Reviewerly/exHarmony huggingface dataset:
Linguistic Imperialism, Toponymy, Semiotics & Taxonomies: The Anglicisation of Irish place names in hegemonic library cataloguing systems

Background. Cataloguing systems are generally assumed to be logical, objective and non-political. In this sense they are often assumed to be rather like maps. However, assumptions around the neutrality of both are erroneous. Maps and cataloguing systems reflect and reproduce dominance and power. In Ireland the six inch to a mile mapping project in the early to mid-1800s is generally accepted as the point at which much of the Anglicisation of Irish place names was formalised. As such it is often assumed that this Anglicisation is a historic event and that similar practices do not continue into the present.Objective. This paper sought to examine how vernacular names for places are treated in a mainstream international library classification and cataloguing system.Methods. The treatment of vernacular place names vis-a-vis English was examined under the dominant Anglo-American Cataloguing Rules, the Dewey Decimal Classification & Relative Index (DDC) and the Resource Description and Access (RDA) system.Results. This paper demonstrates how established international library classification and cataloguing systems continue to explicitly require English forms of names over the vernacular.Contributions. This paper reveals how library classification and cataloguing systems both reinforce the legacy of colonial oppression, and continue to assert the dominance of English. Cataloguing systems may therefore be viewed in terms of their power and purpose, and as such should not be seen as ideologically neutral.
Fuzzy C-Means Clustering Problem Based on Improved DNA Genetic Algorithm and Point Density Weighting

In the every kinds of fuzzy clustering algorithm, the fuzzy c-means clustering (FCM) is widely used in the implementation process, because of it has better local search ability and easy operation. But the fuzzy c-means clustering algorithm also has some inherent flaw and the insufficiency. In this paper, we propose some improvement for this shortcomings. Firstly, we improve the calculation method of the membership degree. Secondly, we join the density in the calculation of the membership degree. At the same time, in order to find the global optimal solution, we use the DNA genetic algorithm to assist the FCM algorithm to jump out of local optimal. In this paper, we use the Matlab2014 to realize the simulation and experiment. Firstly, we utilize the test functions and artificial datasets to prove the effectiveness of the improved DNA genetic algorithm. Secondly, we utilize the UCI data sets to validate the effectiveness of the improved fuzzy c-means algorithm. Finally, the improved fuzzy c-means algorithm was used to realize the classification for the Sogou lab corpus of text and the result proved the validity of the algorithm.
Informatics and information security

Informatics is knowledge of information and how to process it, and information systems management. Because information is encountered almost everywhere in everyday life, and with which we deal more and more, informatics is embedded in almost all the pores of our lives. They represent infrastructure and support other business processes. The challenges in informatics are related to the required quality of services, investments, risks, business ownership and their management. Information is described in the material from a security point of view. In line with this aspect, we require in particular that the information: (a) be available, (b) comprehensive and (c) confidential, to the extent necessary for the needs of the business process implementation. Informatics needs to provide wider support to business processes to the required extent. Therefore, informatics is an area that represents the logistics infrastructure. To a lesser extent, the reverse is true, but informatics is a support for logistics, since it is primarily possible through information processes to carry out logistics.
Language contact in Africa and the African diaspora in the Americas: In honor of John V. Singler. Edited by Cecilia Cutler, Zvjezdana Vrzić &amp; Philipp Angermeyer

Abstract This article reviews Language contact in Africa and the African diaspora in the Americas: In honor of John V. Singler $158
Silicon-based quantum computing: manufacturing and metrology challenges (Conference Presentation)

