pinecone/core-2020-05-10-deduplication huggingface dataset:
Imprecise Bernoulli processes

In classical Bernoulli processes, it is assumed that a single Bernoulli experiment can be described by a precise and precisely known probability distribution. However, both of these assumptions can be relaxed. A first approach, often used in sensitivity analysis, is to drop only the second assumption: one assumes the existence of a precise distribution, but has insufficient resources to determine it precisely. The resulting imprecise Bernoulli process is the lower envelope of a set of precise Bernoulli processes. An alternative approach is to drop both assumptions, meaning that we don't assume the existence of a precise probability distribution and regard the experiment as inherently imprecise. In that case, a single imprecise Bernoulli experiment can be described by a set of desirable gambles. We show how this set can be extended to describe an imprecise Bernoulli process, by imposing the behavioral assessments of epistemic independence and exchangeability. The resulting analysis leads to surprisingly simple mathematical expressions characterizing this process, which turn out to be the same as the ones obtained through the straightforward sensitivity analysis approach
Possibilistic KNN regression using tolerance intervals

International audienceBy employing regression methods minimizing predictive risk, we are usually looking for precise values which tends to their true response value. However, in some situations, it may be more reasonable to predict intervals rather than precise values. In this paper, we focus to find such intervals for the K-nearest neighbors (KNN) method with precise values for inputs and output. In KNN, the prediction intervals are usually built by considering the local probability distribution of the neighborhood. In situations where we do not dispose of enough data in the neighborhood to obtain statistically significant distributions, we would rather wish to build intervals which takes into account such distribution uncertainties. For this latter we suggest to use tolerance intervals to build the maximal specific possibility distribution that bounds each population quantiles of the true distribution (with a fixed confidence level) that might have generated our sample set. Next we propose a new interval regression method based on KNN which take advantage of our possibility distribution in order to choose, for each instance, the value of K which will be a good trade-off between precision and uncertainty due to the limited sample size. Finally we apply our method on an aircraft trajectory prediction problem
Maximin and maximal solutions for linear programming problems with possibilistic uncertainty

We consider linear programming problems with uncertain constraint coefficients described by intervals or, more generally, possi-bility distributions. The uncertainty is given a behavioral interpretation using coherent lower previsions from the theory of imprecise probabilities. We give a meaning to the linear programming problems by reformulating them as decision problems under such imprecise-probabilistic uncer-tainty. We provide expressions for and illustrations of the maximin and maximal solutions of these decision problems and present computational approaches for dealing with them
Evaluating the Uncertainty of a Boolean Formula with Belief Functions

In fault-tree analysis, probabilities of failure of components are often assumed to be precise and the events are assumed to be inde- pendent, but this is not always verified in practice. By giving up some of these assumptions, results can still be computed, even though it may require more expensive algorithms, or provide more imprecise results. Once compared to those obtained with the simplified model, the impact of these assumptions can be evaluated. This paper investigates the case when probability intervals of atomic propositions come from independent sources of information. In this case, the problem is solved by means of belief functions. We provide the general framework, discuss computation methods, and compare this setting with other approaches to evaluating the uncertainty of formulas
An Algorithm for payoff space in C1 parametric games

We present a novel algorithm to determine the payoff-space of certain normal-form C1 parametric games, and - more generally - of continuous families of normal-form C1 games. The algorithm has been implemented by using MATLAB, and it has been applied to several examples. The implementation of the algorithm gives the parametric expressions of the critical zone of any game in the family under consideration both in the bistrategy space and in the payoff space and the graphical representations of the disjoint union (with respect to the parameter set of the parametric game) of the family of all payoff spaces. We have so the parametric representation of the union of all the critical zones. One of the main motivations of our paper is that, in the applications, many normal-form games appear naturally in a parametric fashion; moreover, some efficient models of coopetition are parametric games of the considered type. Specifically, we have realized an algorithm that provides the parametric and graphical representation of the payoff space and of the critical zone of a parametric game in normal-form, supported by a finite family of compact intervals of the real line. Our final goal is to provide a valuable tool to study simply (but completely) normal-form C1-parametric games in two dimensions.
Features and design intent in engineering sketches

