Specification Of Data Types Research Articles

Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire

BackgroundIt has become possible to use data from a patient’s mobile phone as an adjunct or alternative to the traditional self-report and interview methods of symptom assessment in psychiatry. Mobile data–based assessment is possible because of the large amounts of diverse information available from a modern mobile phone, including geolocation, screen activity, physical motion, and communication activity. This data may offer much more fine-grained insight into mental state than traditional methods, and so we are motivated to pursue research in this direction. However, passive data retrieval could be an unwelcome invasion of privacy, and some may not consent to such observation. It is therefore important to measure patients’ willingness to consent to such observation if this approach is to be considered for general use.ObjectiveThe aim of this study was to measure the ownership rates of mobile phones within the patient population, measure the patient population’s willingness to have their mobile phone used as an experimental assessment tool for their mental health disorder, and, finally, to determine how likely patients would be to provide consent for each individual source of mobile phone–collectible data across the variety of potential data sources.MethodsNew patients referred to a tertiary care mood and anxiety disorder clinic from August 2016 to October 2017 completed a survey designed to measure their mobile phone ownership, use, and willingness to install a mental health monitoring app and provide relevant data through the app.ResultsOf the 82 respondents, 70 (85%) reported owning an internet-connected mobile phone. When asked about installing a hypothetical mobile phone app to assess their mental health disorder, 41% (33/80) responded with complete willingness to install with another 43% (34/80) indicating potential willingness to install such an app. Willingness to give permissions for specific types of data varied by data source, with respondents least willing to consent to audio recording and analysis (19% [15/80] willing respondents, 31% [25/80] potentially willing) and most willing to consent to observation of the mobile phone screen being on or off (46% [36/79] willing respondents and 23% [18/79] potentially willing).ConclusionsThe patients surveyed had a high incidence of ownership of internet-connected mobile phones, which suggests some plausibility for the general approach of mental health state inference through mobile phone data. Patients were also relatively willing to consent to data collection from sources that were less personal but expressed less willingness for the most personal communication and location data.

Model of Point Cloud Data Management System in Big Data Paradigm

Modern geoinformation technologies for collecting and processing data, such as laser scanning or photogrammetry, can generate point clouds with billions of points. They provide abundant information that can be used for different types of analysis. Due to its characteristics, the point cloud is often viewed as a special type of geospatial data. In order to efficiently manage such volumes of data, techniques based on a computer cluster have to be used. The Apache Spark framework has proven to be a solution for efficient processing of large volumes of data. This paper thoroughly examines the representation of point cloud data type using Apache Spark constructs. The common operations over point clouds, range queries and k-nearest neighbors queries (kNN) are implemented using Apache Spark DataFrame Application Programming Interface (API). It enabled the design of point cloud related user defined types (UDT) and user defined functions (UDF). The structure of the point cloud for efficient storing in Big Data key-value stores was analyzed and described. The methods presented in this paper were compared to PostgreSQL RDBMS, and the results were discussed.

Exploring Core and Cache Hierarchy Bottlenecks in Graph Processing Workloads

Graph processing is an important analysis technique for a wide range of big data problems. The ability to explicitly represent relationships between entities gives graph analytics significant performance advantage over traditional relational databases. In this paper, we perform an in-depth data-aware characterization of graph processing workloads on a simulated multi-core architecture, find bottlenecks in the core and the cache hierarchy that are not highlighted by previous characterization work, and analyze the behavior of the specific application data type causing the corresponding bottleneck. We find that load-load dependency chains involving different application data types form the primary bottleneck in achieving a high memory-level parallelism in graph processing workloads. We also observe that the private L2 cache has a negligible contribution to performance, whereas the shared L3 cache has higher performance sensitivity. In addition, we present a study on the effectiveness of several replacement policies. Finally, we study the relationship between different graph algorithms and the access volumes to the different data types. Overall, we provide useful insights and guidelines toward developing a more optimized CPU-based architecture for high performance graph processing.

Earthquake Prediction Learning Using the Least Alarm Method

An approach to organization of a system for automatic earthquake prediction is developed. This approach is not bound to any particular type of data, and all data on seismotectonic processes are displayed using the spatial and space-time fields. The prediction field is learned from the feature fields and the sample of predicted earthquakes. The learning method proposed here is the least alarm method. The results of modeling of earthquake prediction for the regions of Japan and Mediterranean are presented. Modeling implied the analysis of prediction fields reflecting stationary and dynamic properties of the seismic process. The results of modeling demonstrate the efficiency of application of the least alarm method to earthquake prediction.

