Collection Of Raw Data Research Articles

N of 1 Data Sharing: The Impact of Data Sharing within the Hematology–Oncology Drug Products Division of the US FDA

Despite both enthusiasm and concern regarding data sharing from clinical trials, few studies have documented the outputs of data sharing. The FDA has a considerable collection of raw data from drugs for their marketing authorization. An examination of data sharing with the FDA provides a snapshot into studies that this may facilitate.

Benefits of and Obstacles to RPA Implementation in Accounting Firms

This paper describes the concept of a novel research planned to be carried out in Polish accounting firms providing accounting services to micro and small enterprises. The accounting firms consist of around 36,000 active entities providing services to as many as about 2 million entrepreneurs. The principal objective of this research is to determine the extent of robotic process automation in Polish accounting firms, as well as to identify the benefits of and obstacles to its implementation. The research is to be structured into 6 tasks, while the methods to be applied include a literature review, interviews with the owners and accountants working in accounting firms, survey methods/pen-and-paper personal interview and computer-assisted web interview, as well as raw data collection and statistical analysis. The results of a preliminary pilot study in two accounting firms are also presented in the paper.

Unmanned Driving Infringement Judgment Based on Wireless Sensor Network Data Mining

Based on the wireless sensor network unmanned driving infringement identification system, this paper focuses on the application of data mining technology and state machine technology and designs and implements a set of practical and effective. Self‐driving cars can reduce the frequency of traffic accidents, alleviate urban traffic congestion, improve people’s travel efficiency, and lower the threshold of driving and other social values. The data processing program and a number of algorithms are given, and a complete set of data processing procedures and algorithms are proposed, including the collection of raw sensor data, the preprocessing of the collected data, and the feature extraction of the processed data. In the experiment, the unmanned driving infringement monitoring network was first designed to conduct real‐time monitoring of unmanned driving infringements during transportation and application. Aiming at the characteristics of unmanned driving infringements, a monitoring network platform was designed for remote control and large‐scale monitoring. Secondly, according to the characteristics of the unmanned driving infringement monitoring sensor network, the unmanned driving infringement node monitoring terminal is designed. The monitoring terminal part mainly designs the sensor module, the wireless communication module, the display warning module power module, and the data mining processing module. The sensor modules, respectively, include temperature, humidity, and concentration sensors, and the communication mode in the communication module mainly adopts Wi‐Fi. At the same time, the research is based on wireless sensor network, combined with data mining technology, puts forward a sensory data display system model based on data mining technology, and conducts an in‐depth analysis of the sensory data display system model, including the logical level of the system, system architecture, and functional modules. Finally, it focuses on the specific application of data mining technology in environmental information analysis and prediction, uses JAVA programming and realizes a data analysis and display system based on wireless sensor network, and verifies the accuracy of the data mining algorithm. The experimental results analyze the application of data mining technology in the driverless infringement determination system and use a large number of unmanned driving infringements to analyze the determination rules, so as to realize the interaction between active people and driverless cars.

The M2 Money Supply, the Economy, and the National Debt: A Mathematical Approach

The United States dollar was tied to a finite standard dating back to 1792. The dollar has survived multiple wars, armed conflicts short of war, economic recessions, and the Great Depression. At the end of World War II, the dollar, by international agreement, effectively became the world’s reserve currency. Other currencies were fixed to the dollar and the dollar was in turn fixed to the gold standard, that is, the value of a dollar was fixed at thirty-five dollars per ounce of gold. In 1971, the United States government severed the dollar from the gold standard leaving the size of the money supply free of any finite limit. Since that time, the United States money supply, national debt, and stock market indices have experienced exponential growth. The purpose of this paper is to investigate mathematically and model the relationship among these key economic indicators. Our work in this paper allows us to gain some insight into how each of these economic indicators can influence one another while giving us a better idea of how the economy functions. We were allowed to collect data for each of the indicators over a certain period which allowed us to demonstrate that a strong correlation exists among these three indicators. After collecting our data and making some logical assumptions, we modeled the money supply as a function of time. In turn, we modeled the Dow Jones stock market index as a function of the money supply. Finally, we produced a model of the National Debt as a function of the Dow Jones stock market index. From the data, we collected and our economic indicator models, we derived a series of differential equations that would help us observe the exponential growth trend of each graphically. By showing a comparison from our collection of raw data versus our results from our model, we conclude that, under prevailing circumstances, it is reasonable to expect growth in all three indicators and increasing market volatility for some time to come. We have answered the question of whether the monetary and fiscal policies of the past half-century have been of some benefit. However, we question whether this trend is good for all Americans and whether it is sustainable in perpetuity. This paper concludes with possible areas of interest warranting additional investigation to enable us to better understand the economy and develop effective financial strategies.

