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See detailLearning and Teaching Geropsychology
Boll, Thomas UL

in Zumbach, Joerg; Bernstein, Douglas; Narciss, Susanne (Eds.) et al International Handbook of Psychology Learning and Teaching (2022)

This chapter presents a broad overview about the essential aspects of learning and teaching geropsychology in tertiary education. After an introduction to the scope of geropsychology and the need for ... [more ▼]

This chapter presents a broad overview about the essential aspects of learning and teaching geropsychology in tertiary education. After an introduction to the scope of geropsychology and the need for geropsychology education, the objectives and basic principles of geropsychology curricula are discussed. Their overall goal is to qualify students and/or professionals to understand and solve psychological aspects of problems of older people in the contexts of practical application, research, and teaching. Specific teaching and learning objectives in geropsychology are described in terms of underlying content dimensions (e.g., areas of acting, functioning, and development of older people; basic components of practical geropsychological acting; target groups and settings) and levels of competency to be acquired (e.g., uni-, multi-structural, relational, and extended abstract level). This is followed by an overview of the core topics of geropsychology. These refer to theoretical (e.g., basic concepts of age, aging and the elderly; action competence of older people; challenges in later life; resources for adaptation; problems of people providing services to older adults), normative (ethical and legal) and methodological foundations (research, assessment, evaluation and intervention methods). This is followed by sections on linking main learning objectives and core topics of geropsychology to courses within study programs (psychological or non-psychological) and about the relations between teaching, learning, and assessment in geropsychology. The chapter concludes with information about resources for these issues including relevant URL links, tips for teaching and annotated references to further reading. [less ▲]

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See detailDecentralization as Disembodiment. Blockchain Justice between Utopia and Myopia
Becker, Katrin UL

Presentation (2022, September 15)

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See detailEpilogue: Darning the Divide - Thinking Counterfactually
Becker, Katrin UL; Lassègue, Jean

Presentation (2022, September 15)

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See detailFNR Highlight: CeMi: Cemeteries & crematoria as public spaces of belonging in Europe.
Kmec, Sonja UL

Diverse speeches and writings (2022)

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See detailCombiner lecture proche et distante: l’exemple de la viralité en ligne
Schafer, Valerie UL

Presentation (2022, September 13)

Cette intervention s’intéressera à la combinaison de la lecture proche et distante, des échelles micro et macro, en proposant de réfléchir aux enjeux, outils, défis et parfois limites de ce qui est ... [more ▼]

Cette intervention s’intéressera à la combinaison de la lecture proche et distante, des échelles micro et macro, en proposant de réfléchir aux enjeux, outils, défis et parfois limites de ce qui est qualifié de scalable reading . Des recherches en cours sur la viralité en ligne et les mèmes, et notamment le cas du Harlem Shake, seront pris pour cas d'étude, avant d’inviter les participants à penser à leur tour leur sujet en terme de scalable reading . [less ▲]

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See detailViralité
Schafer, Valerie UL; Pailler, Fred UL

in Walter, Jacques (Ed.) Publictionnaire. Dictionnaire encyclopédique et critique des publics (2022)

Entry related to virality in this online dictionary on public and audience

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Peer Reviewed
See detailAutomatic Classification of Peer Review Recommendation
Kozlowski, Diego UL; Boothby, Clara; Pei-Ying, Chen et al

Poster (2022, September 08)

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See detailA cosmopolitan international law: the authority of regional inter-governmental organisations to establish international criminal accountability mechanisms
Owiso, Owiso UL

Doctoral thesis (2022)

The overall aim of this thesis is to investigate the potential role of regional inter-governmental organisations (RIGOs) in international criminal accountability, specifically through the establishment of ... [more ▼]