Understanding the Role of the WTO in International Data Flows: Taking the Liberalization or the Regulatory Autonomy Path?

Recent years have witnessed a surge in discussions relating to data and data flow in trade fora. This was predictable given the importance of data for trade in the digital economy, especially e-commerce. However, there is a major discord between WTO members on issues relating to data flows and data localization. This article sets out to understand how data flows across borders and the types of trade restrictive data localization measures members use. The analysis of various restrictions on data flows imposed by states reflects the different objectives behind them, targeting all or specific types of data. Such regulations potentially violate existing WTO commitments. The article concludes with a call for issuing a multilateral amendment of existing norms, and undertaking a data differentiated approach to resolve the deadlock at the WTO.

Relaxed Data Types as Consistency Conditions †

In the quest for higher-performance shared data structures, weakening consistency conditions and relaxing the sequential specifications of data types are two of the primary tools available in the literature today. In this paper, we show that these two approaches are in many cases different ways to specify the same sets of allowed concurrent behaviors of a given shared data object. This equivalence allows us to use whichever description is clearer, simpler, or easier to achieve equivalent guarantees. Specifically, for three common data type relaxations, we define consistency conditions such that the combination of the new consistency condition and an unrelaxed type allows the same behaviors as Linearizability and the relaxed version of the data type. Conversely, for the consistency condition k-Atomicity, we define a new data type relaxation such that the behaviors allowed by the relaxed version of a data type, combined with Linearizability, are the same as those allowed by k-Atomicity and the original type. As an example of the possibilities opened by our new equivalence, we use standard techniques from the literature on consistency conditions to prove that the three data type relaxations we consider are not comparable to one another or to several similar known conditions. Finally, we show a particular class of data types where one of our newly-defined consistency conditions is comparable to, and stronger than, one of the known consistency conditions we consider.

Data Sharing For Precision Medicine: Policy Lessons And Future Directions.

Data sharing is a precondition of precision medicine. Numerous organizations have produced abundant guidance on data sharing. Despite such efforts, data are not being shared to a degree that can trigger the expected data-driven revolution in precision medicine. We set out to explore why. Here we report the results of a comprehensive analysis of data-sharing guidelines issued over the past two decades by multiple organizations. We found that the guidelines overlap on a restricted set of policy themes. However, we observed substantial fragmentation in the policy landscape across specific organizations and data types. This may have contributed to the current stalemate in data sharing. To move toward a more efficient data-sharing ecosystem for precision medicine, policy makers should explore innovative ways to cope with central policy themes such as privacy, consent, and data quality; focus guidance on interoperability, attribution, and public engagement; and promote data-sharing policies that can be adapted to multiple data types.

Dynamical stochastic higher spin vertex models

We introduce a new family of integrable stochastic processes, called dynamical stochastic higher spin vertex models, arising from fused representations of Felder’s elliptic quantum group $$E_{\tau , \eta } ({\mathfrak {s}}{\mathfrak {l}}_2)$$ . These models simultaneously generalize the stochastic higher spin vertex models, studied by Corwin–Petrov and Borodin–Petrov, and are dynamical in the sense of Borodin’s recent stochastic interaction round-a-face models. We provide explicit contour integral identities for observables of these models (when run under specific types of initial data) that characterize the distributions of their currents. Through asymptotic analysis of these identities in a special case, we evaluate the scaling limit for the current of a dynamical version of a discrete-time partial exclusion process. In particular, we show that its scaling exponent is 1 / 4 and that its one-point marginal converges (in a sense of moments) to that of a non-trivial random variable, which we determine explicitly.

Network Traffic Fingerprinting Based on Approximated Kernel Two-Sample Test

Many applications and communication protocols exhibit unique communication patterns that can be exploited to identify them in network traffic. This paper proposes a method to represent these patterns compactly, such that they can be used in different analytical tasks. The method treats each communication as a set of observations of a random variable with unknown probability distribution. This view allows us to derive the representation from a distance between two probability distributions used in maximum mean discrepancy—a non-parametric kernel test. The representation (and distance) can be then easily used in various algorithms for identification of communicating application and data analysis, independently of the specific type of input data.

Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory.

