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Author(s):  
Peter Brenton

Whether community created and driven, or developed and run by researchers, most citizen science projects operate on minimalistic budgets, their capacity to invest in fully featured bespoke software and databases is usually very limited. Further, the increasing number of applications and citizen science options available for public participation creates a confusing situation to navigate. Cloud-based platforms such as BioCollect, iNaturalist, eBird, CitSci.org, and Zooniverse, provide an opportunity for citizen science projects to leverage highly featured functional software capabilities at a fraction of the cost of developing their own, as well as a common channel through which the public can find and access projects. These platforms are also excellent vehicles to facilitate the implementation of data and metadata standards, which streamline interoperability and data sharing. Such services can also embed measures in their design, which uplift the descriptions and quality of data outputs, significantly amplifying their usability and value. In this presentation I outline the experiences of the Atlas of Living Australia on these issues and demonstrate how we are tackling them with the BioCollect and iNaturalist platforms. We also consider the differences and similarities of these two platforms with respect to standards and data structures in relation to suitability for different use cases. You are invited to join a discussion on approaches being adopted and offer insights for improved outcomes.


2022 ◽  
Vol 22 (1) ◽  
pp. 1-26
Author(s):  
Zakaria Benomar ◽  
Francesco Longo ◽  
Giovanni Merlino ◽  
Antonio Puliafito

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.


2021 ◽  
Author(s):  
Bhuvaneswari Sankaranarayanan ◽  
Aria Abubakar ◽  
David F. Allen ◽  
Ivan Diaz Granados

Abstract Log interpretation is the task of analyzing and processing well logs to generate the subsurface properties around wells. A direct application of machine learning (ML) to this task is to train an ML model for predicting properties in target wells given well logs (data) and properties (labels) in a set of training wells in the same field and/or region. Our ML model of choice for predicting the desired properties is the decision tree-based learning algorithm called random forests (RF). We also devise a mechanism to automatically tune the hyperparameters of this algorithm depending on the data in the training wells. This eliminates the tedious task of carefully tuning the hyperparameters for every new set of training wells and provides a one-click solution. In addition to predicting the properties, we compute the uncertainty in the predicted properties in the form of prediction intervals using the concept of quantile regression forests (QRF). We test our workflow on two use cases. First, we consider a petrophysics use case on an unconventional land dataset to predict the petrophysical properties such as water saturation, total porosity, volume of clay, and total organic carbon from petrophysics logs. Then, we consider a geomechanics use case on a conventional offshore dataset to predict the lithology, pore pressure, and rock mechanical properties. We obtain a good prediction performance on both use cases. The uncertainty estimates also complement the ML model's prediction of the properties by explaining the various correlations that are found to be existing among them based on domain knowledge. The entire workflow of automating the tuning of hyperparameters and training the ML model to predict the properties along with its estimate of uncertainty provide a complete solution to apply the ML workflow for automated log interpretation.


2021 ◽  
pp. 1351010X2110455
Author(s):  
David Thery ◽  
David Poirier-Quinot ◽  
Sebastien Jouan ◽  
Brian FG Katz ◽  
Vincent Boccara

Auralization technology has reached a satisfactory level of ecological validity, enabling its use in architectural acoustic design. Only recently have the actual uses of auralization in the consulting community been explored, resulting in the identification of a variety of uses, including (1) to present to clients, (2) to test design ideas, (3) as a verification tool, (4) as a verification tool, (5) as a marketing tool, and (6) to improve internal company discussions. Taking advantage of methodologies from ergonomics research, the present study investigates effective uses through the observation of a collaboration project between an acoustic research team and an acoustic consultant, as a case study. Two spaces have been auralized in the context of the conception of a new skyscraper during the design phase of the project. The two spaces faced different problematics: an Atrium for which three different acoustic treatment options were suggested and experienced through multi-modal auralizations and audio-only auralizations of an Auditorium where an intrusive noise was to be acoustically treated. The ergonomic observation and analysis of this project revealed key impediments to the integration of auralization in common acoustic design practices.


