pinecone/core-2020-05-10-deduplication huggingface dataset:
Three-Dimensional CSEM Modelling on Unstructured Tetrahedral Meshes Using Edge Finite Elements

The last decade has been a period of rapid growth for electromagnetic methods (EM) in geophysics, mostly because of their industrial adoption. In particular, the marine controlled-source electromagnetic method (CSEM) has become an important technique for reducing ambiguities in data interpretation in hydrocarbon exploration. In order to be able to predict the EM signature of a given geological structure, modelling tools provide us with synthetic results which we can then compare to real data. On the other hand and among the modelling methods for EM based upon 3D unstructured meshes, the Nédélec Edge Finite Element Method (EFEM) offers a good trade-off between accuracy and number of degrees of freedom, i.e. size of the problem. Furthermore, its divergence-free basis is very well suited for solving Maxwell’s equation. On top of that, we present the numerical formulation and results of 3D CSEM modelling using the Parallel Edge-based Tool for Geophysical Electromagnetic Modelling (PETGEM) on unstructured tetrahedral meshes. We validated our experiments against quasi-analytical results in canonical models.Authors of this work has received funding from the European Union's Horizon 2020 research and innovation programme under the\ud
Marie-Sklodowska Curie grant agreement No. 644202.\ud
The research leading to these results has received funding from the European\ud
Union's Horizon 2020 Programme (2014-2020) and from Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP)\ud
under the HPC4E Project (www.hpc4e.eu), grant agreement No. 689772.\ud
Authors gratefully acknowledge the support from the Mexican National Council for Science and Technology (CONACyT). Numerical tests in this work were performed on the MareNostrum supercomputer of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (www.bsc.es).Peer ReviewedPostprint (author's final draft
Performance Evaluation of Multiple Cloud Data Centers Allocations for HPC

This paper evaluates the behavior of the Microsoft Azure G5 cloud instance type over multiple Data Centers. The purpose is to identify if there are major differences between them and to help the users choose the best option for their needs. Our results show that there are differences in the network level for the same instance type in different locations and inside the same location at different times. The network performance causes interference in the applications level, as we could verify in our results.This research received funding from the EU H2020 Programme and from MCTI/RNP-Brazil under the HPC4E project, grant agreement\ud
no. 689772. Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations\ud
(see https://www.grid5000.fr). Additional funding was provided by CAPES and Microsoft.Peer ReviewedPostprint (author's final draft
SM4MQ: a semantic model for multidimensional queries

On-Line Analytical Processing (OLAP) is a data analysis approach to support decision-making. On top of that, Exploratory OLAP is a novel initiative for the convergence of OLAP and the Semantic Web (SW) that enables the use of OLAP techniques on SW data. Moreover, OLAP approaches exploit different metadata artifacts (e.g., queries) to assist users with the analysis. However, modeling and sharing of most of these artifacts are typically overlooked. Thus, in this paper we focus on the query metadata artifact in the Exploratory OLAP context and propose an RDF-based vocabulary for its representation, sharing, and reuse on the SW. As OLAP is based on the underlying multidimensional (MD) data model we denote such queries as MD queries and define SM4MQ: A Semantic Model for Multidimensional Queries. Furthermore, we propose a method to automate the exploitation of queries by means of SPARQL. We apply the method to a use case of transforming queries from SM4MQ to a vector representation. For the use case, we developed the prototype and performed an evaluation that shows how our approach can significantly ease and support user assistance such as query recommendation.Peer ReviewedPostprint (author's final draft
Self-Enforcing Access Control for Encrypted RDF

The amount of raw data exchanged via web protocols is\ud
steadily increasing. Although the Linked Data infrastructure could\ud
potentially be used to selectively share RDF data with different individuals\ud
or organisations, the primary focus remains on the unrestricted\ud
sharing of public data. In order to extend the Linked Data paradigm to\ud
cater for closed data, there is a need to augment the existing infrastructure\ud
with robust security mechanisms. At the most basic level both access\ud
control and encryption mechanisms are required. In this paper, we propose\ud
a flexible and dynamic mechanism for securely storing and efficiently\ud
querying RDF datasets. By employing an encryption strategy based on\ud
Functional Encryption (FE) in which controlled data access does not\ud
require a trusted mediator, but is instead enforced by the cryptographic\ud
approach itself, we allow for fine-grained access control over encrypted\ud
RDF data while at the same time reducing the administrative overhead\ud
associated with access control management
Human-to-Human Interaction: the Killer Application of Ubiquitous Computing

