Rapid video assessment for monitoring testing facility fraud

2018 ◽  
Vol 35 (8) ◽  
pp. 1508-1518
Author(s):  
Rosembergue Pereira Souza ◽  
Luiz Fernando Rust da Costa Carmo ◽  
Luci Pirmez
Keyword(s):  
Processing Time ◽  
Threshold Value ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Video Assessment ◽  
Technical Requirements ◽  
Testing Facility ◽  
Rapid Processing ◽  
Temporal Differencing

Purpose The purpose of this paper is to present a procedure for finding unusual patterns in accredited tests using a rapid processing method for analyzing video records. The procedure uses the temporal differencing technique for object tracking and considers only frames not identified as statistically redundant. Design/methodology/approach An accreditation organization is responsible for accrediting facilities to undertake testing and calibration activities. Periodically, such organizations evaluate accredited testing facilities. These evaluations could use video records and photographs of the tests performed by the facility to judge their conformity to technical requirements. To validate the proposed procedure, a real-world data set with video records from accredited testing facilities in the field of vehicle safety in Brazil was used. The processing time of this proposed procedure was compared with the time needed to process the video records in a traditional fashion. Findings With an appropriate threshold value, the proposed procedure could successfully identify video records of fraudulent services. Processing time was faster than when a traditional method was employed. Originality/value Manually evaluating video records is time consuming and tedious. This paper proposes a procedure to rapidly find unusual patterns in videos of accredited tests with a minimum of manual effort.

Predicting the voluntary donation to online content creators

2020 ◽  
Vol 120 (10) ◽  
pp. 1941-1957
Author(s):  
Futao Zhao ◽  
Zhong Yao
Keyword(s):  
Prediction Model ◽  
Performance Metrics ◽  
Impact Factors ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Modal Particles ◽  
Interpretive Analysis ◽  
Text Content ◽  
The Impact

PurposeThe purpose of this paper is to identify the impact factors that might influence audiences' voluntary donation to content creators on the online platforms, and to build an effective prediction model by considering both content and creator-related features.Design/methodology/approachThis study collected the real-world data of content consumption from Xueqiu.com and extracted both content and creator characteristics from the data set. The best donation prediction model based on such features was determined by evaluating four prevalent classifiers with various performance metrics. Furthermore, three feature selection methods were applied to validate the robustness of the constructed model, and then the predictability of different feature groups was examined. Finally, we conducted an interpretive analysis to identify relatively important predictors.FindingsThe experimental results show that the random classifier with all extracted features outperformed other built models and achieved excellent performance, indicating the usefulness of these factors in predicting the donations. Moreover, the predictability of content features was demonstrated to be relatively better than that of creator ones. Finally, several particularly important predictors were identified such as the number of modal particles in the article.Originality/valueThis study is among the first to investigate what factors might drive customers' voluntary donation to content contributors on social websites. Different from previous studies focusing on live video streaming, we expand the research vision by examining the donations to user-generated text content, calling for attention to other important topics in the burgeoning industry.

scholarly journals Flags and anthems: naturalisation effects on income and employment

2017 ◽  
Vol 44 (4) ◽  
pp. 491-504
Author(s):  
Jan-Jan Soon
Keyword(s):  
Causal Effect ◽  
Threshold Value ◽  
Fuzzy Regression ◽  
Host Countries ◽  
Market Outcomes ◽  
Data Set ◽  
Content Type ◽  
Difficult Time ◽  
European Values ◽  
Fuzzy Regression Discontinuity Design

Purpose Even though Europe has recently undergone a difficult time and is recovering from the aftermath of prevalent unemployment, immigrants are still flocking towards Europe and taking up citizenships of their host countries through naturalisation. The purpose of this paper is to look at the how naturalised immigrants fare in terms of income and employment chances, compared to immigrants. Design/methodology/approach Using a fuzzy regression discontinuity design and the 2008 European Values Study integrated data set with a final sample of 4,460 observations, this paper isolates the causal effect of naturalisation on the income and employment chances of immigrants by exploiting exogenous variations generated by the eligibility rules for naturalisation in 41 European countries. Findings Main findings show that the probability of being naturalised increases for eligible immigrants, income and employment chances increase for eligible immigrants, and income and employment chances increase for naturalised immigrants. Research limitations/implications This study has a data limitation, where in using the discontinuity design, there is an unbalanced number of observations to the left and right of the design’s threshold value. Originality/value There are limited studies using causal models or potential outcome frameworks to examine the effect of immigrant naturalisation on labour market outcomes in Europe. This study fills this gap.

