Close to ** percent of surveyed security professionals reported using a centralized SIEM interface to analyze and consolidate their endpoint data. SIEMs (security, information, and event management) are interfaces that make log collection and interpretation much easier by normalizing and categorizing all the information they take in.

All statistics were done in R Studio

Yearly citation counts for the publication titled "General methods for analysing repeated measures".

This paper provides a review of three different advanced machine learning algorithms for anomaly detection in continuous data streams from a ground-test firing of a subscale Solid Rocket Motor (SRM). This study compares Orca, one-class support vector machines, and the Inductive Monitoring System (IMS) for anomaly detection on the data streams. We measure the performance of the algorithm with respect to the detection horizon for situations where fault information is available. These algorithms have been also studied by the present authors (and other co-authors) as applied to liquid propulsion systems. The trade space will be explored between these algorithms for both types of propulsion systems.

The following datasets are used for the Water Rights Demand Analysis project and are formatted to be used in the calculations. The State Water Resources Control Board Division of Water Rights (Division) has developed a methodology to standardize and improve the accuracy of water diversion and use data that is used to determine water availability and inform water management and regulatory decisions. The Water Rights Demand Data Analysis Methodology (Methodology https://www.waterboards.ca.gov/drought/drought_tools_methods/demandanalysis.html ) is a series of data pre-processing steps, R Scripts, and data processing modules that identify and help address data quality issues related to both the self-reported water diversion and use data from water right holders or their agents and the Division of Water Rights electronic water rights data.

For each main and supporting figures, the linear mixed models, statistical inference tests, and p-values are shown. (XLSX)

This dataset contains raw datafiles that support the development of rapid gas chromatography mass spectrometry (GC-MS) methods for seized drug analysis. Files are provided in the native ".D" format collected from an Agilent GC-MS system. Files can be opened using Agilent proprietary software or freely available software such as AMDIS (which can be downloaded at chemdata.nist.gov). Included here is data of seized drug mixtures and adjudicated case samples that were analyzed as part of the method development process for rapid GC-MS. Information about the naming of datafiles and the contents of each mixture and case sample can be found in the associated Excel sheet ("File Names and Comments.xlsx").

This dataset contains all of the supporting materials to accompany Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chapter A3, 454 p., https://doi.org/10.3133/tm4a3. [Supersedes USGS Techniques of Water-Resources Investigations, book 4, chapter A3, version 1.1.]. Supplemental material (SM) for each chapter are available to re-create all examples and figures, and to solve the exercises at the end of each chapter, with relevant datasets provided in an electronic format readable by R. The SM provide (1) datasets as .Rdata files for immediate input into R, (2) datasets as .csv files for input into R or for use with other software programs, (3) R functions that are used in the textbook but not part of a published R package, (4) R scripts to produce virtually all of the figures in the book, and (5) solutions to the exercises as .html and .Rmd files. The suff ...

Question Paper Solutions of chapter Correlation and Regression Analysis of Numerical and statistical Methods, 5th Semester , Bachelor of Computer Application 2020-2021

Background Microarray experiments offer a potent solution to the problem of making and comparing large numbers of gene expression measurements either in different cell types or in the same cell type under different conditions. Inferences about the biological relevance of observed changes in expression depend on the statistical significance of the changes. In lieu of many replicates with which to determine accurate intensity means and variances, reliable estimates of statistical significance remain problematic. Without such estimates, overly conservative choices for significance must be enforced. Results A simple statistical method for estimating variances from microarray control data which does not require multiple replicates is presented. Comparison of datasets from two commercial entities using this difference-averaging method demonstrates that the standard deviation of the signal scales at a level intermediate between the signal intensity and its square root. Application of the method to a dataset related to the β-catenin pathway yields a larger number of biologically reasonable genes whose expression is altered than the ratio method. Conclusions The difference-averaging method enables determination of variances as a function of signal intensities by averaging over the entire dataset. The method also provides a platform-independent view of important statistical properties of microarray data.

