9 research outputs found

    Certifying Correctness for Combinatorial Algorithms : by Using Pseudo-Boolean Reasoning

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    Over the last decades, dramatic improvements in combinatorialoptimisation algorithms have significantly impacted artificialintelligence, operations research, and other areas. These advances,however, are achieved through highly sophisticated algorithms that aredifficult to verify and prone to implementation errors that can causeincorrect results. A promising approach to detect wrong results is touse certifying algorithms that produce not only the desired output butalso a certificate or proof of correctness of the output. An externaltool can then verify the proof to determine that the given answer isvalid. In the Boolean satisfiability (SAT) community, this concept iswell established in the form of proof logging, which has become thestandard solution for generating trustworthy outputs. The problem isthat there are still some SAT solving techniques for which prooflogging is challenging and not yet used in practice. Additionally,there are many formalisms more expressive than SAT, such as constraintprogramming, various graph problems and maximum satisfiability(MaxSAT), for which efficient proof logging is out of reach forstate-of-the-art techniques.This work develops a new proof system building on the cutting planesproof system and operating on pseudo-Boolean constraints (0-1 linearinequalities). We explain how such machine-verifiable proofs can becreated for various problems, including parity reasoning, symmetry anddominance breaking, constraint programming, subgraph isomorphism andmaximum common subgraph problems, and pseudo-Boolean problems. Weimplement and evaluate the resulting algorithms and a verifier for theproof format, demonstrating that the approach is practical for a widerange of problems. We are optimistic that the proposed proof system issuitable for designing certifying variants of algorithms inpseudo-Boolean optimisation, MaxSAT and beyond

    Programming Languages and Systems

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    This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

    Modélisation formelle des systÚmes de détection d'intrusions

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    L’écosystĂšme de la cybersĂ©curitĂ© Ă©volue en permanence en termes du nombre, de la diversitĂ©, et de la complexitĂ© des attaques. De ce fait, les outils de dĂ©tection deviennent inefficaces face Ă  certaines attaques. On distingue gĂ©nĂ©ralement trois types de systĂšmes de dĂ©tection d’intrusions : dĂ©tection par anomalies, dĂ©tection par signatures et dĂ©tection hybride. La dĂ©tection par anomalies est fondĂ©e sur la caractĂ©risation du comportement habituel du systĂšme, typiquement de maniĂšre statistique. Elle permet de dĂ©tecter des attaques connues ou inconnues, mais gĂ©nĂšre aussi un trĂšs grand nombre de faux positifs. La dĂ©tection par signatures permet de dĂ©tecter des attaques connues en dĂ©finissant des rĂšgles qui dĂ©crivent le comportement connu d’un attaquant. Cela demande une bonne connaissance du comportement de l’attaquant. La dĂ©tection hybride repose sur plusieurs mĂ©thodes de dĂ©tection incluant celles sus-citĂ©es. Elle prĂ©sente l’avantage d’ĂȘtre plus prĂ©cise pendant la dĂ©tection. Des outils tels que Snort et Zeek offrent des langages de bas niveau pour l’expression de rĂšgles de reconnaissance d’attaques. Le nombre d’attaques potentielles Ă©tant trĂšs grand, ces bases de rĂšgles deviennent rapidement difficiles Ă  gĂ©rer et Ă  maintenir. De plus, l’expression de rĂšgles avec Ă©tat dit stateful est particuliĂšrement ardue pour reconnaĂźtre une sĂ©quence d’évĂ©nements. Dans cette thĂšse, nous proposons une approche stateful basĂ©e sur les diagrammes d’état-transition algĂ©briques (ASTDs) afin d’identifier des attaques complexes. Les ASTDs permettent de reprĂ©senter de façon graphique et modulaire une spĂ©cification, ce qui facilite la maintenance et la comprĂ©hension des rĂšgles. Nous Ă©tendons la notation ASTD avec de nouvelles fonctionnalitĂ©s pour reprĂ©senter des attaques complexes. Ensuite, nous spĂ©cifions plusieurs attaques avec la notation Ă©tendue et exĂ©cutons les spĂ©cifications obtenues sur des flots d’évĂ©nements Ă  l’aide d’un interprĂ©teur pour identifier des attaques. Nous Ă©valuons aussi les performances de l’interprĂ©teur avec des outils industriels tels que Snort et Zeek. Puis, nous rĂ©alisons un compilateur afin de gĂ©nĂ©rer du code exĂ©cutable Ă  partir d’une spĂ©cification ASTD, capable d’identifier de façon efficiente les sĂ©quences d’évĂ©nements.Abstract : The cybersecurity ecosystem continuously evolves with the number, the diversity, and the complexity of cyber attacks. Generally, we have three types of Intrusion Detection System (IDS) : anomaly-based detection, signature-based detection, and hybrid detection. Anomaly detection is based on the usual behavior description of the system, typically in a static manner. It enables detecting known or unknown attacks but also generating a large number of false positives. Signature based detection enables detecting known attacks by defining rules that describe known attacker’s behavior. It needs a good knowledge of attacker behavior. Hybrid detection relies on several detection methods including the previous ones. It has the advantage of being more precise during detection. Tools like Snort and Zeek offer low level languages to represent rules for detecting attacks. The number of potential attacks being large, these rule bases become quickly hard to manage and maintain. Moreover, the representation of stateful rules to recognize a sequence of events is particularly arduous. In this thesis, we propose a stateful approach based on algebraic state-transition diagrams (ASTDs) to identify complex attacks. ASTDs allow a graphical and modular representation of a specification, that facilitates maintenance and understanding of rules. We extend the ASTD notation with new features to represent complex attacks. Next, we specify several attacks with the extended notation and run the resulting specifications on event streams using an interpreter to identify attacks. We also evaluate the performance of the interpreter with industrial tools such as Snort and Zeek. Then, we build a compiler in order to generate executable code from an ASTD specification, able to efficiently identify sequences of events

