pinecone/core-2020-05-10-deduplication huggingface dataset:
Slice, mine and dice : complexity-aware automated discovery of business process models

Automated process discovery techniques aim at extracting models from information system logs in order to shed light into the business processes supported by these systems.\ud
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Existing techniques in this space are effective when applied to relatively small or regular logs, but otherwise generate large and spaghetti-like models. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. The result is a collection of process models -- each one representing a variant of the business process -- as opposed to an all-encompassing model.\ud
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Still, models produced in this way may exhibit unacceptably high complexity. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically by means of subprocess extraction. The proposed technique allows users to set a desired bound for the complexity of the produced models. Experiments on real-life logs show that the technique produces collections of models that are up to 64% smaller than those extracted under the same complexity bounds by applying existing trace clustering techniques
The Power of Well-Structured Systems

Well-structured systems, aka WSTSs, are computational models where the set of
possible configurations is equipped with a well-quasi-ordering which is
compatible with the transition relation between configurations. This structure
supports generic decidability results that are important in verification and
several other fields.
  This paper recalls the basic theory underlying well-structured systems and
shows how two classic decision algorithms can be formulated as an exhaustive
search for some "bad" sequences. This lets us describe new powerful techniques
for the complexity analysis of WSTS algorithms. Recently, these techniques have
been successful in precisely characterising the power, in a
complexity-theoretical sense, of several important WSTS models like unreliable
channel systems, monotonic counter machines, or networks of timed systems.Comment: Invited tal
Compositional Verification and Optimization of Interactive Markov Chains

Interactive Markov chains (IMC) are compositional behavioural models
extending labelled transition systems and continuous-time Markov chains. We
provide a framework and algorithms for compositional verification and
optimization of IMC with respect to time-bounded properties. Firstly, we give a
specification formalism for IMC. Secondly, given a time-bounded property, an
IMC component and the assumption that its unknown environment satisfies a given
specification, we synthesize a scheduler for the component optimizing the
probability that the property is satisfied in any such environment
PLASMA-lab: A Flexible, Distributable Statistical Model Checking Library

International audienceWe present PLASMA-lab, a statistical model checking (SMC) library that provides the functionality to create custom statistical model checkers based on arbitrary discrete event modelling languages. PLASMA-lab is written in Java for maximum cross-platform compatibility and has already been incorporated in various performance-critical software and embedded hardware platforms. Users need only implement a few simple methods in a simulator class to take advantage of our efficient SMC algorithms. PLASMA-lab may be instantiated from the command line or from within other software. We have constructed a graphical user interface (GUI) that exposes the functionality of PLASMA-lab and facilitates its use as a standalone application with multiple 'drop-in' modelling languages. The GUI adds the notion of projects and experiments, and implements a simple, practical means of distributing simulations using remote clients
Universally Composable Key-Management

International audienceWe present the first universally composable key-management functionality, formalized in the GNUC framework by Hofheinz and Shoup. It allows the enforcement of a wide range of security policies and can be extended by diverse key usage operations with no need to repeat the security proof.We illustrate its use by proving an implementation of a security token secure with respect to arbitrary key-usage operations and explore a proof technique that allows the storage of cryptographic keys externally, a novel development in simulation-based security frameworks
ASICS : authenticated key exchange security incorporating certification systems

Most security models for authenticated key exchange (AKE) do not explicitly model the associated certification system, which includes the certification authority (CA) and its behaviour. However, there are several well-known and realistic attacks on AKE protocols which exploit various forms of malicious key registration and which therefore lie outside the scope of these models. We provide the first systematic analysis of AKE security incorporating certification systems (ASICS). We define a family of security models that, in addition to allowing different sets of standard AKE adversary queries, also permit the adversary to register arbitrary bitstrings as keys. For this model family we prove generic results that enable the design and verification of protocols that achieve security even if some keys have been produced maliciously. Our approach is applicable to a wide range of models and protocols; as a concrete illustration of its power, we apply it to the CMQV protocol in the natural strengthening of the eCK model to the ASICS setting
Mining Malware Specifications through Static Reachability Analysis

