Specification Of Data Types Research Articles

Appraising the decision-making process concerning COVID-19 policy in postsecondary education in Canada: A critical scoping review protocol.

Responses to COVID-19 in Canadian postsecondary education have overhauled usual norms and practices, with policies of unclear rationale implemented under the pressure of a reported public health emergency. To critically appraise the decision-making process informing COVID-19 policy in the postsecondary education sector. Our scoping review will draw from macro and micro theories of public policy, specifically the critical tradition exemplified by Carol Bacchi's approach "What is the problem represented to be" and will be guided by Arksey and O'Malley's framework for scoping reviews and the team-based approach of Levan and colleagues. Data will include diverse and publicly available documents to capture multiple stakeholders' perspectives on the phenomenon of interest and will be retrieved from university newsletters and legal websites using combinations of search terms adapted to specific data types. Two reviewers will independently screen, chart, analyze and synthesize the data. Disagreements will be resolved through full team discussion. Despite the unprecedented nature of the mass medical mandates implemented in the postsecondary sector and their dramatic impact on millions of lives-students, faculty, staff and their families, friends and communities-the decision-making process leading to them has not been documented or appraised. By identifying, summarizing and appraising the evidence, our review should inform practices that can contribute to effective and equitable public health policies in postsecondary institutions moving forward.

Graph-based mobility profiling

The decarbonization of the transport system requires a better understanding of human mobility behavior to optimally plan and evaluate sustainable transport options (such as Mobility as a Service). Current analysis frameworks often rely on specific datasets or data-specific assumptions and hence are difficult to generalize to other datasets or studies. In this work, we present a workflow to identify groups of users with similar mobility behavior that appear across several datasets. Our method does not depend on a specific clustering algorithm, is robust against the choice of hyperparameters, does not require specific labels in the dataset, and is not limited to specific types of tracking data. This allows the extraction of stable mobility profiles based on several small and inhomogeneous tracking data sets. Our method consists of the following main steps: Representing individual mobility using location-based graphs; extraction of graph-based mobility features; clustering using different hyperparameter configurations; group identification using statistical testing. The method is applied to six tracking datasets (Geolife, Green Class 1 + 2, yumuv and two Foursquare datasets) with a total of 1070 users that visit about 3′000’000 different locations with a total tracking duration of over 200′000 days. We can identify and interpret five mobility profiles that appear in all datasets and show how these profiles can be used to analyze longitudinal and cross-sectional tracking studies.

Show and tell: approaches for effective figures

Show and tell: approaches for effective figures

Common data model for COVID-19 datasets.

A global medical crisis like the coronavirus disease 2019 (COVID-19) pandemic requires interdisciplinary and highly collaborative research from all over the world. One of the key challenges for collaborative research is a lack of interoperability among various heterogeneous data sources. Interoperability, standardization and mapping of datasets are necessary for data analysis and applications in advanced algorithms such as developing personalized risk prediction modeling. To ensure the interoperability and compatibility among COVID-19 datasets, we present here a common data model (CDM) which has been built from 11 different COVID-19 datasets from various geographical locations. The current version of the CDM holds 4639 data variables related to COVID-19 such as basic patient information (age, biological sex and diagnosis) as well as disease-specific data variables, for example, Anosmia and Dyspnea. Each of the data variables in the data model is associated with specific data types, variable mappings, value ranges, data units and data encodings that could be used for standardizing any dataset. Moreover, the compatibility with established data standards like OMOP and FHIR makes the CDM a well-designed CDM for COVID-19 data interoperability. The CDM is available in a public repo here: https://github.com/Fraunhofer-SCAI-Applied-Semantics/COVID-19-Global-Model. Supplementary data are available at Bioinformatics online.

Systematic comparison of ranking aggregation methods for gene lists in experimental results.

