Publications

Blockchain-based solution for detecting and preventing fake check scams

Abstract

Fake check scam is one of the most common attacks used to commit fraud against consumers. This fraud is particularly costly for victims because they generally lose thousands of dollars as well as being exposed to judicial proceedings. Currently, there is no existing solution to authenticate checks and detect fake ones instantly. Instead, banks must wait for a period of more than 48 h to detect the scam. In this context, we propose a blockchain-based scheme to authenticate checks and detect fake check scams. Moreover, our approach allows the revocation of used checks. More precisely, our approach helps the banks to share information about provided checks and used ones, without exposing the banks’ customers’ personal data. We demonstrate a proof of concept of our proposed approach using Namecoin and Hyperledger blockchain technologies.

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Pictograms to aid laypeople in identifying the addictiveness of gambling products (PictoGRRed study)

Abstract

The structural addictive characteristics of gambling products are important targets for prevention, but can be unintuitive to laypeople. In the PictoGRRed (Pictograms for Gambling Risk Reduction) study, we aimed to develop pictograms that illustrate the main addictive characteristics of gambling products and to assess their impact on identifying the addictiveness of gambling products by laypeople. We conducted a three-step study: (1) use of a Delphi consensus method among 56 experts from 13 countries to reach a consensus on the 10 structural addictive characteristics of gambling products to be illustrated by pictograms and their associated definitions, (2) development of 10 pictograms and their definitions, and (3) study in the general population to assess the impact of exposure to the pictograms and their definitions (n = 900). French-speaking experts from the panel assessed the addictiveness of gambling products (n = 25), in which the mean of expert’s ratings was considered as the true value. Participants were randomly provided with the pictograms and their definitions, or with a standard slogan, or with neither (control group). We considered the control group as representing the baseline ability of laypeople to assess the addictiveness of gambling products. Each group and the French-speaking experts rated the addictiveness of 14 gambling products. The judgment criterion was the intraclass coefficients (ICCs) between the mean ratings of each group and the experts, reflecting the level of agreement between each group and the experts. Exposure to the pictograms and their definition doubled the ability of laypeople to assess the addictiveness of gambling products compared with that of the group that read a slogan or the control group (ICC = 0.28 vs. 0.14 (Slogan) and 0.14 (Control)). Laypeople have limited awareness of the addictive characteristics of gambling products. The pictograms developed herein represent an innovative tool for universally empowering prevention and for selective prevention.

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PKIs in C-ITS: Security functions, architectures and projects: A survey

Abstract

In the smart cities context, Cooperative Intelligent Transportation Systems (C-ITS) represent one of the main use cases that aim to improve peoples’ daily lives. Within these environments, messages are exchanged continuously. The latter must be secure and must ensure users’ privacy. In this regard, Public Key Infrastructures (PKIs) represent the major solution to meet security needs. In this work, we present a holistic survey that describes all the different functions and services of a C-ITS PKI and focus on the different standards and consortia works that have been adopted to regulate such PKIs. Relying on the survey, we highlight the main research problems and open challenges for ITS PKIs. Then, we propose a generic model for a C-ITS PKI architecture.

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Featured games

Abstract

Feature-based analysis of software product lines and family-based model checking have seen rapid development. Many model checking problems can be reduced to two-player games on finite graphs. A prominent example is mu-calculus model checking, which is generally done by translating to parity games, but also many quantitative model-checking problems can be reduced to (quantitative) games. As part of a program to make game-based model checking available for software product lines, we introduce featured reachability games, featured minimum reachability games, featured discounted games, featured energy games, and featured parity games. We show that all these admit optimal featured strategies, which project to optimal strategies for any product, and how to compute winners and values of such games in a family-based manner.

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GenIDA: An international participatory database to gain knowledge on health issues related to genetic forms of neurodevelopmental disorders

Abstract

Intellectual disability with or without manifestations of autism and/or epilepsy affects 1-2% of the population, and it is estimated that more than 30-50% of these cases have a single genetic cause. More than 1000 genes and recurrent chromosomal abnormalities are involved in these genetic forms of neurodevelopmental disorders, which often remain insufficiently described in terms of clinical spectrum, associated medical problems, etc., due to their rarity and the often-limited number of patients’ phenotypes reported. GenIDA is an international online participatory database that aims to better characterise the clinical manifestations and natural histories of these rare diseases. Clinical information is reported by parents of affected individuals using a structured questionnaire exploring physical parameters, cognitive and behavioural aspects, the presence or absence of neurological disorders or problems affecting major physiological functions, as well as autonomy and quality of life. This strengthens the implication in research of the concerned families. GenIDA aims to construct international cohorts of significant size of individuals affected by a given condition. As of July 2022, GenIDA counts some 1545 documented patient records from over 60 nationalities and collaborates with clinicians and researchers around the world who have access to the anonymized data collected to generate new, medically meaningful information to improve patient care. We present the GenIDA database here, together with an overview of the possibilities it offers to affected individuals, their families, and professionals in charge of the management of genetic forms of neurodevelopmental disorders. Finally, case studies of cohorts will illustrate the usefulness of GenIDA.