    Mean-Field methods for Structured Deep-Learning in Computer Vision

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    In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predict marginals, over single or multiple variables, independently, with high accuracy. However, in many tasks of practical interest, we need to predict jointly several correlated variables. Practical applications include people detection in crowded scenes, image segmentation, surface reconstruction, 3D pose estimation and others. A large part of the research effort in today's computer-vision community aims at finding task-specific solutions to these problems, while leveraging the power of Deep-Learning based classifiers. In this thesis, we present our journey towards a generic and practical solution based on mean-field (MF) inference. Mean-field is a Statistical Physics-inspired method which has long been used in Computer-Vision as a variational approximation to posterior distributions over complex Conditional Random Fields. Standard mean-field optimization is based on coordinate descent and in many situations can be impractical. We therefore propose a novel proximal gradient-based approach to optimizing the variational objective. It is naturally parallelizable and easy to implement. We prove its convergence, and then demonstrate that, in practice, it yields faster convergence and often finds better optima than more traditional mean-field optimization techniques. Then, we show that we can replace the fully factorized distribution of mean-field by a weighted mixture of such distributions, that similarly minimizes the KL-Divergence to the true posterior. Our extension of the clamping method proposed in previous works allows us to both produce a more descriptive approximation of the true posterior and, inspired by the diverse MAP paradigms, fit a mixture of mean-field approximations. We demonstrate that this positively impacts real-world algorithms that initially relied on mean-fields. One of the important properties of the mean-field inference algorithms is that the closed-form updates are fully differentiable operations. This naturally allows to do parameter learning by simply unrolling multiple iterations of the updates, the so-called back-mean-field algorithm. We derive a novel and efficient structured learning method for multi-modal posterior distribution based on the Multi-Modal Mean-Field approximation, which can be seamlessly combined to modern gradient-based learning methods such as CNNs. Finally, we explore in more details the specific problem of structured learning and prediction for multiple-people detection in crowded scenes. We then present a mean-field based structured deep-learning detection algorithm that provides state of the art results on this dataset