International audienceAbstract. The number of malicious software (malware) is growing out of control. Syntactic signature based detection cannot cope with such growth and manual construction of malware signature databases needs to be replaced by computer learning based approaches. Currently, a single modern signature capturing the semantics of a malicious behavior can be used to replace an arbitrarily large number of old-fashioned syntactical signatures. However teaching computers to learn such behaviors is a challenge. Existing work relies on dynamic analysis to extract malicious behaviors, but such technique does not guarantee the coverage of all behaviors. To sidestep this limitation we show how to learn malware signatures using static reachability analysis. The idea is to model binary programs using pushdown systems (that can be used to model the stack operations occurring during the binary code execution), use reachability analysis to extract behaviors in the form of trees, and use subtrees that are common among the trees extracted from a training set of malware files as signatures. To detect malware we propose to use a tree automaton to compactly store malicious behavior trees and check if any of the subtrees extracted from the file under analysis is malicious. Experimental data shows that our approach can be used to learn signatures from a training set of malware files and use them to detect a test set of malware that is 5 times the size of the training set
Full abstraction for fair testing in CCS

20 pages.International audienceIn previous work with Pous, we defined a semantics for CCS which may both be viewed as an innocent presheaf semantics and as a concurrent game semantics. It is here proved that a behavioural equivalence induced by this semantics on CCS processes is fully abstract for fair testing equivalence. The proof relies on a new algebraic notion called playground, which represents the 'rule of the game'. From any playground, two languages, equipped with labelled transition systems, are derived, as well as a strong, functional bisimulation between them
Lending Petri nets and contracts

Choreography-based approaches to service composition typically assume that, after a set of services has been found which correctly play the roles prescribed by the choreography, each service respects his role. Honest services are not protected against adversaries. We propose a model for contracts based on an extension of Petri nets, which allows services to protect themselves while still realizing the choreography. We relate this model with Propositional Contract Logic, by showing a translation of formulae into our Petri nets which preserves the logical notion of agreement, and allows for compositional verification
Extending UPPAAL for the Modeling and Verification of Dynamic Real-Time Systems

Dynamic real-time systems, where the number of processes is not constant and new processes can be created on the fly like in object-based systems and ad-hoc networks, are still lacking a formal framework enabling their verification. Different toolboxes like Uppaal [21], Tina [10], Red [28] and Kronos [29] have been designed to deal with the modeling and analysis of real-time systems. Nevertheless, a shortcoming of these tools is that they can only describe static topologies. Other tools like Spin [18] allow the dynamic creation of processes, but do not consider time aspects. This paper presents a formal framework for modeling and verifying dynamic real-time systems. We introduce callable timed automata as a simple but powerful extension of standard timed automata in which processes may call each other. We show that the semantics of each call event can be interpreted either as an activation of the existing instance of the corresponding automaton (static instantiation), or a creation of a new concurrent instance (dynamic instantiation). We explore both semantical interpretations, static and dynamic, and give for each one the motivation and benefits with illustrating examples. Finally, we report on experiments with a prototype tool, which translates (a subset of) callable timed automata to UPPAAL systems
Transience Bounds for Distributed Algorithms

International audienceA large variety of distributed systems, like some classical synchronizers, routers, or schedulers, have been shown to have a periodic behavior after an initial transient phase (Malka and Rajsbaum, WDAG 1991). In fact, each of these systems satisfies recurrence relations that turn out to be linear as soon as we consider max-plus or min-plus algebra. In this paper, we give a new proof that such systems are eventually periodic and a new upper bound on the length of the initial transient phase. Interestingly, this is the first asymptotically tight bound that is linear in the system size for various classes of systems. Another significant benefit of our approach lies in the straightforwardness of arguments: The proof is based on an easy convolution lemma borrowed from Nachtigall (Math. Method. Oper. Res. 46) instead of purely graph-theoretic arguments and involved path reductions found in all previous proofs
A Mechanized Semantic Framework for Real-Time Systems

