pinecone/core-2020-05-10-deduplication huggingface dataset:
Zenon Modulo: When Achilles Outruns the Tortoise using Deduction Modulo

International audienceWe propose an extension of the tableau-based first order automated theorem prover Zenon to deduction modulo. The theory of deduction modulo is an extension of predicate calculus, which allows us to rewrite terms as well as propositions, and which is well suited for proof search in axiomatic theories, as it turns axioms into rewrite rules. We also present a heuristic to perform this latter step automatically, and assess our approach by providing some experimental results obtained on the benchmarks provided by the TPTP library, where this heuristic is able to prove difficult problems in set theory in particular. Finally, we describe an additional backend for Zenon that outputs proof certificates for Dedukti, which is a proof checker based on the λΠ-calculus modulo
On the relationship between functional encryption, obfuscation, and fully homomorphic encryption

We investigate the relationship between Functional Encryption (FE) and Fully Homomorphic Encryption (FHE), demonstrating that, under certain assumptions, a Functional Encryption scheme supporting evaluation on two ciphertexts implies Fully Homomorphic Encryption. We first introduce the notion of Randomized Functional Encryption (RFE), a generalization of Functional Encryption dealing with randomized functionalities of interest in its own right, and show how to construct an RFE from a (standard) semantically secure FE. For this we define the notion of entropically secure FE and use it as an intermediary step in the construction. Finally we show that RFEs constructed in this way can be used to construct FHE schemes thereby establishing a relation between the FHE and FE primitives. We conclude the paper by recasting the construction of RFE schemes in the context of obfuscation.NSF -National Science Foundatio
Recovering Private Keys Generated with Weak PRNGs

International audienceSuppose that the private key of discrete logarithm-based or factoring-based public-key primitive is obtained by concatenating the outputs of a linear congruential generator. How seriously is the scheme weakened as a result? While linear congruential generators are cryptographically very weak "pseudorandom" number generators, the answer to that question is not immediately obvious, since an adversary in such a setting does not get to examine the outputs of the congruential generator directly, but can only obtain an implicit hint about them—namely the public key. In this paper, we take a closer look at that problem, and show that, in most cases, an attack does exist to retrieve the key much faster than with a naive exhaustive search on the seed of the generator
Gathering and Exclusive Searching on Rings under Minimal Assumptions

International audienceConsider a set of mobile robots with minimal capabilities placed over distinct nodes of a discrete anonymous ring. Asynchronously, each robot takes a snapshot of the ring, determining which nodes are either occupied by robots or empty. Based on the observed configuration, it decides whether to move to one of its adjacent nodes or not. In the first case, it performs the computed move, eventually. The computation also depends on the required task. In this paper, we solve both the well-known Gathering and Exclusive Searching tasks. In the former problem, all robots must simultaneously occupy the same node, eventually. In the latter problem, the aim is to clear all edges of the graph. An edge is cleared if it is traversed by a robot or if both its endpoints are occupied. We consider the exclusive searching where it must be ensured that two robots never occupy the same node. Moreover, since the robots are oblivious, the clearing is perpetual, i.e., the ring is cleared infinitely often. In the literature, most contributions are restricted to a subset of initial configurations. Here, we design two different algorithms and provide a characterization of the initial configurations that permit the resolution of the problems under minimal assumptions
Graph editing to a fixed target.

For a fixed graph H, the H-Minor Edit problem takes as input a graph G and an integer k and asks whether G can be modified into H by a total of at most k edge contractions, edge deletions and vertex deletions. Replacing edge contractions by vertex dissolutions yields the H-Topological Minor Edit problem. For each problem we show polynomial-time solvable and NP-complete cases depending on the choice of H. Moreover, when G is AT-free, chordal or planar, we show that H-Minor Edit is polynomial-time solvable for all graphs H
A Pretty Complete Combinatorial Algorithm for the Threshold Synthesis Problem

A linear pseudo-Boolean constraint (LPB) is an expression of the form  a_1 l_1+...+a_m l_m>= d$, where each l_i is a literal (it assumes the  value 1 or 0 depending on whether a propositional variable x_i is true  or false) and a_1,...,a_m,d are natural numbers. An LPB represents a  Boolean function, and those Boolean functions that can be represented  by exactly one LPB are called threshold functions.  The problem of  finding an LPB representation of a Boolean function if possible is  called threshold recognition problem or threshold synthesis problem.  The problem has an O(m^7t^5) algorithm using linear programming, where  m is the dimension and t the number of clauses in the DNF input. There  is also an entirely combinatorial procedure, which works by  decomposing the DNF and "counting" the variable occurrences in it. We  have implemented both algorithms and report here on the experiments
Embedding agents in business applications using enterprise integration
  patterns

