Publications

Languages of higher-dimensional timed automata

Abstract

We present a new language semantics for real-time concurrency. Its operational models are higher-dimensional timed automata (HDTAs), a generalization of both higher-dimensional automata and timed automata. We define languages of HDTAs as sets of interval-timed pomsets with interfaces. As an application, we show that language inclusion of HDTAs is undecidable. On the other hand, using a region construction we can show that untimings of HDTA languages have enough regularity so that untimed language inclusion is decidable.

Continue reading

GenIDA, a participatory patient registry for genetic forms of intellectual disability provides detailed caregiver reported information on 237 individuals with Koolen-de Vries syndrome

Abstract

Continue reading

Forecasting electricity prices: An optimize then predict-based approach

Abstract

We are interested in electricity price forecasting at the European scale. The electricity market is ruled by price regulation mechanisms that make it possible to adjust production to demand, as electricity is difficult to store. These mechanisms ensure the highest price for producers, the lowest price for consumers and a zero energy balance by setting day-ahead prices, i.e. prices for the next 24h. Most studies have focused on learning increasingly sophisticated models to predict the next day’s 24 hourly prices for a given zone. However, the zones are interdependent and this last point has hitherto been largely underestimated. In the following, we show that estimating the energy cross-border transfer by solving an optimization problem and integrating it as input of a model improves the performance of the price forecasting for several zones together.

Continue reading

An experience report on the optimization of the product configuration system of Renault

Abstract

The problem of configuring a variability model is widespread in many different domains. A leading automobile manufacturer has developed its technology internally to model vehicle diversity. This technology relies on the approach known as knowledge compilation to explore the configurations space. However, the growing variability and complexity of the vehicles’ range hardens the space representation problem and impacts performance requirements. This paper tackles these issues by exploiting symmetries that represent isomorphic parts in the configurations space. A new method describes how these symmetries are exploited and integrated. The extensive experiments we conducted on datasets from the automobile manufacturer show our approach’s robustness and effectiveness: the achieved gain is a reduction of 52.13% in space representation and 49.81% in processing time on average

Continue reading

Optimization of the product configuration system of Renault

Abstract

The problem of configuring a variability model is widespread in many different domains. Renault has developed its technology internally to model vehicle diversity. This technology relies on the approach known as knowledge compilation to explore the configurations space. However, the growing variability and complexity of the vehicles’ range hardens the space representation problem and impacts performance requirements. This paper tackles these issues by exploiting symmetries that represent isomorphic parts in the configurations space. A new method describes how these symmetries are exploited and integrated. The extensive experiments we conducted on datasets from the automobile manufacturer show our approach’s robustness and effectiveness: the achieved gain is a reduction of 52.13% in space representation on average.

Continue reading

A MOP-based implementation for method combinations

Abstract

In traditional object-oriented languages, the dynamic dispatch algorithm is hardwired to select and execute the most specific method in a polymorphic call. In CLOS, the Common Lisp Object System, an abstraction known as “method combinations” allows the programmer to define their own dispatch scheme. When Common Lisp was standardized, method combinations were not mature enough to be fully specified.In 2018, using SBCL as a research vehicle, we analyzed the unfortunate consequences of this under-specification and proposed a layer on top of method combinations designed to both correct a number of observed behavioral inconsistencies, and propose an extension called “alternative combinators”. Following this work, SBCL underwent a number of internal changes that fixed the reported inconsistencies, although in a way that hindered further experimentation.In this paper, we analyze SBCL’s new method combinations implementation and we propose an alternative design. Our solution is standard-compliant so any Lisp implementation can potentially use it. It is also based on the MOP, meaning that it is extensible, which restores the opportunity for further experimentation. In particular, we revisit our former “alternative combinators” extension, broken after 2018, and demonstrate that provided with this new infrastructure, it can be re-implemented in a much simpler and non-intrusive way.

Continue reading

An elegant and fast algorithm for partitioning types

Abstract

We present an improvement on the Maximal Disjoint Type Decomposition algorithm, published previously. The new algorithm is shorter than the previously best known algorithm in terms of lines of code, and performs better in many, but not all, benchmarks. Additionally the algorithm computes metadata which makes the Brzozowski derivative easier to compute–both easier in terms of accuracy and computation time. Another advantage of this new algorithm is its resilience limited SUBTYPEP implementations.

Continue reading

Des joueurs sous écrou : Enquêtes sur les problématiques liées à la pratique des jeux d’argent en population carcérale

Abstract

Continue reading

Dictionnaire des sciences du jeu

Abstract

Continue reading

Metrics for evaluating interface explainability models for cyberattack detection in IoT data

Abstract

The importance of machine learning (ML) in detecting cyberattacks lies in its ability to efficiently process and analyze large volumes of IoT data, which is critical in ensuring the security and privacy of sensitive information transmitted between connected devices. However, the lack of explainability of ML algorithms has become a significant concern in the cybersecurity community. Therefore, explainable techniques are developed to make ML algorithms more transparent, thereby improving trust in attack detection systems by its ability to allow cybersecurity analysts to understand the reasons for model predictions and to identify any limitation or error in the model. One of the key artifacts of explainability is interface explainability models such as impurity and permutation feature importance analysis, Local Interpretable Model-agnostic Explanations (LIME), and SHapley Additive exPlanations (SHAP). However, these models are not able to provide enough quantitative information (metrics) to build complete trust and confidence in the explanations they generate. In this paper, we propose and evaluate metrics such as reliability and latency to quantify the trustworthiness of the explanations and to establish confidence in the model’s decisions to accurately detect and explain cyberattacks in IoT data during the ML process.

Continue reading