pinecone/core-2020-05-10-deduplication huggingface dataset:
Clock Synchronization and Distributed Estimation in Highly Dynamic Networks: An Information Theoretic Approach

International audienceWe consider the External Clock Synchronization problem in dynamic sensor networks. Initially, sensors obtain inaccurate estimations of an external time reference and subsequently collaborate in order to synchronize their internal clocks with the external time. For simplicity, we adopt the drift-free assumption, where internal clocks are assumed to tick at the same pace. Hence, the problem is reduced to an estimation problem, in which the sensors need to estimate the initial external time. This work is further relevant to the problem of collective approximation of environmental values by biological groups. Unlike most works on clock synchronization that assume static networks, this paper focuses on an extreme case of highly dynamic networks. Specifically, we assume a non-adaptive scheduler adversary that dictates in advance an arbitrary, yet independent, meeting pattern. Such meeting patterns fit, for example, with short-time scenarios in highly dynamic settings, where each sensor interacts with only few other arbitrary sensors. We propose an extremely simple clock synchronization algorithm that is based on weighted averages, and prove that its performance on any given independent meeting pattern is highly competitive with that of the best possible algorithm, which operates without any resource or computational restrictions, and knows the meeting pattern in advance. In particular, when all distributions involved are Gaussian, the performances of our scheme coincide with the optimal performances. Our proofs rely on an extensive use of the concept of Fisher information. We use the Cramér-Rao bound and our definition of a Fisher Channel Capacity to quantify information flows and to obtain lower bounds on collective performance. This opens the door for further rigorous quantifications of information flows within collaborative sensors
Node Labels in Local Decision

International audienceThe role of unique node identifiers in network computing is well understood as far as symmetry breaking is concerned. However, the unique identifiers also leak information about the computing environment— in particular, they provide some nodes with information related to the size of the network. It was recently proved that in the context of local decision, there are some decision problems such that (1) they cannot be solved without unique identifiers, and (2) unique node identifiers leak a sufficient amount of information such that the problem becomes solvable (PODC 2013). In this work we give study what is the minimal amount of information that we need to leak from the environment to the nodes in order to solve local decision problems. Our key results are related to scalar oracles f that, for any given n, provide a multiset f (n) of n labels; then the adversary assigns the labels to the n nodes in the network. This is a direct generalisation of the usual assumption of unique node identifiers. We give a complete characterisation of the weakest oracle that leaks at least as much information as the unique identifiers. Our main result is the following dichotomy: we classify scalar oracles as large and small, depending on their asymptotic behaviour, and show that (1) any large oracle is at least as powerful as the unique identifiers in the context of local decision problems, while (2) for any small oracle there are local decision problems that still benefit from unique identifiers
A middle-level ontology for context modelling

Context modelling is one of the stages conducted during the context life cycle. It has the aim of giving meaning and structure to the collected context’s raw data. Although there are different context models proposed in the literature, we have identified some gaps that are not fully covered, particularly related to the reusability of the models themselves and the lack of consolidated and standardized onto-logical resources. To tackle this problem, we adopt a three-layered context ontology perspective and we focus on this paper in the middle layer, which is defined following a prescriptive process and structured in a modular way for supporting reuse.Peer ReviewedPostprint (author's final draft
Incremental checking of OCL constraints with aggregates through SQL

Valid states of data are those satisfying a set of constraints. Therefore, efficiently checking whether some constraint has been violated after a data update is an important problem in data management. We tackle this problem by incrementally checking OCL constraint violations by means of SQL queries. Given an OCL constraint, we obtain a set of SQL queries that returns the data that violates the constraint. In this way, we can check the validity of the data by checking the emptiness of these queries. The queries that we obtain are incremental since they are only executed when some relevant data update may violate the constraint, and they only examine the data related to the update.Peer ReviewedPostprint (author's final draft
Aligning business goals and risks in OSS adoption

Increasing adoption of Open Source Software (OSS) requires a change in the organizational culture and reshaping IT decision-makers mindset. Adopting OSS software components introduces some risks that can affect the adopter organization’s business goals, therefore they need to be considered. To assess these risks, it is required to understand the socio-technical structures that interrelate the stakeholders in the OSS ecosystem, and how these structures may propagate the potential risks to them. In this paper, we study the connection be-tween OSS adoption risks and OSS adopter organizations’ business goals. We propose a model-based approach and analysis framework that combines two ex-isting frameworks: the i* framework to model and reason about business goals, and the RiskML notation to represent and analyse OSS adoption risks. We illus-trate our approach with data drawn from an industrial partner organization in a joint EU project.Peer ReviewedPostprint (author's final draft
Listeria monocytogenes in Milk Products

