This work addresses the automatic alignment of chapters across different Arabic legal texts.We examine two representation strategies: TF-IDF-based lexical embeddings and contextual semantic embeddings generated by AraBERT.These embeddings enable us to assess the semantic proximity between chapters.We then frame the text alignment process as an optimal transport problem, incorporating soft structural constraints.To analyze the impact of method parameters, we use a curated ground-truth dataset derived from a pair of representative texts.

Obfuscation is a software protection technique that transforms a program's binary code to conceal its behavior and to hinder analysis.Conversely, deobfuscation is an adversarial process that seeks to partially or fully remove the applied obfuscation in order to recover the original, unobfuscated code.This work introduces the first deobfuscation framework based on Reinforcement Learning (RL).It models an obfuscated function's binary code using a novel graph representation integrating both data and control-flow.The graph is then progressively simplified through a sequence of graph-edit operations, selected iteratively by a Graph Neural Network (GNN)-based agent operating within a RL pipeline.Experiments demonstrate promising results on Mixed Boolean Arithmetic (MBA) obfuscation, where multiple variants of diverse expressions can successfully be simplified into valid deobfuscated variants.* This work was partially

Neural forecasting methods typically assume Gaussian distributions, focusing on point prediction via MSE minimization.This overlooks heavy-tailed, locally explosive time series where predictive densities exhibit multimodality.We propose a Mixture Density Network with skewed Student-t components for density forecasting.To address extreme event rarity, we develop a dual reweighting strategy with post-hoc recalibration correcting distributional shift.Experiments on noncausal autoregressive processes demonstrate competitive point prediction with well-calibrated uncertainty quantification.

Auto-Adaptive Laplacian Pyramids (ALP) is an iterative kernel-based regression model.It constructs a multi-scale representation of the train data, where the multi-scale modes are average residuals.In this work, we propose two extensions of the model.The first is a hybrid approach that combines ALP with Empirical Mode Decomposition to provide localization in the frequency domain.The second modifies ALP to fit datasets with non-uniform noise, which is achieved by computing the optimal stopping criterion in a point-dependent manner.Experimental results demonstrate these models for solar energy prediction and for forecasting epidemiology infections.

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere.This is particularly adapted for high-dimensional directional data such as texts.We propose in this article to estimate a von Mises mixture using a l1 penalized likelihood.This leads to sparse prototypes that improve both clustering quality and interpretability.We introduce an expectation-maximisation (EM) algorithm for this estimation and show the advantages of the approach on real data benchmark.We propose to explore the trade-off between the sparsity term and the likelihood one with a simple path following algorithm.

In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress.While some domains like web analysis can benefit from this with only minor restrictions, other fields like medicine with patient data are stronger regulated.In particular data privacy plays an important role as recently highlighted by the trustworthy AI initiative of the EU or general privacy regulations in legislation.Another major challenge is, that the required training data is often distributed in terms of features or samples and unavailable for classical batch learning approaches.In 2016 Google came up with a framework, called Federated Learning to solve both of these problems.We provide a brief overview on existing Methods and Applications in the field of vertical and horizontal Federated Learning, as well as Federated Transfer Learning.

In this paper, we address the problem of finding a correspondence, or matching, between the functions of two programs in binary form, which is one of the most common task in binary diffing. We introduce a new formulation of this problem as a particular instance of a graph edit problem over the call graphs of the programs. In this formulation, the quality of a mapping is evaluated simultaneously with respect to both function content and call graph similarities. We show that this formulation is equivalent to a network alignment problem. We propose a solving strategy for this problem based on max-product belief propagation. Finally, we implement a prototype of our method, called QBinDiff, and propose an extensive evaluation which shows that our approach outperforms state of the art diffing tools.

In this paper, we present a novel algorithm to address the Network Alignment problem. It is inspired from a previous message passing framework of Bayati et al. [2] and includes several modifications designed to significantly speed up the message updates as well as to enforce their convergence. Experiments show that our proposed model outperforms other state-of-the-art solvers. Finally, we propose an application of our method in order to address the Binary Diffing problem. We show that our solution provides better assignment than the reference differs in almost all submitted instances and outline the importance of leveraging the graphical structure of binary programs.

Abstract Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State‐of‐the‐Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web‐based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

Abstract Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under‐explored. This state‐of‐the‐art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

The stochastic block model (SBM) [1] describes interactions between nodes of a network following a probabilistic approach. Nodes belong to hidden clusters and the probabilities of interactions only depend on these clusters. Interactions of time varying intensity are not taken into account. By partitioning the whole time horizon, in which interactions are observed, we develop a non stationary extension of the SBM, allowing us to simultaneously cluster the nodes of a network and the fixed time intervals in which interactions take place. The number of clusters as well as memberships to clusters are finally obtained through the maximization of the complete-data integrated likelihood relying on a greedy search approach. Experiments are carried out in order to assess the proposed methodology.

This paper proposes an approach for comparing interlocked board networks over time to test for statistically significant change. In addition to contributing to the conversation about whether the Mizruchi hypothesis (that a disintegration of power is occurring within the corporate elite) holds or not, we propose novel methods to handle a longitudinal investigation of a series of social networks where the nodes undergo a few modifications at each time point. Methodologically, our contribution is two-fold: we extend a Bayesian model hereto applied to compare two time periods to a longer time period, and we define and employ the concept of a hull of a sequence of social networks, which makes it possible to circumvent the problem of changing nodes over time.

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine owner to implement efficiently the adapted maintenance operations (fixing the source of the anomaly) are of crucial importance to reduce the costs attached to unscheduled maintenance. This paper introduces a general methodology that aims at classifying monitoring signals into normal ones and several classes of abnormal ones. The main idea is to leverage expert knowledge by generating a very large number of binary indicators. Each indicator corresponds to a fully parametrized anomaly detector built from parametric anomaly scores designed by experts. A feature selection method is used to keep only the most discriminant indicators which are used at inputs of a Naive Bayes classifier. This give an interpretable classifier based on interpretable anomaly detectors whose parameters have been optimized indirectly by the selection process. The proposed methodology is evaluated on simulated data designed to reproduce some of the anomaly types observed in real world engines.

Taking the temporal dimension into account during the analysis of Web usage data has become a necessity since the way a site is visited may well evolve due to modifications in the structure and content of the site, or even due to changes in the behavior of certain user groups. Consequently, the models associated with these behaviors must be continuously updated. One solution to this problem is to update these models using summaries obtained by means of an evolutionary approach based on clustering methods. To do this, we carry out various clustering strategies that are applied on time sub-periods. We compare the results obtained using this method with those reached by traditional global analysis.

An object-oriented neural network simulator kernel is presented. It as based on a general mathematical model for arbitrary feedforward nets. We propose a C++ implementation of this model which satisfies the following requirements: expandability (allowing an easy implementation of a new neural model), portability and efficiency (the kernel does not increase significantly its computation times for classic models, compared to a direct object-oriented implementation). Learning algorithms such as gradient-based ones can be written for arbitrary nets and are therefore directly available for every particular model.>