Twenty-five years past the Weiser’s vision of Ubiquitous Computing, and there is not a clear understanding of what is or is not a pervasive system. Due to the loose boundaries of such paradigm, almost any kind of remotely ac-cessible networked system is classified as a pervasive system. We think that that is mainly due to the lack of killer applications that could make this vi-sion clearer. Actually, we think that the most promising killer application is already here, but we are so used to it that we do not see it, as a perfect fitting of the Weiser’s vision: the Human-to-Human Interaction mediated by com-puters
Serendipitous Recommendations through Ontology-based Contextual Pre-filtering

Context-aware Recommender Systems aim to provide users with better recommendations for their current situation. Although evaluations of recommender systems often focus on accuracy, it is not the only important aspect. Often recommendations are overspecialized, i.e. all of the same kind. To deal with this problem, other properties can be considered, such as serendipity. In this paper, we study how an ontology-based and context-aware pre-filtering technique which can be combined with existing recommendation algorithm performs in ranking tasks. We also investigate the impact of our method on the serendipity of the recommendations. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can improve the accuracy and serendipity
Fog Computing in Medical Internet-of-Things: Architecture,
  Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
  Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
  Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
  Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
  Smart Healthcare (2017), Springe
A functional analysis of haptic feedback in digital musical instrument interactions

An experiment is presented that measured aspects of functionality, usability and user experience for four distinct types of device feedback. The goal was to analyse the role of haptic feedback in functional digital musical instrument (DMI) interactions. Quantitative and qualitative human–computer interaction analysis techniques were applied in the assessment of prototype DMIs that displayed unique elements of haptic feedback; specifically, full haptic (constant-force and vibrotactile) feedback, constant-force only, vibrotactile only and no feedback. From the analysis, data are presented that comprehensively quantify the effects of feedback in haptic interactions with DMI devices. The investigation revealed that the various types of haptic feedback applied had no significant functional effect upon device performance in pitch selection tasks; however, a number of significant effects were found upon the users’ perception of usability and their experiences with each of the different feedback types
A generic decision support tool for lot-sizing and scheduling problems with setup and due dates

Decision support tools are essential to help in the management of industrial systems at different levels: strategic, to design the system; tactical to plan activities or assign resources; operational to schedule activities. In this paper, we present a generic and modular decision support tool to solve different planning, assignment, scheduling or lot-sizing problems. To the best of our knowledge, such generic tool does not exist. The methodology is illustrated by solving a real world lot-sizing and scheduling problem from a plastic injection company
Frontiers in Wastewater Treatment and Modelling\ud
FICWTM 2017

Today, multidisciplinary is a key to solving issues in the water field. The works in\ud
several International Water Association (IWA) Specialist Groups have demonstrated\ud
the importance of both innovative technologies and mathematical modelling,\ud
and that the exchange of scientific and technical information among researchers and\ud
practitioners involved in these fields is crucial for effectively advancing knowledge.\ud
Mathematical modelling has the advantage of allowing scenario analysis before\ud
designing the real plant in order to ensure an optimized system.\ud
To foster the multidisciplinary collaboration among different water specialists,\ud
the dialogue is a must in order to better share specific knowledge.\ud
With this final aim with the support of the International Water Association\ud
(IWA) and the University of Palermo, Italy it was organized the FICWTM 2017,\ud
Frontiers International Conference on Wastewater Treatment from 21 to 24 of May\ud
2017
Comparison of Two Mathematical Models for Greenhouse Gas Emission from Membrane Bioreactors

In this study two mathematical models (Model I and Model II), able to predict the nitrous oxide (N2O) and carbon dioxide (CO2) emission from an University Cape Town (UCT) – membrane bioreactor (MBR) plant, have been compared. Model I considers the N2O production only during the denitrification. Model II takes into account the two ammonia-oxidizing bacteria (AOB) formation pathways for N2O. Both models were calibrated adopting real data. Results highlight that Model II had a better capability of reproducing the measured data especially in terms of N2O model outputs. Indeed, the average efficiency related to the N2O model outputs was equal to 0.3 and 0.38 for Model I and Model II respectively
The Sludge Dewaterability in Advanced Wastewater Treatment: A Survey of Four Different Membrane BioReactor Pilot Plants

