their actual recipient countries, highlighting mobility trajectories from the moment of fi rst departure and on the process of decision-making regarding their choice of location. Then, we examine further mobility aspirations, which may lead to secondary mobility within or out of the country of residence. In the concluding section, we discuss the consequences of our fi ndings for migration and asylum politics against the background of the “autonomy of migration” framework. [less ▲]

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See detailIs Risk-Neutral Skewness an Indicator of Downside Risk? Evidence from Tail Risk-Taking of Hedge Funds
Lehnert, Thorsten UL

in Journal of Derivatives (2022), 29(3), 30-45

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See detailMACHINE LEARNING IN THE DESIGN SPACE EXPLORATION OF TSN NETWORKS
Mai, Tieu Long UL

Doctoral thesis (2022)

Real-time systems are systems that have specific timing requirements. They are critical systems that play an important role in modern societies, be it for instance control systems in factories or ... [more ▼]

Real-time systems are systems that have specific timing requirements. They are critical systems that play an important role in modern societies, be it for instance control systems in factories or automotives. In recent years, Ethernet has been increasingly adopted as layer 2 protocol in real-time systems. Indeed, the adoption of Ethernet provides many benefits, including COTS and cost-effective components, high data rates and flexible topology. The main drawback of Ethernet is that it does not offer "out-of-the-box" mechanisms to guarantee timing and reliability constraints. This is the reason why time-sensitive networking (TSN) mechanisms have been introduced to provide Quality-of-Service (QoS) on top of Ethernet and satisfy the requirements of real-time communication in critical systems. The promise of Ethernet TSN is the possibility to use a single network for different criticality levels, e.g, critical control traffic and infotainment traffic sharing the same network resources. This thesis is about the design of Ethernet TSN networks, and specifically about techniques that help quantify the extent to which a network can support current and future communication needs. The context of this work is the increasing use of design-space exploration (DSE) in the industry to master the complexity of designing (e.g. in terms of architectural and technological choices) and configuring a TSN network. One of the main steps in DSE is performing schedulability analysis to conclude about the feasibility of a network configuration, i.e., whether all traffic streams satisfy their timing constraints. This step can take weeks of computations for a large set of candidate solutions with the simplest TSN mechanisms, while more complicated TSN mechanisms will require even longer time. This thesis explores the use of Artificial Intelligence (AI) techniques to assist in the design of TSN networks by speeding up the DSE. Specifically, the thesis proposes the use of machine learning (ML) as an alternative approach to schedulability analysis. The application of ML involves two steps. In the first step, ML algorithms are trained with a large set of TSN configurations labeled as feasible or non-feasible. Due to its pattern recognition ability, ML algorithms can predict the feasibility of unseen configurations with a good accuracy. Importantly, the execution time of an ML model is only a fraction of conventional schedulability analysis and remains constant whatever the complexity of the network configurations. Several contributions make up the body of the thesis. In the first contribution, we observe that the topology and the traffic of a TSN network can be used to derive simple features that are relevant to the network feasibility. Therefore, standard and simple machine learning (ML) algorithms such as k-Nearest Neighbors are used to take these features as inputs and predict the feasibility of TSN networks. This study suggests that ML algorithms can provide a viable alternative to conventional schedulability analysis due to fast execution time and high prediction accuracy. A hybrid approach combining ML and schedulability analyses is also introduced to control the prediction uncertainty. In the next studies, we aim at further automating the feasibility prediction of TSN networks with the Graph Neural Network (GNN) model. GNN takes as inputs the raw data from the TSN configurations and encodes them as graphs. Synthetic features are generated by GNN, thus the manual feature selection step is eliminated. More importantly, the GNN model can generalize to a wide range of topologies and traffic patterns, in contrast to the standard ML algorithms tested before that can only work with a fixed topology. An ensemble of individual GNN models shows high prediction accuracies on many test cases containing realistic automotive topologies. We also explore possibilities to improve the performance of GNN with more advanced deep learning techniques. In particular, semi-supervised learning and self-supervised learning are experimented. Although these learning paradigms provide modest improvements, we consider them promising techniques due to the ability to leverage the massive amount of unlabeled training data. While this thesis focuses on the feasibility prediction of TSN configurations, AI techniques have huge potentials to automate other tasks in real-time systems. A natural follow-up work of this thesis is to apply GNN to multiple TSN mechanisms and predict which mechanism can provide the best scheduling solution for a given configuration. Although we need distinct ML models for each TSN mechanism, this research direction is promising as TSN mechanisms may share similar feasibility features and thus transfer learning techniques can be applied to facilitate the training process. Furthermore, GNN can be used as a core block in deep reinforcement learning to find the feasible priority assignment of TSN configurations. This thesis aims to make a contribution towards DSE of TSN networks with AI. [less ▲]

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See detailDie Vielfalt der Sozialen Arbeit in Luxemburg und die Arbeitsmarktzahlen 2021
Böwen, Petra UL; Flammang, Manou Laure

E-print/Working paper (2022)

Dieser Newsletter beschreibt den Arbeitsmarkt der Sozialen Arbeit in Luxemburg 2021, gibt einen Überblick über die Berufsqualifikationen und die Vielfalt der Praxisfelder und spezifischen Arbeitsbereiche.

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See detailWeb Archiving of crisis
Schafer, Valerie UL

Presentation (2022, March 31)

Roundtable and short presentation on Web Archives of Crisis and Conspiracy

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See detailConceptualising the Legal Notion of ‘State of the Art’ in the Context of IT Security
Schmitz, Sandra UL

in Friedewald, Michael; Krenn, Stephan; Schiffner, Stefan (Eds.) et al Privacy and Identity Management. Between Data Protection and Security (2022, March 31)

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See detailRecent Developments and Overview of the Country and Practitioner’s Reports
Cole, Mark David UL

in European Data Protection Law Review (2022), 8(1), 73-77

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See detailSecondary law provisions on media ownership transparency
Cole, Mark David UL

in Capello, Maja (Ed.) Transparency of media ownership (IRIS Special 2-2021) (2022)

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