Response to: Twelve tips for introducing E-portfolios in health professions education: The technologist’s point of view

in Medical Teacher (2023)

Project DragLiner: Harnessing plasma Coulomb drag for satellite deorbiting to keep orbits clean

Scientific Conference (2023, April)

Personal Informatics at Work

Scientific Conference (2023, April)

An ever-increasing amount of sensor technologies surrounds our work environment, collecting data about workers? activity and wellbeing ? often without them having any say in this. This datafication of the ... [more ▼]

An ever-increasing amount of sensor technologies surrounds our work environment, collecting data about workers? activity and wellbeing ? often without them having any say in this. This datafication of the workplace can help find novel ways to stimulate healthy behaviors or increase productivity. However, the ubiquitous usage of data collectors in the workplace can also put employees in a position of power imbalance, where management is frequently aiming at ?optimizing? employees towards normative and more-than-often problematic standards of productivity and wellbeing. Yet, data-tracking practices might not only seek an optimum but also can be interpreted towards self-enhancement (Meissner, 2016). In this sense, optimization is not per se an improvement on previous goals, but rather allows ?the discovery of new opportunities?. Indeed, data can be a means of giving space for users to be curious about their own subjective work experiences. Personal Informatics research has mostly focused on leisure and health-related topics but it takes on different forms, requirements, and ethical considerations for a work context. To explore how such considerations emerge from material practices, we conducted a field study, deploying a research probe called Habilyzer at a workplace (N=5). Findings show that users explored aspects meaningful to them yet highlight discrepancies between the envisioned self-tracking goals and participants? practices. Regarding sensors? open-endedness, a balance between the burden of data collection and the value derived from it appeared critical. We contribute new insights into how open-ended sensor technologies can be designed to support self-tracking practices in the workplace. [less ▲]

Les nouvelles tendances en matière de sécurité pour les serveurs Linux

Article for general public (2023)

Towards Log Slicing

in Dawes, Joshua; Shin, Donghwan; Bianculli, Domenico (Eds.) Towards Log Slicing (2023, April)

This short paper takes initial steps towards developing a novel approach, called log slicing, that aims to answer a practical question in the field of log analysis: Can we automatically identify log ... [more ▼]

This short paper takes initial steps towards developing a novel approach, called log slicing, that aims to answer a practical question in the field of log analysis: Can we automatically identify log messages related to a specific message (e.g., an error message)? The basic idea behind log slicing is that we can consider how different log messages are "computationally related" to each other by looking at the corresponding logging statements in the source code. These logging statements are identified by 1) computing a backwards program slice, using as criterion the logging statement that generated a problematic log message; and 2) extending that slice to include relevant logging statements. The paper presents a problem definition of log slicing, describes an initial approach for log slicing, and discusses a key open issue that can lead towards new research directions. [less ▲]

Superconducting Gravity Meters

in Sideris, Michael G. (Ed.) Encyclopedia of Geodesy (2023)

Protecting Consumers and Capital Markets in the Age of Social Media: the Case of Finfluencers

in Oxford BLB (2023)

Financial influencers and content creators, commonly referred to as ‘Finfluencers’, increasingly provide unpaid or paid finance content on social media. This non-expert financial advice can potentially ... [more ▼]

Financial influencers and content creators, commonly referred to as ‘Finfluencers’, increasingly provide unpaid or paid finance content on social media. This non-expert financial advice can potentially cause significant financial and non-financial harm, especially for financially illiterate consumers. A Financial Conduct Authority study revealed low financial literacy levels among retail investors. For example, 38 per cent of surveyed investors could not list a single reason for investing in their top three investments, with most investors relying on rules of thumb and gut instinct. The shocking findings of the FCA study partly inspired my article Regulating Finfluencers. My paper investigates the practices and business models of Finfluencers and how EU law, three national jurisdictions (the UK, Luxembourg, and Germany), and five social media platforms govern their activities. The primary aim is to comprehend the activities and regulation of Finfluencers and to critically examine whether the current framework adequately protects consumers. [less ▲]

Statelessness in the European Union, Norway and Georgia

Report (2023)

This 2023 EMN inform is the latest in the series of EMN Platform on Statelessness informs, with two others previously published in 2016 and 2020. It is updated with contributions from the participating ... [more ▼]

This 2023 EMN inform is the latest in the series of EMN Platform on Statelessness informs, with two others previously published in 2016 and 2020. It is updated with contributions from the participating EMN Member Countries and EMN Observer Countries (Norway and Georgia). Information related to the United Kingdom (UK) has been removed, as the UK is no longer an EMN Member Country, but UK information remains in the earlier informs. [less ▲]

Learning Optimisation Algorithms over Graphs

Doctoral thesis (2023)

The paradigm of learning to optimise relies on the following principle: instead of designing an algorithm to solve a problem, we design an algorithm which will automate the design of such a solver. The ... [more ▼]

The paradigm of learning to optimise relies on the following principle: instead of designing an algorithm to solve a problem, we design an algorithm which will automate the design of such a solver. The initial idea was to alleviate the limitations stated by the No Free Lunch Theorem by producing an algorithm which efficiency is less dependent upon known instances of the problem to tackle. Hyper-heuristics constitute the main learning-to-optimise techniques. These rely on a high-level algorithm performing a search process into a space of low-level heuristics to tackle a given problem. Because the latter search space is problem-dependent, the vast majority of hyper-heuristics are designed to tackle a specific problem. Due to this lack of generality, existing works fully redesign hyper-heuristics when tackling a new problem, despite the fact that they may share a similar structure. In this dissertation, we tackle this challenge by proposing a generic way for learning to optimise any problem. To this end, this thesis introduces three main contributions: (i) an analysis of the formal functioning of learning-to-optimise techniques; (ii) a model of generic hyper-heuristic, named Algorithm Learner for Graph Optimisation problems (ALGO), constituting the central point of this work; (iii) a real-world use case where we use our generic hyper-heuristic to automate the design of behaviours within a swarm of drones. In the first part, we provide a formalism for optimisation and learning concepts, which we use to describe the large body of knowledge that combines two layers of optimisation and/or learning. We then put an emphasis on approaches using learning to improve an optimisation process, i.e., aiming at learning to optimise. In the second part, we present ALGO, our model of generic hyper-heuristic. We explain how we abstract from a given problem with a graph structure so that it can be used to tackle any optimisation problem. We also detail the steps to follow in order to use ALGO to tackle a given problem. We finally present the modularity of ALGO with inner components that a user can implement. The second part ends with a validation of our model, i.e., using ALGO to tackle a classical optimisation problem. In the third part, we use ALGO to tackle the problem of area surveillance with a swarm of drones. We demonstrate that ALGO constitutes a novel and efficient way to automate the design of such a distributed and multi-objective problem. [less ▲]

Essays On Skills, Labor Market Institutions and Wage Inequality

Doctoral thesis (2023)