A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The gene lists resulting from a group of studies answering the same, or similar, questions can be combined by ranking aggregation methods to find a consensus or a more reliable answer. Evaluating a ranking aggregation method on a specific type of data before using it is required to support the reliability since the property of a dataset can influence the performance of an algorithm. Such evaluation on gene lists is usually based on a simulated database because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. In this study, a group of existing methods and their variations that are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data were used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomic data, with various heterogeneity of quality, noise level and a mix of unranked and ranked data using 20000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (non-small cell lung cancer) and bacteria (macrophage apoptosis) was performed. We summarize the results of our evaluation in a simple flowchart to select a ranking aggregation method, and in an automated implementation using the meta-analysis by information content algorithm to infer heterogeneity of data quality across input datasets. The code for simulated data generation and running edited version of algorithms: https://github.com/baillielab/comparison_of_RA_methods. Code to perform an optimal selection of methods based on the results of this review, using the MAIC algorithm to infer the characteristics of an input dataset, can be downloaded here: https://github.com/baillielab/maic. An online service for running MAIC: https://baillielab.net/maic. Supplementary data are available at Bioinformatics online.

Методика предобработки данных машинного обучения для решения задач компьютерного зрения

In the field of machine learning, there is no single methodology for data preprocessing, since all stages of this process are unique for a specific task. However, a specific data type is used in each direction. The research hypothesis assumes that it is possible to clearly structure the sequences and phases of data preparation for text recognition tasks. The article discusses the basic principles of data preprocessing and the allocation of successive stages as a specific technique for the task of recognizing ABC characters. ETL set images were selected as the source data. Preprocessing included the stages of working with images, at each of which changes were made to the source data. The first step was cropping, which allowed to get rid of unnecessary information in the image. Next, the approach of converting the image to the original aspect ratio was considered and the method of converting from shades of gray to black and white format was determined. At the next stage, the character lines were artificially expanded for better recognition of printed alphabets. At the last stage of data preprocessing, augmentation was performed, which made it possible to better recognize ABC characters regardless of their position in space. As a result, the general structure of the data preprocessing methodology for text recognition tasks was built.

Platform Data Protection from the Competition Law Perspective Based on Specific Conduct and Data Types

In the era of big data, data has gradually become an important commercial resource in the development of the emerging economy. Under the background of frequent disputes over unfair network competition, emerging data rights and interests can be better defined and better protected from the perspective of the Competition Law. When judging whether the conduct is an unfair competition conduct, the performance of the accused conduct and the type of the data involved in the case should be accurately defined, so as to define its legitimacy.

A survey on machine learning in array databases

This paper provides an in-depth survey on the integration of machine learning and array databases. First,machine learning support in modern database management systems is introduced. From straightforward implementations of linear algebra operations in SQL to machine learning capabilities of specialized database managers designed to process specific types of data, a number of different approaches are overviewed. Then, the paper covers the database features already implemented in current machine learning systems. Features such as rewriting, compression, and caching allow users to implement more efficient machine learning applications. The underlying linear algebra computations in some of the most used machine learning algorithms are studied in order to determine which linear algebra operations should be efficiently implemented by array databases. An exhaustive overview of array data and relevant array database managers is also provided. Those database features that have been proven of special importance for efficient execution of machine learning algorithms are analyzed in detail for each relevant array database management system. Finally, current state of array databases capabilities for machine learning implementation is shown through two example implementations in Rasdaman and SciDB.

Partial arithmetical data types of rational numbers and their equational specification

Upon adding division to the operations of a field we obtain a meadow. It is conventional to view division in a field as a partial function, which complicates considerably its algebra and logic. But partiality is one out of a plurality of possible design decisions regarding division. Upon adding a partial division function ÷ to a field Q of rational numbers we obtain a partial meadow Q(÷) of rational numbers that qualifies as a data type. Partial data types bring problems for specifying and programming that have led to complicated algebraic and logical theories – unlike total data types. We discuss four different ways of providing an algebraic specification of this important arithmetical partial data type Q(÷) via the algebraic specification of a closely related total data type. We argue that the specification method that uses a common meadow of rational numbers as the total algebra is the most attractive and useful among these four options. We then analyse the problem of equality between expressions in partial data types by examining seven notions of equality that arise from our methods alone. Finally, based on the laws of common meadows, we present an equational calculus for working with fracterms that is of general interest outside programming theory.

