Improper Data Research Articles

Mediation of Lazy Update Propagation in a Replicated Database over a Decentralized P2P Architecture

While replicating data over a decentralized Peer-to- Peer (P2P) network, transactions broadcasting updates arising from different peers run simultaneously so that a destination peer replica can be updated concurrently, that always causes transaction and data conflicts. Moreover, during data migration, connectivity interruption and network overload corrupt running transactions so that destination peers can experience duplicated data or improper data or missing data, hence replicas remain inconsistent. Different methodological approaches have been combined to solve these problems: the audit log technique to capture the changes made to data; the algorithmic method to design and analyse algorithms and the statistical method to analyse the performance of new algorithms and to design prediction models of the execution time based on other parameters. A Graphical User Interface software as prototype, have been designed with C #, to implement these new algorithms to obtain a database synchronizer-mediator. A stream of experiments, showed that the new algorithms were effective. So, the hypothesis according to which “The execution time of replication and reconciliation transactions totally depends on independent factors.” has been confirmed.

Efficient Aspect Finding in Sentiment Analysis using Optimal Binary Search Tree

Sentiment Analysis is an interesting research area. It useful for analyze the user comments with the help of natural language processing. While consider a huge data and improper data, the sentiment classification with better accuracy is a very big challenge in sentiment analysis. The social media records relevant to Sentiment analysis is a difficult one to identify with a people’s situation, thoughts, and view in the direction of a certain event, which has a number of applications such as election guess and product assessment. This paper focuses on the basic idea of optimal binary search Tree Technique to find the optimal solution (aspect) from a sentence among the several aspects; specifically, we perform the classification process appropriate to various features, which is used to identify the best one aspect among the several aspect terms in a sentence. In the proposed system, We have introduced optimal binary search Tree aspect level sentiment analysis technique. Through this we are getting best and efficient aspect level solution compare to General Tree. The main novelty of the paper is analyzing how aspect sentiment extracted from each word in a sentence through Dictionary based approach with help of the syntactic Patterns. It provides a well understanding of the appropriate role of every words to obtain optimal Aspect in an efficiency way.

Analysis on the Information Management of Social Security

The social security undertaking is a relatively complicated and large-scale social system project. It requires multiple government departments to cooperate with each other and participate in management. Therefore, problems such as poor information communication, unclear division of duties, and improper data storage are inevitable. And the management agencies caused a lot of inconvenience. Many insured people could not receive insurance benefits on time due to various reasons, which caused inconvenience to the people’s life and increased social unrest factors.

Impact of matrix effects and ionization efficiency in non-quantitative untargeted metabolomics.

LC-MS-based untargeted metabolomics has become increasingly popular due to the vast amount of information gained in a single analysis. Many studies utilize metabolomics to profile metabolic changes in various representative biofluids, tissues, or other sample types. Most analyses are performed measuring changes in the metabolic pool of a single biological matrix due to an altered phenotype, such as disease versus normal. Measurements in such experiments are typically highly reproducible with little variation due to analytical and technological advancements in mass spectrometry. With the expanded application of metabolomics into various non-analytical scientific disciplines, the emergence of studies comparing the signal intensities of specific analytes across different biological matrices (e.g. plasma vs. urine) is becoming more common, but the matrix effect between sample types is often neglected. Additionally, the practice of comparing the signal intensities of different analytes and correlating to relative abundance is also increasingly prevalent, but the response ratio between analytes due to differences in ionization efficiency is not always accounted for in data analysis. This report serves to communicate and raise awareness of these two well-recognized issues to prevent improper data interpretation in the field of metabolomics. We demonstrate the impact of matrix effects and ionization efficiency with labeled analytical standards in human plasma, serum, and urine and describe how the direct comparison of non-quantitative signal intensities between biofluids, as well as between different analytes in the same biofluid, in untargeted metabolomics is inaccurate without proper response corrections. Human plasma, serum, and urine (n = 4 technical replicates per biofluid) were spiked with a panel of labeled internal standards all at identical concentrations, simultaneously extracted, and analyzed by UHPLC-HRMS. Signal intensities were compared for demonstration of the impact of matrix effects in untargeted metabolomics. A neat mixture of two co-eluting, structurally-similar labeled standards at the same concentration was also analyzed to demonstrate the effect of ionization efficiency on signal intensity. Despite being spiked at identical concentrations, labeled standards we examined in this study showed significant differences in their signal intensities between biofluids, as well as from each other in the same biofluid, due to matrix effects. Co-eluting standards were also found to yield significantly different signal intensities at identical concentrations due to differences in ionization efficiency. Due to the presence of matrix effects in untargeted, non-quantitative metabolomics, the signal intensity of any single analyte cannot be directly compared to the signal intensity of that same analyte (or any other analyte) between any two different matrices. Due to differences in ionization efficiency, the signal intensity of any single analyte cannot be directly compared to the signal intensity of any other analyte, even in the same matrix.