FPETD: Fault‐Tolerant and Privacy‐Preserving Electricity Theft Detection

Electricity theft occurs from time to time in the smart grid, which can cause great losses to the power supplier, so it is necessary to prevent the occurrence of electricity theft. Using machine learning as an electricity theft detection tool can quickly lock participants suspected of electricity theft; however, directly publishing user data to the detector for machine learning‐based detection may expose user privacy. In this paper, we propose a real‐time fault‐tolerant and privacy‐preserving electricity theft detection (FPETD) scheme that combines n‐source anonymity and a convolutional neural network (CNN). In our scheme, we designed a fault‐tolerant raw data collection protocol to collect electricity data and cut off the correspondence between users and their data, thereby ensuring the fault tolerance and data privacy during the electricity theft detection process. Experiments have proven that our dimensionality reduction method makes our model have an accuracy rate of 92.86% for detecting electricity theft, which is much better than others.

Selecting Content Analysis Approach in Social Networking Sites: What’s the Best for Cyberbullying Studies?

Insight into students’ behaviour and interaction is essential when it comes to improving the development and implementation of educational policies. Content analysis is a staple method to study online communication. However, there seems to be no clear reference for researchers to decide which approach is suitable to address a particular purpose. So, this study aims to recommend the most suitable content analysis approach for SNS research. This study extracted relevant studies from open-access databases and analysed the full-texts with NVivo 12. From a raw data collection of 120 texts, this study identified 19 approaches. This study recommends four approaches which are ideal to study cyberbullying among students in the higher education context, specifically on the types, styles, motives, and solutions of cyberbullying. First is Neuendorf’s descriptive content analysis approach to study about the types. Second is Hijmans’ rhetorical content analysis to study the styles. Third is Hsieh and Shannon’s directed content analysis approach to study the motives. Fourth is Miles and Huberman’s collaborative social content analysis to study the solutions. These four approaches have different focuses that seem to be the perfect match to study different aspects of online interaction on SNS, particularly in the context of cyberbullying.

Komposisi Karawitan dalam Perspektif Estetika Posmodern

Purpose: This article examines some works of musical composition which are dissected by the aesthetic approach of postmodernism. Research methods: The method used is a qualitative method by collecting data through observation, literature review, and document review (discography). Data analysis uses tri angulation of data from raw data collection (scripts), selection, processing, to drawing conclusions. Results and discussion: The idioms along with the aesthetic characteristics of postmodernism are applied to uncover the phenomenon of postmodernity in musical compositions with diverse ideas and perspectives. Even now, there have been a variety of new terms such as new music for new gamelan which are conceptually not familiar to the public and need to be discussed further.Implication: Various musical phenomena as well as aesthetic concepts have moved towards musical progression which not only breaks down shapes and structures but also musical understanding that has undergone a paradigm referring to the 'deconstruction' concepts of the search for identity in the era of local-global aesthetic struggles.

First Assessment of Geophysical Sensitivities from Spaceborne Galileo and BeiDou GNSS-Reflectometry Data Collected by the UK TechDemoSat-1 Mission

The UK’s TechDemoSat-1 (TDS-1), launched 2014, has demonstrated the use of global positioning system (GPS) signals for monitoring ocean winds and sea ice. Here it is shown, for the first time, that Galileo and BeiDou signals detected by TDS-1 show similar promise. TDS-1 made seven raw data collections, recovering returns from Galileo and BeiDou, between November 2015 and March 2019. The retrieved open ocean delay Doppler maps (DDMs) are similar to those from GPS. Over sea ice, the Galileo DDMs show a distinctive triple peak. Analysis, adapted from that for GPS DDMs, gives Galileo’s signal-to-noise ratio (SNR), which is found to be inversely sensitive to wind speed, as for GPS. A Galileo track transiting from open ocean to sea ice shows a strong instantaneous SNR response. These results demonstrate the potential of future spaceborne constellations of GNSS-R (global navigation satellite system–reflectometry) instruments for exploiting signals from multiple systems: GPS, Galileo, and BeiDou.

