pinecone/core-2020-05-10-deduplication huggingface dataset:
Capability Maturity Model for Collaborative Networks  Based on Extended Axiomatic Design Theory

The paradigm of forming and sustaining Collaborative Networks as environments that create Virtual Organisations (VOs) assumes that effective (and efficient) enterprise engineering (EE) capabilities and processes are available. However, these processes are only effective if they produce VOs which have sufficiently limited complexity, because as complexity grows, the VO's behaviour becomes increasingly harder to predict under all circumstances.  This paper proposes the use of EE methods based on Extended Axiomatic Design Theory to limit the complexity of VOs -- and of the CN itself. We introduce process- and people capability maturity levels, whereupon higher maturity implies higher probability of success of CNs in creating and maintaining VOs, and success of the VOs themselves, and formulate strategies for capability-improvement, intended to achieve higher levels of EE maturity.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Understanding TwitterTM use among Parliament representatives: A genre analysis

Published version of a chapter published in the book: Electronic Participation. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-23333-3_1This article examines parliament representatives’ Twitter- contributions (tweets). First, the genre of communication approach is introduced to identify common characteristics and communication patterns. Second, the findings are analysed using various eDemocracy models and deliberative standards to identify to what extent these tweets could be characterized as part of a deliberative discussion. The tweets are mainly dominated by five communication purposes; providing links to information sources for other Twitter users, to inform about the representative’s ongoing activities, to express views on topical issues, introducing non-political (private) content and participating in online discussions with other parliament representatives. Other less frequent communication patterns include tweets attracting attention to the representative’s own blogs, requests for input from readers and finally discussions with citizens. The analysed tweets generally did not meet deliberative standards and are dominated by politicians disseminating information and discussing with other parliament representatives. We conclude by arguing that the parliament representatives’ Twitter use is linked to the Liberal Democracy model, where the main purpose is to disseminate information to electors, and provide information on ongoing activities to the audience
Design Thinking and Participation: Lessons Learned from Three Case Studies

Part 3: Understanding eParticipationInternational audienceThis paper examines how design thinking and serious games approaches can be used to support participation through the analysis of three case studies. Indeed we will analyze these approaches in three different contexts: (i) a state-owned multi-utilities company; (ii) a political party; (iii) an information system strategic committee. Our analysis framework relies on the concepts of "perceived usefulness" and "perceived ease of use" and we will use it to discuss the lessons learned. Our main finding is that these approaches really contributing in making complex and abstract matters more "tangible" and thus understandable
Open Quantum Systems. An Introduction

We revise fundamental concepts in the dynamics of open quantum systems in the
light of modern developments in the field. Our aim is to present a unified
approach to the quantum evolution of open systems that incorporates the
concepts and methods traditionally employed by different communities. We
present in some detail the mathematical structure and the general properties of
the dynamical maps underlying open system dynamics. We also discuss the
microscopic derivation of dynamical equations, including both Markovian and
non-Markovian evolutions.Comment: 100 pages, 3 figures. Updated version with typos corrected. Preprint
  version of the published boo
Image segmentation of cross-country scenes captured in IR spectrum

Computer vision has become a major source of information for autonomous
navigation of robots of various types, self-driving cars, military robots and
mars/lunar rovers are some examples. Nevertheless, the majority of methods
focus on analysing images captured in visible spectrum. In this manuscript we
elaborate on the problem of segmenting cross-country scenes captured in IR
spectrum. For this purpose we proposed employing salient features. Salient
features are robust to variations in scale, brightness and view angle. We
suggest the Speeded-Up Robust Features as a basis for our salient features for
a number of reasons discussed in the paper. We also provide a comparison of two
SURF implementations. The SURF features are extracted from images of different
terrain types. For every feature we estimate a terrain class membership
function. The membership values are obtained by means of either the multi-layer
perceptron or nearest neighbours. The features' class membership values and
their spatial positions are then applied to estimate class membership values
for all pixels in the image. To decrease the effect of segmentation blinking
that is caused by rapid switching between different terrain types and to speed
up segmentation, we are tracking camera position and predict features'
positions. The comparison of the multi-layer perception and the nearest
neighbour classifiers is presented in the paper. The error rate of the terrain
segmentation using the nearest neighbours obtained on the testing set is
16.6+-9.17%.Comment: Corrected version of the chapter published in Advances in Robotics
  and Virtual Reality, Volume 26 of the series Intelligent Systems Reference
  Library pp 227-24
Impact of Innovative Agricultural Practices of Carbon Sequestration on Soil Microbial Community.

