Graduate School of Computer Science and Advanced Technologies

Paragraph justification is based primarily on shrinking or stretching the interword blanks. While the blanks on a line are all scaled by the same amout, the amount in question varies from line to line. The quality of a paragraph's typographic color largely depends on the aforementioned variation being as small as possible. Yet, TEX'S paragraph justification algorithm addresses this problem in a rather coarse fashion. In this paper, we propose a refinement to the algorithm allowing to improve the situation without disturbing the general behavior of the algorithm too much, and without the need for manual intervention. We analyze the impact of our refinement on a large number of experiments through several statistical estimators. We also exhibit a number of typographical traits relatedto whitespace distribution that we believe may contribute to our perception of homogeneousness.

With the Internet of Things (IoT) growth and customer expectations, the importance of data streaming and streaming processing has increased.Data Streaming refers to the concept where data is processed and transmitted continuously and in real-time without necessarily being stored in a physical location.Personal health monitors and home security systems are examples of data streaming sources.This paper presents a systematic mapping study of the performance analysis of Data Streaming systems in the context of Containerization and Microservices.The research aimed to identify the main methods, tools, and techniques used in the last five years for the execution of this type of study.The results show that there are still few performance evaluation studies for this system niche, and there are gaps that must be filled, such as the lack of analytical modeling and the disregard for communication protocols' influence.

We generalize the results in [3] giving a reduction algorithm allowing to compute the index of seaweed subalgebras of classical simple Lie algebras. We thus are able to obtain the index of some interesting families of seaweed subalgebras and to give new examples of large classes of Frobenius Lie algebras among them.

The intrusion detection systems are vital for the sustainability of Cooperative Intelligent Transportation Systems (C-ITS) and the detection of sybil attacks are particularly challenging. In this work, we propose a novel approach for the detection of sybil attacks in C-ITS environments. We provide an evaluation of our approach using extensive simulations that rely on real traces, showing our detection approach's effectiveness.

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at <a href='https://github.com/RagMeh11/QU-BraTS'>https://github.com/RagMeh11/QU-BraTS</a>

Abstract We revisit the problem of reducing incompletely specified Mealy machines with reactive synthesis in mind. We propose two techniques: the former is inspired by the tool MeMin [1] and solves the minimization problem, the latter is a novel approach derived from simulation-based reductions but may not guarantee a minimized machine. However, we argue that it offers a good enough compromise between the size of the resulting Mealy machine and performance. The proposed methods are benchmarked against MeMin on a large collection of test cases made of well-known instances as well as new ones.

International audience

Abstract. In this article, we are interested in the implementation of an open-source low-level architecture (critical system) adapted to agile and easily replicable close-range remote sensing robots operating in multiple evolution domains. After reviewing the existing autopilots responding to these needs, we discuss the available hardware solutions and their limits. Then, we propose an original solution (software and hardware) that we developed to obtain a universal low-level architecture for all our exploration robots, whatever their environment of evolution, and the steps needed to make it run on our chosen family of micro-controllers: the ESP32. Finally, we present the operational results obtained on our different platforms (land, surface, submarine and air), their limits and the envisaged perspectives.

Modern parallel SAT solvers rely heavily on effective clause sharing policies for their performance. The core problem being addressed by these policies can be succinctly stated as “the problem of identifying high-quality learnt clauses”. These clauses, when shared between the worker nodes of parallel solvers, should lead to better performance. The term “high-quality clauses” is often defined in terms of metrics that solver designers have identified over years of empirical study. Some of the more well-known metrics to identify high-quality clauses for sharing include clause length, literal block distance (LBD), and clause usage in propagation.In this paper, we propose a new metric aimed at identifying high-quality learnt clauses and a concomitant clause-sharing policy based on a combination of LBD and community structure of Boolean formulas. The concept of community structure has been proposed as a possible explanation for the extraordinary performance of SAT solvers in industrial instances. Hence, it is a natural candidate as a basis for a metric to identify high-quality clauses. To be more precise, our metric identifies clauses that have low LBD and low community number as ones that are high-quality for applications such as verification and testing. The community number of a clause C measures the number of different communities of a formula that the variables in C span. We perform extensive empirical analysis of our metric and clause-sharing policy, and show that our method significantly outperforms state-of-the-art techniques on the benchmark from the parallel track of the last four SAT competitions.

In this paper, we present a machine learning based splitting heuristic for divide-and-conquer parallel Boolean SAT solvers. Splitting heuristics, whether they are look-ahead or look-back, are designed using proxy metrics, which when optimized, approximate the true metric of minimizing solver runtime on sub-formulas resulting from a split. The rationale for such metrics is that they have been empirically shown to be excellent proxies for runtime of solvers, in addition to being cheap to compute in an online fashion. However, the design of traditional splitting methods are often ad-hoc and do not leverage the copious amounts of data that solvers generate.