A methodology for classification and validation of customer datasets

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dongyun Nie ◽  
Paolo Cappellari ◽  
Mark Roantree
Keyword(s):  
Initial Step ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Customer Data ◽  
Average Value ◽  
Customer Lifetime ◽  
Customer Segment ◽  
High Level

Purpose The purpose of this paper is to develop a method to classify customers according to their value to an organization. This process is complicated by the disconnected nature of a customer record in an industry such as insurance. With large numbers of customers, it is of significant benefit to managers and company analysts to create a broad classification for all customers. Design/methodology/approach The initial step is to construct a full customer history and extract a feature set suited to customer lifetime value calculations. This feature set must then be validated to determine its ability to classify customers in broad terms. Findings The method successfully classifies customer data sets with an accuracy of 90%. This study also discovered that by examining the average value for key variables in each customer segment, an algorithm can label the group of clusters with an accuracy of 99.3%. Research limitations/implications Working with a real-world data set, it is always the case that some features are unavailable as they were never recorded. This can impair the algorithm’s ability to make good classifications in all cases. Originality/value This study believes that this research makes a novel contribution as it automates the classification of customers but in addition, the approach provides a high-level classification result (recall and precision identify the best cluster configuration) and detailed insights into how each customer is classified by two validation metrics. This supports managers in terms of market spend on new and existing customers.

scholarly journals Team problem solving and motivation under disorganization – an agent-based modeling approach

2017 ◽  
Vol 23 (1/2) ◽  
pp. 46-65 ◽  
Author(s):  
Dinuka Herath ◽  
Joyce Costello ◽  
Fabian Homberg
Keyword(s):  
Problem Solving ◽  
Agent Based Modeling ◽  
Hierarchical Level ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Non Profit ◽  
Agent Based ◽  
Prime Objective ◽  
Better Than

Purpose This paper aims at simulating on how “disorganization” affects team problem solving. The prime objective is to determine how team problem solving varies between an organized and disorganized environment also considering motivational aspects. Design/methodology/approach Using agent-based modeling, the authors use a real-world data set from 226 volunteers at five different types of non-profit organizations in Southwest England to define some attributes of the agents. The authors introduce the concepts of natural, structural and functional disorganization while operationalizing natural and functional disorganization. Findings The simulations show that “disorganization” is more conducive for problem solving efficiency than “organization” given enough flexibility (range) to search and acquire resources. The findings further demonstrate that teams with resources above their hierarchical level (access to better quality resources) tend to perform better than teams that have only limited access to resources. Originality/value The nuanced categories of “(dis-)organization” allow us to compare between various structural limitations, thus generating insights for improving the way managers structure teams for better problem solving.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

Light touch, heavy hand: principals and data-use PLCs

2017 ◽  
Vol 55 (4) ◽  
pp. 376-389 ◽  
Author(s):  
Alice Huguet ◽  
Caitlin C. Farrell ◽  
Julie A. Marsh
Keyword(s):  
Professional Learning ◽  
Low Income ◽  
Equal Opportunity ◽  
Data Use ◽  
Opportunity To Learn ◽  
School Level ◽  
Comparative Case Study ◽  
Data Set ◽  
Content Type ◽  
Principal's Role

Purpose The use of data for instructional improvement is prevalent in today’s educational landscape, yet policies calling for data use may result in significant variation at the school level. The purpose of this paper is to focus on tools and routines as mechanisms of principal influence on data-use professional learning communities (PLCs). Design/methodology/approach Data were collected through a comparative case study of two low-income, low-performing schools in one district. The data set included interview and focus group transcripts, observation field notes and documents, and was iteratively coded. Findings The two principals in the study employed tools and routines differently to influence ways that teachers interacted with data in their PLCs. Teachers who were given leeway to co-construct data-use tools found them to be more beneficial to their work. Findings also suggest that teachers’ data use may benefit from more flexibility in their day-to-day PLC routines. Research limitations/implications Closer examination of how tools are designed and time is spent in data-use PLCs may help the authors further understand the influence of the principal’s role. Originality/value Previous research has demonstrated that data use can improve teacher instruction, yet the varied implementation of data-use PLCs in this district illustrates that not all students have an equal opportunity to learn from teachers who meaningfully engage with data.

A dynamic simulator for the ergonomics evaluation of powered torque tools for human assembly

2017 ◽  
Vol 37 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Haluk Ay ◽  
Anthony Luscher ◽  
Carolyn Sommerich
Keyword(s):  
Risk Factors ◽  
Joint Torque ◽  
Human Hand ◽  
Model Parameters ◽  
Test Rig ◽  
Musculoskeletal Discomfort ◽  
Data Set ◽  
Content Type ◽  
Working Posture ◽  
Displacement Measurements