peer-reviewedMilk and milk products are frequently identified as vectors for transmission of Listeria monocytogenes. Milk can be contaminated at farm level either by indirect external contamination from the farm environment or less frequently by direct contamination of the milk from infection in the animal. Pasteurisation of milk will kill L. monocytogenes, but post-pasteurisation contamination, consumption of unpasteurised milk and manufacture of unpasteurised milk products can lead to milk being the cause of outbreaks of listeriosis. Therefore, there is a concern that L. monocytogenes in milk could lead to a public health risk. To protect against this risk, there is a need for awareness surrounding the issues, hygienic practices to reduce the risk and adequate sampling and analysis to verify that the risk is controlled. This review will highlight the issues surrounding L. monocytogenes in milk and milk products, including possible control measures. It will therefore create awareness about L. monocytogenes, contributing to protection of public health
Generic aspects of skyrmion lattices in chiral magnets

Magnetic skyrmions are topologically non-trivial spin whirls that may not be
transformed continuously into topologically trivial states such as
ferromagnetic spin alignment. In recent years lattice structures composed of
skyrmions have been discovered in certain bulk chiral magnets with
non-centrosymmetric crystal structures. The magnetic phase diagrams of these
materials share remarkable similarities despite great variations of the
characteristic temperature, field, and length scales and regardless whether the
underlying electronic state is that of a metal, semiconductor, or insulator.Comment: 38 pages, 8 figure
A Predictability Algorithm for Distributed Discrete Event Systems

International audiencePredictability is considered as a crucial system property that determines with certainty the future occurrence of a fault based on a sequence of observations on system model. There are very few works done on the predictability problem for discrete event systems, which is however extremely important for developing critical complex systems. In this paper, we propose a formal sufficient and necessary condition for this property before presenting a new algorithm based on it, which is extendible from a centralized framework to a distributed one. Both are formally presented, as well as experimental results that show the efficiency of our approach
A logical approach for behavioural composition of scenario-based models

As modern systems become more complex, design approaches model different aspects of the system separately. When considering (intra and inter) system interactions, it is usual to model individual scenarios using UML’s sequence diagrams. Given a set of scenarios we then need to check whether these are consistent and can be combined for a better understanding of the overall behaviour. This paper addresses this by presenting a novel formal technique for composing behavioural models at the metamodel level through exact metamodel restriction (EMR). In our approach a sequence diagram can be completely described by a set of logical constraints at the metamodel level. When composing sequence diagrams we take the union of the sets of logical constraints for each diagram and additional behavioural constraints that describe the matching composition glue. A formal semantics for composition in accordance with the glue guides our model transformation to Alloy. Alloy’s fully automated constraint solver gives us the solution. Our technique has been implemented as an Eclipse plugin SD2Alloy.Postprin
Gateway selection optimization in Hybrid MANET-Satellite network

In this paper, we study the problem of gateway placement in an hybrid mobile ad hoc – satellite network. We propose a genetic algorithm based approach to solve this multi-criteria optimization problem. The analysis of the proposed algorithm is made by means of simulations. Topology dynamics are also taken into account since the node mobility will impact the gateway placement decisions. Our solution shows promising results and displays unmatched flexibility with respect to the optimization criteria
A Large-Scale Community Questions Classification Accounting for Category Similarity: An Exploratory?

The paper reports on a large-scale topical categorization of questions from a Russian community question answering (CQA) service Otvety@Mail.Ru. We used a data set containing all the questions (more than 11 millions) asked by Otvety@Mail.Ru users in 2012. This is the first study on question categorization dealing with non-English data of this size. The study focuses on adjusting category structure in order to get more robust classification results. We investigate several approaches to measure similarity between categories: the share of identical questions, language models, and user activity. The results show that the proposed  approach is promising.14-07-00589; RFBR; Russian Foundation for Basic Research
Towards crowdsourcing and cooperation in linguistic resources

Linguistic resources can be populated with data through the use of such
approaches as crowdsourcing and gamification when motivated people are
involved. However, current crowdsourcing genre taxonomies lack the concept of
cooperation, which is the principal element of modern video games and may
potentially drive the annotators' interest. This survey on crowdsourcing
taxonomies and cooperation in linguistic resources provides recommendations on
using cooperation in existent genres of crowdsourcing and an evidence of the
efficiency of cooperation using a popular Russian linguistic resource created
through crowdsourcing as an example.Comment: 11 pages, 2 figures, accepted to RuSSIR 2014, the final publication
  is available at link.springer.co
Introduction to Formal Concept Analysis and Its Applications in
  Information Retrieval and Related Fields