Full Mean Square Performance Bounds on Quaternion Estimators for Improper Data

Novel physical insights are provided into the mean square performance bounds of quaternion-valued widely linear (WL), semi-widely linear (SWL), and strictly linear estimators for the generality of quaternion-valued Gaussian data. This is achieved by first defining three kinds of complementary mean square errors (CMSEs) of these estimators and by further exploiting the corresponding degrees of $\mathbb {H}$ -improperness (second-order noncircularity). Next, the investigation of the bounds of the attainable CMSEs by these classes of estimators shows that only a joint consideration of the proposed CMSE analysis and the standard MSE analysis provides enough degrees of freedom for a detailed account of the MSE performance. The so-established framework for the analysis of estimators for $\mathbb {H}$ -improper data is shown to be capable of measuring error power distribution for each data channel, an important finding, which is not possible to obtain through the standard MSE analysis only. Simulations in the system identification setting support the analysis.

Pseudo Epidemic of Diarrhea Incidence: A Month Post Tsunami in Central Sulawesi, Indonesia

Introduction:On Friday, September 28, 2018, the 7.4 Richter Scale earthquake hit Central Sulawesi and was followed by a tsunami. Within a month after the unpredictable earthquake and tsunami, a 773 aftershock earthquake was noted. These events took a major toll on the population in the affected areas. 2,086 people died and more than four thousand people were injured. 1.373 people went missing and 206.494 were evacuated. Surveillance data from November 4, 2018, to October 24, 2018, showed that an increased number of illnesses such as diarrhea was the second leading reported cases. Data showed that the number of diarrhea cases was 3.350 with two peaks of epidemic curves on October 10 and 22, 2018.Aim:To verify the diarrhea outbreak after the tsunami in Palu, Donggala, and Sigi District.Methods:Verification of medical records at six selected primary healthcare institutions with the highest number of cases of diarrhea.Results:A pseudo-epidemic of diarrhea occurred. A high number of diarrhea cases occurred due to double reporting and misdiagnosed cases. Investigation reports showed that liquid defecation was considered diarrhea even though it occurred less than three times a day. The follow-up activity was contacting data entry managers to revise data, disseminate findings during the daily meeting of the health-related officers, and broadcasting findings through a WhatsApp group of provincial and district surveillance officers. Post-investigation, the number of diarrhea incidences was lower and the peak was not shown on the epidemic curve. It can be interpreted that a diarrhea outbreak did not occur in the tsunami-affected area in the Palu, Donggala, and Sigi districts.Discussion:During a time of disaster, a chaotic situation led to improper data collection. Data verification should be conducted to assure the validity of reported data.

Evaluation of Users’ Knowledge and Concerns of Biometric Passport Systems

The increase in terrorism and identity fraud has forced governments worldwide to make a combined effort to enhance the security of national borders. Biometric passports are the emergent identity travel document deployed in guaranteeing the safekeeping of the entry point of the border and limiting the usage of counterfeit documents. This study analyzes users’ concerns and threats to the biometric passport delivery system in Uganda, where the first biometric passports are planned for rollout in 2019. We used a mixed approach to compute and articulate the results. Factors impacting fear of technology like disclosure of personal data, improper data transmission, and data abuse were determined. Relevance knowledge of preferred technology such as the personal experience of the technology, data privacy awareness and perceived usefulness was confirmed. Threats and attacks on the technology such as counterfeit and brute-force were identified. It is important for policymakers and security expertise to understand that biometric technologies evoke fears of privacy and public liberties infringements. Therefore, end user’s acceptance of biometric passports will be dependent on the degree of trust in the technology itself and in those operating the applications.

