pinecone/core-2020-05-10-deduplication huggingface dataset:
Automatically generating streamlined constraint models with ESSENCE and CONJURE

Streamlined constraint reasoning is the addition of uninferred constraints to a constraint model to reduce the search space, while retaining at least one solution. Previously, effective streamlined models have been constructed by hand, requiring an expert to examine closely solutions to small instances of a problem class and identify regularities. We present a system that automatically generates many conjectured regularities for a given Essence specification of a problem class by examining the domains of decision variables present in the problem specification. These conjectures are evaluated independently and in conjunction with one another on a set of instances from the specified class via an automated modelling tool-chain comprising of Conjure, Savile Row and Minion. Once the system has identified effective conjectures they are used to generate streamlined models that allow instances of much larger scale to be solved. Our results demonstrate good models can be identified for problems in combinatorial design, Ramsey theory, graph theory and group theory - often resulting in order of magnitude speed-ups.Postprin
Solving Segment Routing Problems with Hybrid Constraint Programming Techniques

Segment routing is an emerging network technology that exploits the existence of several paths between a source and a destination to spread the traffic in a simple and elegant way. The major commercial network vendors already support segment routing, and several Internet actors are ready to use segment routing in their network. Unfortunately, by changing the way paths are computed, segment routing poses new op- timization problems which cannot be addressed with previous research contributions. In this paper, we propose a new hybrid constraint pro- gramming framework to solve traffic engineering problems in segment routing. We introduce a new representation of path variables which can be seen as a lightweight relaxation of usual representations. We show how to define and implement fast propagators on these new variables while reducing the memory impact of classical traffic engineering models. The efficiency of our approach is confirmed by experiments on real and artificial networks of big Internet actors
Broken Triangles Revisited

A broken triangle is a pattern of (in)compatibilities between assignments  in a binary CSP (constraint satisfaction problem). In the absence  of certain broken triangles, satisfiability-preserving domain reductions  are possible via merging of domain values. We investigate the possibility  of maximising the number of domain reduction operations by the  choice of the order in which they are applied, as well as their interaction  with arc consistency operations. It turns out that it is NP-hard to choose  the best order
A Microstructure-based Family of Tractable Classes for CSPs

The study of tractable classes is an important issue in Artificial Intelligence, especially in Constraint Satisfaction Problems. In this context, the Broken  Triangle Property (BTP) is a state-of-the-art microstructure-based tractable class  which generalizes well-known and previously-defined tractable classes, notably the set of instances whose constraint graph is a tree. In this paper, we propose  to extend and to generalize this class using a more general approach based on a parameter k which is a given constant. To this end, we introduce the k-BTP property (and the class of instances satisfying this property) such that we have  2-BTP = BTP, and for k > 2, k-BTP is a relaxation of BTP in the sense that k-BTP is a subset of (k + 1)-BTP. Moreover, we show that if k-TW is the class of instances  having tree-width bounded by a constant k, then k-TW is a subset of (k + 1)-BTP. Concerning tractability, we show that instances satisfying k-BTP and which are  strong k-consistent are tractable, that is, can be recognized and solved in polynomial time. We also study the relationship between k-BTP and the approach of  Naanaa who proposed a set-theoretical tool, known as the directional rank, to extend tractable classes in a parameterized way. Finally we propose an experimental  study of 3-BTP which shows the practical interest of this class, particularly w.r.t. the practical solving of instances satisfying 3-BTP and for other instances, w.r.t.  to backdoors based on this tractable class
Nonlinear Background Filter to Improve Pedestrian Detection

In this paper, we propose a simple nonlinear filter which improves the detection of pedestrians walking in a video. We do so by first cumulating temporal gradient of moving objects into a motion history image (MHI). Then we apply to each frame of the video a motion-guided nonlinear filter whose goal is to smudge out background details while leaving untouched foreground moving objects. The resulting blurry-background image is then fed to a pedestrian detector. Experiments reveal that for a given miss rate, our motion-guided nonlinear filter can decrease the number of false positives per image (FPPI) by a factor of up to 26. Our method is simple, computationally light, and can be applied on a variety of videos to improve the performances of almost any kind of pedestrian detectors.Peer reviewe
Egocentric video personalization in cultural experiences scenarios

