pinecone/core-2020-05-10-deduplication huggingface dataset:
Primordial Generation of Magnetic Fields

We reexamine generation of the primordial magnetic fields, at temperature
$T>80$TeV, by applying a consistent kinetic theory framework which is suitably
modified to take the quantum anomaly into account. The modified kinetic
equation can reproduce the known quantum field theoretic results upto the
leading orders. We show that our results qualitatively matches with the earlier
results obtained using heuristic arguments. The modified kinetic theory can
give the instabilities responsible for generation of the magnetic field due to
chiral imbalance in two distinct regimes: a) when the collisions play a
dominant role and b) when the primordial plasma can be regarded as
collisionless. We argue that the instability developing in the collisional
regime can dominate over the instability in the collisionless regime.Comment: 7 pages. To appear in the proceedings of XXI DAE-BRNS High Energy
  Physics Symposium, IIT Guwahati, India, December 201
Study of Performance of Bakelite Resistive Plate Chamber (RPC)

Resistive Plate Chamber (RPC) is a type of gaseous detector having excellent
time and position resolutions. VECC is involved in the R\&D of indigenously
developed bakelite RPCs. The largest size of bakelite RPC developed in India is
100 cm $\times$ 100 cm. We present here the test results of a bakelite sample
along with the cosmic ray test results of a bakelite RPC (30 cm $\times$ 30 cm
$\times$ 0.2cm) fabricated at VECC. The steps taken towards the development of
a large size (240 cm $\times$ 120 cm $\times$ 0.2 cm) bakelite RPC have also
been discussed.Comment: Springer Proceedings in Physics 174, Springer International
  Publishing Switzerland 201
Study of photon detection efficiency and position resolution of BESIII
  electromagnetic calorimeter

We study the photon detection efficiency and position resolution of the
electromagnetic calorimeter (EMC) of the BESIII experiment. The control sample
of the initial-state-radiation (ISR) process of $e^+e^-\rightarrow \gamma
\mu^+\mu^-$ is used at $J/\psi$ and $\psi(3770)$ resonances for the EMC
calibration and photon detection efficiency study. Photon detection efficiency
is defined as the predicted photon, obtained by performing a kinematic fit with
two muon tracks, matched with real photons in the EMC. The spatial resolution
of the EMC is defined as the separation in polar ($\theta$) and azimuthal
($\phi$) angles between charged track and associated cluster centroid on the
front face of the EMC crystals.Comment: 5 page
SmartKeepers: A Decentralized, Secure, and Flexible Social Platform for Coworkers

International audienceCoworking has emerged as an attractive model for organization. It relies on a highly dynamic collaborations among different partners. Traditional centralized social platform lack fundamental requirements for such collaborations potentially short, that are the management of the dynamic topology of such professional networks, privacy, data exchanges and ownership. In this paper, we present SmartKeepers, a decentralized and secure environment for coworking activity. Each user physically owns her node that she plugs in and out as she moves from one collaborative space to another. The system supports a large variety of network topologies and is fully interoperable with W3C compliant solutions. We showcase the cogency of the Semantic Web for building decentralized and secure services while keeping every user at the core of the data ownership process
DBpedia's triple pattern fragments: usage patterns and insights

Queryable Linked Data is published through several interfaces, including SPARQL endpoints and Linked Data documents. In October 2014, the DBpedia Association announced an official Triple Pattern Fragments interface to its popular DBpedia dataset. This interface proposes to improve the availability of live queryable data by dividing query execution between clients and servers. In this paper, we present a usage analysis between November 2014 and July 2015. In 9 months time, the interface had an average availability of 99.99 %, handling 16,776,170 requests, 43.0% of which were served from cache. These numbers provide promising evidence that low-cost Triple Pattern Fragments interfaces provide a viable strategy for live applications on top of public, queryable datasets
De-anonymizable location cloaking for privacy-controlled mobile systems

© Springer International Publishing Switzerland 2015. The rapid technology upgrades of mobile devices and the popularity of wireless networks significantly drive the emergence and development of Location-based Services (LBSs), thus greatly expanding the business of online services and enriching the user experience. However, the personal location data shared with the service providers also leave hidden risks on location privacy. Location anonymization techniques transform the exact location of a user into a cloaking area by including the locations of multiple users in the exposed area such that the exposed location is indistinguishable from that of the other users. However in such schemes, location information once perturbed cannot be recovered from the cloaking region and as a result, users of the location cannot obtain fine granular information even when they have access to it. In this paper, we propose Dynamic Reversible Cloaking (DRC) a new de-anonymziable location cloaking mechanism that allows to restore the actual location from the perturbed information through the use of an anonymization key. Extensive experiments using realistic road network traces show that the proposed scheme is efficient, effective and scalable
Opinion dynamics: models, extensions and external effects

