pinecone/core-2020-05-10-deduplication huggingface dataset:
GAMPP: Genetic algorithm for UAV mission planning problems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-25017-5_16Due to the rapid development of the UAVs capabilities, these are being incorporated into many fields to perform increasingly complex tasks. Some of these tasks are becoming very important because they involve a high risk to the vehicle driver, such as detecting forest fires or rescue tasks, while using UAVs avoids risking human lives. Recent researches on artificial intelligence techniques applied to these systems provide a new degree of high-level autonomy of them. Mission planning for teams of UAVs can be defined as the planning process of locations to visit (way-points) and the vehicle actions to do (loading/dropping a load, taking videos/pictures, acquiring information), typically over a time period. Currently, UAVs are controlled remotely by human operators from ground control stations, or use rudimentary systems. This paper presents a new Genetic Algorithm for solving Mission Planning Problems (GAMPP) using a cooperative team of UAVs. The fitness function has been designed combining several measures to look for optimal solutions minimizing the fuel consumption and the mission time (or makespan). The algorithm has been experimentally tested through several missions where its complexity is incrementally modified to measure the scalability of the problem. Experimental results show that the new algorithm is able to obtain good solutions improving the runtime of a previous approach based on CSPs.This work is supported by Comunidad Autónoma de Madrid under project CIBERDINE S2013/ICE-3095, Spanish Ministry of Science and Education under Project Code TIN2014-56494-C4-4-P and Savier Project (Airbus Defence & Space, FUAM-076915). The authors would like to acknowledge the support obtained from Airbus Defence & Space, specially from Savier Open Innovation project members: José Insenser, César Castro and Gemma Blasco
A reasoning module for distributed clinical decision support systems

One of the main challenges in distributed clinical decision support systems is to ensure that the flow of information is kept. The failure of one or more components should not bring down an entire system. Moreover, it should not impair any decision processes that are taking place in a functioning component. This work describes a decision module that is capable of managing states of incomplete information which result from the failure of communication between components or delays in making the information available. The framework is also capable of generating scenarios for situations in which there are information gaps. The proposal is described through an example about colon cancer staging.(undefined)info:eu-repo/semantics/publishedVersio
An automated framework for the management of P2P traffic in ISP infrastructures

Peer-to-Peer (P2P) is nowadays a widely used paradigm underpinning the deployment of several Internet services and applications. However, the management of P2P traffic aggregates is not an easy task for Internet Service Providers (ISPs). In this perspective, and considering an expectable proliferation in the use of such ap- plications, future networks require the development of smart mechanisms fostering an easier coexistence between P2P applications and ISP infrastructures. This paper aims to contribute for such research efforts presenting a framework incorporating useful mechanisms to be activated by network administrators, being also able to operate as an automated management tool dealing with P2P traffic aggregates.FCT - Fundação para a Ciência e Tecnologia Portugal in the scope of the project: UID/CEC/00319/201
A geographic opportunistic forwarding strategy for vehicular named data networking

Studies in Computational Intelligence, 616Recent advanced intelligent devices enable vehicles to retrieve information while they are traveling along a road. The store-carry-and-forward paradigm has a better performance than traditional communication due to the tolerance to intermittent connectivity in vehicular networks. Named Data Networking is an alternative to IP-based networks for data retrieval. On account of most vehicular applications taking interest in geographic location related information, this paper propose a Geographical Opportunistic Forwarding Protocol (GOFP) to support geo-tagged name based information retrieval in Vehicle Named Data Networking (V-NDN). The proposed protocol adopts the opportunistic forwarding strategy, and the position of interest and trajectories of vehicles are used in forwarding decision. Then the ONE simulator is extended to support GOFP and simulation results show that GOFP has a better performance when compared to other similar protocols in V-NDN.Fundamental Research Funds of Jilin University, No. 450060491509FCT - Fundação para a Ciência e Tecnologia Portugal in the scope of the project: UID/CEC/00319/201
Characterization and Estimation of the Variations of a Random Convex Set by Its Mean n-Variogram: Application to the Boolean Model