Quali-Mat

While sports activity recognition is a well studied subject in mobile, wearable and ubiquitous computing, work to date mostly focuses on recognition and counting of specific exercise types. Quality assessment is a much more difficult problem with significantly less published results. In this work, we present Quali-Mat: a method for evaluating the quality of execution (QoE) in exercises using a smart sports mat that can measure the dynamic pressure profiles during full-body, body-weight exercises. As an example, our system not only recognizes that the user is doing push-ups, but also distinguishes 5 subtly different types of push-ups, each of which (according to sports science literature and professional trainers) has a different effect on different muscle groups. We have investigated various machine learning algorithms targeting the specific type of spatio-temporal data produced by the pressure mat system. We demonstrate that computationally efficient, yet effective Conv3D model outperforms more complex state-of-the-art options such as transfer learning from the image domain. The approach is validated through an experiment designed to cover 47 quantifiable variants of 9 basic exercises with 12 participants. Overall, the model can categorize 9 exercises with 98.6% accuracy / 98.6% F1 score, and 47 QoE variants with 67.3% accuracy / 68.1% F1 score. Through extensive discussions with both the experiment results and practical sports considerations, our approach can be used for not only precisely recognizing the type of exercises, but also quantifying the workout quality of execution on a fine time granularity. We also make the Quali-Mat data set available to the community to encourage further research in the area.

CLPA: Clean-Label Poisoning Availability Attacks Using Generative Adversarial Nets

Poisoning attacks are emerging threats to deep neural networks where the adversaries attempt to compromise the models by injecting malicious data points in the clean training data. Poisoning attacks target either the availability or integrity of a model. The availability attack aims to degrade the overall accuracy while the integrity attack causes misclassification only for specific instances without affecting the accuracy of clean data. Although clean-label integrity attacks are proven to be effective in recent studies, the feasibility of clean-label availability attacks remains unclear. This paper, for the first time, proposes a clean-label approach, CLPA, for the poisoning availability attack. We reveal that due to the intrinsic imperfection of classifiers, naturally misclassified inputs can be considered as a special type of poisoned data, which we refer to as "natural poisoned data''. We then propose a two-phase generative adversarial net (GAN) based poisoned data generation framework along with a triplet loss function for synthesizing clean-label poisoned samples that locate in a similar distribution as natural poisoned data. The generated poisoned data are plausible to human perception and can also bypass the singular vector decomposition (SVD) based defense. We demonstrate the effectiveness of our approach on CIFAR-10 and ImageNet dataset over a variety type of models. Codes are available at: https://github.com/bxz9200/CLPA.

Data Privacy Concerns as a Source of Resistance to Complete Mobile Data Collection Tasks Via a Smartphone App

Abstract Smartphones present many interesting opportunities for survey research, particularly through the use of mobile data collection applications (apps). There is still much to learn, however, about how to integrate apps in general population surveys. Recent studies investigating hypothetical willingness to complete mobile data collection tasks via an app suggest there may be substantial resistance, in particular, due to concerns around data privacy. There is not much evidence about how privacy concerns influence actual decisions to participate in app-based surveys. Theoretical approaches to understanding privacy concerns and survey participation decisions would suggest that the influence of the former over the latter is likely to vary situationally. In this paper, we present results from a methodological experiment conducted in the context of a three-wave probability-based online panel survey of the general population as part of the 2019 Swiss Election Study (“Selects”) testing different ways of recruiting participants to an app. Questions included at wave 1 about online data privacy concerns and comfort sharing different types of data with academic researchers allow us to assess their impact on both hypothetical willingness to download a survey app for completing questionnaires, to take and share photos, and to share the smartphone’s GPS location and actual completion of these tasks. Our findings confirm that general concerns about online data privacy do influence hypothetical willingness to complete mobile data collection tasks, but may be overridden by how comfortable people feel about sharing specific types of data with researchers. When it comes to actual compliance with task requests, however, neither privacy concerns nor comfort sharing data seem to matter. We conclude with recommendations for exploring these relationships further in future app-based studies.