Concurrent systems consist of many components which may execute in parallel and are complex to design, to analyze, to verify, and to implement. The complexity increases if the systems have real-time constraints, which are very useful in avionic, spatial and other kind of embedded applications. In this paper we present a logical framework for defining and validating real-time formalisms as well as reasoning methods over them. For this purpose, we have implemented in the Coq proof assistant well known semantic domains for real-time systems based on labelled transitions systems and timed runs. We experiment our framework by considering the real-time CSP-based language fiacre, which has been defined as a pivot formalism for modeling languages (aadl, sdl, ...) used in the TOPCASED project. Thus, we define an extension to the formal semantic models mentioned above that facilitates the modeling of fine-grained time constraints of fiacre. Finally, we implement this extension in our framework and provide a proof method environment to deal with real-time system in order to achieve their formal certification
Efficient top-k spatial distance joins

Conference Theme: Advances in Spatial and Temporal DatabasesLNCS v. 8098 entitled: Advances in spatial and temporal databases : 13th International Symposium, SSTD 2013 ... proceedings Embargo till 2014-Aug-01Consider two sets of spatial objects R and S, where each object is assigned a score (e.g., ranking). Given a spatial distance threshold ε and an integer k, the top-k spatial distance join (k- SDJ) returns the k pairs of objects, which have the highest combined score (based on an aggregate function γ) among all object pairs in R x S which have spatial distance at most ε. Despite the practical application value of this query, it has not received adequate attention in the past. In this paper, we fill this gap by proposing methods that utilize both location and score information from the objects, enabling top-k join computation by accessing a limited number of objects. Extensive experiments demonstrate that a technique which accesses blocks of data from R and S ordered by the object scores and then joins them using an aR-tree based module performs best in practice and outperforms alternative solutions by a wide margin. © 2013 Springer-Verlag.postprin
Sparse Coding and Mid-Level Superpixel-Feature for l0-Graph Based Unsupervised Image Segmentation

International audienceWe propose in this paper a graph-based unsupervised segmentation approach that combines superpixels, sparse representation, and a new mid-level feature to describe superpixels. Given an input image, we first extract a set of interest points either by sampling or using a local feature detector, and we compute a set of low-level features associated with the patches centered at the interest points. We define a low-level dictionary as the collection of all these low-level features. We call superpixel a region of an oversegmented image obtained from the input image, and we compute the low-level features associated with it. Then we compute for each superpixel a mid-level feature defined as the sparse coding of its low-level features in the aforementioned dictionary. These mid-level features not only carry the same information as the initial low-level features, but also carry additional contextual cue. We use the superpixels at several segmentation scales, their associated mid-level features, and the sparse representation coefficients to build graphs at several scales. Merging these graphs leads to a bipartite graph that can be partitioned using the Transfer Cut algorithm. We validate the proposed mid-level feature framework on the MSRC dataset, and the segmented results show improvements from both qualitative and quantitative viewpoints compared with other state-of-the-art methods
An Automated Visual Inspection System for the Classification of the Phases of Ti-6Al-4V Titanium Alloy

Metallography is the science of studying the physical properties of metal microstructures, by means of microscopes. While traditional approaches involve the direct observation of the acquired images by human experts, Com-puter Vision techniques may help experts in the analysis of the inspected mate-rials. In this paper we present an automated system to classify the phases of a Titanium alloy, Ti-6Al-4V. Our system has been tested to analyze the final products of a Friction Stir Welding process, to study the states of the micro-structures of the welded material
A novel Border Identification algorithm based on an “Anti-Bayesian” paradigm

Published version of a chapter in the book: Computer Analysis of Images and Patterns. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-40261-6_23Border Identification (BI) algorithms, a subset of Prototype Reduction Schemes (PRS) aim to reduce the number of training vectors so that the reduced set (the border set) contains only those patterns which lie near the border of the classes, and have sufficient information to perform a meaningful classification. However, one can see that the true border patterns (“near” border) are not able to perform the task independently as they are not able to always distinguish the testing samples. Thus, researchers have worked on this issue so as to find a way to strengthen the “border” set. A recent development in this field tries to add more border patterns, i.e., the “far” borders, to the border set, and this process continues until it reaches a stage at which the classification accuracy no longer increases. In this case, the cardinality of the border set is relatively high. In this paper, we aim to design a novel BI algorithm based on a new definition for the term “border”. We opt to select the patterns which lie at the border of the alternate class as the border patterns. Thus, those patterns which are neither on the true discriminant nor too close to the central position of the distributions, are added to the “border” set. The border patterns, which are very small in number (for example, five from both classes), selected in this manner, have the potential to perform a classification which is comparable to that obtained by well-known traditional classifiers like the SVM, and very close to the optimal Bayes’ bound
On achieving near-optimal “Anti-Bayesian” Order Statistics-Based classification fora asymmetric exponential distributions

