scholarly journals Signatures of personality on dense 3D facial images

2016 ◽  
Author(s):  
Sile Hu ◽  
Jieyi Xiong ◽  
Pengcheng Fu ◽  
Lu Qiao ◽  
Jingze Tan ◽  
...  
Keyword(s):  
Big Five ◽  
Image Data ◽  
Partial Least Square ◽  
Least Square ◽  
High Dimensional ◽  
Human Face ◽  
Big Five Model ◽  
The Face ◽  
Quantitative Analyses ◽  
Facial Images

AbstractIt has long been speculated that there exist cues on human face that allow observersto make reliable judgments of others’personality traits. However, direct evidences ofassociation between facial shapes and personality are missing. This study assessed thepersonality attributes for 834 Han Chinese volunteers (405 males and 429 females) utilizing the five-factor personality model (the ‘Big Five’ model), and collected their neutral 3D facial images. Dense anatomical correspondence was established across the 3D facial images to allow high-dimensional quantitative analyses on the face phenotypes. Two different approaches, Composite Partial Least Square Component(CPLSC) and principle component analysis (PCA) were used to test the associations between the self-testedpersonality scores and the dense 3D face image data. Among the fivepersonality factors, Agreeableness and Conscientiousness in male, and Extraversion in female were significantly associated to specific facial patterns. The personality-related facial patterns were extracted and their effects were extrapolated on simulated 3Dfacial models.

Locus Of Control Sebagai Varibel Moderasi Hubungan Kompleksitas Tugas Dan Kinerja Auiditor

2018 ◽  
Vol 16 (2) ◽  
pp. 113
Author(s):  
Sri Hastuti ◽  
Siti Sundari
Keyword(s):  
Locus Of Control ◽  
Big Five ◽  
Partial Least Square ◽  
Least Square

Research Objectives to prove the influence of the complexity of the tasks faced by the Auditor on performance in carrying out duties as an Auditor. The complexity of tasks related to various problems in the company requires locus of control from internal and external to maintain independence and competence.The first auditor performance case occurred in 2002 with the disclosure of the Enron case involving the KAP in the big five, Athur Anderson. In 2008 the Telkom case affected the closure of KAP Edy Priyanto, and there were still many other cases which were violations of the accountant's code of ethics.This research is in the form of quantitative, with proof of the complexity of the task and locus of control on the performance of the auditor. Sample 46 Junior auditors from several KAPs in Surabaya, using the Partial Least Square test, the result that the complexity of the task affects the performance of the Auditor and the interaction of the complexity of the task with locus of control does not affect the performance of the Auditor.

Developing a Conceptual Framework using Exploratory Testing to Improve Software Quality in SME's

2018 ◽  
Vol 7 (4.1) ◽  
pp. 42
Author(s):  
Murugan Thangiah ◽  
Shuib Basri ◽  
Dhanapal Durai Dominic
Keyword(s):  
Conceptual Framework ◽  
Structural Equation ◽  
Reliability And Validity ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Survey Questionnaire ◽  
Exploratory Testing ◽  
Quantitative Analyses ◽  
Future Work

In order to create quality software with standards and agreeing principles, the Small and Medium Software Enterprises (SME’s) faces many challenges and issues due to variety of reasons. Starting from the requirement analysis phase, the challenges emerge and continue until the project nears its completion, before being released to the customers or stakeholders. Various issues surfaced during the SDLC phases are identified and analyzed in the study. A Conceptual Framework using Exploratory Testing has been developed based on the study and the quantitative analyses were conducted using survey questionnaire. In this research paper the data analysis of the quantitative survey is conducted using Partial Least Square Structural Equation Modeling.  The reliability and validity of the data is evaluated and presented in this paper which is essential to develop the conceptual framework. Further analysis of the survey questionnaire will be carried on and it will be reported in future work. 

scholarly journals EXPLORING GOAL COMMITMENT PREDICTORS OF BOUNDARY ROLE PERSONS (BRP)