A Scalable, Standards-Based Approach for IoT Data Sharing and Ecosystem Monetization

The full potential of IoT data is often unrealized because data are restricted to single-purpose use-cases or accessible to a few, siloed users. The opportunities are significantly greater when data can be shared using a marketplace concept. This provides a framework where many suppliers of data can interact with consumers of data, such as AI developers and IoT service providers. Their marketplace interactions provide a means to identify demand for specific types of data and, to incentivize suppliers to offer high quality and dependable data. Standardization is a pre-requisite for an effective marketplace in terms of setting the rules for interactions and in establishing a technical baseline for data supply and consumption. This paper introduces an IoT data marketplace framework and illustrates its application through a set of smart city and intelligent transport system deployments. It highlights use of oneM2M, which is an open standard for the middleware layer in the IoT technology stack. oneM2M’s middleware capabilities reside between IoT devices and communications networks and, the AI/ML and IoT applications that consume IoT data. The proposed IoT data marketplace also includes licensing, usage tracking and secured data sharing features. These provide marketplace users with the tools to control data sharing and monetization. Through reference to multiple deployments of the IoT data marketplace, the paper highlights the benefits of standardization for replicable solutions in multiple IoT segments, interoperability, and business model innovation involving multi-stakeholder ecosystems.

Specifying geospatial data product characteristics for forest and fuel management applications

One of the greatest challenges for land managers is to maintain a multitude of ecosystem services while reducing hazards posed by wildfires, insect outbreaks, and other disturbances accelerating due to climate change. In response to limited available resources and improved technical abilities, natural resource managers are increasingly using geospatial data to plan and evaluate their management actions. Large amounts of public resources are invested in research and development to improve geospatial datasets, yet there is limited knowledge about the specific data types and data characteristics that clients (e.g. land managers) prefer. Our overall objective was to investigate what geospatial data characteristics are preferred by natural resource professionals to monitor and manage forests and fuels across large landscapes. We performed an online survey and collected supplemental data at a subsequent workshop during the 2020 Operational Lidar Inventory meeting to investigate preferred data use and data characteristics of data users of the Pacific Northwest. Our online survey was completed by 69 respondents represented by managers and natural resource professionals from tribal/state, federal, academic, and industry/consulting entities. We found that metrics related to species composition, total biomass/timber volume, and vegetation height were the most preferred attributes, yet preference differed slightly by employment type. From the workshop we found that metric preferences depend upon which management priorities are central to the management application. There was preference for data with Landsat pixel-level (30 m) spatial resolution, annual temporal resolution, and at regional spatial extents. To maintain viable ecosystem services in the long term, it is important to understand the metrics and their data characteristics that are most useful. We conclude that our study is a useful way to understand (a) how to improve the data utility for the users (clients) and (b) the development and investment needs for the data developers and funders.

Totalising Partial Algebras

We will examine totalising a partial operation in a general algebra by using an absorbtive element, bottom, such as an error flag. We then focus on the simplest example of a partial operation, namely subtraction on the natural numbers: n - m is undefined whenever n < m. We examine the use of bottom in algebraic structures for the natural numbers, especially semigroups and semirings. We axiomatise this totalisation process and introduce the algebraic concept of a team, being an additive cancellative semigroup with totalised subtraction. Also, with the natural numbers in mind, we introduce the property of being generated by an iterative function, which we call a splinter. We prove a number of theorems about the algebraic specification of datatypes of natural numbers.

Blockchain for deep learning: review and open challenges.

Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today’s deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly trustworthy deep learning frameworks.