In this paper we propose a novel approach for egocentric video personalization in a cultural experience scenario, based on shots automatic labelling according to different semantic dimensions, such as web leveraged knowledge of the surrounded cultural Points Of Interest, information about stops and moves, both relying on geolocalization, and camera’s wearer behaviour. Moreover we present a video personalization web system based on shots multi-dimensional semantic classification, that is designed to aid the visitor to browse and to retrieve relevant information to obtain a customized video. Experimental results show that the proposed techniques for video analysis achieve good performances in unconstrained scenario and user evaluation tests confirm that our solution is useful and effective
Hierarchical image segmentation relying on a likelihood ratio test

International audienceHierarchical image segmentation provides a set of image seg-mentations at different detail levels in which coarser details levels can be produced by simple merges of regions from segmentations at finer detail levels. However, many image segmentation algorithms relying on similarity measures lead to no hierarchy. One of interesting similarity measures is a likelihood ratio, in which each region is modelled by a Gaussian distribution to approximate the cue distributions. In this work, we propose a hierarchical graph-based image segmentation inspired by this likelihood ratio test. Furthermore, we study how the inclusion of hierarchical property have influenced the computation of quality measures in the original method. Quantitative and qualitative assessments of the method on three well known image databases show efficiency
A tool to support the creation of datasets of tampered videos

Digital Video Forensics is getting a growing interest from the Multimedia research community, as the need for methods to validate the authenticity of a video content is increasing with the number of videos freely available to the digital users. Unlike Digital Image Forensics, to our knowledge, there are not standard datasets to test video forgery detection techniques. In this paper we present a new tool to support the users in creating datasets of tampered videos. We furthermore present our own dataset and we discuss some remarks about how to create forgeries difficult to be detected by an observer, to the naked eye
En plein air visual agents

Nowadays, machine learning is playing a dominant role in most challenging computer vision problems. This paper advocates an extreme evolution of this interplay, where visual agents continuously process videos and interact with humans, just like children, exploiting life–long learning computational schemes. This opens the challenge of en plein air visual agents, whose behavior is progressively monitored and evaluated by novel mechanisms, where dynamic man-machine interaction plays a fundamental role. Going beyond classic benchmarks, we argue that appropriate crowd-sourcing schemes are suitable for performance evaluation of visual agents operating in this framework. We provide a proof of concept of this novel view, by showing methods and concrete solutions for en plein air visual agents. Crowdsourcing evaluation is reported, along with a life–long experiment on “The Aristocats” cartoon. We expect that the proposed radically new framework will stimulate related approaches and solutions
Generalization-Based k-Anonymization

Microaggregation is an anonymization technique consisting on partitioning the data into clusters no smaller than k elements and then replacing the whole cluster by its prototypical representant. Most of microaggregation techniques work on numerical attributes. However, many data sets are described by heterogeneous types of data, i.e., nu- merical and categorical attributes. In this paper we propose a new mi- croaggregation method for achieving a compliant k-anonymous masked file for categorical microdata based on generalization. The goal is to build a generalized description satisfied by at least k domain objects and to replace these domain objects by the description. The way to construct that generalization is similar that the one used in growing decision trees. Records that cannot be generalized satisfactorily are discarded, therefore some information is lost. In the experiments we performed we prove that the new approach gives good results. © Springer International Publishing Switzerland 2015.This  research  is  partially  funded  by  the  Spanish  MICINN  projects  COGNITIO (TIN-2012-38450-C03-03), EdeTRI (TIN2012-39348-C02-01) and COPRIVACY (TIN2011-27076-C03-03), the grant 2009-SGR-1434 from the Generalitat de Catalunya, and the European Project DwB (Grant Agreement Number 262608)Peer reviewe
The Complexity of 3-Valued Lukasiewicz Rules

It is known that determining the satisfiability of n-valued Łukasiewicz rules is NP-complete for n ≥ 4, as well as that it can be solved in time linear in the length of the formula in the Boolean case (when n = 2). However, the complexity for n = 3 is an open problem. In this paper we formally prove that the satisfiability problem for 3-valued Łukasiewicz rules is NP-complete. Moreover, we also prove that when the consequent of the rule has at most one element, the problem is polynomially solvable. © Springer International Publishing Switzerland 2015.Research partially supported by the Generalitat de Catalunya grant AGAUR 2014-SGR-118, and the Ministerio de Economía y Competividad projects AT CONSOLIDER CSD2007-0022, INGENIO 2010, CO-PRIVACY TIN2011-27076-C03-03, EDETRI TIN2012-39348-C02-01 and HeLo TIN2012-33042. The second author was supported by Mobility Grant PRX14/00195\ud
of the Ministerio de Educación, Cultura y DeportePeer reviewe
Estimating unknown values in reciprocal intuitionistic preference relations via asymmetric fuzzy preference relations