We investigate the problem of determining design intent from engineering sketches: what did the designer have in mind when sketching a component? Specifically, we consider the unidirectional reverse mapping from form features, as determined from an input sketch, to design features, representing the design intent present in the designer’s mind. We introduce a list of com- mon engineering form features. For each, we list which geometrical cues may be helpful in identifying these features in design sketches, and we list the design features which such form features commonly imply. We show that a reductionist approach which decomposes a diagram into form features can be used to deduce the design intent of the object portrayed in a drawing. We supply experimental results in support of this idea
Temporal Semantic Centrality for the Analysis of Communication Networks

International audienceUnderstanding communication structures in huge and versatile online communities becomes a major issue. In this paper we propose a new metric, the Semantic Propagation Probability, that characterizes the user's ability to propagate a concept to other users, in a rapid and focused way. The message semantics is analyzed according to a given ontology. We use this metric to obtain the Temporal Semantic Centrality of a user in the community. We propose and evaluate an efficient implementation of this metric, using real-life ontologies and data sets
On the systematic development of domain-specific mashup tools for end users

The recent emergence of mashup tools has refueled research on end user development, i.e., on enabling end-users without programming skills to compose their own applications. Yet, similar to what happened with analogous promises in web service composition and business process management, research has mostly focused on technology and, as a consequence, has failed its objective. In this paper, we propose a domain-specific approach to mashups that is aware of the terminology, concepts, rules, and conventions (the domain) the user is comfortable with. We show what developing a domain-specific mashup tool means, which role the mashup meta-model and the domain model play and how these can be merged into a domain-specific mashup meta-model. We exemplify the approach by implementing a mashup tool for a specific domain (research evaluation) and describe the respective user study. The results of the user study confirm that domain-specific mashup tools indeed lower the entry barrier to mashup development. © 2012 Springer-Verlag
A K-Based Formal Framework for Domain-Specific Modelling Languages

International audienceWe propose a formal approach for the definition of domain-specific modelling languages (DSMLs). The approach uses basic Model-Driven Engineering artifacts for defining a DSML's syntax (using metamodels) and its operational semantics (using model transformations). We give formal meanings to these artifacts by mapping them to the K semantic framework. The mapping is implemented in the Rascal metaprogramming language. Since the resulting K definitions are executable, one obtains an execution engine for DSMLs and gains acces to K's formal analysis tools. We illustrate the approach on xSPEM, a language for describing the execution of tasks constrained by time, precedence, and resources
Profiling and framing structures for pervasive information systems development

Pervasive computing is a research field of computing technology that aims to achieve a new computing paradigm. Software engineering has been, since its existence, subject of research and improvement in several areas of interest. Model-Based/Driven Development (MDD) constitutes an approach to software design and development that potentially contributes to: concepts closer to domain and reduction of semantic gaps; automation and less sensitivity to technological changes; capture of expert knowledge and reuse. This paper presents a profiling and framing structure approach for the development of Pervasive Information Systems (PIS). This profiling and framing structure allows the organization of the functionality that can be assigned to computational devices in a system and of the corresponding development structures and models, being. The proposed approach enables a structural approach to PIS development. The paper also presents a case study that allowed demonstrating the applicability of the approach.Fundação para a Ciência e a Tecnologia (FCT
BiQL: A query language for analyzing information networks

One of the key steps in data analysis is the exploration of data. For traditional relational data, this process is facilitated by rela- tional database management systems and the aggregates and rankings they can compute. However, for the exploration of graph data, relational databases may not be most practical and scalable. Many tasks related to exploration of information networks involve computation and analy- sis of connections (e.g. paths) between concepts. Traditional relational databases offer no specific support for performing such tasks. For in- stance, a statistic such as the shortest path between two given nodes cannot be computed by a relational database. Surprisingly, tools for querying graph and network databases are much less well developed than for relational data, and only recently an increasing number of studies are devoted to graph or network databases. Our position is that the devel- opment of such graph databases is important both to make basic graph mining easier and to prepare data for more complex types of analysis.
In this chapter, we present the BiQL data model for representing and manipulating information networks. The BiQL data model consists of two parts: a data model describing objects, link, domains and networks, and a query language describing basic network manipulations. The main focus here lies on data preparation and data analysis, and less on data mining or knowledge discovery tasks directly.status: publishe
Link and node prediction in metabolic networks with probabilistic logic

Information on metabolic processes for hundreds of organisms is available in public databases. However, this information is often incomplete or affected by uncertainty. Systems capable to perform automatic curation of these databases and capable to suggest pathway-holes fillings are therefore needed. To this end such systems should exploit data available from related organisms and cope with heterogeneous sources of information (e.g. phylogenetic relations).  Here we start to investigate two fundamental problems concerning automatic metabolic networks curation, namely link prediction and node prediction using ProbLog, a simple yet powerful extension of the logic programming language Prolog with independent random variables.status: publishe
Patterns and logic for reasoning with networks