Author(s):  
Christopher Allan O'Neill ◽  
Mark Andrejevic ◽  
Neil Selwyn ◽  
Xin Gu ◽  
Gavin Smith

In this paper we analyse data gathered through facial recognition tradeshow ethnographies and interviews with members of the biometrics industry, as we consider recent shifts in industry discourse towards promoting the ‘ethical’ use of biometric technology. As the biometrics industry increasingly moves towards a ‘Video Surveillance as a Service’ (VSaaS) model, the study of facial recognition infrastructures is becoming a crucial aspect of the interrogation of the Internet of Things. We demonstrate that the facial recognition industry is acutely aware of critiques of facial recognition cameras and biometric technologies as enabling social harms related to intrusiveness and bias (see Stark, 2019), and that members of the industry are keen to promote a more prosocial public image of the technology. Towards this end we find that biometric monitoring of children has gained a prominent place in the promotion of facial recognition technologies as a mode of ‘careful’ surveillance. We identify three key ‘use cases’ in which the face of the child takes on a prominent role as justifying and legitimating the use of facial recognition technologies – in the auditing of humanitarian food supply programs, in the detection of so-called ‘staging’ of family units at the US border, and in the detection of underage gambling in Australia. We argue that the immanent ‘ethical’ framing of the child’s face in this context serves to obscure the political ramifications of the extension of facial recognition and of biometric surveillance tools more broadly.


2021 ◽  
Vol 54 (5) ◽  
Author(s):  
Sergey Stepanov

X-ray Server (https://x-server.gmca.aps.anl.gov) is a collection of programs for online modelling of X-ray diffraction and scattering. The dynamical diffraction program is the second most popular Server program, contributing 34% of total Server usage. It models dynamical X-ray diffraction from strained crystals and multilayers for any Bragg-case geometry including grazing incidence and exit. This paper reports on a revision of equations used by the program, which yields ten times faster calculations in most use cases, on implementing calculations of X-ray standing waves and on adding new options for modelling diffraction from monolayers.


Semantic Web ◽  
2021 ◽  
pp. 1-21
Author(s):  
Gustavo Candela ◽  
Pilar Escobar ◽  
María Dolores Sáez ◽  
Manuel Marco-Such

Cultural heritage institutions are exploring Semantic Web technologies to publish and enrich their catalogues. Several initiatives, such as Labs, are based on the creative and innovative reuse of the materials published by cultural heritage institutions. In this way, quality has become a crucial aspect to identify and reuse a dataset for research. In this article, we propose a methodology to create Shape Expressions definitions in order to validate LOD datasets published by libraries. The methodology was then applied to four use cases based on datasets published by relevant institutions. It intends to encourage institutions to use ShEx to validate LOD datasets as well as to promote the reuse of LOD, made openly available by libraries.


Author(s):  
David Golightly ◽  
Ken Pierce ◽  
Roberto Palacin ◽  
Carl Gamble

Simulation is an important tool to support rail decarbonisation but can be challenging due to heterogeneous models, simulation tools and skill sets, and concerns around intellectual property. Multi-modelling, a proven methodology in sectors such as aerospace and automotive, uses Functional Mock-up Interface (FMI) and co-simulation to potentially overcome these problems. This paper presents a feasibility study of multi-modelling for rail decarbonisation, using a combination of audit of current state of the art, technical implementation and stakeholder consultation. The audit showed that while current uptake of FMI in rail is low, there is potential to repurpose models from pre-existing tools and apply them within multi-modelling. The technical feasibility assessment demonstrated how multi-modelling could generate flexible simulation outputs to identify decarbonisation systems effects both for urban and mainline rail, including rapid integration of pre-existing MATLAB Simulink models. Work with industry stakeholders identified use cases where multi-modelling would benefit rail decarbonisation, as well as barriers and enablers to adoption. Overall, the study demonstrates the feasibility and considerations for multi-modelling to support rail decarbonisation efforts, and the future developments necessary for wider rollout.


Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2161
Author(s):  
Martin Rudigier ◽  
Georg Nestlinger ◽  
Kailin Tong ◽  
Selim Solmaz

Automated vehicles we have on public roads today are capable of up to SAE Level-3 conditional autonomy according to the SAE J3016 Standard taxonomy, where the driver is the main responsible for the driving safety. All the decision-making processes of the system depend on computations performed on the ego vehicle and utilizing only on-board sensor information, mimicking the perception of a human driver. It can be conjectured that for higher levels of autonomy, on-board sensor information will not be sufficient alone. Infrastructure assistance will, therefore, be necessary to ensure the partial or full responsibility of the driving safety. With higher penetration rates of automated vehicles however, new problems will arise. It is expected that automated driving and particularly automated vehicle platoons will lead to more road damage in the form of rutting. Inspired by this, the EU project ESRIUM investigates infrastructure assisted routing recommendations utilizing C-ITS communications. In this respect, specially designed ADAS functions are being developed with capabilities to adapt their behavior according to specific routing recommendations. Automated vehicles equipped with such ADAS functions will be able to reduce road damage. The current paper presents the specific use cases, as well as the developed C-ITS assisted ADAS functions together with their verification results utilizing a simulation framework.


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