Context

With the growth of social media and the spread of the Internet, the user's opinion become accessible in public forums. It became then possible to analyse and extract knowledge based on the textual data published by users, through the application of Natural Language Processing and Text Mining techniques. In this dissertation, these techniques are used to, based on comments posted by users on YouTube, extract information about Usability, User Experience (UX), and Perceived Health Impacts related to Quality of Life (H-QoL). This analysis focus on videos about the Just Dance series, one of the most popular interactive dance video games.

Just Dance belongs in a category of games whose purpose goes beyond entertainment - serious games - among which there is a specific type of games, exergames, which aim is to promote physical activity. Despite their positive influence on the health of their users, these often stop playing after a short period of time, leading to the loss of benefits in the medium and long term. It is in this context that the need to better understand the experience and opinions of players arises, especially how they feel and how they like to interact, so that the knowledge generated can be used to redesign games, so that these can increasingly address the preferences of end-users.

It is with this purpose, that in a serious game it is necessary to assure not only the fundamental characteristics of the functioning system, but also to provide the best possible experience and, at the same time, to understand if these positively impact players' lives. In this way, this work analyses three dimensions, observing, besides Usability and UX aspects, also H-QoL, in the corpus extrated.

To meet the objectives, a tool was developed that extracts information from user comments on YouTube, a social media network that despite being one of the most popular, still has been little explored as a source for opinion mining. To extract information about Usability, UX and H-QoL, a pre-established vocabulary was used with an approach based on the lexicon of the English idiom and its semantic relations. In this way, the presence of 38 concepts (five of Usability, 18 of UX, and 15 of H-QoL) was annotated, and the sentiment of each comment was also analysed. Given the lack of a vocabulary that allowed for the analysis of the dimension related to H-QoL, the concepts identified in the World Health Organization's WHOQOL-100 questionnaire were validated for user opinion mining purposes with ten specialists in the Health and Quality of Life domains.

The results of the information extration are displayed in a public dashboard that allows visitors to explore and analyse the existing data. Until the moment of this work, 543 405 comments were collected from 32 158 videos, in which about 52% contain information related to the three dimensions. The performance of this annotation process, as measured through human validation with eight collaborators, obtained an general efficacy of 85%.

Content

There are three datasets related with Just Dance game on YouTube, with: - All the user's comments extracted, with some informations about them and with sentiment analysis - Analytics collected from YouTube, related with comments, videos and channels - All the data analyzed in the work, with the annotation of the 38 concepts under study

Project

Developed by Renato Santos in the context of the Master Degree in Informatics Engineering, DEI-FCTUC, dissertation titled "Analysing Usability, User Experience, and Perceived Health Impacts related to Quality of Life based on Users' Opinion Mining", under the supervision of Paula Alexandra Silva and Joel Perdiz Arrais.

More information

Check more about this project: https://linktr.ee/justdanceproject

Contact

If you have any questions or suggestions, please e-mail us on renatojms@student.dei.uc.pt

Abstract VisRes is a graphical tool for displaying and analysing resonance data. Files of eigenphase sums, generated during the computation of electron and photon interactions with atoms and molecules, can be read and the data displayed both as continuous and discontinuous graphs. Resonance resolution can be improved by interactively computing additional data points and merging them with the displayed data. Resonance positions and widths are determined by fitting user selected data points by the Breit...

Title of program: VisRes 2.00 Catalogue Id: ADJE_v1_0

Nature of problem Electron and photon collisions with atoms and molecules give rise to quasi-bound states with long lifetimes. These resonances are exhibited in theoretical computations by a rapid increase in the eigenphase sum of approximately pi radians. VisRes is a graphical tool designed to enable the user to read files of eigenphase sums, to detect resonances, and to determine accurately their positions and widths.

Versions of this program held in the CPC repository in Mendeley Data ADJE_v1_0; VisRes 2.00; 10.1016/S0010-4655(98)00067-8

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)

Regression analysis of the business environment well-being index is presented.

This manual provides guidance on how to create a pair analysis file and on the appropriate weights and design variables needed to analyze pair data, and it provides example code in multiple software packages.