This paper addresses the issue of integrating agents with a variety of
external resources and services, as found in enterprise computing environments.
We propose an approach for interfacing agents and existing message routing and
mediation engines based on the endpoint concept from the enterprise integration
patterns of Hohpe and Woolf. A design for agent endpoints is presented, and an
architecture for connecting the Jason agent platform to the Apache Camel
enterprise integration framework using this type of endpoint is described. The
approach is illustrated by means of a business process use case, and a number
of Camel routes are presented. These demonstrate the benefits of interfacing
agents to external services via a specialised message routing tool that
supports enterprise integration patterns
Data abstraction in coordination constraints

Communications in Computer and Information Science 393, 2013This paper studies complex coordination mechanisms based on constraint satisfaction. In particular, it focuses on data-sensitive connectors from the Reo coordination language. These connectors restrict how and where data can flow between loosely-coupled components taking into account the data being exchanged. Existing engines for Reo provide a very limited support for data-sensitive connectors, even though data constraints are captured by the original semantic models for Reo. When executing data-sensitive connectors, coordination constraints are not exhaustively solved at compile time but at runtime on a per-need basis, powered by an existing SMT (satisfiability modulo theories) solver.To deal with a wider range of data types and operations, we abstract data and reduce the original constraint satisfaction problem to a SAT problem, based on a variation of predicate abstraction. We show soundness and completeness of the abstraction mechanism for well-defined constraints, and validate our approach by evaluating the performance of a prototype implementation with different test cases, with and without abstraction
Complex Self-reproducing Systems

International audienceCellular automata and L-Systems are well-known formal models to describe the behaviour of biological processes. They are discrete dynamical systems, each of which can have complex and varied behaviour. Here, we study a class of substitutive systems incorporating properties of both cellular automata and L-systems, that exhibits self-reproducing behaviour. A one-dimensional array of cells is considered. Each cell has a set of modes or states which are determined by a number from $\Z/n\Z^*$ ($n$ prime). The behaviour of a cell depends on the states of its neighbours and obeys to an additive rule. It has also a cell-division mode, which allows the line of cells to grow. The behaviour of such a model can be complex, but, using algebraic techniques, we prove that it can describe a reproducing system
DESIGNING A GENERALIZED MULTIPLE CRITERIA DECISION SUPPORT SYSTEM

Decision support systems are of many kinds depending on the
models and techniques employed in them. Multiple criteria
decision making techniques constitute an important class of DSS
with unique software requirements. This paper stresses the
importance of interactive MCDM methods since these facilitate
learning through all stages of the decision making process. We
first describe some features of Multiple Criteria Decision Support
Systems ( MCDSSs) that distinguish them from classical DSSs. We
then outline a software architecture for a MCDSS which has three
basic components: a Dialog Manager, an MCDM Model Manager, and a
Data Manager. We describe the interactions that occur between
these three software components in an integrated MCDSS and outline
a design for the Data Manager which is based on a concept of
levels of data abstraction.Information Systems Working Papers Serie
Additive, Dynamic and Multiplicative Regression

We survey and compare model-based approaches to regression for cross-sectional and longitudinal data which extend the classical parametric linear model for Gaussian responses in several aspects and for a variety of settings. Additive models replace the sum of linear functions of regressors by a sum of smooth functions. In dynamic or state space models, still linear in the regressors, coefficients are allowed to vary smoothly with time according to a Bayesian smoothness prior. We show that this is equivalent to imposing a roughness penalty on time-varying coefficients. Admitting the coefficients to vary with the values of other covariates, one obtains a class of varying-coefficient models (Hastie and Tibshirani, 1993), or in another interpretation, multiplicative models. The roughness penalty approach to non- and semiparametric modelling, together with Bayesian justifications, is used as a unifying and general framework for estimation. The methodological discussion is illustrated by some real data applications.
Different Nonlinear Regression Models with Incorrectly Observed Covariates

We present quasi-likelihood models for different regression problems when one of the explanatory variables is measured with heteroscedastic error. In order to derive models for the observed data the conditional mean and variance functions of the regression models are only expressed through functions of the observable covariates. The latent covariable is treated as a random variable that follows a normal distribution. Furthermore it is assumed that enough additional information is provided to estimate the individual measurement error variances, e.g. through replicated measurements of the fallible predictor variable. The discussion includes the polynomial regression model as well as the probit and logit model for binary data, the Poisson model for count data and ordinal regression models.
Guided Self-Organization of Input-Driven Recurrent Neural Networks