BackgroundMicrobial genome sequencing is now being routinely used in many clinical and public health laboratories. Understanding how to report complex genomic test results to stakeholders who may have varying familiarity with genomics—including clinicians, laboratorians, epidemiologists, and researchers—is critical to the successful and sustainable implementation of this new technology; however, there are no evidence-based guidelines for designing such a report in the pathogen genomics domain. Here, we describe an iterative, human-centered approach to creating a report template for communicating tuberculosis (TB) genomic test results.MethodsWe used Design Study Methodology—a human centered approach drawn from the information visualization domain—to redesign an existing clinical report. We used expert consults and an online questionnaire to discover various stakeholders’ needs around the types of data and tasks related to TB that they encounter in their daily workflow. We also evaluated their perceptions of and familiarity with genomic data, as well as its utility at various clinical decision points. These data shaped the design of multiple prototype reports that were compared against the existing report through a second online survey, with the resulting qualitative and quantitative data informing the final, redesigned, report.ResultsWe recruited 78 participants, 65 of whom were clinicians, nurses, laboratorians, researchers, and epidemiologists involved in TB diagnosis, treatment, and/or surveillance. Our first survey indicated that participants were largely enthusiastic about genomic data, with the majority agreeing on its utility for certain TB diagnosis and treatment tasks and many reporting some confidence in their ability to interpret this type of data (between 58.8% and 94.1%, depending on the specific data type). When we compared our four prototype reports against the existing design, we found that for the majority (86.7%) of design comparisons, participants preferred the alternative prototype designs over the existing version, and that both clinicians and non-clinicians expressed similar design preferences. Participants showed clearer design preferences when asked to compare individual design elements versus entire reports. Both the quantitative and qualitative data informed the design of a revised report, available online as a LaTeX template.ConclusionsWe show how a human-centered design approach integrating quantitative and qualitative feedback can be used to design an alternative report for representing complex microbial genomic data. We suggest experimental and design guidelines to inform future design studies in the bioinformatics and microbial genomics domains, and suggest that this type of mixed-methods study is important to facilitate the successful translation of pathogen genomics in the clinic, not only for clinical reports but also more complex bioinformatics data visualization software.

Compression of Textual Column-Oriented Data

Column-oriented data are well suited for compression. Since values of the same column are stored contiguously on disk, the information entropy is lower if compared to the physical data organization of conventional databases. There are many useful light-weight compression techniques targeted at specific data types and domains, like integers and small lists of distinct values, respectively. However, compression of textual values formed by skewed and high-cardinality words is usually restricted to variations of the LZ compression algorithm. So far there are no empirical evaluations that verify how other sophisticated compression methods address columnar data that store text. In this paper we shed a light on this subject by revisiting concepts of those algorithms. We also analyse how they behave in terms of compression and speed when dealing with textual columns where values appear in adjacent positions.

Framework to Classify and Analyze Social Media Content

In the last decade, a large amount of data has been published in different fields and can be used as a data source for research and study. However, identifying a specific type of data requires processing, which involves machine learning classifying techniques. To facilitate this, we propose a general framework that can be applied to any social media content to develop an intelligent system. The framework consists of three main parts: an interface, classifier and ana-lyzer. The analyzer uses media recognition to identify specific features. Then, the classifier uses these features and involves them in the classification process. The interface organizes the interaction between the system compo-nents. We tested the framework and developed a system to be applied to im-age-based social media networks (Instagram). The system was implemented as a mobile application (My Interests) that works as a recommendation and filtering system for Instagram users and reduces the time they spend on irre-levant information. It analyzes the images, categorizes them, identifies the in-teresting ones, and finally, reports the results. We used the Cloud Vision API as a tool to analyze the images and extract their features. Furthermore, we adapted support vector machine (SVM), a machine learning method, to classify images and to predict the preferred ones.

On the information content of discrete phylogenetic characters.

Phylogenetic inference aims to reconstruct the evolutionary relationships of different species based on genetic (or other) data. Discrete characters are a particular type of data, which contain information on how the species should be grouped together. However, it has long been known that some characters contain more information than others. For instance, a character that assigns the same state to each species groups all of them together and so provides no insight into the relationships of the species considered. At the other extreme, a character that assigns a different state to each species also conveys no phylogenetic signal. In this manuscript, we study a natural combinatorial measure of the information content of an individual character and analyse properties of characters that provide the maximum phylogenetic information, particularly, the number of states such a character uses and how the different states have to be distributed among the species or taxa of the phylogenetic tree.

Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

Report from ACM MMSys 2017

The ACM Multimedia Systems Conference (MMSys) provides a forum for researchers to present and share their latest research findings in multimedia systems. It is a unique event targeting "multimedia systems" from various angles and views across all domains instead of focusing on a specific aspect or data type. ACM MMSys'17 was held in Taipei, Taiwan in June 20-23, 2017.