The wasted activated sludge dewaterability represents a major concern\ud
for Wastewater Treatment Plants (WWTPs) managers. Indeed, whereas the\ud
dewatered sludge could represents a re-usable matrix, the principal drawback\ud
related to the wasted sludge dewaterability is the high water content due to the\ud
presence of extracellular polymeric substances (EPS) that allow the trapping of\ud
water molecules within the bio sludge flocs. In order to provide an outlook of\ud
the dewaterability features of activated sludge derived from advanced WWTP,\ud
the present research reports a long term survey (over two years) aimed at\ud
assessing the principal dewaterability parameters of the sludge wasted from\ud
different Membrane BioReactor pilot plants
Greenhouse Gas Emissions from Membrane Bioreactors

Nowadays, it is widely accepted that wastewater treatment plants (WWTPs) are significant sources of greenhouse gas (GHG) emission, contributing to the anthropogenic sources. Among the GHG emitted from WWTPs, nitrous oxide (N2O) has been identified of having the major interest/concern, since its high global warming potential (GWP), is 298 times higher than that of CO2 and also to its capability to react with stratospheric ozone causing the layer depletion. Up to now, most of the experimental investigations have been carried out on conventional activated sludge (CAS) processes. The knowledge of N2O emission from advanced technologies such membrane bioreactors (MBRs) is still very limited. The present paper is aimed at providing a picture of the GHG emissions from MBR systems. In particular, data of N2O acquired from pilot plant systems monitoring are here presented. The key aim of the study was to highlight the effect of wastewater features and operational conditions on N2O production/emission from MBRs
Effect of Temperature on N2O and NO Emission in a Partial Nitrification SBR Treating Reject Wastewater

Temperature is a very important parameter during nitritation, having\ud
a direct effect on ammonia oxidation rate (AOR) and enzymatic activities which\ud
relate to both N2O and NO emission. This study aims at investigating the effect\ud
of temperature on AOR, N2O and NO production in an enriched ammonia\ud
oxidizing bacteria (AOB) sequencing batch reactor (SBR) performing partial\ud
nitrification (PN) of synthetic reject wastewater. To achieve that, a SBR was\ud
subject to several shifts in temperature (in the range of 30 to 15 °C, 5 °C for\ud
each decrease). Cycle studies, which contain two aeration phases, were conducted\ud
under each temperature. The results showed that AOR specific exponentially\ud
correlates with the temperature during the temperature decreasing\ud
experiments. With the decrease of the temperature, N2O firstly increased and\ud
then dropped to very low levels along with the decrease of the AOR, unlike NO\ud
that did not show any apparent connection with the temperature
A Graphical User Interface as a DSS Tool for GHG Emission Estimation from Water Resource Recovery Facilities

A Grafical User Interface (GUI) for the greenhouse gas (GHG) emissions from WWTPs based on four models aimed at quantifying the gas emissions from the aerated tanks (i.e. CAS and MBR reactor), aerobic digesters, secondary clarifiers and anaerobic digesters have been englobed in a GUI in order to provide a valid decision support system (DSS) to the practitioners. The GUI allows to estimate such emissions for the different WWTP phases considered. The GUI has been developed on MATLAB platform and provides as output the GHG emissions in terms of CO2 and N2O fluxes
Bacterial Community Structure of an IFAS-MBRs Wastewater Treatment Plant

In this work, the bacterial community putatively involved in BNR events of a UCT-MBMBR pilot plant was elucidated by both culture-dependent and metagenomics DNA analyses. The presence of bacterial isolates belonging to Bacillus (in the anoxic compartment) and to Acinetobacter, Stenotrophomonas, Rhodococcus, Escherichia and Aeromonas (in the aerobic compartment) is in agreement with the nitrification/denitrification processes observed in the plant. Moreover, the study of bacterial community structure by NGS revealed a microbial diversity suggesting a biochemical complexity which can be further explored and exploited to improve UCT-MBMBR plant performance
UCT-MBR vs IFAS-UCT-MBR for Wastewater Treatment: A Comprehensive Comparison Including N2O Emission

In this study the performance (in terms of carbon and nutrient removal) and N2O emission of two plant configurations adopting innovative technologies were investigated. With this regards, an University Cape Town (UCT) membrane bioreactor (MBR) plant and an Integrated Fixed Film Activated Sludge (IFAS) -UCT-MBR plant were monitored. Both plants treat real wastewater under two different values of the influent carbon nitrogen ratio (C/N = 5 mgCOD/mgN and C/N = 10 mgCOD/mgN). Results have shown the\ud
highest carbon and nutrients removal efficiencies for the IFAS-UCT-MBR configuration during both the two investigated C/N values. Furthermore, the lowest N2O emission occurred for the IFAS-UCT-MBR
Towards a structured process modeling method : building the prescriptive modeling theory