A data-driven machine learning integrated wearable medical sensor framework for elderly care service

Recently, Wearable Medical Devices (WMDs) is a promising medical technology to improve patient treatment in various medical perspectives. However, the traditional model-driven framework faces challenges on narrow reconstruction ratio and compression ratio due to improper measurement of signals in sensors. In this research, a Machine Learning assisted Integrated Data-driven framework (MLA-IDDF) that can acquire signal features based on personalized characteristics from elderly patients to improve compressive sensing performance with a reduced number of measurements for precise model. The system coordinates the collection and processing of raw sensor data from a variety of sources in a common information database. MLA-IDDF develops semantic models to describe patient’s conditions, and decision-making mechanisms based on the interpretation of collected data that provides accurate observations about elderly patients. The Simulation analysis at the lab scale shows the performance of the system and user satisfaction assessments confirmed that the system is superior to other conventional systems.

Blockchain-Based Data-Preserving AI Learning Environment Model for AI Cybersecurity Systems in IoT Service Environments

Artificial intelligence (AI) has a limitation in that it is only in the passive cognition area, so its operating process is not transparent; therefore, the technology relies on learning data. Since raw data for AI learning are processed and inspected manually to assure high quality for sophisticated AI learning, human errors are inevitable, and damaged and incomplete data and differences from the original data may lead to unexpected outputs of AI learning for which processed data are used. In this context, this research examines cases where AI learning data were inaccurate, in terms of cybersecurity, and the need for learning data management before machine learning through analysis of cybersecurity attack techniques, and we propose the direction of establishing a data-preserving AI system, which is a blockchain-based learning data environment model to verify the integrity of learning data. The data-preserving AI learning environment model is expected to prevent cyberattacks and data deterioration that may occur when data are provided and utilized in an open network for the processing and collection of raw data.

A Novel Sparse Representation and Softmax Method for Human Activity Identification in Healthcare Systems

Human activity identification has been attracting extensive research attention due to its prominent applications in healthcare systems such as healthcare monitoring and rehabilitation process. Traditional methods are greatly dependent on hand-crafted feature extraction, hampering their generalization performance. In this research, a novel sparse representation and softmax (SRS) method is presented for human activity identification to reduce the computation complexity of the task and improve the accuracy of classification. The multi-class classifier based on the softmax function is firstly introduced to improve sensor data classification performance. Sparse representation technology is then applied in our work to extract human activity features from sensor data. The output of the classifier model, taking raw sensor data after transforming into a high-dimensional feature space as input, provides a normalization of the probability distribution of activity categories, thereby ensuring accuracy and efficiency under diverse human activities. Experiments on a collection of raw sensor data from wireless sensor networks demonstrate the identification accuracy of our approach compared with nearest neighbor, naive Bayesian classifier, and support vector machine methods. The F1-score of the proposed method is respectively 14.1%, 19.6%, and 6.8% higher than the approaches mentioned above, indicating the effectiveness of SRS.

A distributed incremental update scheme for probability distribution of wind power forecast error

Due to the uncertainty of distributed wind generations (DWGs), a better understanding of the probability distributions (PDs) of their wind power forecast errors (WPFEs) can help market participants (MPs) who own DWGs perform better during trading. Under the premise of an accurate PD model, considering the correlation among DWGs and absorbing the new information carried by the latest data are two ways to maintain an accurate PD. These two ways both require the historical and latest wind power and forecast data of all DWGs. Each MP, however, only has access to the data of its own DWGs and may refuse to share these data with MPs belonging to other stakeholders. Besides, because of the endless generation of new data, the PD updating burden increases sharply. Therefore, a distributed strategy is used to avoid raw data collection. In addition, the incremental learning strategy is further applied to reduce the updating burden. Finally, a distributed incremental update scheme is proposed to make each MP continually acquire the latest conditional PD of its DWGs’ WPFE. Specifically, the Gaussian-mixture-model-based (GMM-based) joint PD is first used to characterize the correlation among DWGs. Then, a distributed modified incremental GMM algorithm is proposed to enable MPs to update the parameters of the joint PD in a distributed and incremental manner. After that, a distributed derivation algorithm is further proposed to make MPs derive their conditional PD of WPFE from the joint one in a distributed way. Combining the two original algorithms, the complete distributed incremental update scheme is finally achieved, by which each MP can continually obtain its latest conditional PD of its DWGs’ WPFE via neighborhood communication and local calculation with its own data. The effectiveness, correctness, and efficiency of the proposed scheme are verified using the dataset from the NREL.