Over the last 20 years there has been a steady increase in fundamental physics research and the hardware development needed to realize a quantum computer. However, in the last 5 years we have seen a dramatic increase in the interest from industry, including a variety of major semiconductor and technology leaders such as IBM, Google and Intel. This is the result of both a better understanding of the variety of problems that can be addressed and the impact that will come with quantum computing along with the emergence of a couple of potentially scalable hardware solutions. In a recent interview with a leading manufacturer, an aggressive strategy was laid out to achieve an operational 1000-qubit quantum computer within 5 years that can solve a variety of problems not addressable by conventional computing and the 10-year prospect to achieve a 1 million qubit machine that would “profoundly change society”. The major technology leaders view quantum information (QI) as a technology that they must begin to develop and understand today. Augmenting existing technology with QI is desirable, but , there are a variety of technical challenges to making silicon-based QI systems with the needed quantum properties and coherence times. New challenges in atomic precision and individual atom or electron effects that fundamentally affect device performance bring on a variety of new manufacturing and metrology challenges. Measuring electron spin coherence, exchange energies, and qubit fidelity are entirely new measurements for the semiconductor industry. Even the resolution of TEM may be inadequate, as TEM of a nominally 80 nm thick slice of silicon which averages many atoms together in a measurement where only one or two atoms can deeply change device performance, coherence times, or introduce charge offsets. After developing some of the first cold atom and ion trap quantum information demonstrations at NIST, we are now investing significantly in developing the supporting metrology and modeling methods needed to help industry develop and commercialize this new technology. In this presentation we will provide an overview of hardware solutions for quantum computing, future metrology needs and challenges to enable manufacturable quantum computers. Although industry leaders are now investing in superconducting qubits and silicon-based qubits, the two leading technologies, this talk will primarily focus on the principles of solid state silicon QI operation, manufacturing metrology solutions, and measurements needed to develop and evaluate viable technological solutions.
Numerical Algorithmic Science and Engineering within Computer Science: Rationale, Foundations and Organization

A re-calibration is proposed for "numerical analysis" as it arises specifically within the broader, embracing field of modern computer science (CS). This would facilitate research into theoretical and practicable models of real-number computation at the foundations of CS, and it would also advance the instructional objectives of the CS field. Our approach is premised on the key observation that the great "watershed" in numerical computation is much more between finite- and infinite-dimensional numerical problems than it is between discrete and continuous numerical problems. A revitalized discipline for numerical computation within modern CS can more accurately be defined as "numerical algorithmic science &amp; engineering (NAS&amp;E), or more compactly, as "numerical algorithmics," its focus being the algorithmic solution of numerical problems that are either discrete, or continuous over a space of finite dimension, or a combination of the two. It is the counterpart within modern CS of the numerical analysis discipline, whose primary focus is the algorithmic solution of continuous, infinite-dimensional numerical problems and their finite-dimensional approximates, and whose specialists today have largely been repatriated to departments of mathematics. Our detailed overview of NAS&amp;E from the viewpoints of rationale, foundations, and organization is preceded by a recounting of the role played by numerical analysts in the evolution of academic departments of computer science, in order to provide background for NAS&amp;E and place the newly-emerging discipline within its larger historical context.
Migrating Ariadne from Drupal to a Static Site

At the start of 2019 Ariadne[1] moved from being a Drupal[2] site to being a static site. This moverealised a number of benefits for the journal including an improvement in the site performance anda reduction in the ongoing effort required for site maintenance.
Assessment of Light Microscopy, Rapid Diagnostic Test and Polymerase Chain Reaction on the Diagnosis of Malaria Infection in Infected Individuals in some South-South States of Nigeria