Biomine and ProbLog are two frameworks to implement bisociative information networks (BisoNets). They combine structured
data representations with probabilities expressing uncertainty. While Biomine is based on
graphs, ProbLog's core language is that of the logic programming
language Prolog. 
This chapter provides an overview of important concepts, terminology,
and reasoning tasks addressed in the two systems. It does so in 
an informal way, focusing on intuition rather than on mathematical
definitions. It aims at bridging the gap between network
representations and logical ones.status: publishe
MCTS Experiments on the Voronoi Game

International audienceMonte-Carlo Tree Search (MCTS) is a powerful tool in games with a finite branching factor. This paper describes an artificial player playing the Voronoi game, a game with an infinite branching factor. First, this paper shows how to use MCTS on a discretization of the Voronoi game, and the effects of enhancements such as RAVE and Gaussian processes (GP). A first set of experimental results shows that MCTS with UCB+RAVE or with UCB+GP are first good solutions for playing the Voronoi game without domain-dependent knowledge. Second, this paper shows how to greatly improve the playing level by using geometrical knowledge about Voronoi diagrams, the balance of diagrams being the key concept. The second set of experimental results shows that a player using MCTS and geometrical knowledge outperforms the player without knowledge
Taming the cloud: Safety, certification and compliance for software services - Keynote at the Workshop on Engineering Service-Oriented Applications (WESOA) 2011

The maturity of IT processes, such as software development, can be and is often certified. Current trends in the IT industry suggest that software systems in the future will be very different from their counterparts today, with an increasing adoption of the Service-Oriented Architecture (SOA) design pattern and the deployment of Software-as-a-Service (SaaS) on Cloud infrastructures. In this talk we discuss some issues surrounding engineering Software Services for Cloud infrastructures and highlight the need for enhanced control, service-level agreement and compliance mechanisms for Software Services. Cloud Infrastructures and Service Mash-ups
A new intuitionistic fuzzy rough set approach for decision support

The rough set theory was proved of its effectiveness in dealing with the imprecise and ambiguous information. Dominance-based Rough Set Approach (DRSA), as one of the extensions, is effective and fundamentally important for Multiple Criteria Decision Analysis (MCDA). However, most of existing DRSA models cannot directly examine uncertain information within rough boundary regions, which might miss the significant knowledge for decision support. In this paper, we propose a new believe factor in terms of an intuitionistic fuzzy value as foundation, further to induce a kind of new uncertain rule, called believable rules, for better performance in decision-making. We provide an example to demonstrate the effectiveness of the proposed approach in multicriteria sorting and also a comparison with existing representative DRSA models.Department of Computing2012-2013 > Academic research: refereed > Chapter in an edited book (author)preprint_postprin
Analysis of Trivium Using Compressed Right Hand Side Equations

We study a new representation of non-linear multivariate
equations for algebraic cryptanalysis. Using a combination of multiple
right hand side equations and binary decision diagrams, our new representation
allows a very efficient conjunction of a large number of separate
equations. We apply our new technique to the stream cipher Trivium
and variants of Trivium reduced in size. By merging all equations into
one single constraint, manageable in size and processing time, we get a
representation of the Trivium cipher as one single equation
Implementing information-theoretically secure oblivious transfer from packet reordering

If we assume that adversaries have unlimited computational capabilities, secure computation between mutually distrusting players can not be achieved using an error-free communication medium. However, secure multi-party computation becomes possible when a noisy channel is available to the parties. For instance, the Binary Symmetric Channel (BSC) has been used to implement Oblivious Transfer (OT), a fundamental primitive in secure multi-party computation. Current research is aimed at designing protocols based on real-world noise sources, in order to make the actual use of information-theoretically secure computation a more realistic prospect for the future. In this paper, we introduce a modified version of the recently proposed Binary Discrete-time Delaying Channel (BDDC), a noisy channel based on communication delays. We call our variant Reordering Channel (RC), and we show that it successfully models packet reordering, the common behavior of packet switching networks that results in the reordering of the packets in a stream during their transit over the network. We also show that the protocol implementing oblivious transfer on the BDDC can be adapted to the new channel by using a different sending strategy, and we provide a functioning implementation of this modified protocol. Finally, we present strong experimental evidence that reordering occurrences between two remote Internet hosts are enough for our construction to achieve statistical security against honest-but-curious adversaries
Kernel-based logical and relational learning with kLog for hedge cue detection