Employees’ privacy perceptions: exploring the dimensionality and antecedents of personal data sensitivity and willingness to disclose

AbstractThe processing of employees’ personal data is dramatically increasing, yet there is a lack of tools that allow employees to manage their privacy. In order to develop these tools, one needs to understand what sensitive personal data are and what factors influence employees’ willingness to disclose. Current privacy research, however, lacks such insights, as it has focused on other contexts in recent decades. To fill this research gap, we conducted a cross-sectional survey with 553 employees from Germany. Our survey provides multiple insights into the relationships between perceived data sensitivity and willingness to disclose in the employment context. Among other things, we show that the perceived sensitivity of certain types of data differs substantially from existing studies in other contexts. Moreover, currently used legal and contextual distinctions between different types of data do not accurately reflect the subtleties of employees’ perceptions. Instead, using 62 different data elements, we identified four groups of personal data that better reflect the multi-dimensionality of perceptions. However, previously found common disclosure antecedents in the context of online privacy do not seem to affect them. We further identified three groups of employees that differ in their perceived data sensitivity and willingness to disclose, but neither in their privacy beliefs nor in their demographics. Our findings thus provide employers, policy makers, and researchers with a better understanding of employees’ privacy perceptions and serve as a basis for future targeted research on specific types of personal data and employees.

Influential methods reports for group-randomized trials and related designs.

This article identifies the most influential methods reports for group-randomized trials and related designs published through 2020. Many interventions are delivered to participants in real or virtual groups or in groups defined by a shared interventionist so that there is an expectation for positive correlation among observations taken on participants in the same group. These interventions are typically evaluated using a group- or cluster-randomized trial, an individually randomized group treatment trial, or a stepped wedge group- or cluster-randomized trial. These trials face methodological issues beyond those encountered in the more familiar individually randomized controlled trial. PubMed was searched to identify candidate methods reports; that search was supplemented by reports known to the author. Candidate reports were reviewed by the author to include only those focused on the designs of interest. Citation counts and the relative citation ratio, a new bibliometric tool developed at the National Institutes of Health, were used to identify influential reports. The relative citation ratio measures influence at the article level by comparing the citation rate of the reference article to the citation rates of the articles cited by other articles that also cite the reference article. In total, 1043 reports were identified that were published through 2020. However, 55 were deemed to be the most influential based on their relative citation ratio or their citation count using criteria specific to each of the three designs, with 32 group-randomized trial reports, 7 individually randomized group treatment trial reports, and 16 stepped wedge group-randomized trial reports. Many of the influential reports were early publications that drew attention to the issues that distinguish these designs from the more familiar individually randomized controlled trial. Others were textbooks that covered a wide range of issues for these designs. Others were "first reports" on analytic methods appropriate for a specific type of data (e.g. binary data, ordinal data), for features commonly encountered in these studies (e.g. unequal cluster size, attrition), or for important variations in study design (e.g. repeated measures, cohort versus cross-section). Many presented methods for sample size calculations. Others described how these designs could be applied to a new area (e.g. dissemination and implementation research). Among the reports with the highest relative citation ratios were the CONSORT statements for each design. Collectively, the influential reports address topics of great interest to investigators who might consider using one of these designs and need guidance on selecting the most appropriate design for their research question and on the best methods for design, analysis, and sample size.

Classification of TCGA-related research articles based on cancer types and experimental strategy

The Cancer Genome Atlas (TCGA) was launched in the year 2006 to accelerate the comprehensive understanding of the genetics of cancer using advanced high throughput technologies. It has been helping to generate new cancer therapies, prognostics methods, diagnostic methods and preventive strategies. This has led to an exponential increase in scientific articles utilising and corroborating information and data from TGCA to various related studies. With this exponential increase in the number of articles, it is challenging to identify specific articles of interest for a particular cancer type. It is even more challenging to identify articles by specific data types. In this work, we have built a web-tool, CTP (Classification of TCGA Publications), to systematically classify the articles that are utilising TCGA data. This tool enables users to access all the relevant articles available for a particular cancer type or experimental strategy utilising TCGA data.

Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization.

The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling-a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js, its source code, and documentation are available at https://gosling.js.org.