Namur Digital Institute

Purpose Because new-age technologies are gaining a broader interest among service scholars and practitioners, it is critical to identify these technologies and examine the roles they play. The examination needs to be conducted to design engaging customer and service experiences in new phygital settings that connect physical and digital environments. This review article aims to provide researchers with a new comprehensive and integrative extended reality technology (ERT) framework. The framework serves as the basis for an all-inclusive view of ERT types in order to explore the different types of technology used to design phygital customer and service experiences. Design/methodology/approach This article reviews prior works on the role technology plays in terms of customer experiences across various fields of research, including consumer, marketing and service literature. Adopting an experiential and phygital perspective as well as considering a consumer standpoint, this article defines the scope of the ERT framework by identifying categories of new-age technologies and their effects related to the design of phygital customer and service experiences. Findings The ERT framework proposed in this article offers directions for future research by adopting an experiential approach to technologies in order to categorize additional technological devices, platforms and tools that can be considered in the design of phygital experiences following several extension processes. These processes can enhance the cognitive, social, sensory and contextual dimensions of the phygital experience and thus create a continuum in terms of customer value from physical to digital settings and vice versa. Research limitations/implications Companies and service providers may benefit from a new, comprehensive, focused framework that assembles different types of technology. The technologies can be utilized to design engaging customer and service experiences that deliver customer value from physical to digital spaces and inversely. Originality/value No prior works have proposed a comprehensive ERT framework for service research following an experiential perspective and a consumer view of the experience occurring in a new setting: phygital. By embracing the ERT framework provided in this article, future service scholars can examine the dynamics and types of technologies that can positively or negatively affect the design of consumption and service experiences in phygital settings.

We propose a model for feature-aware discrete-time Markov chains, called FDTMC, as a basis for verifying probabilistic properties, e.g., Reliability and availability, of product lines. To verify such properties on FDTMC, we compare three techniques. First, we experiment with two different parametric techniques to obtain this formula: the classical one builds it from the model as whole, and a new one that builds it compositionally from a sequence of modules. Finally, we propose a new technique that performs a bounded verification for the whole product line, and thus takes advantage of the high probability of common behaviors of the product line. It computes an approximate formula, represented as an arithmetic decision diagram. Experimental results on a vital signal monitoring system prototype are provided and compared for these techniques aiming at analysing them for scalability issues of size and computational time. They show complementary advantages, and we provide criteria to choose a technique depending on the characteristics of the model.

Online platforms have created content moderation systems, particularly in relation to tackling illegal content online. This study reviews and assesses the EU regulatory framework on content moderation and the practices by key online platforms. On that basis, it makes recommendations to improve the EU legal framework within the context of the forthcoming Digital Services Act.<br/>This document was provided by the Policy Department for Economic, Scientific and Quality of Life Policies at the request of the committee on Internal Market and Consumer Protection (IMCO).

Abstract The complexity of cyber–physical systems ( CPSs ) is commonly addressed through complex workflows , involving models in a plethora of different formalisms , each with their own methods, techniques, and tools. Some workflow patterns , combined with particular types of formalisms and operations on models in these formalisms, are used successfully in engineering practice. To identify and reuse them, we refer to these combinations of workflow and formalism patterns as modelling paradigms . This paper proposes a unifying (Descriptive) Framework to describe these paradigms, as well as their combinations. This work is set in the context of Multi-Paradigm Modelling ( MPM), which is based on the principle to model every part and aspect of a system explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s) and workflows. The purpose of the Descriptive Framework presented in this paper is to serve as a basis to reason about these formalisms, workflows, and their combinations. One crucial part of the framework is the ability to capture the structural essence of a paradigm through the concept of a paradigmatic structure . This is illustrated informally by means of two example paradigms commonly used in CPS: Discrete Event Dynamic Systems and Synchronous Data Flow. The presented framework also identifies the need to establish whether a paradigm candidate follows, or qualifies as, a (given) paradigm. To illustrate the ability of the framework to support combining paradigms, the paper shows examples of both workflow and formalism combinations. The presented framework is intended as a basis for characterisation and classification of paradigms, as a starting point for a rigorous formalisation of the framework (allowing formal analyses), and as a foundation for MPM tool development.