AMMonitor: Remote monitoring of biodiversity in an adaptive framework with r

Abstract Ecological research and management programs are increasingly using autonomous monitoring units (AMUs) to collect large volumes of acoustic and/or photo data to address pressing management objectives or research goals. The data management requirements of an AMU‐based monitoring effort are often overwhelming, with a considerable amount of processing to translate raw data into models and analyses that have research and management utility. We created the r package AMMonitor to simplify the process of moving from remotely collected data to analysis and results, using a comprehensive SQLite database for data management that tracks all components of a remote monitoring program. This framework enables the tracking of analyses and research/management objectives through time. We illustrate the AMMonitor approach with the example of evaluating an occurrence‐based management objective for a target species. First, we provide an overview of the database and data management approach. Next, we illustrate a few available workflows: temporally adaptive sampling, automated detection of species sounds from acoustic recordings and aggregation of automated detections into an encounter history for use in an occupancy analysis, the outcome of which can be analysed with respect to the motivating management objective. Without a comprehensive framework for efficiently moving from raw remote monitoring data collection to results and analysis, monitoring programs are limited in their capacity to systematically characterize ecological processes and inform management decisions through time. AMMonitor provides an option for such a framework. Code, comprehensive documentation and step‐by‐step examples are available online at https://code.usgs.gov/vtcfwru/AMMonitor

A qualitative study of the operating room experience of patients who underwent surgery under spinal anesthesia: "It was like an adventure".

This study aimed to explore the experiences of the people who underwent orthopedic surgery under spinal anesthesia and to report their feelings and thoughts. The study was carried out using a qualitative approach. Twenty-one patients were interviewed who underwent orthopedic surgery on the first or second postoperative day. Content analysis was performed after the collection of raw data. NVIVO 12 Pro software was used for data analysis. The frequency count (f) and participant codes (P) were used for the presentation of the findings. The themes and frequency counts obtained by analyzing the interviews with the patients were as follows: "Time passed like watching a movie" (f = 213), "Like an adventure" (f = 587), and "See, feel, look" (f = 405). Five of 21 participants (23.8%) stated that they would not recommend spinal anesthesia. The findings generally indicated the anxiety caused by the unknown, fear in the preanesthetic period, operation experienced like an adventure, and a process generally completed with satisfaction.

From Bits of Data to Bits of Knowledge-An On-Board Classification Framework for Wearable Sensing Systems.

Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.

Lightweight Privacy-Preserving Raw Data Publishing Scheme

Data publishing or data sharing is an important part of analyzing network environments and improving the Quality of Service (QoS) in the Internet of Things (IoT). In order to stimulate data providers (i.e., IoT end-users) to contribute their data, privacy requirement is necessary when data is collected and published. In traditional privacy preservation techniques, such as k-anonymity, data aggregation and differential privacy, data is modified, aggregated, or added noise, the utility of the published data are reduced. Privacy-preserving raw data publishing is a more valuable solution, and <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="liu-ieq1-2974183.gif"/></alternatives></inline-formula>-source anonymity based raw data collection is most promising by delinking raw data and their sources. In this article, a lightweight raw data collection scheme for publishing is proposed, in which the rawness and the unlinkability of published data are all really guaranteed with Shamir&#x2019;s secret sharing, and shuffling algorithm. Moreover, it is lightweight and practical for the IoT environment by the performance evaluation.