2018 ◽  
Vol 2 (2) ◽  
pp. 99
Author(s):  
Nurus Sa’adah ◽  
Fathul Himam ◽  
Achmad Sobirin
Keyword(s):  
High Risk ◽  
Role Conflict ◽  
Basic Characteristic ◽  
Goal Commitment ◽  
Partial Least Square ◽  
Least Square ◽  
Communication Climate ◽  
External Agents ◽  
The Face ◽  
Influence Attempts

As the face of the organization, BRPs were the target of representations and influence attempts by external agents. In effect, the BRPs were both the influencer and the recipient of influence from insiders and outsiders. This basic characteristic led potentially to higher levels of role conflict and tension for the BRPs than other organization members. Because of high risk and challenges, BRPs had to have goal commitment to maintain their loyalty. This study aimed to explore goal commitment predictors of BRPs. Data collection involved 162 colleges promotion officers in Yogyakarta and it was analysed through Partial Least Square (PLS). The results indicated that R-square on the goal commitment  was 0,398. The effect of conscientiousness on goal commitment was indicated by a correlation of 0,323 and t=4,245; the effect of communication climate on goal commitment was indicated by a correlation value of 0,206 and t=2,545; the effect of equity reward on goal commitment was indicated by a correlation value of 0,092 and t=1,534; the effect of opponent cooperation on goal commitment was indicated by a correlation value of 0,203 and t=2,915. The conclusion was communication climate, opponent cooperation, and conscientiousness influenced goal commitment significantly, but equity reward did not influence goal commitment significantly.

The Face-Space Typicality Paradox: Understanding the Face-Space Metaphor

1998 ◽  
Vol 51 (3) ◽  
pp. 475-483 ◽  
Author(s):  
A. Mike Burton ◽  
John R. Vokey
Keyword(s):  
Face Processing ◽  
Dimensional Space ◽  
Short Note ◽  
High Dimensional ◽  
Human Face ◽  
Face Space ◽  
The Face

Some recent accounts of human face processing use the idea of “face space”, considered to be a multi-dimensional space whose dimensions correspond to ways in which faces can vary. Within this space, “typicality” is sometimes taken to reflect the proximity of a face to its local neighbours. Intuitions about the distribution of faces within the space may suggest that the majority of faces will be “typical” in these terms. However, when typicality measures are taken, researchers very rarely find that faces cluster at the “typical” end of the scale. In this short note we attempt to resolve this paradox and point out that reasoning about high dimensional distributions requires that some specific assumptions are made explicit.

scholarly journals PENERAPAN KEMOMETRIK PADA METODE CITRA DIGITAL UNTUK ANALISIS ION MERKURI (II) DENGAN INDIKATOR NANOPARTIKEL PERAK

Alotrop ◽  
2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Angga Aprian Dinata ◽  
M. Lutfi Firdaus ◽  
Rina Elvia
Keyword(s):  
Quantitative Analysis ◽  
Digital Image ◽  
Limit Of Detection ◽  
Principal Component Regression ◽  
Image Data ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Image Method ◽  
Rgb Values

Digital image method in quantitative analysis usually uses one of the RGB primary color components (Red, Green, Blue), so that not all digital image data can be extracted. Then needed a method that can render the whole RGB values as variables in quantitative analysis are known as chemometric. This research aims to know the influence of the application of chemometric against the sensitivity of the digital image. Chemometry method used is the Principal Component Regression (PCR) and Partial Least Square (PLS) using Unscramber X software from Camo software, USA.. This method is applied for the quantitative analysis of Mercury (II) ion with silver nanoparticles (NPP) immobilization on filter paper indicator. The research results showed that chemometric has a good influence against the level of the Limit of Detection (LOD) of the digital image, where the level of LOD with chemometric application of the Principal Component Regression (PCR) is 0.4311 ppb, and Partial Least Square (PLS) is  0.4310 ppb smaller than without the application of chemometric Single Linear Regression (SLR) at 0.837 ppb. 