Purpose The purpose of this study is to design and develop a testing device to simulate interaction between human hand–arm dynamics, right-angle (RA) computer-controlled power torque tools and joint-tightening task-related variables. Design/methodology/approach The testing rig can simulate a variety of tools, tasks and operator conditions. The device includes custom data-acquisition electronics and graphical user interface-based software. The simulation of the human hand–arm dynamics is based on the rig’s four-bar mechanism-based design and mechanical components that provide adjustable stiffness (via pneumatic cylinder) and mass (via plates) and non-adjustable damping. The stiffness and mass values used are based on an experimentally validated hand–arm model that includes a database of model parameters. This database is with respect to gender and working posture, corresponding to experienced tool operators from a prior study. Findings The rig measures tool handle force and displacement responses simultaneously. Peak force and displacement coefficients of determination (R2) between rig estimations and human testing measurements were 0.98 and 0.85, respectively, for the same set of tools, tasks and operator conditions. The rig also provides predicted tool operator acceptability ratings, using a data set from a prior study of discomfort in experienced operators during torque tool use. Research limitations/implications Deviations from linearity may influence handle force and displacement measurements. Stiction (Coulomb friction) in the overall rig, as well as in the air cylinder piston, is neglected. The rig’s mechanical damping is not adjustable, despite the fact that human hand–arm damping varies with respect to gender and working posture. Deviations from these assumptions may affect the correlation of the handle force and displacement measurements with those of human testing for the same tool, task and operator conditions. Practical implications This test rig will allow the rapid assessment of the ergonomic performance of DC torque tools, saving considerable time in lineside applications and reducing the risk of worker injury. DC torque tools are an extremely effective way of increasing production rate and improving torque accuracy. Being a complex dynamic system, however, the performance of DC torque tools varies in each application. Changes in worker mass, damping and stiffness, as well as joint stiffness and tool program, make each application unique. This test rig models all of these factors and allows quick assessment. Social implications The use of this tool test rig will help to identify and understand risk factors that contribute to musculoskeletal disorders (MSDs) associated with the use of torque tools. Tool operators are subjected to large impulsive handle reaction forces, as joint torque builds up while tightening a fastener. Repeated exposure to such forces is associated with muscle soreness, fatigue and physical stress which are also risk factors for upper extremity injuries (MSDs; e.g. tendinosis, myofascial pain). Eccentric exercise exertions are known to cause damage to muscle tissue in untrained individuals and affect subsequent performance. Originality/value The rig provides a novel means for quantitative, repeatable dynamic evaluation of RA powered torque tools and objective selection of tightening programs. Compared to current static tool assessment methods, dynamic testing provides a more realistic tool assessment relative to the tool operator’s experience. This may lead to improvements in tool or controller design and reduction in associated musculoskeletal discomfort in operators.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

Use of Basu-Dhar bivariate geometric distribution in the analysis of the reliability of component series systems

2019 ◽  
Vol 36 (4) ◽  
pp. 569-586
Author(s):  
Ricardo Puziol Oliveira ◽  
Jorge Alberto Achcar
Keyword(s):  
Bayesian Approach ◽  
Geometric Distribution ◽  
Bivariate Distribution ◽  
Dependence Structure ◽  
Series System ◽  
Engineering Applications ◽  
Data Set ◽  
Content Type ◽  
Bivariate Geometric Distribution ◽  
Series Systems

Purpose The purpose of this paper is to provide a new method to estimate the reliability of series system by using a discrete bivariate distribution. This problem is of great interest in industrial and engineering applications. Design/methodology/approach The authors considered the Basu–Dhar bivariate geometric distribution and a Bayesian approach with application to a simulated data set and an engineering data set. Findings From the obtained results of this study, the authors observe that the discrete Basu–Dhar bivariate probability distribution could be a good alternative in the analysis of series system structures with accurate inference results for the reliability of the system under a Bayesian approach. Originality/value System reliability studies usually assume independent lifetimes for the components (series, parallel or complex system structures) in the estimation of the reliability of the system. This assumption in general is not reasonable in many engineering applications, since it is possible that the presence of some dependence structure between the lifetimes of the components could affect the evaluation of the reliability of the system.

The effect of actual workplace learning on job satisfaction and organizational commitment

2019 ◽  
Vol 31 (8) ◽  
pp. 481-497 ◽  
Author(s):  
Geunpil Ryu ◽  
Seong-Gin Moon
Keyword(s):  
Job Satisfaction ◽  
Organizational Commitment ◽  
Human Resource Development ◽  
Workplace Learning ◽  
Learning Experience ◽  
Work Attitudes ◽  
Moderating Effect ◽  
Data Set ◽  
Content Type ◽  
Learning Programs

Purpose This study aims to examine the effect of workplace learning experience and intrinsic learning motive on job satisfaction and organizational commitment. In addition, the study examined the moderating effect of intrinsic learning motives on the relationship between learning experience and job satisfaction and organizational commitment. Design/methodology/approach The current research used the Human Capital Corporate Panel survey data set, which aimed to explore how human resource development practices influence corporate performance. In all, 10,003 samples from 441 companies were used for data analysis. Findings Results indicate that taking part in workplace learning programs positively affects job satisfaction and organizational commitment. Likewise, intrinsic learning motives are also positively related to work attitudes. However, no interaction effect between the intrinsic learning motive and the learning experience was found, which may imply that an autonomous extrinsic learning motive is a better predictor for explaining job satisfaction than is a purely intrinsic learning motive within an organizational context. Originality/value Little research has examined the actual effect of workplace learning programs on employees’ attitudes regarding job satisfaction and organizational commitment. Furthermore, to the authors’ knowledge, no research has examined the moderating effect of intrinsic learning motive with workplace learning experience on employees’ positive work attitudes.

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