The overall aim of this thesis is to investigate the potential role of regional inter-governmental organisations (RIGOs) in international criminal accountability, specifically through the establishment of criminal accountability mechanisms, and to make a case for RIGOs’ active involvement. The thesis proceeds from the assumption that international criminal justice is a cosmopolitan project that demands that a tenable conception of state sovereignty guarantees humanity’s fundamental values, specifically human dignity. Since cosmopolitanism emphasises the equality and unity of the human family, guaranteeing the dignity and humanity of the human family is therefore a common interest of humanity rather than a parochial endeavour. Accountability for international crimes is one way through which human dignity can be validated and reaffirmed where such dignity has been grossly and systematically assaulted. Therefore, while accountability for international crimes is primarily the obligation of individual sovereign states, this responsibility is ultimately residually one of humanity as a whole, exercisable through collective action. As such, the thesis advances the argument that states as collective representations of humanity have a responsibility to assist in ensuring accountability for international crimes where an individual state is either genuinely unable or unwilling by itself to do so. The thesis therefore addresses the question as to whether RIGOs, as collective representations of states and their peoples, can establish international criminal accountability mechanisms. Relying on cosmopolitanism as a theoretical underpinning, the thesis examines the exercise of what can be considered as elements of sovereign authority by RIGOs in pursuit of the cosmopolitan objective of accountability for international crimes. In so doing, the thesis interrogates whether there is a basis in international law for such engagement, and examines how such engagement can practically be undertaken, using two case studies of the European Union and the Kosovo Specialist Chambers and Specialist Prosecutor’s Office, and the African Union and the (proposed) Hybrid Court for South Sudan. The thesis concludes that general international law does not preclude RIGOs from exercising elements of sovereign authority necessary for the establishment of international criminal accountability mechanisms, and that specific legal authority to engage in this regard can then be determined by reference to the doctrine of attributed/conferred powers and the doctrine of implied powers in interpreting the legal instruments of RIGOs. Based on this conclusion, the thesis makes a normative case for an active role for RIGOs in the establishment of international criminal accountability mechanisms, and provides a practical step-by-step guide on possible legal approaches for the establishment of such mechanisms by RIGOs, as well as guidance on possible design models for these mechanisms. [less ▲]

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See detailTranslanguaging pedagogy and creative activism for early education in multilingual Luxembourg
Aleksic, Gabrijela UL

Scientific Conference (2022, September 07)

Luxembourg is a highly linguistically and culturally diverse country. In early education, there are 64 % of four-year old children who not speak Luxembourgish at home (MENJE, 2018). From 2017 ... [more ▼]

Luxembourg is a highly linguistically and culturally diverse country. In early education, there are 64 % of four-year old children who not speak Luxembourgish at home (MENJE, 2018). From 2017, multilingual early education is mandatory, which obliges teachers to develop children’s Luxembourgish, familiarizing them with French, and valuing their home languages. Therefore, the present project aimed to: (1) offer an 18-hours professional development (PD) course in translanguaging pedagogy to 40 teachers over 6 months, (2) strengthen home-school collaboration, and (3) support children’s linguistic, socio-emotional, and cognitive development and engagement in the classroom. The results from teacher questionnaires, focus groups, and interviews, showed that there was some positive change regarding the attitudes towards children’s home languages. The interviews with 17 parents indicated that there was a need for more home-school collaboration. The tests in early literacy and numeracy with 23 preschool children over one year, identified that children performed higher in their home languages. The video observations showed that when the teachers used children’s languages in the classroom, this impacted positively their well-being. Following the principles of creative activism, the author produced: (1) the website with over 100 practical activities on how teachers can involve children’s languages and families, (2) the illustrated book Sumak for parents, showing difficulties with integration in a new country, and (3) the illustrated book, Mara’s song for preschool children, showing how Mara finds her way in the new classroom. [less ▲]

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See detailGenetic stratification of motor and QoL outcomes in Parkinson's disease in the EARLYSTIM study
Weiss, Daniel; Landoulsi, Zied UL; May, Patrick UL et al

in Parkinsonism and Related Disorders (2022)

Purpose The decision for subthalamic deep brain stimulation (STN-DBS) in Parkinson's disease (PD) relies on clinical predictors. Whether genetic variables could predict favourable or unfavourable ... [more ▼]