Rapid diagnosis and effective treatment of cases is the main objectives of control programmes in malaria endemic areas as malaria is one of the most deadly infections in some of the African countries. Its diagnosis is vital towards the effectiveness of antimalarial drug used. Routine microscopically examination, rapid diagnostic test and Polymerase chain reaction were compared. A total of Eight hundred and forty six positive samples were collected across the four Southern states (Bayelsa, Rivers, Edo and Delta) of Nigeria. Results: Out of the 192 samples screened from Bayelsa state 124(64.6%) were positive for microscopy and 68(35.4%) negative, 57(29.7%) were positive for rapid diagnostic test (RDT) and 135(70.3%) were negative, 150(78.1%) were positive for PCR and 42(21.9%) negative. Out of the 218 samples screened from Rivers, 135(61.9%) were positive for microscopy and 83(38.1%) negative, 90(41.3%) were positive for RDT and 128 (58.7%) negative, 150(68.8%) were positive for PCR and 68(31.2%) negative. Out of the 196 samples screened from Edo, 103(52.6%) were positive for microscopy and 93(47.4%) were negative, 63(32.1%) were positive for RDT and 133(67.9%) were negative, 123(62.8%) were positive for PCR and 73(37.2%) negative. Out of the 240 samples screened from Delta, 121(50.4%) were positive for microscopy and 119(49.6%) were negative, 78(32.5%) were positive for RDT while 162(67.5%) were negative, 141(58.8%) were positive for PCR while 99(41.2%) were negative. PCR technique which is the most sensitive diagnostic tool though more expensive should be recommended also routine microscopy and RDT should not be withdrawn for prompt diagnosis and cost effectiveness.
Using Latent Class Analysis to Identify ARDS Sub-phenotypes for Enhanced Machine Learning Predictive Performance

In this work, we utilize Machine Learning for early recognition of patients at high risk of acute respiratory distress syndrome (ARDS), which is critical for successful prevention strategies for this devastating syndrome. The difficulty in early ARDS recognition stems from its complex and heterogenous nature. In this study, we integrate knowledge of the heterogeneity of ARDS patients into predictive model building. Using MIMIC-III data, we first apply latent class analysis (LCA) to identify homogeneous sub-groups in the ARDS population, and then build predictive models on the partitioned data. The results indicate that significantly improved performances of prediction can be obtained for two of the three identified sub-phenotypes of ARDS. Experiments suggests that identifying sub-phenotypes is beneficial for building predictive model for ARDS.
Blockchain Solutions for Multi-Agent Robotic Systems: Related Work and Open Questions

The possibilities of decentralization and immutability make blockchain probably one of the most breakthrough and promising technological innovations in recent years. This paper presents an overview, analysis, and classification of possible blockchain solutions for practical tasks facing multi-agent robotic systems. The paper discusses blockchain-based applications that demonstrate how distributed ledger can be used to extend the existing number of research platforms and libraries for multi-agent robotic systems.
Image Perception and Diagnostic Accuracy of Digital Mammography: An Editorial

The use of screening mammography is generally accepted by the radiology community as an effective, although imperfect, means of screening for breast cancer. Various means of improving the accuracy of screening mammography have been proposed, including use of CAD and double reading. Double reading of mammograms has been demonstrated to be an effective method for improving the quality of mammography reading, however it increases the cost of the screening mammogram since at least two radiologists time is required. Call backs may also be increased, depending on the algorithm utilized for paired reading. In those areas where double reading is widely utilized, various methods of pairing radiologists for the double reads are used.
Quantitative Depth Quality Assessment of RGBD Cameras At Close Range Using 3D Printed Fixtures

Mobile robots that manipulate their environments require high-accuracy scene understanding at close range. Typically this understanding is achieved with RGBD cameras, but the evaluation process for selecting an appropriate RGBD camera for the application is minimally quantitative. Limited manufacturer-published metrics do not translate to observed quality in real-world cluttered environments, since quality is application-specific. To bridge the gap, we present a method for quantitatively measuring depth quality using a set of extendable 3D printed fixtures that approximate real-world conditions. By framing depth quality as point cloud density and root mean square error (RMSE) from a known geometry, we present a method that is extendable by other system integrators for custom environments. We show a comparison of 3 cameras and present a case study for camera selection, provide reference meshes and analysis code, and discuss further extensions.
Multiliteracies in Teaching Materials of Fiction Prose Study based Ador-Meverlee