Recently, social phenomena have received a lot of attention not only from
social scientists, but also from physicists, mathematicians and computer
scientists, in the emerging interdisciplinary field of complex system science.
Opinion dynamics is one of the processes studied, since opinions are the
drivers of human behaviour, and play a crucial role in many global challenges
that our complex world and societies are facing: global financial crises,
global pandemics, growth of cities, urbanisation and migration patterns, and
last but not least important, climate change and environmental sustainability
and protection. Opinion formation is a complex process affected by the
interplay of different elements, including the individual predisposition, the
influence of positive and negative peer interaction (social networks playing a
crucial role in this respect), the information each individual is exposed to,
and many others. Several models inspired from those in use in physics have been
developed to encompass many of these elements, and to allow for the
identification of the mechanisms involved in the opinion formation process and
the understanding of their role, with the practical aim of simulating opinion
formation and spreading under various conditions. These modelling schemes range
from binary simple models such as the voter model, to multi-dimensional
continuous approaches. Here, we provide a review of recent methods, focusing on
models employing both peer interaction and external information, and
emphasising the role that less studied mechanisms, such as disagreement, has in
driving the opinion dynamics. [...]Comment: 42 pages, 6 figure
Collaborative cloud computing application for water resources based on open source software

This article presents research and development of a collaboration cloud computing application for water resources based on open source software. The cloud computing application is using a hybrid deployment model of public – private cloud, running on two separate virtual machines (VMs). The first one (VM1) is running on Amazon web services (AWS) and the second one (VM2) is running on a Xen cloud platform. The collaborative cloud application has four web services for 1) data infrastructure (DI), 2) support for water resources modelling (WRM), 3) user management and 4) water resources optimization (WRO). The most important characteristic of the cloud application is a real-time geo-collaboration platform supporting multiple users. This research shows the capability to scale and distribute the cloud application between several VMs. The cloud application was successfully tested in the Zletovica case study in a distributed computer environment with concurred multiple access
Challenges for the Animation of Expressive Virtual Characters: The Standpoint of Sign Language and Theatrical Gestures

International audienceDesigning and controlling virtual characters endowed with expressive gestures requires the modeling of multiple processes, involving high-level abstract representations to low-level sensorimotor models. An expressive gesture is here defined as a meaningful bodily motion which intrinsically associates sense, style, and expressiveness. The main challenges rely both on the capability to produce a large spectrum of parametrized actions executed with some variability in various situations, and on the biological plausibility of the motion of the virtual characters. The goals of the paper are twofold. First we review the different formalisms used to describe expressive gestures, from notations to computational languages. Secondly we identify and discuss remaining challenges in the generation of expressive virtual characters. The different models and formalisms are illustrated more particularly for theatrical and sign language gestures
On Multiview Analysis for Fingerprint Liveness Detection

Fingerprint recognition systems, as any other biometric system, can be subject to attacks, which are usually carried out using artificial fingerprints. Several approaches to discriminate between live and fake fingerprint images have been presented to address this issue. These methods usually rely on the analysis of individual features extracted from the fingerprint images. Such features represent different and complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. However, very little work in this direction has been reported in the literature. In this work, we present the results of a preliminary investigation on multiview analysis for fingerprint liveness detection. Experimental results show the effectiveness of such approach, which improves previous results in the literatur
Optimizing the Data Adaptive Dual Domain Denoising Algorithm

International audienceThis paper presents two new strategies that greatly improve the execution time of the DA3D Algorithm, a new denoising algorithm with state-of-the-art results. First, the weight map used in DA3D is implemented as a quad-tree. This greatly reduces the time needed to search the minimum weight, greatly reducing the overall computation time. Second, a simple but effective tiling strategy is shown to work in order to allow the parallel execution of the algorithm. This allows the implementation of DA3D in a parallel architecture. Both these improvements do not affect the quality of the output
Families of the Granules for Association Rules and Their Properties

We employed the granule (or the equivalence class) defined by a descriptor in tables, and investigated rough set-based rule generation. In this paper, we consider the new granules defined by an implication, and propose a family of the granules defined by an implication in a table with exact data. Each family consists of the four granules, and we show that three criterion values, support, accuracy, and coverage, can easily be obtained by using the four granules. Then, we extend this framework to tables with non-deterministic data. In this case, each family consists of the nine granules, and the minimum and the maximum values of three criteria are also obtained by using the nine granules. We prove that there is a table causing support and accuracy the minimum, and generally there is no table causing support, accuracy, and coverage the minimum. Finally, we consider the application of these properties to Apriori-based rule generation from uncertain data. These properties will make Apriori-based rule generation more effective.10th International Conference, RSKT 2015, Held as Part of the International Joint Conference on Rough Sets, IJCRS 2015, November 20-23, 2015, Tianjin, Chin
New neighborhood based rough sets