This chapter deals with the impact on soil microbiology of innovative management techniques for enhancing carbon sequestration. With in the MESCOSAGR project, the effect of different field treatments was investigated at three experimental sites differing in pedo-climatic characteristics. Several microbiological parameters were evaluated to describe the composition of soil microbial communities involved in the carbon cycle, as well as to assess microbial biomass and activity. Results indicated that both compost and catalyst amendments to field soils under maize or wheat affected microbial dynamics and activities, though with out being harmful to microbial communities
ProMISE: a Process Metamodelling Method for Information Systems Engineering

International audienceProcesses play a great part in information systems engineering projects success. There are a lot of process models and metamodels; however, the "one size fits all" motto has to be moderated: models have to be adapted to the specificities of the organizations or the projects. In order to help method engineers building adapted process models, we propose a method to build process metamodels and to instantiate them according to the organizations context. Our method consists of selecting the concepts needed from a conceptual graph, gathering the current knowledge of metamodelling concepts for information systems engineering processes, and integrating them in a new process metamodel that will be instantiated for any project in an organization. This method is supported by a tool
Profiling Power Consumption on Desktop Computer Systems

Background. Energy awareness in the ICT has become an important issue: ICT is both a key player in energy efficiency, and a power drainer. Focusing on software, recent work suggested the existence of a relationship between power consumption, software configuration and usage patterns in computer systems. Aim. The aim of this work was collecting and analysing power consumption data of a general-purpose computer system, simulating common usage scenarios, in order to extract a power consumption profile for each scenario. Methods. We selected a desktop system running Windows XP as a test machine. Meanwhile, we developed 11 usage scenarios, classified by their functionality, and automated by a GUI testing tool. Then, we conducted several test runs of the scenarios, collecting power consumption data by means of a power meter. Results. Our analysis resulted in an estimation of a power consumption value for each scenario and software application used, obtaining that each single scenario introduced an overhead from 2 to 11 Watts, corresponding to an increase of about 12%. Conclusions. We determined that software and its usage patterns impacts consistently on the power consumption of computer systems. Further work will be devoted to evaluate how power consumption is affected by the usage of specific system resources, like processors, disks, memory et
On the combination of TDDFT with molecular dynamics: New developments

In principle, we should not need the time-dependent extension of density-functional theory (TDDFT) for excitations, and in particular not for Molecular Dynamics (MD) studies: the theorem by Hohenberg and Kohn teaches us that for any observable that we wish to look at (including dynamical properties or observables dependent on excited states) there is a corresponding functional of the ground-state density. Yet the unavailability of such magic functionals in many cases (the theorem is a non-constructive existence result) demands the development and use of the alternative exact reformulation of quantum mechanics provided by TDDFT. This theory defines a convenient route to electronic excitations and to the dynamics of a many-electron system subject to an arbitrary time-dependent perturbation. This is, in fact, the main purpose of inscribing TDDFT in a MD framework -the inclusion of the effect of electronic excited states in the dynamics. However, as we will show in this review, it may not be the only use of TDDFT in this context. In this manuscript, we review two recent proposals: In Section 1.2, we show how TDDFT can be used to design efficient gsBOMD algorithms -even if the electronic excited states are in this case not relevant. The work described in Section 1.3 addresses the problem of mixed quantum-classical systems at thermal equilibrium.J. L. Alonso, A. Castro and P. Echenique acknowledge support from the research grants E24/3 (DGA, Spain), FIS2009-13364-C02-01 (MICINN, Spain). P. Echenique acknowledges support from the research grant 200980I064 (CSIC, Spain). A. Rubio acknowledges funding by the Spanish MEC (FIS2007-65702- C02-01), ACI-promciona project (ACI2009-1036), “Grupos Consolidados UPV/EHU del Gobierno Vasco” (IT-319-07), the European Research Council through the advance grant DYNamo (267374), and the European Community through projects e-I3 ETSF (Contract No. 211956) and THEMA (228539)
ASAP : towards accurate, stable and accelerative penetrating-rank estimation on large graphs

Pervasive web applications increasingly require a measure of similarity among objects. Penetrating-Rank (P-Rank) has been one of the promising link-based similarity metrics as it provides a comprehensive way of jointly encoding both incoming and outgoing links into computation for emerging applications. In this paper, we investigate P-Rank efficiency problem that encompasses its accuracy, stability and computational time. (1) We provide an accuracy estimate for iteratively computing P-Rank. A symmetric problem is to find the iteration number K needed for achieving a given accuracy ε. (2) We also analyze the stability of P-Rank, by showing that small choices of the damping factors would make P-Rank more stable and well-conditioned. (3) For undirected graphs, we also explicitly characterize the P-Rank solution in terms of matrices. This results in a novel non-iterative algorithm, termed ASAP , for efficiently computing P-Rank, which improves the CPU time from O(n 4) to O( n 3 ). Using real and synthetic data, we empirically verify the effectiveness and efficiency of our approaches
Flow monitoring experiences at the ethernet-layer