Purpose The decision for subthalamic deep brain stimulation (STN-DBS) in Parkinson's disease (PD) relies on clinical predictors. Whether genetic variables could predict favourable or unfavourable decisions is under investigation. Objective First, we aimed to reproduce the previous observation that SNCA rs356220 was associated with favourable STN-DBS motor response. In additional exploratory analyses, we studied if other PD risk and progression variants from the latest GWAS are associated with therapeutic outcome. Further, we evaluated the predictive value of polygenic risk scores. Methods We comprehensively genotyped patients from the EarlyStim cohort using NeuroChip, and assessed the clinico-genetic associations with longitudinal outcome parameters. Results The SNCA rs356220 variant did not predict UPDRS III outcomes. However, it was associated with quality of life improvement in secondary analyses. Several polymorphisms from previously identified GWAS hits predicted motor or quality of life outcomes in DBS patients. Polygenic risk scores did not predict any outcome parameter. Conclusions Our findings support the hypothesis that different common genetic markers are associated with favourable quality of life outcomes of STN-DBS in PD. These findings can be the basis for further validation in larger and independent cohorts. [less ▲]

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See detailAbstracts of the 11th DACH+ Conference on Energy Informatics (S53-Taxonomy of Local Flexibility Markets)
Potenciano Menci, Sergio UL

in Energy Informatics (2022, September 07), 5

Flexibility has risen as a potential solution and complement for system operators’ current and future problems (e.g., congestion, voltage) caused by integrating distributed renewable resources (e.g., wind ... [more ▼]

Flexibility has risen as a potential solution and complement for system operators’ current and future problems (e.g., congestion, voltage) caused by integrating distributed renewable resources (e.g., wind, solar) and electric vehicles. In parallel, local flexibility markets (LFM) emerge as a possible smart grid solution to bridge between flexibility-seeking customers and flexibility-offering customers in localized areas. Nevertheless, there is no unique, standard, or simple solution to tackle all the problems system operators and other energy actors face. Therefore, many local flexibility market concepts, initiatives (projects), and companies have developed various solutions over the last few years. At the same time, they increased the complexity of the topic. Thus, this research paper aims to describe several local flexibility market concepts, initiatives (projects), and companies in Europe. To do so, we propose a taxonomy derived from LFMs descriptions. We use the taxonomy-building research method proposed by [1] to develop our taxonomy. Moreover, we use the smart grid architecture model (SGAM) as a structural and foundation guideline. Given the numerous and diverse LFM solutions, we delimit the taxonomy by considering solutions focused on congestion management on medium and low voltage (meta-characteristic). [less ▲]

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See detailOptimal industrial flexibility scheduling based on generic data format
Bahmani, Ramin UL; van Stiphoudt, Christine UL; Potenciano Menci, Sergio UL et al

in Energy Informatics (2022, September 07), 5

The energy transition into a modern power system requires energy flexibility. Demand Response (DR) is one promising option for providing this flexibility. With the highest share of final energy ... [more ▼]

The energy transition into a modern power system requires energy flexibility. Demand Response (DR) is one promising option for providing this flexibility. With the highest share of final energy consumption, the industry has the potential to offer DR and contribute to the energy transition by adjusting its energy demand. This paper proposes a mathematical optimization model that uses a generic data model for flexibility description. The optimization model supports industrial companies to select when (i.e., at which time), where (i.e., in which market), and how (i.e., the schedule) they should market their flexibility potential to optimize profit. We evaluate the optimization model under several synthetic use cases developed upon the learnings over several workshops and bilateral discussions with industrial partners from the paper and aluminum industry. The results of the optimization model evaluation suggest the model can fulfill its purpose under different use cases even with complex use cases such as various loads and storages. However, the optimization model computation time grows as the complexity of use cases grows. [less ▲]

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See detailWCET and Priority Assignment Analysis of Real-Time Systems using Search and Machine Learning
Lee, Jaekwon UL

Doctoral thesis (2022)

Real-time systems have become indispensable for human life as they are used in numerous industries, such as vehicles, medical devices, and satellite systems. These systems are very sensitive to violations ... [more ▼]