We review attempts that have been made towards understanding the
computational properties and mechanisms of input-driven dynamical systems like
RNNs, and reservoir computing networks in particular. We provide details on
methods that have been developed to give quantitative answers to the questions
above. Following this, we show how self-organization may be used to improve
reservoirs for better performance, in some cases guided by the measures
presented before. We also present a possible way to quantify task performance
using an information-theoretic approach, and finally discuss promising future
directions aimed at a better understanding of how these systems perform their
computations and how to best guide self-organized processes for their
optimization.Comment: 23 pages, to appear in M. Prokopenko (ed.), Guided Self-Organization:
  Inception, Springer, 201
Evaluation of AFIS-Ranked latent fingerprint matched templates

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-53842-1_20The methodology currently practiced in latent print examination (known as ACE-V) yields only a decision as result, namely individualization, exclusion or inconclusive. From such a decision, it is not possible to express the strength of opinion of a forensic examiner quantitatively with a scientific basis to the criminal justice system. In this paper, we propose a framework to generate a score from the matched template generated by the forensic examiner. Such a score can be viewed as a measure of confidence of a forensic examiner quantitatively, which in turn can be used in statistics-based evidence evaluation framework, for e.g, likelihood ratio. Together with the description and evaluation of new realistic forensic case driven score computation, we also exploit the developed experimental framework to understand more about matched templates in forensic fingerprint databases.R.K. and R.W. are supported by Marie Curie Fellowships under project BB-for2 (FP7-ITN-238803). This work has also been partially supported by Spanish Guardia Civil, Cátedra UAM-Telefónica, and projects Bio-Shield (TEC2012-34881) and Contexts (S2009/TIC-1485)
Comparative analysis of the variability of facial landmarks for forensics using CCTV images

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-53842-1_35Proceedings of the 6th Pacific-Rim Symposium, PSIVT 2013, Guanajuato, Mexico, October 28-November 1, 2013.This paper reports a study of the variability of facial landmarks in a forensic scenario using images acquired from CCTV images. This type of images presents a very low quality and a large range of variability factors such as differences in pose, expressions, occlusions, etc. Apart from this, the variability of facial landmarks is affected by the precision in which the landmarks are tagged. This process can be done manually or automatically depending on the application (e.g., forensics or automatic face recognition, respectively). This study is carried out comparing both manual and automatic procedures, and also 3 distances between the camera and the subjects. Results show that landmarks located in the outer part of the face (highest end of the head, ears and chin) present a higher level of variability compared to the landmarks located the inner face (eye region, and nose). This study shows that the landmark variability increases with the distance between subject and camera, and also the results of the manual and automatic approaches are similar for the inner facial landmarks.This work has been partially supported by a contract with Spanish Guardia Civil and projects BBfor2 (FP7-ITN-238803), Bio-Shield (TEC2012-34881), Contexts (S2009/TIC-1485), TeraSense (CSD2008-00068) and “Catedra UAM-Telefonica”
When Optimization Is Just an Illusion

Bio-inspired optimization algorithms have been successfully applied to solve many problems in engineering, science, and economics. In computer science bio-inspired optimization has different applications in different domains such as software engineering, networks, data mining, and many others. However, some applications may not be appropriate or even correct. In this paper we study this phenomenon through a particular method which applies the genetic algorithms on a time series classification task to set the weights of the similarity measures used in a combination that is used to classify the time series. The weights are supposed to be obtained by applying an optimization process that gives optimal classification accuracy. We show in this work, through examples, discussions, remarks, explanations, and experiments, that the aforementioned method of optimization is not correct and that completely randomly-chosen weights for the similarity measures can give the same classification accuracy
A Pre-initialization Stage of Population-Based Bio-inspired Metaheuristics for Handling Expensive Optimization Problems

Metaheuristics are probabilistic optimization algorithms which are applicable to a wide range of optimization problems. Bio-inspired, also called nature-inspired, optimization algorithms are the most widely-known metaheuristics. The general scheme of bio-inspired algorithms consists in an initial stage of randomly generated solutions which evolve through search operations, for several generations, towards an optimal value of the fitness function of the optimization problem at hand. Such a scenario requires repeated evaluation of the fitness function. While in some applications each evaluation will not take more than a fraction of a second, in others, mainly those encountered in data mining, each evaluation may take up several minutes, hours, or even more. This category of optimization problems is called expensive optimization. Such cases require a certain modification of the above scheme. In this paper we present a new method for handling expensive optimization problems. This method can be applied with different population-based bio-inspired optimization algorithms. Although the proposed method is independent of the application to which it is applied, we experiment it on a data mining task
Discrete rigid transformation graph search for 2D image registration