Intuitionistic preference relations are becoming increasingly important in the field of group decision making since they present a flexible and simple way to the experts to provide their preference relations, while at the same time allowing them to accommodate a certain degree of hesitation inherent to all decision making processes. In this contribution, we prove the mathematical equivalence between the set of asymmetric fuzzy preference relations and  the set of reciprocal intuitionistic fuzzy preference relations. This result is exploited to tackle the presence of incomplete reciprocal intuitionistic  fuzzy preference relation in decision making by developing a consistency driven estimation procedure  via the corresponding equivalent incomplete asymmetric fuzzy preference relation
ASP, amalgamation, and the conceptual blending workflow

We present a framework for conceptual blending – a concept invention method that is advocated in cognitive science as a fundamental, and uniquely human engine for creative thinking. Herein, we employ the search capabilities of ASP to find commonalities among input concepts as part of the blending process, and we show how our approach fits within a generalised conceptual blending workflow. Specifically, we orchestrate ASP with imperative Python programming, to query external tools for theorem proving and colimit computation. We exemplify our approach with an example of creativity in mathematics. © Springer International Publishing Switzerland 2015.This work is supported by the 7th Framework Programme for Research of the European Commission funded COINVENT project (FET-Open grant number: 611553). M. Eppe is supported by the German Academic Exchange ServicePeer Reviewe
A formal theory of justifications

We develop an abstract theory of justifications suitable for describing the semantics of a range of logics in knowledge representation, computational and mathematical logic. A theory or program in one of these logics induces  a semantical structure called a justification frame. Such a justification frame defines a class of justifications each of which embodies a potential {\em reason} why its facts are true. By defining various evaluation functions for these justifications, a range of  different semantics are obtained. By allowing nesting of justification frames, various language constructs can be integrated in a seamless way.  The theory provides elegant and compact formalisations of existing and new semantics in logics of various areas,  showing unexpected  commonalities and interrelations, and creating opportunities for new expressive knowledge representation formalisms.status: publishe
Implementing Preferences with asprin

asprin offers a framework for expressing and evaluating combinations of quantitative and qualitative preferences among the stable models of a logic program. In this paper, we demonstrate the generality and flexibility of the methodology by showing how easily existing preference relations can be implemented in asprin. Moreover, we show how the computation of optimal stable models can be improved by using declarative heuristics. We empirically evaluate our contributions and contrast them with dedicated implementations. Finally, we detail key aspects of asprin’s implementation.Full Tex
Compacting Boolean formulae for inference in probabilistic logic programming

Knowledge compilation converts Boolean formulae for which some inference tasks are computationally expensive into a representation where the same tasks are tractable. ProbLog is a state-of-the-art Probabilistic Logic Programming system that uses knowledge compilation to reduce the expensive probabilistic inference to an efficient weighted model counting. Motivated to improve ProbLog's performance we present an approach that optimizes Boolean formulae in order to speed-up knowledge compilation. We identify 7 Boolean subformulae patterns that can be used to re-write Boolean formulae. We implemented an algorithm with polynomial complexity which detects and compacts 6 of these patterns. We employ our method in the inference pipeline of ProbLog and conduct extensive experiments. We show that our compaction method improves knowledge compilation and consecutively the overall inference performance. Furthermore, using compaction reduces the number of time-outs, allowing us to solve previously unsolvable problems.status: publishe
Automated inference of rules with exception from past legal cases using ASP

© Springer International Publishing Switzerland 2015.In legal reasoning, different assumptions are often considered when reaching a final verdict and judgement outcomes strictly depend on these assumptions. In this paper, we propose an approach for generating a declarative model of judgements from past legal cases, that expresses a legal reasoning structure in terms of principle rules and exceptions. Using a logic-based reasoning technique, we are able to identify from given past cases different underlying defaults (legal assumptions) and compute judgements that cover all possible cases (including past cases) within a given set of relevant factors. The extracted declarative model of judgements can then be used to make deterministic automated inference on future judgements, as well as generate explanations of legal decisions
Moment-Generating Algorithm for Response Time in Processor Sharing Queueing Systems