The Molecular Methods Market size was valued at USD 2.0 billion in 2023 and is projected to reach USD 3.17 billion by 2032, exhibiting a CAGR of 6.8 % during the forecasts period. Molecular Methods Market refers to the machinery and procedures that pertain to the analysing and modifying of macromolecules such as DNA, RNA and proteins at their molecular level. These have been espoused more in diagnostics, research, forensic medicine and for individual patient profiles. The fields in which these tools may be used are PCR, sequencing, microarrays, and gene editing. This market is growing due to factors like technology, changing genetic disorders’ incidence, and the need for precision medicine. Trends are the use of artificial intelligence and machine learning for data analysis, portable, user-friendly devices, and the increased collaboration between universities and industry participants with ideas implementation into applications.

The article analyzes the methods for measuring the radio frequency parameters of 5G subscriber equipment in the millimeter wave range for the scenario of autonomous deployment (SA) of the 5G RAN radio access network and shows the features of these measurements associated with the need to carry out them using Over-the-Air methods. The issues of analyzing the parameters being tested

ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

These data refer to the paper “Upcoming issues, new methods: using Interactive Qualitative Analysis (IQA) in Management Research”. This article is a guide to the application of the IQA method in management research and the files available refer to: 1. 1-Affinities, definitions, and cards produced by focus group.docx: all cards, affinities and definitions create by focus group session.docx 2. 2-Step-by-step - Analysis procedures.docx: detailed data analysis procedures.docx 3. 3-Axial Coding Tables – Individual Interviews.docx: detailed axial coding procedures.docx 4. 4-Theoretical Coding Table – Individual Interviews.docx: detailed theoretical coding procedures.docx

Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyse animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analysing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analysing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g. sex) on the number and combined strength of an individual's network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In grouping-event based networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analysing and interpreting their own network data using these methods.

This dataset contains the original data, analysis data, and a results synopsis of 12 slug tests performed in 7 wells completed in unconfined fractured bedrock near the North Shore of Lake Superior in Minnesota. Aquifers tested include extrusive and intrusive volcanic rocks and slate. Estimated hydraulic conductivity range from 10.2 to 2x10-6 feet/day. Mean and median hydraulic conductivity are 3.7 and 1.6, respectively. The highest and lowest hydraulic conductivities were in slate and fractured lava, respectively. Compressed air and traditional displacement-tube methods were employed. Water levels were measured with barometrically compensated (11 tests) and absolute pressure transducers (1 test) and recorded with data loggers. Test data were analyzed with AQTESOLV software using the unconfined KGS (Hyder and others, 1994; 9 tests) and Bower-Rice, 1976 models (3 tests).This dataset contains the original data, analysis data, and a results synopsis of 12 slug tests performed in 7 wells completed in unconfined fractured bedrock near the North Shore of Lake Superior in Minnesota. Aquifers tested include extrusive and intrusive volcanic rocks and slate. Estimated hydraulic conductivity range from 10.2 to 2x10-6 feet/day. Mean and median hydraulic conductivity are 3.7 and 1.6, respectively. The highest and lowest hydraulic conductivities were in slate and fractured lava, respectively. Compressed air and traditional displacement-tube methods were employed. Water levels were measured with barometrically compensated (11 tests) and absolute pressure transducers (1 test) and recorded with data loggers. Test data were analyzed with AQTESOLV software using the unconfined KGS (Hyder and others, 1994; 9 tests) and Bower-Rice, 1976 models (3 tests). Data files include the original recorded data, data files transformed into a form necessary for AQTESLOV, AQTESOLV analysis files and results files, and a compilation of well information and slug-test results. All files are formatted as tab-delimited ASCII except for the AQTESOVE analysis and results files, which are proprietary aqt and PDF files respectively. For convenience, a Microsoft Excel file is included that contains a synopsis of the well data and slug-test results, original recorded, transformed, and plotted slug-test data, data formats, constants and variables used in the data analysis, and notes about each test. Data files include the original recorded data, data files transformed into a form necessary for AQTESLOV, AQTESOLV analysis files and results files, and a compilation of well information and slug-test results. All files are formatted as tab-delimited ASCII except for the AQTESOVE analysis and results files, which are proprietary aqt and PDF files respectively. For convenience, a Microsoft Excel file is included that contains a synopsis of the well data and slug-test results, original recorded, transformed, and plotted slug-test data, data formats, constants and variables used in the data analysis, and notes about each test.