    Discretization reaction-diffusion models with finite difference method

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    Discretization model is a continuous model transformation procedure to model discrete. Discretization is done using advanced finite difference method, by analogy differential equations using limit rules, with different equations using the different between discrete time points. The model used in this paper is a model of reaction-diffusion (Turing) that represents the diffusion of fluid in the cells that cause the cells to move. Finite difference method is a numerical method that can be used to solve partial differential equations. Methods used explicit finite difference scheme developed for the time difference and central difference for the space to complete the reactiondiffusion equation (Turing). Based on the numerical solution obtained then the amount of domain growth does not affect the stability of reaction-diffusion models (Turing)

    LANDSLIDE INVESTIGATION IN THE RÍO AGUAS CATCHMENT, SOUTHEAST SPAIN

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    Many remote and/or rural communities Uve under the threat of major landslide activity. These remote areas are also increasingly the focus of development programmes which, without careful consideration of the ground conditions, will be at risk. The cost of mitigating against landslide activity can also be extremely high, possibly making prevention or avoidance the better long-term option. It is, therefore, of prime importance to assess the type and magnitude of the landslide activity affecting an area, particularly during the feasibility and planning stages of any project. The most straightforward approach to any landslide investigation is the compilation of a landslide inventory and the development of a geological and geomorphological ground mode! for the area under investigation. Previous landslide inventory-based projects (for example, in UK, Hong Kong and Nepal) have utilised either desk study or remotely sensed sources (aerial photographs or satellite imagery) with only limited field mapping, have focused on wet monsoonal environments in either mountainous and/or heavily populated areas and have usually been completed without reference to a ground model for the area being investigated. Therefore, an investigation of the landslide activity affecting the 425 kmÂČ Rio Aguas Catchment area has been completed. This is a remote and rural part of southeastern Spain, which has an arid to semi-arid climate and is periodically affected by both earthquake and flash-flood activity. Through a combination of aerial photographic interpretation (API) and field verification and mapping, over 300 landslides have been mapped and documented. These data have been used to develop a landslide inventory and a conceptual geological and geomorphological ground model of the study area, with respect to the landslide activity. The landslide inventory data have been used to complete a statistical analysis investigating the factors that control the distribution, style and mechanisms of the landslide activity, as well as to examine the relationships between landslide volume, runout length and angle of reach. The basis of the ground model is a project-derived terrain classification for the study area. The data analysis has shown that although a variety of landslide failure mechanisms are seen within the study area, the majority of the landsides are rock falls and topples and/or occur within incised sections of the drainage network. The analysis has also shown that the landslide activity is controlled by a combination of the discontinuities within the rock mass, as well as contrasts in the permeability and stiffness of the rock masses/types involved. The influence of human activity, as well as tectonic activity, rainfall and expansive day soils has also been considered. However, a lack of detailed historical landslide and rainfall data limits the conclusions that can be drawn. The mapped landslide distribution (supported by the geological and geomorphological ground model) has highlighted that the majority of the landslides in the Rio Aguas Catchment are related to a major river capture and modification of the drainage network that occurred approximately 100Ka BP, and that they are a key component of the geomorphological processes active within the study area. This river capture, driven by differential tectonic uplift between sedimentary basins, has caused a wave of incision to pass through a substantial section of the south central part of the study area leading to the oversteepening of slopes, the incision of the drainage network and the majority of the landslide activity that is seen within the study area. The development of the drainage network has been recorded by a series of river terrace deposits that reflect the overall tectonically induced incision as well as the variable Quaternary climate. These river terrace deposits have been used to provide a relative temporal framework for the landslide activity, in the absence of any dated landslide chronology

    Programming Languages and Systems

    Get PDF
    This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems
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