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How to help digital-native students to successfully take control of their learning: A return of 8 years of experience on a computer science e-learning platform in higher education

Abstract

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In pursuit of the hidden features of GNN’s internal representations

Abstract

We consider the problem of explaining Graph Neural Networks (GNNs). While most attempts aim at explaining the final decision of the model, we focus on the hidden layers to examine what the GNN actually captures and shed light on the hidden features built by the GNN. To that end, we first extract activation rules that identify sets of exceptionally co-activated neurons when classifying graphs in the same category. These rules define internal representations having a strong impact in the classification process. Then - this is the goal of the current paper - we interpret these rules by identifying a graph that is fully embedded in the related subspace identified by the rule. The graph search is based on a Monte Carlo Tree Search directed by a proximity measure between the graph embedding and the internal representation of the rule, as well as a realism factor that constrains the distribution of the labels of the graph to be similar to that observed on the dataset. Experiments including 6 real-world datasets and 3 baselines demonstrate that our method DISCERN generates realistic graphs of high quality which allows providing new insights into the respective GNN models.

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Topology-aware method to segment 3D plan tissue images

Abstract

The study of genetic and molecular mechanisms underlying tissue morphogenesis has received a lot of attention in biology. Especially, accurate segmentation of tissues into individual cells plays an important role for quantitative analyzing the development of the growing organs. However, instance cell segmentation is still a challenging task due to the quality of the image and the fine-scale structure. Any small leakage in the boundary prediction can merge different cells together, thereby damaging the global structure of the image. In this paper, we propose an end-to-end topology-aware 3D segmentation method for plant tissues. The strength of the method is that it takes care of the 3D topology of segmented structures. The keystone is a training phase and a new topology-aware loss - the CavityLoss - that are able to help the network to focus on the topological errors to fix them during the learning phase. The evaluation of our method on both fixed and live plant organ datasets shows that our method outperforms state-of-the-art methods (and contrary to state-of-the-art methods, does not require any post-processing stage). The code of CavityLoss is freely available at https://github.com/onvungocminh/CavityLoss

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On GNN explainability with activation rules

Abstract

GNNs are powerful models based on node representation learning that perform particularly well in many machine learning problems related to graphs. The major obstacle to the deployment of GNNs is mostly a problem of societal acceptability and trustworthiness, properties which require making explicit the internal functioning of such models. Here, we propose to mine activation rules in the hidden layers to understand how the GNNs perceive the world. The problem is not to discover activation rules that are individually highly discriminating for an output of the model. Instead, the challenge is to provide a small set of rules that cover all input graphs. To this end, we introduce the subjective activation pattern domain. We define an effective and principled algorithm to enumerate activations rules in each hidden layer. The proposed approach for quantifying the interest of these rules is rooted in information theory and is able to account for background knowledge on the input graph data. The activation rules can then be redescribed thanks to pattern languages involving interpretable features. We show that the activation rules provide insights on the characteristics used by the GNN to classify the graphs. Especially, this allows to identify the hidden features built by the GNN through its different layers. Also, these rules can subsequently be used for explaining GNN decisions. Experiments on both synthetic and real-life datasets show highly competitive performance, with up to 200% improvement in fidelity on explaining graph classification over the SOTA methods.

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A modern C++ point of <i>view</i> of programming in image processing

Abstract

C++ is a multi-paradigm language that enables the programmer to set up efficient image processing algorithms easily. This language strength comes from many aspects. C++ is high-level, so this enables developing powerful abstractions and mixing different programming styles to ease the development. At the same time, C++ is low-level and can fully take advantage of the hardware to deliver the best performance. It is also very portable and highly compatible which allows algorithms to be called from high-level, fast-prototyping languages such as Python or Matlab. One fundamental aspects where C++ shines is generic programming. Generic programming makes it possible to develop and reuse bricks of software on objects (images) of different natures (types) without performance loss. Nevertheless, conciliating genericity, efficiency, and simplicity at the same time is not trivial. Modern C++ (post-2011) has brought new features that made it simpler and more powerful. In this paper, we focus on some C++20 aspects of generic programming: ranges, views, and concepts, and see how they extend to images to ease the development of generic image algorithms while lowering the computation time.

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