Flow monitoring is a scalable technology for providing summaries of network activity. Being deployed at the IP-layer, it uses fixed flow definitions, based on fields of the IP-layer and higher layers. Since several backbone network operators are considering the deployment of (Carrier) Ethernet in their Next-Generation Network, flow monitoring should also evolve in that direction. In order to do flow monitoring at the Ethernet-layer, Ethernet header fields need to be considered in flow definitions. IPFIX provides the flexibility to change the definition of flows, incorporating information from several layers in the network (including non-IP fields). The deployment of IPFIX is still at an early stage, which means that use cases for Ethernet-layer monitoring are not well known yet. This paper provides an overview of the usability of IPFIX at the Ethernet-layer and presents several use cases in which Ethernet-layer flow monitoring provides new insights and different views on a network
Regular and Singular Boundary Problems in Maple

We describe a new Maple package for treating boundary problems for linear
ordinary differential equations, allowing two-/multipoint as well as Stieltjes
boundary conditions. For expressing differential operators, boundary
conditions, and Green's operators, we employ the algebra of
integro-differential operators. The operations implemented for regular boundary
problems include computing Green's operators as well as composing and factoring
boundary problems. Our symbolic approach to singular boundary problems is new;
it provides algorithms for computing compatibility conditions and generalized
Green's operators.Comment: 14 pages; Berlin/Heidelberg, Springe
OUC's participation in the 2010 INEX Book Track

In this article we describe the Oslo University College’s participation in the INEX 2010 Book track. The OUC has submitted retrieval results for the “prove it” task with traditional relevance detection combined with some rudimental detection of confirmation. We call for a broader discussion of a more meaning-oriented (semantics-aware) approach to retrieval in digitized books, with the “prove it” task (classifiable as a simple semantics- aware retrieval activity) providing the INEX milieu with a suitable context to start this discussion
The INEX 2010 Interactive Track: An Overview

In the paper we present the organization of the INEX 2010 interactive track. For the 2010 experiments the iTrack has gathered data on user search behavior in a collection consisting of book metadata taken from the online bookstore Amazon and the social cataloguing application LibraryThing. The collected data represents traditional bibliographic metadata, user-generated tags and reviews and promotional texts and reviews from publishers and professional reviewers. In this year’s experiments we designed two search task categories, which were set to represent two different stages of work task processes. In addition we let the users create a task of their own, which is used as a control task. In the paper we describe the methods used for data collection and the tasks performed by the participants
Semantics-based web service discovery using information retrieval techniques

This paper demonstrates an experimental study that examines the accuracy of various information retrieval techniques for Web service discovery. The main goal of this research is to evaluate algorithms for semantic web service discovery. The evaluation is comprehensively benchmarked using more than 1,700 real-world WSDL documents from INEX 2010 Web Service Discovery Track dataset. For automatic search, we successfully use Latent Semantic Analysis and BM25 to perform Web service discovery. Moreover, we provide linking analysis which automatically links possible atomic Web services to meet the complex requirements of users. Our fusion engine recommends a final result to users. Our experiments show that linking analysis can improve the overall performance of Web service discovery. We also find that keyword-based search can quickly return results but it has limitation of understanding users’ goals
Polynomial conjunctive query rewriting under unary inclusion dependencies

Ontology-based data access (OBDA) is widely accepted as an important ingredient of the new generation of information systems. In the OBDA paradigm, potentially incomplete relational data is enriched by means of ontologies, representing intensional knowledge of the application domain. We consider the problem of conjunctive query answering in OBDA. Certain ontology languages have been identified as FO-rewritable (e.g., DL-Lite and sticky-join sets of TGDs), which means that the ontology can be incorporated into the user's query, thus reducing OBDA to standard relational query evaluation. However, all known query rewriting techniques produce queries that are exponentially large in the size of the user's query, which can be a serious issue for standard relational database engines. In this paper, we present a polynomial query rewriting for conjunctive queries under unary inclusion dependencies. On\ud
the other hand, we show that binary inclusion dependencies do not admit\ud
polynomial query rewriting algorithms
Multiplying the Mileage of Your Dataset with Subwindowing