Real-time systems have become indispensable for human life as they are used in numerous industries, such as vehicles, medical devices, and satellite systems. These systems are very sensitive to violations of their time constraints (deadlines), which can have catastrophic consequences. To verify whether the systems meet their time constraints, engineers perform schedulability analysis from early stages and throughout development. However, there are challenges in obtaining precise results from schedulability analysis due to estimating the worst-case execution times (WCETs) and assigning optimal priorities to tasks. Estimating WCET is an important activity at early design stages of real-time systems. Based on such WCET estimates, engineers make design and implementation decisions to ensure that task executions always complete before their specified deadlines. However, in practice, engineers often cannot provide a precise point of WCET estimates and they prefer to provide plausible WCET ranges. Task priority assignment is an important decision, as it determines the order of task executions and it has a substantial impact on schedulability results. It thus requires finding optimal priority assignments so that tasks not only complete their execution but also maximize the safety margins from their deadlines. Optimal priority values increase the tolerance of real-time systems to unexpected overheads in task executions so that they can still meet their deadlines. However, it is a hard problem to find optimal priority assignments because their evaluation relies on uncertain WCET values and complex engineering constraints must be accounted for. This dissertation proposes three approaches to estimate WCET and assign optimal priorities at design stages. Combining a genetic algorithm and logistic regression, we first suggest an automatic approach to infer safe WCET ranges with a probabilistic guarantee based on the worst-case scheduling scenarios. We then introduce an extended approach to account for weakly hard real-time systems with an industrial schedule simulator. We evaluate our approaches by applying them to industrial systems from different domains and several synthetic systems. The results suggest that they are possible to estimate probabilistic safe WCET ranges efficiently and accurately so the deadline constraints are likely to be satisfied with a high degree of confidence. Moreover, we propose an automated technique that aims to identify the best possible priority assignments in real-time systems. The approach deals with multiple objectives regarding safety margins and engineering constraints using a coevolutionary algorithm. Evaluation with synthetic and industrial systems shows that the approach significantly outperforms both a baseline approach and solutions defined by practitioners. All the solutions in this dissertation scale to complex industrial systems for offline analysis within an acceptable time, i.e., at most 27 hours. [less ▲]

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See detailMulti-objective Robust Machine Learning For Critical Systems With Scarce Data
Ghamizi, Salah UL

Doctoral thesis (2022)

With the heavy reliance on Information Technologies in every aspect of our daily lives, Machine Learning (ML) models have become a cornerstone of these technologies’ rapid growth and pervasiveness. In ... [more ▼]