Neighborhood based rough sets are important generalizations of the classical rough sets of Pawlak, as neighborhood operators generalize equivalence classes. In this article, we introduce nine neighborhood based operators and we study the partial order relations between twenty-two different neighborhood operators obtained from one covering. Seven neighborhood operators result in new rough set approximation operators. We study how these operators are related to the other fifteen neighborhood based approximation operators in terms of partial order relations, as well as to seven non-neighborhood-based rough set approximation operators
An evaluation methodology for concept maps mined from lecture notes: an educational perspective

Revised Selected Papers from 6th International Conference, CSEDU 2014 Barcelona, Spain, April 1–3, 2014Concept maps are effective tools that assist learners in organising and representing knowledge. Recent efforts in the area of concept mapping work toward semi- or fully automated approaches to extract concept maps from various text sources such as text books. The motivation for this research is twofold: novice learners require substantial assistance from experts in constructing their own maps, introducing additional hurdles, and alternatively, the workload required by academics in manually constructing expert maps is substantial and repetitive. A key limitation of an automated concept map generation is the lack of an evaluation framework to measure the quality of concept maps. The most common evaluation mechanism is measuring the overlap between machine-generated elements (e.g. concepts) with expert maps using relevancy measures such as precision and recall. However, in the educational context, the majority of knowledge presented is relevant to the learner, resulting in a large amount of information being retrieved for knowledge organisation. Therefore, this paper introduces a machine-based approach to evaluate the relative importance of knowledge by comparing with human judgment. We introduce three ranking models and conclude that the structural features are positively correlated with human experts (rs ~ 1) for courses with rich content and good structure (well-fitted).Thushari Atapattu, Katrina Falkner, and Nickolas Falkne
Semi-supervised fuzzy-rough feature selection

With the continued and relentless growth in dataset sizes in recent times, feature or attribute selection has become a necessary step in tackling the resultant intractability. Indeed, as the number of dimensions increases, the number of corresponding data instances required in order to generate accurate models increases exponentially. Fuzzy-rough set-based feature selection techniques offer great flexibility when dealing with real-valued and noisy data; however, most of the current approaches focus on the supervised domain where the data object labels are known. Very little work has been carried out using fuzzy-rough sets in the areas of unsupervised or semi-supervised learning. This paper proposes a novel approach for semi-supervised fuzzy-rough feature selection where the object labels in the data may only be partially present. The approach also has the appealing property that any generated subsets are also valid (super)reducts when the whole dataset is labelled. The experimental evaluation demonstrates that the proposed approach can generate stable and valid subsets even when up to 90% of the data object labels are missing
Simulation of stochastic Volterra equations driven by space--time L\'evy
  noise

In this paper we investigate two numerical schemes for the simulation of
stochastic Volterra equations driven by space--time L\'evy noise of pure-jump
type. The first one is based on truncating the small jumps of the noise, while
the second one relies on series representation techniques for infinitely
divisible random variables. Under reasonable assumptions, we prove for both
methods $L^p$- and almost sure convergence of the approximations to the true
solution of the Volterra equation. We give explicit convergence rates in terms
of the Volterra kernel and the characteristics of the noise. A simulation study
visualizes the most important path properties of the investigated processes
Towards an OSS adoption business impact assessment

Nowadays, the adoption of Open Source Software (OSS) by organizations is becoming a strategic need in a wide variety of application areas.  Organizations adopt OSS in very diverse ways. The way in which they adopt OSS affects and shapes their businesses. Therefore, knowing the impact of different OSS adoption strategies in the context of an organization may help improving the processes undertaken inside this organization and ultimately pave the road to strategic moves. However, there is a lack of support for assessing the impact of the OSS adoption over the business of the adopter organizations. Based on the goal-oriented characterization of some OSS adoption strategies, in this paper, we propose a preliminary approach to assess the business impact of the OSS adoption strategies over the adopter organizations. The proposal is based on the Business Model Canvas and graph theory notions to support the elicitation and assessment of the impact of each goal over the adopter organization.  We illustrate the application of the approach in the context of a telecommunications company.Peer ReviewedPostprint (author's final draft
Simplicity is not simple: how business architecture in one of Belgium's biggest companies can be simple and easy to use