Author version made available in accordance with the publisher's policy.This study is focused on improving the classification performance of EEG data through the use of some data restructuring methods. In this study, the impact of having more training instances/samples vs. using shorter window sizes is investigated. The BCI2003 IVa dataset is used to examine the results. The results not surprisingly indicate that, up to a certain point, having higher numbers of training instances significantly improves the classification performance while the use of shorter window sizes tends to worsen performance in a way that usually cannot fully be compensated for by the additional instances, but tends to provide useful gain in overall performance for small divisors into two or three subepochs. We have moreover determined that use of an incomplete set of overlapping windows can have little effect, and is inapplicable for the smallest divisors, but that use of overlapping subepochs from three specific non-overlapping areas (start, middle and end) of a superepoch tends to contribute significant additional information. Examination of a division into five equal non-overlapping areas indicates that for some subjects the first or last fifth contributes significantly less information than the middle three fifths
Preconditioner-Based Contact Response and Application to Cataract Surgery

International audienceIn this paper we introduce a new method to compute, in real-time, the physical behavior of several colliding soft-tissues in a surgical simulation. The numerical approach is based on finite element modeling and allows for a fast update of a large number of tetrahedral elements. The speed-up is obtained by the use of a specific preconditioner that is updated at low frequency. The preconditioning enables an optimized computation of both large deformations and precise contact response. Moreover, homogeneous and inhomogeneous tissues are simulated with the same accuracy. Finally, we illustrate our method in a simulation of one step in a cataract surgery procedure, which require to handle contacts with non homogeneous objects precisely
Biomechanical Simulation of Electrode Migration for Deep Brain Stimulation

International audienceDeep Brain Stimulation is a modern surgical technique for treating patients who suffer from affective or motion disorders such as Parkinson's disease. The efficiency of the procedure relies heavily on the accuracy of the placement of a micro-electrode which sends electrical pulses to a specific part of the brain that controls motion and affective symptoms. However, targeting this small anatomical structure is rendered difficult due to a series of brain shifts that take place during and after the procedure. This paper introduces a biomechanical simulation of the intra and postoperative stages of the procedure in order to determine lead deformation and electrode migration due to brain shift. To achieve this goal, we propose a global approach, which accounts for brain deformation but also for the numerous interactions that take place during the procedure (contacts between the brain and the inner part of the skull and falx cerebri, effect of the cerebro-spinal fluid, and biomechanical interactions between the brain and the electrodes and cannula used during the procedure). Preliminary results show a good correlation between our simulations and various results reported in the literature
Neural Process Reconstruction from Sparse User Scribbles

We present a novel semi-automatic method for segmenting neural processes in large, highly anisotropic EM (electron microscopy) image stacks. Our method takes advantage of sparse scribble annotations provided by the user to guide a 3D variational segmentation model, thereby allowing our method to globally optimally enforce 3D geometric constraints on the segmentation. Moreover, we leverage a novel algorithm for propagating segmentation constraints through the image stack via optimal volumetric pathways, thereby allowing our method to compute highly accurate 3D segmentations from very sparse user input. We evaluate our method by reconstructing 16 neural processes in a 1024×1024×50 nanometer-scale EM image stack of a mouse hippocampus. We demonstrate that, on average, our method is 68% more accurate than previous state-of-the-art semi-automatic methods.Engineering and Applied Science
Detection of Neuron Membranes in Electron Microscopy Images Using Multi-scale Context and Radon-Like Features

Automated neural circuit reconstruction through electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that exploits multi-scale contextual information together with Radon-like features (RLF) to learn a series of discriminative models. The main idea is to build a framework which is capable of extracting information about cell membranes from a large contextual area of an EM image in a computationally efficient way. Toward this goal, we extract RLF that can be computed efficiently from the input image and generate a scale-space representation of the context images that are obtained at the output of each discriminative model in the series. Compared to a single-scale model, the use of a multi-scale representation of the context image gives the subsequent classifiers access to a larger contextual area in an effective way. Our strategy is general and independent of the classifier and has the potential to be used in any context based framework. We demonstrate that our method outperforms the state-of-the-art algorithms in detection of neuron membranes in EM images.Engineering and Applied Science
Detecting Outlying Subjects in High-Dimensional Neuroimaging Datasets with Regularized Minimum Covariance Determinant

International audienceMedical imaging datasets used in clinical studies or basic research often comprise highly variable multi-subject data. Statistically-controlled inclusion of a subject in a group study, i.e. deciding whether its images should be considered as samples from a given population or whether they should be rejected as outlier data, is a challenging issue. While the informal approaches often used do not provide any statistical assessment that a given dataset is indeed an outlier, traditional statistical procedures are not well-suited to the noisy, high-dimensional, settings encountered in medical imaging, e.g. with functional brain images. In this work, we modify the classical Minimum Covariance Determinant approach by adding a regularization term, that ensures that the estimation is well-posed in high-dimensional settings and in the presence of many outliers. We show on simulated and real data that outliers can be detected satisfactorily, even in situations where the number of dimensions of the data exceeds the number of observations