Published version of a Chapter in the book: Computer Analysis of Images and Patterns. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-40261-6_44This paper considers the use of Order Statistics (OS) in the theory of Pattern Recognition (PR). The pioneering work on using OS for classification was presented in [1] for the Uniform distribution, where it was shown that optimal PR can be achieved in a counter-intuitive manner, diametrically opposed to the Bayesian paradigm, i.e., by comparing the testing sample to a few samples distant from the mean - which is distinct from the optimal Bayesian paradigm. In [2], we showed that the results could be extended for a few symmetric distributions within the exponential family. In this paper, we attempt to extend these results significantly by considering asymmetric distributions within the exponential family, for some of which even the closed form expressions of the cumulative distribution functions are not available. These distributions include the Rayleigh, Gamma and certain Beta distributions. As in [1] and [2], the new scheme, referred to as Classification by Moments of Order Statistics (CMOS), attains an accuracy very close to the optimal Bayes’ bound, as has been shown both theoretically and by rigorous experimental testing
Effectively delivering XML information in periodic broadcast environments

Existing data placement algorithms for wireless data broad-
cast generally make assumptions that the clients' queries are already known and the distribution of access frequencies of their queries can be obtained a priori. Unfortunately, these assumptions are not realistic in most real life applications because new mobile clients may join in anytime and clients may be reluctant to disclose their queries (due to privacy concerns). In this work, we study the data placement problem of periodic XML data broadcast in which similar assumptions can be avoided. This is an important issue, particularly when XML becomes prevalent in
today's mobile computing devices. Taking advantage of the structured characteristics of XML data, we are able to generate effective broadcast programs based purely on XML data on the server without any knowledge of the clients' access patterns. This not only makes our work distinguished from previous studies, but also enables it to have broader
applicability.We present a theoretical analysis of the problem and discuss structural sharing in XML data which forms the basis of our novel greedy data placement algorithm. Finally, we evaluate the proposed placement
algorithm through a set of experiments and the results show that our algorithm can effectively place XML data on air and significantly improve the overall access efficiency.
GUN: An Efficient Execution Strategy for Querying the Web of Data

International audienceLocal-As-View (LAV) mediators provide a uniform interface to a federation of heterogeneous data sources, attempting to execute queries against the federation. LAV mediators rely on query rewriters to translate mediator queries into equivalent queries on the federated data sources. The query rewriting problem in LAV mediators has shown to be NP-complete, and there may be an exponential number of rewritings, making unfeasible the execution or even generation of all the rewritings for some queries. The complexity of this problem can be particularly impacted when queries and data sources are described using SPARQL conjunctive queries, for which millions of rewritings could be generated. We aim at providing an efficient solution to the problem of executing LAV SPARQL query rewritings while the gathered answer is as complete as possible. We formulate the Result-Maximal k-Execution problem (ReMakE) as the problem of maximizing the query results obtained from the execution of only k rewritings. Additionally, a novel query execution strategy called GUN is proposed to solve the ReMakE problem. Our experimental evaluation demonstrates that GUN outperforms traditional techniques in terms of answer completeness and execution time
Complex matching of RDF datatype properties

Property mapping is a fundamental component of ontology matching, and yet there is little support that goes beyond the identification of single property matches. Real data often requires some degree of composition, trivially exemplified by the mapping of "first name" and "last name" to "full name" on one end, to complex matchings, such as parsing and pairing symbol/digit strings to SSN numbers, at the other end of the spectrum. In this paper, we propose a two-phase instance-based technique for complex datatype property matching. Phase 1 computes the Estimate Mutual Information matrix of the property values to (1) find simple, 1:1 matches, and (2) compute a list of possible complex matches. Phase 2 applies Genetic Programming to the much reduced search space of candidate matches to find complex matches. We conclude with experimental results that illustrate how the technique works. Furthermore, we show that the proposed technique greatly improves results over those obtained if the Estimate Mutual Information matrix or the Genetic Programming techniques were to be used independently. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40285-2_18