International audienceIn this paper we propose a method to characterize and estimate the variations of a random convex set Ξ<sub>0</sub> in terms of shape, size and direction. The mean <i>n</i>-variogram <i>γ</i> <sup>(<i>n</i>)</sup><sub>Ξ 0</sub> : (<i>u</i><sub>1</sub> · · · <i>u</i><sub>n</sub>) ↦ E[<i>ν</i><sub><i>d</i></sub> (Ξ<sub>0</sub> ∩(Ξ<sub>0</sub> −<i>u</i><sub>1</sub>) · · ·∩ (Ξ<sub>0</sub> − <i>u</i><sub>n</sub>))] of a random convex set Ξ<sub>0</sub> on R<sup><i>d</i></sup> reveals information on the <i>n</i><sup><i>th<i></sup> order structure of Ξ<sub>0</sub>. Especially we will show that considering the mean <i>n</i>-variograms of the dilated random sets Ξ<sub>0</sub> &#8853 <i>rK</i> by an homothetic convex family <i>rK</i><sub><i>r</i>>0</sub>, it's possible to estimate some characteristic of the <i>n</i><sup><i>th</i></sup> order structure of Ξ<sub>0</sub>. If we make a judicious choice of <i>K</i>, it provides relevant measures of Ξ<sub>0</sub>. Fortunately the germ-grain model is stable by convex dilatations, furthermore the mean <i>n</i>-variogram of the primary grain is estimable in several type of stationary germ-grain models by the so called <i>n</i>-points probability function. Here we will only focus on the Boolean model, in the planar case we will show how to estimate the <i>n</i><sup><i>th</i></sup> order structure of the random vector composed by the mixed volumes <sup>t</sup> (<i>A</i>(Ξ<sub>0</sub>), <i>W</i>(Ξ<sub>0</sub>, <i>K</i>)) of the primary grain, and we will describe a procedure to do it from a realization of the Boolean model in a bounded window. We will prove that this knowledge for all convex body <i>K</i> is sufficient to fully characterize the so called difference body of the grain Ξ<sub>0</sub> &#8853 ˘Ξ<sub>0</sub>. we will be discussing the choice of the element <i>K</i>, by choosing a ball, the mixed volumes coincide with the Minkowski's functional of Ξ<sub>0</sub> therefore we obtain the moments of the random vector composed of the area and perimeter  <sup>t</sup>(<i>A</i>(Ξ<sub>0</sub>), <i>U</i> (Ξ)). By choosing a segment oriented by <i>&#952</i> we obtain estimates for the moments of the random vector composed by the area and the Ferret's diameter in the direction <i>&#952</i>, <sup>t</sup>((<i>A</i>(Ξ<sub>0</sub>), <i>H</i><sub>Ξ 0</sub>(<i>&#952</i>)). Finally, we will evaluate the performance of the method on a Boolean model with rectangular grain for the estimation of the second order moments of the random vectors <sup><i>t</i></sup>(<i>A</i>(Ξ<sub>0</sub>), <i>U</i> (Ξ<sub>0</sub>)) and <sup><i>t</i></sup>((<i>A</i>(Ξ<sub>0</sub>), <i>H</i><sub>Ξ 0</sub> (<i>&#952</i>))
Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation

International audienceStochastic watershed is an image segmentation technique based on mathematical morphology which produces a probability density function of image contours. Estimated probabilities depend mainly on local distances between pixels. This paper introduces a variant of stochastic watershed where the probabilities of contours are computed from a Gaussian model of image regions. In this framework, the basic ingredient is the distance between pairs of regions, hence a distance between normal distributions. Hence several alternatives of statistical distances for normal distributions are compared, namely Bhattacharyya distance, Hellinger metric distance and Wasserstein metric distance
Quantization of hyperspectral image manifold using probabilistic distances