As organizations are becoming more complex, Enterprise Architecture (EA) serves as an important means to align the strategy with the operations and to achieve business/IT (i.e., Information Technology) alignment. Although numerous approaches have been designed for large enterprises, little EA research was oriented towards small- and medium-sized enterprises (SMEs). However, both organizational types are fundamentally different and require a tailored approach. Therefore, CHOOSE was designed as an EA approach that is in accordance with the needs of SMEs. By performing a case study in the department of a large enterprise, this paper aims to investigate how CHOOSE can be used outside its original context. More specifically, it will be examined how the metamodel and modeling method could be extended to deliver an overview and valuable insights about a complex business reality. To realize this, potential solutions for the encountered issues are formulated and evaluated by the involved business stakeholders
Testing a selection of BPMN tools for their support of modelling guidelines

BPMN has become the de facto standard notation for process modelling. Past research has demonstrated the need for modelling guidelines to improve the quality of process models. In previous research we collected a set of practical guidelines through a systematic literature survey and classified those in different categories. In this paper we test a selection of BPMN tools for their support for these guidelines, and report on existing support per category of guideline and the kinds of support used by the tool to support the different guidelines. The results give insight into which domains of guidelines are well supported and which lack support from BPMN tools. Further, different preferences of the vendors are observed regarding the methods of support they implement in their tools.status: publishe
Fast and robust variational optical flow for high-resolution images using SLIC superpixels

We show how pixel-based methods can be applied to a sparse image representation resulting from a superpixel segmentation. On this sparse image representation we only estimate a single motion vector per superpixel, without working on the full-resolution image. This allows the accelerated processing of high-resolution content with existing methods. The use of superpixels in optical flow estimation was studied before, but existing methods typically estimate a dense optical flow field - one motion vector per pixel - using the full-resolution input, which can be slow. Our novel approach offers important speed-ups compared to dense pixel-based methods, without significant loss of accuracy
Content-Fragile Commutative Watermarking-Encryption Based on Pixel Entropy

Content-fragile commutative watermarking-encryption requires that both the content-fragile image signature and the watermarking process are invariant under encryption. The pixel entropy, being dependent on first-order image statistics only, is invariant under permutations. In the present paper we embed semi-fragile signatures based on pixel entropy by using a histogram-based watermarking algorithm, which is also invariant to permutations. We also show how the problem of collisions, i.e. different images having the same signature, can be overcome in this approach, if embedder and encryptor share a common secret
An H.264 sensor aided encoder for aerial video sequences with in-the-loop metadata enhancement

Unmanned Aerial Vehicles (UAVs) are often employed to capture high resolution images in order to perform image mosaicking and/or 3D reconstruction. Images are usually stored on-board or sent to the ground using still image or video data compression. Still image encoders are preferred when low frame rates are involved, because video coding systems are based on motion estimation and compensation algorithms which fail when the motion vectors are significantly long. The latter is the case of low frame rate videos, in which the overlapping between subsequent frames is very small. In this scenario, UAVs attitude and position metadata from the Inertial Navigation System (INS) can be employed to estimate global motion parameters without video analysis. However, a low complexity analysis can refine the motion field estimated using only the metadata. In this work, we propose to use this refinement step in order to improve the position and attitude estimation produced by the navigation system with the aim of maximizing the encoder performance. Experiments on both simulated and real world video sequences confirm the effectiveness of the proposed approac
A Game Engine as a Generic Platform for Real-Time Previz-on-Set in Cinema Visual Effects

International audienceWe present a complete framework designed for film production requiring live (pre) visualization. This framework is based on a famous game engine, Unity.  Actually, game engines possess many advantages that can be directly exploited in real-time pre-vizualization, where real and virtual worlds have to be mixed. In the work presented here, all the steps are performed in Unity: from acquisition to rendering. To perform real-time compositing that takes into account occlusions that occur between real and virtual elements as well as to manage physical interactions of real characters towards virtual elements, we use a low resolution depth map sensor coupled to a high resolution film camera. The goal of our system is to give the film director's creativity a flexible and powerful tool on stage, long before post-production
Cloud Data Auditing Using Proofs of Retrievability

Cloud servers offer data outsourcing facility to their clients. A client
outsources her data without having any copy at her end. Therefore, she needs a
guarantee that her data are not modified by the server which may be malicious.
Data auditing is performed on the outsourced data to resolve this issue.
Moreover, the client may want all her data to be stored untampered. In this
chapter, we describe proofs of retrievability (POR) that convince the client
about the integrity of all her data.Comment: A version has been published as a book chapter in Guide to Security
  Assurance for Cloud Computing (Springer International Publishing Switzerland
  2015