International audienceRigid image registration is an essential image processing task, with a large body of applications. This problem is usually formulated in the continuous domain, most often in an optimization framework. This strategy is suitable whenever the estimated transformation is associated with interpolation. In the case of purely discrete applications, e.g., for template-based segmentation or classification, it is however preferable to avoid digitizing the result again after transformation. In this article, we deal with this point of view in the 2D case. Based on a fully discrete framework, we explicitly explore the parameter space of rigid transformations. This exploration leads to a local search scheme that can be involved in combinatorial optimization strategies
Modeling parsimonious putative regulatory networks: complexity and heuristic approach

International audienceA relevant problem in systems biology is the description of the regulatory interactions between genes. It is observed that pairs of genes have significant correlation through several experimental conditions. The question is to find causal relationships that can explain this experimental evidence. A putative regulatory network can be represented by an oriented weighted graph, where vertices represent genes, arcs represent predicted regulatory interactions and the arc weights represent the $p$-value of the prediction. Given such graph, and experimental evidence of correlation between pairs of vertices, we propose an abstraction and a method to enumerate all parsimonious subgraphs that assign causality relationships compatible with the experimental evidence. When the problem is modeled as the minimization of a global weight function, we show that the enumeration of scenarios is a hard problem. As an heuristic, we model the problem as a set of independent minimization problems, each solvable in polynomial time, which can be combined to explore a relevant subset of the solution space. We present a logic-programming formalization of the model implemented using Answer Set Programming. We show that, when the graph follows patterns that can be found in real organisms, our heuristic finds solutions that are good approximations to the full model. We encoded these approach using Answer Set Programming, applied this to a specific case in the organism \emph{E.\,coli} and compared the execution time of each approach
Modular Synthesis of Sketches Using Models

One problem with the constraint-based approaches to synthesis that have become popular over the last few years is that they only scale to relatively small routines, on the order of a few dozen lines of code. This paper presents a mechanism for modular reasoning that allows us to break larger synthesis problems into small manageable pieces. The approach builds on previous work in the verification community of using high-level specifications and partially interpreted functions (we call them models) in place of more complex pieces of code in order to make the analysis modular.
The main contribution of this paper is to show how to combine these techniques with the counterexample guided synthesis approaches used to efficiently solve synthesis problems. Specifically, we show two new algorithms; one to efficiently synthesize functions that use models, and another one to synthesize functions while ensuring that the behavior of the resulting function will be in the set of behaviors allowed by the model. We have implemented our approach on top of the open-source Sketch synthesis system, and we demonstrate its effectiveness on several Sketch benchmark problems.National Science Foundation (U.S.) (Grant NSF-1116362)National Science Foundation (U.S.) (Grant NSF-1139056)United States. Dept. of Energy (Grant DE-SC0005372
Research in global software engineering: a systematic snapshot

This paper reports our extended analysis of the recent literature addressing global software engineering (GSE), using a new Systematic Snapshot Mapping (SSM) technique. The primary purpose of this work is to understand what issues are being addressed and how research is being carried out in GSE – and comparatively, what work is not being conducted. We carried out the analysis in two stages. In the first stage we analyzed 275 papers published between January 2011 and June 2012, and in the second stage we augmented our analysis by considering a further 26 papers (from the 2013 International Conference on Global Software Engineering (ICGSE’13). Our results reveal that, currently, GSE studies are focused on management- and infrastructure-related factors, using principally evaluative research approaches. Most of the studies are conducted at the organizational level, mainly using methods such as interviews, surveys, field studies and case studies. The USA, India and China are major players in GSE, with USA-India collaborations being the most frequently studied, followed by USA-China. While a considerable number of GSE-related studies have been published since January 2011 they are currently quite narrowly focused, on exploratory research and explanatory theories, and the critical research paradigm has been untouched. An absence of formulative research, experimentation and simulation, and a related focus on evaluative approaches, all suggest that existing tools, methods and approaches from related fields are being tested in the GSE context, even though these may not be inherently applicable to the additional scale and complexity of GSE
A Hybrid Threat Detection and Security Adaptation System for Industrial Wireless Sensor Networks

International audienceWireless Sensor Networks (WSNs) led the way to new forms of communications, which extend today the Internet paradigm to unforeseen boundaries. The legacy industry, however, is slower to adopt this technology, mainly for security reasons. Self-managed security systems allowing a quicker detection of and better resilience to attacks, may counterbalance this reluctance. We propose in this paper a hybrid threat detection and security adaptation system, designed to run on top of industrial WSNs. We explain why this system is suitable for architectures mainly composed of constrained or sleeping devices, while being able to achieve a fair level of autonomous security