International audienceA technique of spatial-spectral quantization of hyperspectral images is introduced. Thus a quantized hyperspectral image is just summarized by K spectra which represent the spatial and spectral structures of the image. The proposed technique is based on α−connected components on a region adjacency graph. The main ingredient is a dissimilarity metric. In order to choose the metric that best fit the hyperspectral data manifold, a comparison of different probabilistic dissimilarity measures is achieved
Similarity joins and beyond: an extended set of binary operators with order

Similarity joins are troublesome database operators that often produce results much larger than the user really needs or expects. In order to return the similar elements, similarity joins also require sorting during the retrieval process, although order is a concept not supported in the relational model. This paper proposes a solution to solve those two issues extending the similarity join concept to a broader set of binary operators, which aims at retrieving the most similar pairs and embedding the sorting operation only as an internal processing step, so as to comply with the relational theory. Additionally, our extension allows to explore another useful condition not previously considered in the similarity retrieval: the negation of predicates. Experiments performed on real and synthetic data show that our operators are fast enough to be used in real applications and scale well both for multidimensional and non-dimensional metric data.FAPESPCNPQCAPESRescuer (EU Commission Grant 614154 and CNPQ/MCTI Grant 490084/2013-3)International Conference on Similarity Search and Applications - SISAP (8. 2015 Glasgow
Diversity in similarity joins

With the increasing ability of current applications to produce and consume more complex data, such as images and geographic information, the similarity join has attracted considerable attention. However, this operator does not consider the relationship among the elements in the answer, generating results with many pairs similar among themselves, which does not add value to the final answer. Result diversification methods are intended to retrieve elements similar enough to satisfy the similarity conditions, but also considering the diversity among the elements in the answer, producing a more heterogeneous result with smaller cardinality, which improves the meaning of the answer. Still, diversity have been studied only when applied to unary operations. In this paper, we introduce the concept of diverse similarity joins: a similarity join operator that ensures a smaller, more diversified and useful answers. The experiments performed on real and synthetic datasets show that our proposal allows exploiting diversity in similarity joins without diminish their performance whereas providing elements that cover the same data space distribution of the non-diverse answers.FAPESPCNPQCAPESRescuer (EU Commission Grant 614154 and CNPQ/MCTI Grant 490084/2013-3)International Conference on Similarity Search and Applications - SISAP (8. 2015 Glasgow
Improving metric access methods with bucket files

Modern applications deal with complex data, where retrieval by similarity plays an important role in most of them. Complex data whose primary comparison mechanisms are similarity predicates are usually immersed in metric spaces. Metric Access Methods (MAMs) exploit the metric space properties to divide the metric space into regions and conquer efficiency on the processing of similarity queries, like range and k-nearest neighbor queries. \ud
Existing MAM use homogeneous data structures to improve query execution, pursuing the same techniques employed by traditional methods developed to retrieve scalar and multidimensional data. In this paper, we combine hashing and hierarchical ball partitioning approaches to achieve a hybrid index that is tuned to improve similarity queries targeting complex data sets, with search algorithms that reduce total execution time by aggressively reducing the number of distance calculations. We applied our technique in the Slim-tree and performed experiments over real data sets showing that the proposed technique is able to reduce the execution time of both range and k-nearest queries to at least half of the Slim-tree. Moreover, this technique is general to be applied over many existing MAM.CAPESCNPqFAPESPInternational Conference on Similarity Search and Applications - SISAP (8. 2015 Glasgow
Aspects of particle filtering in high-dimensional spaces