With the heavy reliance on Information Technologies in every aspect of our daily lives, Machine Learning (ML) models have become a cornerstone of these technologies’ rapid growth and pervasiveness. In particular, the most critical and fundamental technologies that handle our economic systems, transportation, health, and even privacy. However, while these systems are becoming more effective, their complexity inherently decreases our ability to understand, test, and assess the dependability and trustworthiness of these systems. This problem becomes even more challenging under a multi-objective framework: When the ML model is required to learn multiple tasks together, behave under constrained inputs or fulfill contradicting concomitant objectives. Our dissertation focuses on the context of robust ML under limited training data, i.e., use cases where it is costly to collect additional training data and/or label it. We will study this topic under the prism of three real use cases: Fraud detection, pandemic forecasting, and chest x-ray diagnosis. Each use-case covers one of the challenges of robust ML with limited data, (1) robustness to imperceptible perturbations, or (2) robustness to confounding variables. We provide a study of the challenges for each case and propose novel techniques to achieve robust learning. As the first contribution of this dissertation, we collaborate with BGL BNP Paribas. We demonstrate that their overdraft and fraud detection systems are prima facie robust to adversarial attacks because of the complexity of their feature engineering and domain constraints. However, we show that gray-box attacks that take into account domain knowledge can easily break their defense. We propose, CoEva2 adversarial fine-tuning, a new defense mechanism based on multi-objective evolutionary algorithms to augment the training data and mitigate the system’s vulnerabilities. Next, we investigate how domain knowledge can protect against adversarial attacks through multi-task learning. We show that adding domain constraints in the form of additional tasks can significantly improve the robustness of models to adversarial attacks, particularly for the robot navigation use case. We propose a new set of adaptive attacks and demonstrate that adversarial training combined with such attacks can improve robustness. While the raw data available in the BGL or Robot Navigation is vast, it is heavily cleaned, feature-engineered, and annotated by domain experts (which are expensive), and the end training data is scarce. In contrast, raw data is scarce when dealing with an outbreak, and designing robust ML systems to predict, forecast, and recommend mitigation policies is challenging. In particular, for small countries like Luxembourg. Contrary to common techniques that forecast new cases based on previous data in time series, we propose a novel surrogate-based optimization as an integrated loop. It combines a neural network prediction of the infection rate based on mobility attributes and a model-based simulation that predicts the cases and deaths. Our approach has been used by the Luxembourg government’s task force and has been recognized with a best paper award at KDD2020. Our following work focuses on the challenges that pose cofounding factors to the robustness and generalization of Chest X-ray (CXR) classification. We first investigate the robustness and generalization of multi-task models, then demonstrate that multi-task learning, leveraging the cofounding variables, can significantly improve the generalization and robustness of CXR classification models. Our results suggest that task augmentation with additional knowledge (like extraneous variables) outperforms state-of-art data augmentation techniques in improving test and robust performances. Overall, this dissertation provides insights into the importance of domain knowledge in the robustness and generalization of models. It shows that instead of building data-hungry ML models, particularly for critical systems, a better understanding of the system as a whole and its domain constraints yields improved robustness and generalization performances. This dissertation also proposes theorems, algorithms, and frameworks to effectively assess and improve the robustness of ML systems for real-world cases and applications. [less ▲]

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See detailArtificial Intelligence-enabled Automation For Ambiguity Handling And Question Answering In Natural-language Requirements
Ezzini, Saad UL

Doctoral thesis (2022)

Requirements Engineering (RE) quality control is a crucial step for a project’s success. Natural Language (NL) is by far the most commonly used means for capturing requirement specifications. Despite ... [more ▼]

Requirements Engineering (RE) quality control is a crucial step for a project’s success. Natural Language (NL) is by far the most commonly used means for capturing requirement specifications. Despite facilitating communication, NL is prone to quality defects, one of the most notable of which is ambiguity. Ambiguous requirements can lead to misunderstandings and eventually result in a system that is different from what is intended, thus wasting time, money, and effort in the process. This dissertation tackles selected quality issues in NL requirements: • Using Domain-specific Corpora for Improved Handling of Ambiguity in Requirements: Syntactic ambiguity types occurring in coordination and prepositional-phrase attachment structures are prevalent in requirements (in our document collection, as we discuss in Chapter 3, 21% and 26% of the requirements are subject to coordination and prepositional-phrase attachment ambiguity analysis, respectively). We devise an automated solution based on heuristics and patterns for improved handling of coordination and prepositional-phrase attachment ambiguity in requirements. As a prerequisite for this research, we further develop a more broadly applicable corpus generator that creates a domain-specific knowledge resource by crawling Wikipedia. • Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study: Anaphoric ambiguity is another prevalent ambiguity type in requirements. Estimates from the RE literature suggest that nearly 20% of industrial requirements contain anaphora [1, 2]. We conducted a multi-solution study for anaphoric ambiguity handling. Our study investigates six alternative solutions based on three different technologies: (i) off-the-shelf natural language processing (NLP), (ii) recent NLP methods utilizing language models, and (iii) machine learning (ML). • AI-based Question Answering Assistant for Analyzing NL Requirements: Understanding NL requirements requires domain knowledge that is not necessarily shared by all the involved stakeholders. We develop an automated question-answering assistant that supports requirements engineers during requirements inspections and quality assurance. Our solution uses advanced information retrieval techniques and machine reading comprehension models to answer questions from the same requirement specifications document and/or an external domain-specific knowledge resource. All the research components in this dissertation are tool-supported. Our tools are released with open-source licenses to encourage replication and reuse. [less ▲]

Detailed reference viewed: 34 (6 UL)