Malware Detection Based on Multiple PE Headers Identification and Optimization for Specific Types of Files

This paper follows our previous research in which we made a basic experiment to find out if it is possible to detect malware by multiple PE header detection. The previous results show us that there is a considerable amount of malwares that connect themselves to another file. This paper summarizes our previous results, updates the results and also expands them by adding an optimization method and also by including the scan of another (specific) types of data. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Sparse representation approach to data compression for strain-based traffic load monitoring: A comparative study

Among different Structural Health Monitoring (SHM) systems applied on bridges, Bridge Weight-in-Motion (BWIM) is probably the one with widest applications worldwide. Briefly, BWIM uses on-structure sensors that are able to acquire signals sensitive to traffic load events, which can be used as an indirect indicator of the load magnitude. The sampling rate required for this is relatively high (at least 10 Hz), which usually lead to databases with sizes that might reach the order of gigabytes. It is impractical to process this volume of information in the context of infrastructure asset management. Hence, an effective and efficient method for the compression and storage of BWIM data is becoming mandatory. In this paper, sparse representation algorithms have been innovatively applied to the BWIM data compression. A comparative study is performed based on measurements collected from a real bridge, by exploring different methods including Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and two dictionary learning methods, i.e. Compressive Sensing (CS) and K-means Singular Value Decomposition (K-SVD). It has been found that the K-SVD method shows the best performance when applied to this specific type of data, while the DWT method using Haar wavelet is the most computationally efficient. Nearly lossless reconstruction of the signal is achieved by using K-SVD with less than 0.1% reserved coefficients, which gives evidence that dictionary learning technologies are feasible to guarantee the same level of information even with much smaller databases. Therefore, the utilization of dictionary learning is a clear step forward towards higher levels of efficiency in the compression and storage of data collected by SHM systems.

Towards unified quality verification of synthetic count data with countsimQC.

SummaryStatistical tools for biological data analysis are often evaluated using synthetic data, designed to mimic the features of a specific type of experimental data. The generalizability of such evaluations depends on how well the synthetic data reproduce the main characteristics of the experimental data, and we argue that an assessment of this similarity should accompany any synthetic dataset used for method evaluation. We describe countsimQC, which provides a straightforward way to generate a stand-alone report that shows the main characteristics of (e.g. RNA-seq) count data and can be provided alongside a publication as verification of the appropriateness of any utilized synthetic data.Availability and implementation countsimQC is implemented as an R package (for R versions ≥ 3.4) and is available from https://github.com/csoneson/countsimQC under a GPL (≥2) license.

Online social network data as sociometric markers.

Data from online social networks carry enormous potential for psychological research, yet their use and the ethical implications thereof are currently hotly debated. The present work aims to outline in detail the unique information richness of this data type and, in doing so, to support researchers when deciding on ethically appropriate ways of collecting, storing, publishing, and sharing data from online sources. Focusing on the very nature of social networks, their structural characteristics, and depth of information, we provide a detailed and accessible account of the challenges associated with data management and data storage. In particular, the general nonanonymity of network data sets is discussed, and an approach is developed to quantify the level of uniqueness that a particular online network bestows upon the individual maintaining it. Using graph enumeration techniques, we show that comparatively sparse information on a network is suitable as a sociometric marker that allows for the identification of an individual from the global population of online users. The impossibility of anonymizing specific types of network data carries implications for ethical guidelines and research practice. At the same time, network uniqueness opens up opportunities for novel research in psychology. (PsycINFO Database Record

Apheresis data registration in WWA registry–10-year experience of our center

IntroductionThe WAA Registry allows detailed registration of hemapheresis data. Our center registers results there as well. We summarize our results as compared to those of the WAA Registry. Materials and methodsHemapheresis results are registered in the WAA Registry in Umea, Sweden. The patients’ identity is protected by coding. General data (age, gender, weight, procedure, technique used etc.) or special data (occurrence and type of adverse reactions, health condition, quality of life etc.) are completed in a pre-defined form. ResultsIn 2006-2016, we registered 7,927 hemaphereses in 956 patients in the WAA Registry; 40.4% in men and 59.6% in women aged 53±15years. There were mostly no significant differences in the individual interventions between our center and the WAA Registry; only the share of cascade filtrations/rheophereses is quite different (9 times higher in our center − 18.2% of interventions as compared to 2.1% in the WAA Registry). The share of photophereses (32.1%) is relatively high − due to cooperation with the bone marrow transplantations department. Discussion and conclusionIn regular quality assessment, one center usually does not have enough data and experience with some diseases or interventions; therefore, comparison with the WAA Registry results is valuable not only for the quality of interventions but also for side effect prevention. On the other hand, the advantage is that every center has its unique code and may work quite independently (quick and independent non-competitive assessments). Five-minute duration of registration is advantageous in a time-demanding work; moreover, the registration is free.