Response times are arguably the most representative and important metric for measuring the performance of modern computer systems. Further, service level agreements (SLAs), ranging from data centres to smartphone users, demand quick and, equally important, predictable response times. Hence, it is necessary to calculate moments, at least, and ideally response time distributions, which is not straightforward. A new moment-generating algorithm for calculating response times analytically is obtained, based on M/M/1 processor sharing (PS) queueing models. This algorithm is compared against existing work on response times in M/M/1-PS queues and extended to M/M/1 discriminatory PS queues. Two real-world case studies are evaluated
On generalized processor sharing and objective functions: analytical framework

Today, telecommunication networks host a wide range of heterogeneous services. Some demand strict delay minima, while others only need a best-effort kind of service. To achieve service differentiation, network traffic is partitioned in several classes which is then transmitted according to a flexible and fair scheduling mechanism. Telecommunication networks can, for instance, use an implementation of Generalized Processor Sharing (GPS) in its internal nodes to supply an adequate Quality of Service to each class. GPS is flexible and fair, but also notoriously hard to study analytically. As a result, one has to resort to simulation or approximation techniques to optimize GPS for some given objective function. In this paper, we set up an analytical framework for two-class discrete-time probabilistic GPS which allows to optimize the scheduling for a generic objective function in terms of the mean unfinished work of both classes without the need for exact results or estimations/approximations for these performance characteristics. This framework is based on results of strict priority scheduling, which can be regarded as a special case of GPS, and some specific unfinished-work properties in two-class GPS. We also apply our framework on a popular type of objective functions, i.e., convex combinations of functions of the mean unfinished work. Lastly, we incorporate the framework in an algorithm to yield a faster and less computation-intensive result for the optimum of an objective function
Outline of a formal theory of processes and events, and why GIScience needs one

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23374-1_1It has often been noted that traditional GIScience, with its focus on data-modelling functions such as the input, storage, retrieval, organisation, manipulation, and presentation of data, cannot readily accommodate the process-modelling functions such as explanation, prediction, and simulation which it is increasingly acknowledged should form an essential element of the GI scientist’s toolkit. Although there are doubtless many different reasons for this seeming incompatibility, this paper singles out for consideration the different views of time presupposed by the two kinds of function: on the one hand, the ‘frozen’ historical time required by data modelling, and on the other, the ‘fluid’ experiential time required by process modelling. Whereas the former places an emphasis on events as discrete completed wholes, the latter is concerned with on-going continuous processes as they evolve from moment to moment. In order to reconcile the data-modelling and process-modelling requirements of GIScience, therefore, a formal theory of processes and events is developed, within which their fundamental properties can be made explicit independently of any specific implementation context, and their relationships systematically investigated
More than a list: what outdoor free listings of landscape categories reveal about commonsense geographic concepts and memory search strategies

Categorization is central to abstraction from real world geographic phenomena to computation representations, and as such has been the subject of considerable research. We report on one common approach, free listing, in an outdoor setting and explore terms elicited in response to the question ‘What is there for you in a landscape?’. We collected term lists, and explanations for the strategies used from 89 participants in two mountain and one parkland setting. We analyzed results not only using term frequency, but also by cognitive sali- ency, exploring list structures, and building aggregated networks visualizing links between terms. We observed memory search strategies, such as exploiting and switching semantic clusters in our data, with participants using for example not only the local setting to start clusters, but also memories of familiar land- scapes to switch between clusters. Our results reveal that simple free listing ex- periments can help us understand how categories are linked, and also highlight ways in which landscapes are conceptualized
Extracting causal rules from spatio-temporal data

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23374-1_2This paper is concerned with the problem of detecting causality in spatiotemporal data. In contrast to most previous work on causality, we adopt a logical rather than a probabilistic approach. By defining the logical form of the desired causal rules, the algorithm developed in this paper searches for instances of rules of that form that explain as fully as possible the observations found in a data set. Experiments with synthetic data, where the underlying causal rules are known, show that in many cases the algorithm is able to retrieve close approximations to the rules that generated the data. However, experiments with real data concerning the movement of fish in a large Australian river system reveal significant practical limitations, primarily as a consequence of the coarse granularity of such movement data. In response, instead of focusing on strict causation (where an environmental event initiates a movement event), further experiments focused on perpetuation (where environmental conditions are the drivers of ongoing processes of movement). After retasking to search for a different logical form of rules compatible with perpetuation, our algorithm was able to identify perpetuation rules that explain a significant proportion of the fish movements. For example, approximately one fifth of the detected long-range movements of fish over a period of six years were accounted for by 26 rules taking account of variations in water-level alone.EPSRCAustralian Research Council (ARC) under the Discovery Projects Schem