A Decentralized Personal Data Store based on Ethereum: Towards GDPR Compliance

Sharing personal data with service providers is a fundamental resource for the times we live in. But data sharing represents an unavoidable issue, due to improper data treatment, lack of users' awareness to whom they are sharing with, wrong or excessive data sharing from end users who ignore they are exposing personal information. The problem becomes even more complicate if we try to consider the devices around us: how to share devices we own, so that we can receive pervasive services, based on our contexts and device functionalities. The European Authority has provided the General Data Protection Regulation (GDPR), in order to implement protection of sensitive data in each EU member, throughout certification mechanisms (according to Art. 42 GDPR). The certification assures compliance to the regulation, which represent a mandatory requirement for any service which may come in contact with sensitive data. Still the certification is an open process and not constrained by strict rule. In this paper we describe our decentralized approach in sharing personal data in the era of smart devices, being those considered sensitive data as well. Having in mind the centrality of users in the ownership of the data, we have proposed a decentralized Personal Data Store prototype, which stands as a unique data sharing endpoint for third party services. Even if blockchain technologies may seem fit to solve the issue of data protection, because of the absence of a central authority, they lay to additional concerns especially relating such technologies with specifications described in the regulation. The current work offers a contribution in the advancements of personal data sharing management systems in a distributed environment by presenting a real prototype and an architectural blueprint, which advances the state of the art in order to meet the GDPR regulation. Address those arisen issues, from a technological perspective, stands as an important challenge, in order to empower end users in owning their personal data for real.

Detecting Topics of Chat Discussions in A Computer Supported Collaborative Learning (CSCL) Environment

Learning groups' conversations in computer supported collaborative learning (CSCL) environments result in significant information regarding the content of the course. This information is beneficial for instructors to analyze learners' activities during their collaboration process. In understanding these activities and performance of learners, the topic of conversation is important. The purpose of the study is to detect topics of chat discussions conducted by groups of learners while collaboratively studying in an online CSCL environment called Virtual Math Teams (VMT). We implemented the study in the context of a graduate level course during one term in a large state university in Turkey. Participants are MSc and PhD students registered to the course and divided over five groups of three students. We combined and employed methods of data mining, social network analysis, and topic detection to identify topics of learners' discussions. Our data analysis process aims to identify the task related topics occurred in chat discussion of learning teams. In our analysis we followed the stages of data preprocessing, segmentation analysis, and topic detection. Our purpose with the preprocessing stage was eliminating improper data for the main analysis and making the data ready for analysis stage. Therefore, our final corpus was shaped to involve 95% of initial chat messages. Segmentation analysis aims to explore organization of chat discussion and divides the chat logs into more manageable units according to their corresponding contents. In total, we resulted 294 segments including task related and non-task related ones. The topic detection analysis explored the content of chat segments and revealed the major subject of discussions with the use of latent semantic analysis, which is applied to find content similarity among segments and indicative words produced through the use of two mode network analysis.

Dynamic Outlier Detection in the Calibration by Comparison Method Applied to Strain Gauge Weight Sensors.

The present work proposes a robust method of analyzing sets of data series shifted in time in respect to each other utilizing the process of dynamic calibration by comparison. Usually the Pearson’s correlation analysis coefficient is applied for this purpose. However, in some cases the method does not bring satisfactory results, as it can be seen in the results of the research conducted for the purpose of this paper. The Dynamic Time Warping method may be the solution to this problem, as it appears to be more efficient while comparing the shapes of calibration characteristics done with the use of the Pearson’s method. The presented method may also be applied to eliminate dynamic outliers collected in the process of recurrence examination or the analysis of strain gauge weight sensors hysteresis. This fact also makes the method a good tool for eliminating improper data series which might appear in the calibration process due to, e.g., malfunctioning devices installed in the calibration stand. The article presents an example of using the proposed method in eliminating improper dynamic characteristics obtained in a simulated calibration stand. Moreover, a comparative analysis performed on the simulation data is also presented in the article, as well as the result of the laboratory experiment.

Widely linear complex partial least squares for latent subspace regression

Abstract The method of partial least squares (PLS) has become a preferred tool for ill-posed linear estimation problems in the real domain, both in the regression and correlation analysis context. However, many modern applications involve complex-valued data (e.g. smart grid, sensor networks) and would benefit from corresponding well-posed latent variable regression analyses. To this end, we propose a PLS algorithm for physically meaningful latent subspace regression with complex-valued data. For rigour, this is achieved by taking into account full complex second-order augmented statistics to produce a robust widely linear estimator for general improper complex-valued data which may be highly correlated or colinear. The so-derived widely linear complex PLS (WL-CPLS) is shown to allow for effective joint latent variable decomposition of complex-valued data, while accounting for computational intractabilities in the calculation of a generalised inverse. This makes it possible to also determine the joint-subspace identified within the proposed algorithm, when applied to univariate outputs. The analysis is supported through both simulations on synthetic data and a real-world application of frequency estimation in unbalanced power grids. Finally, the ability of WL-CPLS to identify physically meaningful components is demonstrated through simultaneous complex covariance matrix diagonalisation.