In their effort to control and manage processes, organizations often create process models. The quality of such models is not always optimal, because it is challenging for a modeler to translate her mental image of the process into a formal process description. In order to support this complex human processing task, we are developing a smart process modeling method. This paper describes how we have built the underlying prescriptive theory, which is constructed from existing evidence about successful information processing techniques in cognitive psychology
Keystroke Inference Using Smartphone Kinematics

The use of smartphones is becoming ubiquitous in modern society, these very personal devices store large amounts of personal information and we use these devices to access everything from our bank to our social networks, we communicate using these devices in both open one-to-many communications and in more closed, private one-to-one communications. In this paper we have created a method to infer what is typed on a device purely from how the device moves in the user’s hand. With very small amounts of training data (less than the size of a tweet) we are able to predict the text typed on a device with accuracies of up to 90%. We found no effect on this accuracy from how fast users type, how comfortable they are using smartphone keyboards or how the device was held in the hand. It is trivial to create an application that can access the motion data of a phone whilst a user is engaged in other applications, the accessing of motion data does not require any permission to be granted by the user and hence represents a tangible threat to smartphone users
Identifying changes in the cybersecurity threat landscape using the LDA-Web Topic Modelling Data Search Engine.

Successful Cybersecurity depends on the processing of vast quantities of data from a diverse range of sources such as police reports, blogs, intelligence reports, security bulletins, and news sources. This results in large volumes of unstructured text data that is difficult to manage or investigate manually. In this paper we introduce a tool that summarises, categorises and models such data sets along with a search engine to query the model produced from the data. The search engine can be used to find links, similarities and differences between different documents in a way beyond the current search approaches. The tool is based on the probabilistic topic modelling technique which goes further than the lexical analysis of documents to model the subtle relationships between words, documents, and abstract topics. It will assists researchers to query the underlying models latent in the documents and tap into the repository of documents allowing them o be ordered thematically
Its Not All About the Money: Self-efficacy and Motivation in Defensive and Offensive Cyber Security Professionals

Two important factors that define how humans go about performing tasks are self-efficacy and motivation. Through a better understanding of these factors, and how they are displayed by professionals in different roles within the cyber security discipline we can start to explore better ways to exploit the human capability within our cyber security. From our study of 137 cyber security professionals we found that those in attack-focussed roles displayed significantly higher-levels of self-efficacy than those in defensive-focussed roles. We also found those in attack-focussed roles demonstrated significantly higher levels of intrinsic motivation and significantly lower levels of externally regulated motivation. It should be noted we found no correlation with age or experience with either the focus of the practitioners task (whether offensive or defensive focussed) or their levels of motivation or self-efficacy. These striking findings further highlight the differences between those performing tasks that are self-described as offensive and those that are self-described as defensive. This also demonstrates the asymmetry that has long existed in cyber security from both a technical and opportunity viewpoint also exists in the human dimension
Secure and Trustable Distributed Aggregation based on Kademlia

Aggregation of values that need to be kept confidential while guaranteeing
the robustness of the process and the correctness of the result is required in
an increasing number of applications. We propose an aggregation algorithm,
which supports a large spectrum of potential applications including complex
voting protocols. It relies on the distributed hash table Kademlia, used in
BitTorrent, for pseudonymous communication between randomly predetermined peers
to ensure a high degree of confidentiality which does not solely relies on
cryptography. The distribution of data and computation limits the potential for
data breaches, and reduces the need for institutional trust. Experimental
results confirm the complexity of O(n) for n peers allowing for large-scale
applications
Supporting Privacy by Design using Privacy Process Patterns

Advances in Information and Communication Technology (ICT) have had significant impact on every-day life and have allowed us to share, store and manipulate information easily and at any time. On the other hand, such situation also raises important privacy concerns. To deal with such concerns, the literature has identified the need to introduce a Privacy by Design (PbD) approach to support the elicitation and analysis of privacy requirements and their implementation through appropriate Privacy Enhancing Technologies. However, and despite all the work presented in the literature, there is still a gap between privacy design and implementation. This paper presents a set of Privacy Process Patterns that can be used to bridge that gap. To demonstrate the practical application of such patterns, we instantiate them in JavaScript Object Notation (JSON), we use them in conjunction with the Privacy Safeguard (PriS) methodology and we apply them to a real case study