L ingénierie des lignes de produits logiciels (LdPs) est un paradigme pour la modélisation et le développement de familles de systèmes logiciels plutôt que de systèmes logiciels individuels. Son objectif porte sur les moyens de produire et maintenir efficacement des produits logiciels similaires en exploitant ce qu ils ont en commun et en gérant ce qui varie entre eux. Par analogie, la pratique dans l industrie automobile est de construire une ligne de production dans laquelle des variations personnalisées mais tout de même similaires de modèles de voitures sont produits. Les feature models (FMs) sont une représentation fondamentale pour spécifier et raisonner sur la commonalité et la variabilité des LdPs en termes de features (caractéristiques). Les FMs deviennent de plus en plus complexes, manipulés par plusieurs développeurs ou organisations, utilisés pour décrire des features à divers niveaux d abstraction et qui sont mises en relation de différentes façons. Maintenir un seul gros FM n est ni réaliste ni souhaitable. Au contraire une tendance forte est de considérer de multiples FMs. Dans cette thèse, nous développons les fondations théoriques et un support pratique pour gérer de multiples FMs. Nous concevons et développons un ensemble d opérateurs de composition et de décomposition (aggregate, merge, slice) pour supporter la séparation des préoccupations. Les opérateurs sont formellement définis et implémentés avec un algorithme qui garantit des propriétés sémantiques. Nous montrons comment les opérateurs de composition et de décomposition peuvent être combinés ensemble ou avec d autres opérateurs d édition ou de raisonnement pour réaliser des taches complexes. Nous proposons un langage textuel, FAMILIAR (pour FeAture Model scrIpt Language for manIpulation and Automatic Reasoning), qui fournit une solution opérationnelle à la gestion de multiples FMs à large échelle. Un utilisateur des FMs peut combiner les différents opérateurs et manipuler un ensemble restreint de concepts (FMs, features, configurations, etc.) en utilisant une notation concise et des facilités linguistiques. FAMILIAR cache les détails d implémentations (e.g., solveurs) et est supporté par un environnement de développement complet. Nous décrivons plusieurs applications de ces opérateurs et utilisations de FAMILIAR dans différents domaines (messagerie médicale, vidéo protection) et pour différents objectifs (conception de workflows scientifiques, modélisation de la variabilité des exigences à l exécution, rétro ingénierie), démontrant l applicabilité à la fois des opérateurs et du langage de support. Sans les nouvelles capacités fournies par les opérateurs et FAMILIAR, certaines opérations d analyse et de raisonnement n auraient pas été possibles dans les différents cas d études. Pour conclure, nous discutons les différentes perspectives de recherche à moyen terme (opérateurs, langage, éléments de validation) et à long terme (e.g. relations entre les FMs et les autres modèles).

The recent advances in deep learning have been beneficial to automatic sign language recognition (SLR). However, free-to-access, usable, and accessible tools are still not widely available to the deaf community. The need for a sign language-to-text dictionary was raised by a bilingual deaf school in Belgium and linguist experts in sign languages (SL) in order to improve the autonomy of students. To meet that need, an efficient SLR system was built based on a specific transformer model. The proposed system is able to recognize 700 different signs, with a top-10 accuracy of 83%. Those results are competitive with other systems in the literature while using 10 times less parameters than existing solutions. The integration of this model into a usable and accessible web application for the dictionary is also introduced. A user-centered human-computer interaction (HCI) methodology was followed to design and implement the user interface. To the best of our knowledge, this is the first publicly released sign language-to-text dictionary using video captured by a standard camera.

E-government refers to the use of Information and Communication Technologies by governments to deliver their information and services in an optimal way. For a long time, traditional systems development methods such as the waterfall model have been prevailing in the development of e-government services. However, these methods fail to welcome the changing requirements of citizens and to facilitate collaboration with a higher number of stakeholders. Agile software development methods implement practices that can increase responsiveness and collaboration. However, the implementation of agile methods faces challenges sometimes linked with intrinsic characteristics of governments. In this paper, we identify and validate challenges that practitioners face when developing e-government services in an Agile way. In order to reach that goal, we have organized three focus groups with practitioners in Belgium. The identification of these challenges will set the foundation for the tailoring of agile methods to the specificities of e-government.