Technical requirements for organising successful mobility campaigns in citizen observatories

Traditional travel surveys are usually organised in a top-down manner, when data collection is commissioned by transport authorities. Recently, however, there has been an increasing tendency towards bottom-up initiatives when citizens and stakeholders initiate and implement data collection campaigns in citizen observatories. Citizen observatories are usually based on participatory sensing, i.e. data collection by volunteers using smart mobile devices. The ubiquity of smartphones and hardware developments make it a potentially useful instrument to have travel behaviour registered. However, creating a new citizen observatory remains technically difficult and labour-intensive. Today, citizen observatories have to be developed from scratch, which acts as a significant barrier to their implementation. Recently, an open, reusable and reconfigurable citizen observatory platform has been proposed. Through such a platform, ICT-agnostic stakeholders are able to initiate new citizen observatories in various domains.In this paper we have developed a general technical framework for a platform that can allow stakeholders to create citizen observatories, with the aim of collecting data on travel behaviour. The framework is based on the available functionalities of travel survey applications, elaborating on the potential limitations and enhancements of a citizen observatory platform compared to travel survey apps. First, we compared the functionalities of smartphone-based travel surveys and citizen observatories, which exposed the main areas of concern of citizen observatories compared to traditional travel surveys. Next, we developed a framework for the technical requirements to organise a citizen observatory for mobility. The three main stages in this framework are the collection of raw data, processing it into valuable behavioural and environmental information (e.g. travel modes, air pollution) and the feedback of this data to all stakeholders. Data validation should be applied in each stage and it is important to ensure user acceptance during the entire lifespan of the campaign. We highlighted points for attention that should be considered when developing a citizen observatory for mobility. Defining the required accuracy, representativeness and reliability in a specific campaign are key questions in the context of an acceptable rate of battery drain. The technical requirements developed in this paper reflect a ‘best-case scenario’ that will later be tested with specific use cases.

Processing of Raw GNSS Reflectometry Data From TDS-1 in a Backscattering Configuration

Global navigation satellite system reflectometry (GNSS-R) has found many applications in the field of Earth observation including ocean wind-speed detection, ice altimetry, soil moisture monitoring, and more. The main focus of GNSS-R research to date has been on forward-scattered reflections, but theoretical work has proposed a backscattering regime and associated new application opportunities, including marine target detection. This article discusses the methods and results of processing the U.K. TechDemoSat-1 raw data collections in a backscattering regime for the first time, with initial results from sea ice datasets presented. The research has also identified a key problem with the backscatter method-for certain geometries the power from the specular point (forward scattered) may contaminate the data. The theory behind this and a method for predicting such occurrences is also discussed.

Situational Awareness in Distribution Grid Using Micro-PMU Data: A Machine Learning Approach

The recent development of distribution-level phasor measurement units, also known as micro-PMUs, has been an important step toward achieving situational awareness in power distribution networks. The challenge however is to transform the large amount of data that is generated by micro-PMUs to actionable information and then match the information to use cases with practical value to system operators. This open problem is addressed in this paper. First, we introduce a novel data-driven event detection technique to extract events from the extremely large collection of raw micro-PMU data. Subsequently, a data-driven event classifier is developed to effectively classify power quality events. Importantly, we use field expert knowledge and utility records to conduct an extensive data-driven event labeling . Moreover, certain aspects from event detection analysis are adopted as additional features to be fed into the classifier model. In this regard, a multi-class support vector machine classifier is trained and tested over 15 days of real-world data from two micro-PMUs on a distribution feeder in Riverside, CA, USA. In total, we analyze 1.2 billion measurement points and 10,700 events. The effectiveness of the developed event classifier is compared with prevalent multi-class classification methods, including ${k}$ -nearest neighbor method as well as decision-tree method. Importantly, two real-world use-cases are presented for the proposed data analytics tools, including remote asset monitoring and distribution-level oscillation analysis.

GIS-based models for transport network emergency management

The authors investigated the methods of structural-parametric synthesis, mathematical modeling and optimal control of transport networks from the position of the system analysis. The goals of ensuring the transport infrastructure safety with the positions of the transport network optimal load are determined especially in case of emergency situations. The paper describes the requirements for the development of a mathematical model of the transport network loading. A large number of studies on the transport network load is carried out in the environment of the “ITSGIS” intelligent transport geographic information system and was aimed at the development of the algorithm for automatic detection of emergency situations and congestion. The emergency situation types are characterized by its location, i.e. its coordinates on the electronic map. The emergency time is considered from various positions, such as time of occurrence, normative and actual time of elimination. The geofences of an emergency situation influence are divided into areas of direct and indirect emergency situation influence on transport processes and transport infrastructure objects. On the basis of the constructed models developed management methods of transport network loading and emergency situations analysis, including the collection and processing of raw data, the emergency situations determination and their characteristics, the optimal response strategy choice, the performance of management functions.