Streamlining cardiopulmonary exercise testing for use as a screening and tracking tool in primary care.

Cardiopulmonary exercise testing (CPET) using a spectrum of different approaches demonstrates usefulness for objectively assessing patient disease severity in clinical and research settings. Still, an absence of trained specialists and/or improper data interpretation techniques can pose major limitations to the effective use of CPET for the clinical classification of patients. This study aimed to test an automated disease likelihood scoring algorithm system based on cardiopulmonary responses during a simplified step-test protocol. For patients with heart failure (HF), pulmonary hypertension (PAH), obstructive lung disease (OLD), or restrictive lung disease (RLD), we compared patient scores stratified into one of four “silos” generated from our novel algorithm system against patient evaluations provided by expert clinicians. Patients with HF (n = 12), PAH (n = 9), OLD (n = 16), or RLD (n = 10) performed baseline pulmonary function testing followed by submaximal step-testing. Breath-by-breath measures of ventilation and gas exchange, in addition to oxygen saturation and heart rate were collected continuously throughout testing. The algorithm demonstrated close alignment with patient assessments provided by clinical specialists: HF (r = 0.89, P < 0.01); PAH (r = 0.88, P < 0.01); OLD (r = 0.70, P < 0.01); and RLD (r = 0.88, P < 0.01). Furthermore, the algorithm was capable of differentiating major disease from other disease pathologies. Thus, in a clinically relevant manner, these data suggest this simplified automated disease algorithm scoring system used during step-testing to identify the likelihood that patients have HF, PAH, OLD, or RLD closely correlates with patient assessments conducted by trained clinicians.

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies.

Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: https://github.com/WandeRum/GSimp.

Structuring Data for Intelligent Predictive Maintenance in Asset Management

Predictive maintenance (PdM) within asset management improves savings in operational cost, productivity, and safety management capabilities. While PdM can be administered using various methods, growing interest in Artificial Intelligence (AI) has lead to current state of the art PdM relying on machine learning (ML) technology. Like other tools used in PdM for asset management, standards for applying ML technology for PdM are required. This work introduces a standard of practice in regards to usage of asset data to develop ML analytic tools for PdM. It provides a standard method for ensuring asset data is in a form conducive to ML algorithms, and ensuring retention of asset information necessary for optimum PdM during the data transform. In the ML domain, it has been proven through research initiatives that the data structure used to train and test ML algorithms has a great impact on their performance and accuracy. Using poorly trained models for estimation due to improper data usage, can leave some AI-based PdM tools vulnerable to high rates of inaccurate estimations. Thus, leading to value loss during an asset’s life cycle.

COMMITMENT TO TEAM GOALS IN SPORT: ADAPTATION OF GOAL COMMITMENT SCALE TO SPORT FIELD

The aim of this study was to revise Goal Commitment Scale, developed by Hollenbeck, Williams and Klein (1989) with 9 items, revised by Klein, Wesson, Hollenbeck and Wright (2001) to 5 items and adapted to Turkish by Senel and Yildiz (2016) to measure commitment to team goals and analyze validation and reliability in a sample including athletes. The Turkish version of the 5-item Goal Commitment Scale was revised by taking expert opinion to measure commitment to team goals. The Revised form was sent to at least 2-year licensed athletes on the Internet, and data was collected from 400 athletes. The data were examined and 25 improper data were excluded. Collected data were analyzed in SPSS by using Exploratory Factor Analysis and in AMOS (Analysis of Moment Structure) program by using Confirmatory Factor Analysis. Results showed that the revised commitment scale that measures commitment to team goals could be used for samples including athletes. Article visualizations:

A hybrid data mining approach on BIM-based building operation and maintenance

Huge amounts of data are generated daily during the operation and maintenance (O&M) phase of buildings. These accumulated data have the potential to provide deep information that can help improve facility management. Building Information Model/Modeling (BIM) technology has proven potential in O&M management in some studies, making it possible to store massive data. However, the complex and non-intuitive data records, as well as inaccurate manual inputs, raise difficulties in making full use of information in current O&M activities. This paper aims to address these problems by proposing a BIM-based Data Mining (DM) approach for extracting meaningful laws and patterns, as well as detecting improper records. In this approach, the BIM database is first transformed into a data warehouse. After that, three DM methods are combined to find useful information from the BIM. Specifically, the cluster analysis can find relationships of similarity among records, the outlier detection detects manually input improper data and keeps the database fresh, and the improved pattern mining algorithm finds deeper logic links among records. Particular emphasis is put on introducing the algorithms and how they should be used by building managers. Hence, the value of BIM is increased based on rules, extracted from data of O&M phase that appear irregular and disordered. Validated by an integrated on-site practice in an airport terminal, the proposed DM methods are helpful in prediction, early warning, and decision making, leading to the improvements of resource usage and maintenance efficiency during the O&M phase.

Modeling of Deceleration Fuel Cut-off for LPG Fuelled Engine using Fuzzy Logic Controller

At the time of deceleration, continuous LPG flow in LPG fuelled engine causing over fuel consumption and increasing exhaust emissions, while the engine does not need fuel. Therefore, this paper presents a simulation of deceleration fuel cut-off (DFCO) system. Given that the fuel system control is complex and non-linear, modeling with fuzzy logic controller (FLC) has been selected because of simple, easy to understand and tolerant to improper data. The engine modeling is divided into several sections, including intake manifold dynamics and engine dynamics. The input values were processed by the membership function. A series of simulation results indicate that DFCO can be applied. The combination of throttle valve position, engine speed and manifold pressure is able to cut LPG flow at deceleration. As a conclusion, DFCO system is promising to be applied on LPG-fuelled vehicles for saving fuel and reducing emissions.

Improved continuous query plan with cluster weighted dominant querying in synthetic datasets

The arrival of large voluminous continuous queries sets for a given query leads an insignificant insights. The elimination of certain data tuples occurs in order to balance the system load. The streaming query removes the improper data tuples and uses proper data tuples in the form of defined tables or sets. However, major drawback occurs due to unbounded streaming and inadequate access to end data. Due to such constraints, many stream processing methods makes the processed data unavailable for any applications or to the related queries of neighborhood branches. This paper avoids such problems during the process of data tuples at the generation of queries. The study uses a streaming model that executes effective query plans in continuous data. The streaming model aims reduce the communication cost and improves the scalability of continuous aggregation queries. It sub-divides the client query and executes it over data aggregators within the incoherent limit. A weighted dominant query algorithm is formulated to provide the top dominant value in each sub-query clusters. This reduces the cost for computation in synthetic databases. The experimental results proved that the proposed model with weighted dominant query algorithm effectively improves scalability by reducing the computational cost.

Power System Thévenin Equivalent Estimation Based on Phasor Measurements

This paper presents a novel algorithm based on phasor measurements to online estimation of power system Thevenin Equivalent (TE) from a generator terminal. Three consecutive phasor measurements of generator terminal voltage and current are used to estimate the system TE. In a real network, system frequency deviation from its nominal value will produce some phase drift in consecutive phasors reported by Phasor Measurements Units (PMUs). To correct the phase drift and synchronizing the reported phasors to the same reference, an improved triangulation method is developed. The improved method uses a virtual vector in its structure. Using this vector alleviates the negative impacts of improper data resulting from noise or disturbances which make the simple triangulation method to be useless. The algorithm is tested on standard NE-39 network and is implemented in Shahid Beheshti University (SBU) protection laboratory to verify the results in a practical area. The obtained results show that the new method can efficiently estimate the in online mode in steady-state and transient conditions.

AFR and fuel cut-off modeling of LPG-fueled engine based on engine, transmission, and brake system using fuzzy logic controller (FLC)

During deceleration, continuous fuel flows into the engine not only causing over fuel consumption but also increasing exhausts emissions. Therefore, this paper presents a simulation of AFR and fuel cut-off modeling in the LPG-fueled vehicle using Fuzzy Logic Controller (FLC). The third generation of LPG kits (Liquid Phase Injection, LPI) was chosen due to its technological equivalency to EFI gasoline engine and promising to be developed. Given that the fuel system control is complex and non-linear, FLC has been selected because of simple, easy to understand, and tolerant to improper data. Simulation results show that the AFR and fuel cut-off controller able to maintenance AFR at the stoichiometric range during normal operation and able to cut the fuel flow at deceleration time for saving fuel and reducing emissions.