Anti-unification in logic programming refers to the process of capturing common syntactic structure among given goals, computing a single new goal that is more general called a generalization of the given goals. Finding an arbitrary common generalization for two goals is trivial, but looking for those common generalizations that are either as large as possible (called largest common generalizations) or as specific as possible (called most specific generalizations) is a non-trivial optimization problem, in particular when goals are considered to be \textit{unordered} sets of atoms. In this work we provide an in-depth study of the problem by defining two different generalization relations. We formulate a characterization of what constitutes a most specific generalization in both settings. While these generalizations can be computed in polynomial time, we show that when the number of variables in the generalization needs to be minimized, the problem becomes NP-hard. We subsequently revisit an abstraction of the largest common generalization when anti-unification is based on injective variable renamings, and prove that it can be computed in polynomially bounded time.

Industry 4.0 and recent deep learning progress make it possible to solve problems that traditional methods could not. This is the case for anomaly detection that received a particular attention from the machine learning community, and resulted in a use of generative adversarial networks (GANs). In this work, we propose to use intermediate patches for the inference step, after a WGAN training procedure suitable for highly imbalanced datasets, to make the anomaly detection possible on full size Printed Circuit Board Assembly (PCBA) images. We therefore show that our technique can be used to support or replace actual industrial image processing algorithms, as well as to avoid a waste of time for industries.

Model-driven engineering is a promising software development methodology that has been investigated in the context of blockchain-based information systems development. Many approaches propose to specify and generate individual components part of such systems&#x2019; architectures using this methodology. In this paper, we provide a high-level overview of the different types of components that can be generated using model-driven engineering in the blockchain context, and of the potential benefits that it could bring in that context. We organize these findings in a framework called MDE4BBIS, which can help identify opportunities to leverage model-driven engineering for different architectural layers in blockchain-based information systems, and promotes an integrated approach.

Non-linear dimensionality reduction techniques, such as tSNE, are widely used to visualize and analyze high-dimensional datasets. While non-linear projections can be of high quality, it is hard, or even impossible, to interpret the dimensions of the obtained embeddings. This paper adapts LIME to locally explain t-SNE embeddings. More precisely, the sampling and black-box-querying steps of LIME are modified so that they can be used to explain t-SNE locally. The result of the proposal is to provide, for a particular instance x and a particular t-SNE embedding Y, an interpretable model that locally explains the projection of x on Y.

The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.

Relational database management systems (RDBMS) and<br/>NoSQL database systems have been built in order to meet<br/>dierent requirements. Organisations are therefore increasingly<br/>using hybrid data persistence architectures, that we<br/>call hybrid database systems or hybrid polystores. The evolution<br/>of such systems is more complex as NoSQL technologies<br/>imply new challenges such as handling data heterogeneity.<br/>Existing work on NoSQL database evolution exposes the<br/>dierent problems regarding data heterogeneity and mainly<br/>provide technology-specic solutions. In this PhD research<br/>we propose to apply a schema engineering approach to handle<br/>hybrid database system evolution at a conceptual level.<br/>Our starting point is an existing unied conceptual data<br/>model for hybrid databases. Our thesis objectives include<br/>(1) to enrich this conceptual data model with generic schema<br/>evolution operators, (2) to specify the semantics of those<br/>operators depending on the underlying data models, (3) to<br/>design a generic evolution framework and (4) to develop its<br/>proof-of-concept implementation. Our approach will help to<br/>propagate conceptual schema changes to all impacted software<br/>artefacts, namely native data structures, database contents,<br/>queries and programs.

TThis study reviews all the rules adopted during the 8th Parliamentary legislature (2014-2019) to strengthen the Digital Single Market. On that basis, the report analyses the rights and obligations as well as the institutions and procedures created or improved in the main policy fields of the Digital Single Market (e-commerce and online platforms, e-government, data and AI, cybersecurity, consumer protection and electronic communications networks and services). Finally, the report identifies remaining gaps and possible actions for the forthcoming Parliament’s legislature. This study has been prepared for the IMCO Committee at the request of the Policy Department A of the European Parliament.