This paper is a tutorial on Formal Concept Analysis (FCA) and its
applications. FCA is an applied branch of Lattice Theory, a mathematical
discipline which enables formalisation of concepts as basic units of human
thinking and analysing data in the object-attribute form. Originated in early
80s, during the last three decades, it became a popular human-centred tool for
knowledge representation and data analysis with numerous applications. Since
the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics
include Information Retrieval with a focus on visualisation aspects, Machine
Learning, Data Mining and Knowledge Discovery, Text Mining and several others
Semantic publishing challenge : assessing the quality of scientific output by information extraction and interlinking

The Semantic Publishing Challenge series aims at investigating novel approaches for improving scholarly publishing using Linked Data technology. In 2014 we had bootstrapped this effort with a focus on extracting information from non-semantic publications - computer science workshop proceedings volumes and their papers - to assess their quality. The objective of this second edition was to improve information extraction but also to interlink the 2014 dataset with related ones in the LOD Cloud, thus paving the way for sophisticated end-user services
Towards systematic evaluation of multi-agent systems in large scale and dynamic logistics

A common hypothesis in multi-agent systems (MAS) literature is that decentralized MAS are better at coping with dynamic and large scale problems compared to centralized algorithms. Existing work investigates this hypothesis in a limited way, often with no support for further evaluation, slowing down the advance of more general conclusions. Investigating this hypothesis more systematically is time consuming as it requires four main components: (1) formal metrics for the variables of interest, (2) a problem instance generator using these metrics, (3) (de)centralized algorithms and (4) a simulation platform that facilitates the execution of these algorithms. Present paper describes the construction of an instance generator based on previously established formal metrics and simulation platform with support for (de)centralized algorithms. Using our instance generator, a benchmark logistics dataset with varying levels of dynamism and scale is created and we demonstrate how it can be used for systematically evaluating MAS and centralized algorithms in our simulator. This benchmark dataset is essential for enabling the adoption of a more thorough and systematic evaluation methodology, allowing increased insight in the strengths and weaknesses of both
the MAS paradigm and operational research methods.status: publishe
Managing Autonomous Mobility on Demand Systems for Better Passenger
  Experience

Autonomous mobility on demand systems, though still in their infancy, have
very promising prospects in providing urban population with sustainable and
safe personal mobility in the near future. While much research has been
conducted on both autonomous vehicles and mobility on demand systems, to the
best of our knowledge, this is the first work that shows how to manage
autonomous mobility on demand systems for better passenger experience. We
introduce the Expand and Target algorithm which can be easily integrated with
three different scheduling strategies for dispatching autonomous vehicles. We
implement an agent-based simulation platform and empirically evaluate the
proposed approaches with the New York City taxi data. Experimental results
demonstrate that the algorithm significantly improve passengers' experience by
reducing the average passenger waiting time by up to 29.82% and increasing the
trip success rate by up to 7.65%
Probabilistic Perception Revision in AgentSpeak(L)

Agent programming is mostly a symbolic discipline and, as such, draws little benefits from probabilistic areas as machine learning and graphical models. However, the greatest objective of agent research is the achievement of autonomy in dynamical and complex environments — a goal that implies embracing uncertainty and therefore the entailed representations, algorithms and techniques. This paper proposes an innovative and conflict free two layer approach to agent programming that uses already established methods and tools from both symbolic and probabilistic artificial intelligence. Moreover, this method is illustrated by means of a widely used agent programming example, GOLDMINERS
There are Two Sides to Every Question - Controller Versus Attacker.

We investigate security enforcement mechanisms that run in parallel with a system; the aim is to check and modify the run-time behaviour of a possible attacker in order to guarantee that the system satisfies some security policies. We focus on a CSP-like quantitative process-algebra to model such processes. Weights on actions are modelled with semirings, which represent a parametric structure where to cast different metrics. The basic tools are represented by a quantitative logic and a model checking function. First, the behaviour of the system is removed from the parallel computation with respect to some security property to be satisfied. Secondly, what remains is refined in two formulas with respect to the given operator executed by a controller. The result describes what a controller has to do to prevent a given attack
Fast and Accurate Pedestrian Detection in a Truck’s Blind Spot Camera

We propose a multi-pedestrian detection and tracking framework targeting a specific application: detecting vulnerable road users in a truck’s blind spot zone. Existing safety solutions are not able to handle this problem completely. Therefore we aim to develop an active safety system which warns the truck driver if pedestrians are present in the truck’s blind spot zone, using solely the vision input from the truck’s blind spot camera. This is not a trivial task, since—aside from the large distortion induced by such cameras—the application inherently requires real-time operation while at the same time attaining very high accuracy. To achieve this, we propose a fast and efficient pedestrian detection and tracking framework based on our novel perspective warping window approach. Experiments on real-life data show that our approach achieves excellent accuracy results at real-time performance, using a single core CPU implementation only.status: publishe
A Reactive Competitive Emotion Selection System

