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

Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying physics. A line of work relies on learning representations where the dynamics of the underlying phenomenon can be described by a linear operator, based on the Koopman operator theory. However, despite being able to provide reliable long-term predictions for some dynamical systems in ideal situations, the methods proposed so far have limitations, such as requiring to discretize intrinsically continuous dynamical systems, leading to data loss, especially when handling incomplete or sparsely sampled data. Here, we propose a new deep Koopman framework that represents dynamics in an intrinsically continuous way, leading to better performance on limited training data, as exemplified on several datasets arising from dynamical systems.

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.

Nowadays, cyberattacks have become a significant concern for individuals, organizations, and governments. These attacks can take many forms, and the consequences can be severe. In order to protect ourselves from these threats, it is essential to employ a range of different strategies and techniques like detection of patterns, classification of system behaviors against previously known attacks, and anomaly detection techniques. This way, we can identify unknown forms of attacks. Few of these existing techniques seem to fully utilize the potential of mathematical approaches such as spectral graph analysis. This domain is made of tools able to extract important topological features of a graph by computing its Laplacian matrix and its corresponding spectrum. This framework can provide valuable insights into the underlying structure of a network, which can be used to detect cyberthreats. Indeed, significant changes in the topology of the graph result in significant changes in the spectrum of the Laplacian matrix. For this reason, we propose here to address this issue by considering the network as a dynamic graph composed of nodes (devices) and edges (requests between devices), to study the evolution of the Laplacian spectrum, and to compute metrics on this evolving spectrum. This way, we should be able to detect suspicious behaviors which may indicate that an attack is occurring.

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.

Interactive image segmentation is an important application in computer vision for selecting objects of interest in images. Several interactive segmentation methods are based on distance transform algorithms. However, the most known distance transform, geodesic distance, is sensitive to noise in the image and to seed placement. Recently, the Dahu pseudo-distance, a continuous version of the minimum barrier distance (MBD), is proved to be more powerful than the geodesic distance in noisy and blurred images. This paper presents a method for combining the Dahu pseudo-distance with edge information in a graph-cut optimization framework and leveraging each’s complementary strengths. Our method works efficiently on both 2D/3D images and videos. Results show that our method achieves better performance than other distance-based and graph-cut methods, thereby reducing the user’s efforts.

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.

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.