While past research on product configuration has focused on knowledge representation and automated reasoning, researchers have paid less attention to the design and evaluation of user experience (UX). For product configurators like for other interactive applications, UX is of paramount importance. This is all the more true since configurators are often primary points of contact between a merchant and its customers, and business-critical assets meant to maximize sales.<br/>Over the years, the HCI (Human-Computer Interaction) community has defined standard guidelines for interactive applications. Yet, previous studies suggested that in practice product configurators of- ten differ from such guidelines. In this preliminary study, we address two main research questions: (1) To what extent do existing configurators deviate from HCI guidelines? (2) Why do those deviations appear?<br/>We first present a synthesis of the main well-established guidelines from the HCI community. We complement these observations with an analysis of the literature to collect more HCI issues and to better understand their origins. Then, we proceed to studying a sample of 50 real-world configurators to observe whether, and to which extent, they comply with the HCI guidelines. We then conclude the paper with directions for future research, emphasizing HCI challenges that are specific to configurators.<br/>This study is part of a broader PhD project which ultimate goal is to define a set of guidelines specifically intended to optimize the UX of sales configurators.

Recently, the concept of Pervasive Augmented Reality (PAR) was introduced. It is defined as a continuous use of Augmented Reality (AR), where the information displayed comes from applications simultaneously opened by the user or from situated and embedded information placed by various sources. This can lead to potential information clutter. Additionally, PAR environments apply physical constraints over users due to the nature of their displayed information, forcing users to walk to get to situated or embedded information in the environment if they want more details. This work explores the use of the World-in-Miniature (WiM) metaphor inside a PAR context and extends this technique to better fit the constraints implied by such a context: 1) search and filter information of use wherever it might be, 2) access potentially distant and/or occluded information without any required physical movement, and 3) keep context of distant and situated information. Our contributions are an extension to the WiM metaphor and the evaluation of a fully working prototype through the User Experience Questionnaire (UEQ) with results coming from 26 participants.

The digital age has brought about significant changes in the way we communicate, access information, and conduct our daily lives. However, not all individuals have equal access to and proficiency with digital technology. This study examines how digital vulnerability, digital ability, and digital literacy vary across individuals with different education levels and income levels. This study finds that there is significant variation in digital vulnerability, digital ability, and digital literacy across education levels and income levels in the platform economy. Specifically, the higher the education level, the higher the digital vulnerability score and the lower the digital ability score. Similarly, the higher the income level, the lower the digital vulnerability score and the lower the digital ability score. Additionally, significant variation in digital literacy was found across education and income levels. These findings suggest that socio-economic factors such as education and income level are important predictors of digital inequality, and that individuals with higher education and income levels may be more cautious with online platforms and thus experience higher levels of digital vulnerability and lower digital ability.

With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research is<br/>done on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.

Configurators are widespread applications where users can tailor products (i.e. goods or services) to their needs by selecting options and setting parameter values. Constraints over these options exist to avoid building invalid products. Thus, when the user attempts to combine incompatible options, the configurator should raise an error and help the user repair her configuration, that is, change the selected options to obtain a valid product. In this paper, we observe how 54 configurators from different industries handle this repair mechanism. We show that in a majority of cases, the configuration interfaces exhibit bad practices that impede an effective usage of repair, thereby impoverishing user experience.

A Strategy Map is a tool that depicts the interrelationships between the key performance indicators of a company. Strategy Maps are considered as Decision Support Systems by allowing the user to understand the consequences of a decision on other indicators of the business which is crucial in decision-making. To this date, the majority of the practical development of Strategy Maps is based on the knowledge and intuition of experts of the company regardless of the methodology used. These "soft data" present a number of drawbacks when implementing Strategy Maps: in accuracy, in completeness and a lack of longitudinal perspective. Currently, technological innovations enable to collect, store and analyze more data. These "hard data" are a powerful source of information used in Decision Support Systems to enhance strategic decision-making. We suggest to integrate hard data in the development process of the Strategy Maps in order to increase their reliability. This paper presents the outline of a research project related to the use of hard data in Strategy Maps. Five research questions are presented in order to contribute to the current literature with theoretical conclusions, methodological propositions and empirical demonstrations.