Nonlinear data assimilation is high on the agenda in all fields of the geosciences as with ever increasing model resolution and inclusion of more physical (biological etc.) processes, and more complex observation operators the data-assimilation problem becomes more and more nonlinear. The suitability of particle filters to solve the nonlinear data assimilation problem in high-dimensional geophysical problems will be discussed. Several existing and new schemes will be presented and it is shown that at least one of them, the Equivalent-Weights Particle Filter, does indeed beat the curse of dimensionality and provides a way forward to solve the problem of nonlinear data assimilation in high-dimensional systems
Quotienting the delay monad by weak bisimilarity

The delay datatype was introduced by Capretta as a means to deal with partial functions (as in computability theory) in Martin-Löf type theory. It is a monad and it constitutes a constructive alternative to the maybe monad. It is often desirable to consider two delayed computations equal, if they terminate with equal values, whenever one of them terminates. The equivalence relation underlying this identification is called weak bisimilarity. In type theory, one commonly replaces quotients with setoids. In this approach, the delay monad quotiented by weak bisimilarity is still a monad. In this paper, we consider Hofmann's alternative approach of extending type theory with inductive-like quotient types. In this setting, it is difficult to define the intended monad multiplication for the quotiented datatype. We give a solution where we postulate some principles, crucially proposition extensionality and the (semi-classical) axiom of countable choice. We have fully formalized our results in the Agda dependently typed programming language
A Functorial Bridge between the Infinitary Affine Lambda-Calculus and Linear Logic

International audienceIt is a well known intuition that the exponential modality of linear logic may be seen as a form of limit. Recently,Mellì es, Tabareau and Tasson gave a categorical account for this intuition, whereas the first author provided a topological account, based on an infinitary syntax. We relate these two different views by giving a categorical version of the topological construction, yielding two benefits: on the one hand, we obtain canonical models of the infinitary affine lambda-calculus introduced by the first author; on the other hand, we find an alternative formula for computing free commutative comonoids in models of linear logic with respect to the one presented byMellì es et al
First Steps Towards Cumulative Inductive Types in CIC

Having the type of all types in a type system results in paradoxes like Russel’s paradox. Therefore type theories like predicative calculus of inductive constructions (pCIC) - the logic of the Coq proof assistant - have a hierarchy of types Type0, Type1, Type2, . . . , where Type0 : Type1, Type1 : Type2, . . . . In a cumulative type system, e.g., pCIC, for a term t such that t: Type i we also have that t: Typei + 1. The system pCIC has recently been extended to support universe polymorphism, i.e., definitions can be parametrized by universe levels. This extension does not support cumulativity for inductive types. For example, we do not have that a pair of types at levels i and j is also considered a pair of types at levels i + 1 and j + 1.
In this paper, we discuss our on-going research on making inductive types cumulative in the pCIC. Having inductive types be cumulative alleviates some problems that occur while working with large inductive types, e.g., the category of small categories, in pCIC.
We present the pCuIC system which adds cumulativity for inductive types to pCIC and briefly discuss some of its properties and possible extensions. We, in addition, give a justification for the introduced cumulativity relation for inductive types.status: publishe
Matching and merging scenarios automatically with Alloy

The design of large systems often involves the creation of models that describe partial specifications. Model composition is the process of combining partial models to create a single coherent model. This paper presents an automatic composition technique for creating a sequence diagram from partial specifications captured in multiple sequence diagrams with the help of Alloy. Our contribution is twofold: a novel true-concurrent semantics for sequence diagram composition, and a model-driven transformation of sequence diagrams to Alloy that preserves the semantics of composition defined. We have created a tool SD2Alloy that implements the technique as follows: two given sequence diagrams are transformed into two Alloy models, and merged according to a set of syntactic logical constraints describing how their elements should be matched. These constraints are in accordance to our compositional semantics. The technique can also be used to detect problems and inconsistencies in the composition of diagrams.Postprin
Rational Partial Choice Functions and Their Application to Belief Revision.