This article aims to report on the multiliteracies that can be mastered by students taking part in the subject of Fiction Prose Study using Ador-Meverlee based teaching materials (advance organizer-meaningful verbal learning-elaboration) at the Faculty of Teachers Training and Education, Muhammadiyah University of Bengkulu.The research design that is used in compiling teaching materials is Research and Development with three main steps.The stages of the activities carried out are: theoretical studies and research, planning and production activities.The results of the research stated that the structure of the Ador-Meverlee based teaching materials for Fiction Prose Study consisted of: instructional material structure, language structure, understanding structure, display structure, text structure, and stimulant structure.The preparation of the structures of the Ador-Meverlee based teaching materials for Fiction Prose Study fulfills multimodal learning, fulfills the principle of multiliteracies learning: interpretation, collaboration, convention, cultural knowledge, problem solving, reflection, and language use, also fulfilling the stages of multiliteracies learning.In addition, the Ador-Meverlee based teaching materials for Fiction Prose Study can facilitate students in multiliteracies efforts in the form of : spatial-visual, linguistic-verbal, kinesthetic, interpersonal.moral
Research on Behavior Recognition Algorithm Based on Compact Representation of Low-level Feature

Recently, the bag of visual words (BOVW) model based on spatial-temporal interest points (STIPs) is used more and more widely in the field of behavior recognition. however, the model ignores temporal order between frames and intra frame position information of interest points. In the paper, an algorithm is proposed to acquire the geometrical and temporal distribution of STIPs. Firstly, the STIPs mutual information(STIPsMI) algorithm based on Co-occurrence matrix is proposed to describe the spatial-temporal relationship of STIPs between different visual words. Then the descriptor is concatenated with the BOVW histogram as the final descriptor. The two authoritative human action datasets were used to test the algorithm in paper: the KTH and the UCF sports. Experimental results verify the robustness of the algorithm and better than BOVW model and other mainstream methods.
Sampling from a Normal Distribution

Code: https://www.zoology.ubc.ca/~whitlock/Kingfisher Funding:  University of British Columbia Project Leader:  Mike Whitlock Programmers:  Boris Dalstein, Mike Whitlock & Zahraa Almasslawi Art:  Derek Tan Testing:  Melissa Lee, Gaitri Yapa & Bruce Dunham Thanks to:  Darren Irwin, Dolph Schluter, Nancy Heckman, Kaylee Byers, Brandon Doty, Kim Gilbert, Sally Otto, Wilson Whitlock, Jeff Whitlock, Jeremy Draghi, Karon MacLean, Fred Cutler, Diana Whistler, Andrew Owen, Mike Marin, Leslie Burkholder, Eugenia Yu, Doug Bonn, Michael Scott, the UBC Physics Learning Group & the UBC Flex Stats initiative for numerous suggestions and improvements.
Cultural Analytics of Large Datasets from Flickr

Deluge became a metaphor to describe the amount of information to which we are subjected, and very often we feel we are drowning while our access to information is rising. Devising mechanisms for exploring massive image sets according to perceptual attributes is still a challenge, even more when dealing with user-generated social media content. Such images tend to be heterogenous, and using metadata-only can be misleading. This paper describes a set of tools designed to analyze large sets of user-created art related images using image features describing color, texture, composition and orientation. The proposed pipeline permits to discriminate Flickr groups in terms of feature vectors and clustering parameters. The algorithms are general enough to be applied to other domains in which the main question is about the variability of the images.
Financial Openness and Income Inequality: Do Institutions matter for Africa?