We present a reactive emotion selection system designed to be used in a robot that needs to respond autonomously to relevant events. A variety of emotion selection models based on “cognitive appraisal” theories exist, but the complexity of the concepts used by most of these models limits their use in robotics. Robots have physical constrains that condition their understanding of the world and limit their capacity to built the complex concepts needed for such models. The system presented in this paper was conceived to respond to “disturbances” detected in the environment through a stream of images, and use this low-level information to update emotion intensities. They are increased when specific patterns, based on Tomkins’ affect theory, are detected or reduced when it is not. This system could also be used as part of (or as first step in the incremental design of) a more cognitively complex emotional system for autonomous robots
Interactively Illustrating the Context-Sensitivity of Aristotelian Diagrams

This paper studies the logical context-sensitivity of Aristotelian diagrams. I propose a new account of measuring this type of context-sensitivity, and illustrate it by means of a small-scale example. Next, I turn toward a more large-scale case study, based on Aristotelian diagrams for the categorical statements with subject negation. On the practical side, I describe an interactive application that can help to explain and illustrate the phenomenon of context-sensitivity in this particular case study. On the theoretical side, I show that applying the proposed measure of context-sensitivity leads to a number of precise yet
highly intuitive results.status: publishe
Multi-Domain Adapted Machine Translation Using Unsupervised Text Clustering

Domain Adaptation in Machine Translation means to take a machine translation system that is restricted to work in a specific context and to enable the system to translate text from a different domain. The paper presents a two-step domain adaptation strategy, by first making use of unlabeled training material through an unsupervised algorithm, the Self-Organizing Map, to create auxiliary language models, and then to include these models dynamically in a machine translation pipelineAuthor postprint - The original publication is available at www.springerlink.co
Descriptive indexicals, propositional attitudes and the double role of context

This paper offers an account of some uses of indexicals in the context\ud
of propositional attitude ascriptions, i. e. reports that concern the cognitive relations\ud
people bring to bear on propositions. While the contribution of indexicals\ud
to the truth conditions of an utterance is usually singular, their interpretation is\ud
general in the case of so called descriptive uses. I will propose an interpretation\ud
of the descriptive uses of indexicals via a mechanism of descriptive anaphora and\ud
apply this mechanism to the case of attitude ascriptions. I will emphasize the role\ud
of context both in the suppression of the default referential reading of the indexical,\ud
as well as in the reconstruction of the relevant interpretation of the whole\ud
utterance
The Self-Adaptive Context Learning Pattern: Overview and Proposal

Over the years, our research group has designed and developed many self-adaptive multi-agent systems to tackle real-world complex problems, such as robot control and heat engine optimization. A recurrent key feature of these systems is the ability to learn how to handle the context they are plunged in, in other words to map the current state of their perceptions to actions and effects. This paper presents the pattern enabling the dynamic and interactive learning of the mapping between context and actions by our multi-agent systems
Three-loop HTLpt thermodynamics at finite temperature and chemical
  potential

In this proceedings we present a state-of-the-art method of calculating
thermodynamic potential at finite temperature and finite chemical potential,
using Hard Thermal Loop perturbation theory (HTLpt) up to
next-to-next-leading-order (NNLO). The resulting thermodynamic potential
enables us to evaluate different thermodynamic quantities including pressure
and various quark number susceptibilities (QNS). Comparison between our
analytic results for those thermodynamic quantities with the available lattice
data shows a good agreement.Comment: 5 pages, 6 figures, conference proceedings of XXI DAE-BRNS HEP
  Symposium, IIT Guwahati, December 2014; to appear in 'Springer Proceedings in
  Physics Series
Vector meson spectral function and dilepton rate in an effective mean
  field model

We have studied the vector meson spectral function (VMSF) in a hot and dense
medium within an effective QCD model namely the Nambu-Jona-Lasinio (NJL) and
its Polyakov Loop extended version (PNJL) with and without the effect of
isoscalar vector interaction (IVI). The effect of the IVI has been taken into
account using the ring approximation. We obtained the dilepton production rate
(DPR) using the VMSF and observed that at moderate temperature it is enhanced
in the PNJL model as compared to the NJL and Born rate due to the suppression
of color degrees of freedom.Comment: 5 pages, 7 figures, conference proceedings of the XXI DAE-BRNS HEP
  Symposium, IIT Guwahati, December 2014; to appear in 'Springer Proceedings in
  Physics Series