Necessary and sufficient conditions for choice functions to be rational have been intensively studied in the past. However, in these attempts, a choice function is completely specified. That is, given any subset of options, called an issue, the best option over that issue is always known, whilst in real-world scenarios, it is very often that only a few choices are known instead of all. In this paper, we study partial choice functions and investigate necessary and sufficient rationality conditions for situations where only a few choices are known. We prove that our necessary and sufficient condition for partial choice functions boils down to the necessary and sufficient conditions for complete choice functions proposed in the literature. Choice functions have been instrumental in belief revision theory. That is, in most approaches to belief revision, the problem studied can simply be described as the choice of possible worlds compatible with the input information, given an agent’s prior belief state. The main effort has been to devise strategies in order to infer the agents revised belief state. Our study considers the converse problem: given a collection of input information items and their corresponding revision results (as provided by an agent), does there exist a rational revision operation used by the agent and a consistent belief state that may explain the observed results
A Distance-Based Paraconsistent Semantics for DL-Lite

DL-Lite is an important family of description logics. Recently, there is an increasing interest in handling inconsistency in DL-Lite as the constraint imposed by a TBox can be easily violated by assertions in ABox in DL-Lite. In this paper, we present a distance-based paraconsistent semantics based on the notion of feature in DL-Lite, which provides a novel way to rationally draw meaningful conclusions even from an inconsistent knowledge base. Finally, we investigate several important logical properties of this entailment relation based on the new semantics and show its promising advantages in non-monotonic reasoning for DL-Lite.Full Tex
Airborne Ultrasonic Tactile Display BCI

This chapter presents results of our project, which studied whether
contactless and airborne ultrasonic tactile display (AUTD) stimuli delivered to
a user's palms could serve as a platform for a brain computer interface (BCI)
paradigm. We used six palm positions to evoke combined somatosensory brain
responses to implement a novel contactless tactile BCI. This achievement was
awarded the top prize in the Annual BCI Research Award 2014 competition. This
chapter also presents a comparison with a classical attached vibrotactile
transducer-based BCI paradigm. Experiment results from subjects performing
online experiments validate the novel BCI paradigm.Comment: arXiv admin note: substantial text overlap with arXiv:1404.418
The Cube of Opposition and the Complete Appraisal of Situations by Means of Sugeno Integrals

The cube of opposition is a logical structure that underlies many information representation settings. When applied to multiple criteria decision, it displays various possible aggregation attitudes. Situations are usually assessed by combinations of properties they satisfy, but also by combinations of properties they do not satisfy. The cube of opposition applies to qualitative evaluation when criteria are weighted as well as in the general case where any subset of criteria may be weighted for expressing synergies between them, as for Sugeno integrals. Sugeno integrals are well-known as a powerful qualitative aggregation tool which takes into account positive synergies between properties. When there are negative synergies between properties we can use the so-called desintegral associated to the Sugeno integral. The paper investigates the use of the cube of opposition and of the if-then rules extracted from these integrals and desintegrals in order to better describe acceptable situations
Safe Suggestions Based on Type Convertibility to Guide Workflow Composition

International audienceThis paper proposes an interactive approach that guides users in the step-by-step composition of services by providing safe suggestions based on type convertibility. Users specify the points of the workflow (called the focus) they want to complete, and our approach suggests services and connections whose data types are compatible with the focus. We prove the safeness (every step produces a well-formed workflow) and the completeness (every well-formed workflow can be built) of our approach
Experimenting Analogical Reasoning in Recommendation

Recommender systems aim at providing suggestions of interest for end-users. Two main types of approach underlie existing recommender systems: content-based methods and collaborative filtering. In this paper, encouraged by good results obtained in classification by analogical proportion-based techniques, we investigate the possibility of using analogy as the main underlying principle for implementing a prediction algorithm of the collaborative filtering type. The quality of a recommender system can be estimated along diverse dimensions. The accuracy to predict user’s rating for unseen items is clearly an important matter. Still other dimensions like coverage and surprise are also of great interest. In this paper, we describe our implementation and we compare the proposed approach with well-known recommender systems