There has been little empirical literature on the relationship between financial openness and income inequality in Africa. Using a panel threshold estimation technique we examine whether the effect of financial openness on income inequality is dependent to the institutional quality. We adopt the methodology proposed by Hansen (1999) on a panel dataset of 21 African countries over the period 1985-2014. Our results confirm the existence of a two-regime split. In fact, financial openness seems to significantly widen the income inequality in countries with low level of institutional quality while it does not have a significant effect in countries with relatively high institutional quality. These results concur with a well-established literature and demonstrate that financial openness-income inequality link is contingent on the quality of the political and institutional environment in the African countries.
Cloud based data storage by securing it through face recognition mode

Trust of Clients cannot be completely trusted or beneficial through the cloud. Many information gets corrupted due to the unwanted users by the employees in the hierarchy of an Organization. Separate Organization handles the data securely, but in many cases, data is being given away for money because of the single level Management. We move forward to an advanced safe Technology to overcome this issue, where data are downloaded by an Organization or a set of admins in a hierarchy through face detection mode. If any of the admin rejects in accepting the request the file cannot be downloaded and thereby the data is made secure. Advanced Encryption Algorithm is the medium to make this technology secure and beneficial. Many such technologies can offer such a new form of security based work for efficient and brand form of storage process in the software as well as any other field in cloud computing.
Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning

Inspired by the cognitive process of humans and animals, Curriculum Learning (CL) trains a model by gradually increasing the difficulty of the training data. In this paper, we study whether CL can be applied to complex geometry problems like estimating monocular Visual Odometry (VO). Unlike existing CL approaches, we present a novel CL strategy for learning the geometry of monocular VO by gradually making the learning objective more difficult during training. To this end, we propose a novel geometry-aware objective function by jointly optimizing relative and composite transformations over small windows via bounded pose regression loss. A cascade optical flow network followed by recurrent network with a differentiable windowed composition layer, termed CL-VO, is devised to learn the proposed objective. Evaluation on three real-world datasets shows superior performance of CL-VO over state-of-the-art feature-based and learning-based VO.
Supporting Factors to Get Coverage of Malaria Mass Blood Survey (MBS) Above 80%: Lesson Learn From Gripit Village, Banjarmangu Sub District, Banjarnegara District

Malaria in Gripit Village, Banjarmangu Sub district was increased at the end of 2017, by discovery of 14 patients with malaria indegenous. Mass Blood Survey (MBS) activity was carried out in this village because malaria transmission still occured even though Mass Fever Survey has been held and malaria patient without specific symptoms were found. The purpose of this paper is to analyze factors that support the coverage of malaria MBS above 80%. The method was observation by using observation guideline before and during MBS implementation. MBS was done in 2 days in January 2018. The result showed that the MBS coverage in Desa Gripit was 86.38%, the supporting factors were socialization before the activity, MBS was held together with the distribution of the mosquito net (something was obtained), MBS was done whole day (morning, noon, evening), monitoring coverage in MBS process, and active role from health cadres to persuate the villagers. MBS coverage more than 80% supported by multifactor.
DLF GRT Opportunity

Founded in the winter of 2017, the Digital Library Federation’s interest group on Government Records Transparency and Accountability seeks to support a broader culture of records transparency in the digital age. We use a variety of methods to support cross-disciplinary conversation, collaboration, and action around improving access to local, state and federal records and publications (as well as information, data, and documents). Over the past years, we’ve hosted open presentations on topics including the 2020 Census, federal records transparency and its relationship to immigrant justice, Title 44 reform, FOIA and state-level freedom of information laws, and civic data initiatives.
Robust, fast and accurate: a 3-step method for automatic histological image registration

We present a 3-step registration pipeline for differently stained histological serial sections that consists of 1) a robust pre-alignment, 2) a parametric registration computed on coarse resolution images, and 3) an accurate nonlinear registration. In all three steps the NGF distance measure is minimized with respect to an increasingly flexible transformation. We apply the method in the ANHIR image registration challenge and evaluate its performance on the training data. The presented method is robust (error reduction in 99.6% of the cases), fast (runtime 4 seconds) and accurate (median relative target registration error 0.19%).
A Tutorial on Sim-ATAV: Simulation-based Adversarial Testing Framework for Autonomous Vehicles

Testing autonomous vehicles in simulation environments is crucial. Sim-ATAV is an open-source framework developed for experimenting with different test generation techniques in simulation environments for research purposes. This document provides a tutorial on Sim-ATAV with a running example.