Hedge cue detection is a Natural Language Processing (NLP) task that consists of determining whether sentences contain hedges. These linguistic devices indicate that authors do not or cannot back up their
opinions or statements with facts. This binary classification problem, i.e. distinguishing factual versus uncertain sentences, only recently received attention in the NLP community. We use kLog, a new logical and relational language for kernel-based learning, to tackle this problem. We present results on the CoNLL 2010 benchmark dataset that consists of a set of paragraphs from Wikipedia, one of the domains in which uncertainty detection has become important. Our approach shows competitive results compared to state-of-the-art systems.status: publishe
Relational learning for spatial relation extraction from natural language

Automatically extracting spatial information is a challenging novel task with many applications. We formalize it as an information extraction step required for a mapping from natural language to formal spatial representation. Sentences may give rise to multiple spatial realtions between words representing landmarks, trajectors and spatial indicators. Our contribution is to formulate the extraction task as a relational learning problem, for which we employ the recently introduced kLog framework. We discuss representational and modeling aspects, kLog’s flexibility in our task and we present current experimental results.status: publishe
Chemistry by number theory

Aspects of elementary number theory pertaining to the golden ratio and
the golden spiral are shown to be related to and therefore of importance in the
simulation of chemical phenomena. Readily derived concepts include atomic structure,
electronegativity, bond order, the theory of covalent interaction and aspects
of molecular chirality. The physical interpretation of the results implicates the 4D
structure of space-time as a fundamental consideration. The implied classical nature
of 3D molecular structure identifies molecular mechanics as an ideal method for
structure optimization, and it is shown that the parameters may be related to number
theory. All results point at a 4D wave structure of electrostatic charge.Alexander von Humboldt Foundationhttp://www.springer.com/series/430hb2014ai201
Calculation of atomic structure

The Thomas–Fermi and Hartree–Fock calculations of non-hydrogen
atomic structure rely on complicated numerical computations without a simple
visualizable physical model. A new approach, based on a spherical wave structure of
the extranuclear electron density on atoms, self-similar to prominent astronomical
structures, simplifies the problem by orders of magnitude. It yields a normalized
density distribution which is indistinguishable from the TF function and produces
radial distributions, equivalent to HF results. Extended to calculate atomic ionization
radii, it yields more reliable values than SCF simulation of atomic compression. All
empirical parameters used in the calculation are shown to be consistent with the
spherical standing-wave model of atomic electron density.http://www.springer.com/series/430hj201
Covalent interaction

Reviewed in historical context, bond order emerges as a vaguely defined
concept without a clear theoretical basis. As an alternative, the spherical standingwave
model of the extranuclear electronic distribution on an atom provides a
simple explanation of covalent bond order as arising from the constructive and
destructive interference of wave patterns. A quantitative measure derives from a
number pattern that relates integer and half-integer bond orders through series of
Fibonacci numbers, consistent with golden-spiral optimization. Unlike any previous
definition of bond order, this approach is shown to predict covalent bond length,
dissociation energy and stretching force constants for homonuclear interactions that
are quantitatively correct. The analysis is supported by elementary number theory
and involves atomic number and the golden ratio as the only parameters. Validity
of the algorithm is demonstrated for heteronuclear interactions of any order. An
exhaustive comparison of calculated dissociation energies and interatomic distance
in homonuclear diatomic interaction, with experimental data from critical review,
is tabulated. A more limited survey of heteronuclear interactions confirms that
the numerical algorithms are generally valid. The large group of heteronuclear
hydrides is of particular importance to demonstrate the utility of the method, and
molecular hydrogen is treated as a special case. A simple formula that describes the
mutual polarization of heteronuclear pairs of atoms, in terms of valence densities
derived from a spherical-wave structure of extranuclear electronic charge, is used
to calculate the dipole moments of diatomic molecules. Valence density depends on
the volume of the valence sphere as determined by the atomic ionization radius,
and the interatomic distance is determined by the bond order of the diatomic
interaction. The results are in satisfactory agreement with literature data and should
provide a basis for the calculation of more complex molecular dipole moments.
The diatomic CO is treated as a special case, characteristic of all interactions
traditionally identified as dative bonds.http://www.springer.com/series/430hj201