scholarly journals PENGARUH DIMENSI KEPRIBADIAN THE BIG FIVE PERSONALITY TERHADAP ORGANIZATIONAL CITIZENSHIP BEHAVIOR (OCB)

2018 ◽  
Vol 3 (1) ◽  
pp. 88
Author(s):  
Ria Lestari Pangastuti
Keyword(s):  
Student Affairs ◽  
Organizational Citizenship ◽  
Big Five ◽  
Organizational Citizenship Behavior ◽  
Sem Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Citizenship Behavior ◽  
Big Five Personality ◽  
Personality Dimension

The reasearch aim is for analizing the influence of personality dimension “The Big Five Personality” towards ORGANIZATIONAL Citizenship Behavior (OCB) (Studi Case Of Student Affairs Employees At X University). Population of this research is all employees of X University and sample is the employees who are at student affairs unit (50 people). The data were collected by sharing the quesioner to 50 respondents. The analysis of this research is Partial Least Square Structural EQUTION Model (PLS-SEM). Analysis result of PLS-SEM is there is significant influence of personality dimension, “The Big Five Personality” among five personality dimensions, alturism has the highest significance.

scholarly journals Impact of Personality Traits on Knowledge Hiding: A Comparative Study on Technology-Based Online and Physical Education

2021 ◽  
Vol 12 ◽  
Author(s):  
Jian Wu
Keyword(s):  
Personality Traits ◽  
Structural Model ◽  
Measurement Model ◽  
Partial Least Square ◽  
Least Square ◽  
Psychological Ownership ◽  
Equation Modeling ◽  
Big Five Model ◽  
Knowledge Hiding ◽  
Model Algorithm

Knowledge hiding has been a variable of interest that has led to major intangible losses to organizations, especially in this pandemic era when everything has shifted to online platforms and social media. Knowledge hiding has taken a new turn into the field of knowledge management. Moreover, the major players in knowledge hiding are the personality characteristics of individuals that have now found a way of expression without coming into the spotlight. This study is a necessary one in this time of online working environments where the role of personality traits and psychological ownership has been explored to understand their impact on the knowledge hiding within the organizations of China, and furthermore, to understand what role social status plays in moderating these relationships. The sampling design used is convenient random sampling with a sample size of 298 managers. This study has used the software Smart-PLS 3.3.3 for analyzing the data. The data relied on and was validated using preliminary tests of reliability and discriminant and convergent validities using the measurement model algorithm. Further, the partial least square technique was used to find the equation modeling for the variables, with the help of a structural model algorithm using 500 iterations for bootstrapping. The findings of the current study show that the personality traits of the “BIG FIVE” model positively predict knowledge hiding, except for openness to experience. At the same time, psychological ownership plays a partial mediating role.

scholarly journals Marching Cubes Algorithm for Fast 3D Modeling of Human Face by Incremental Data Fusion

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xiangsheng Huang ◽  
Xinghao Chen ◽  
Tao Tang ◽  
Ziling Huang
Keyword(s):  
3D Modeling ◽  
Color Image ◽  
Image Data ◽  
Ray Casting ◽  
Human Face ◽  
Marching Cubes ◽  
Polygonal Mesh ◽  
Face Region ◽  
Marching Cubes Algorithm ◽  
The Face

We present a 3D reconstruction system to realize fast 3D modeling using a vision sensor. The system can automatically detect the face region and obtain the depth data as well as color image data once a person appears in front of the sensor. When the user rotates his head around, the system will track the pose and integrate the new data incrementally to obtain a complete model of the personal head quickly. In the system, iterative closest point (ICP) algorithm is first used to track the pose of the head, and then a volumetric integration method is used to fuse all the data obtained. Third, ray casting algorithm extracts the final vertices of the model, and finally marching cubes algorithm generates the polygonal mesh of the reconstructed face model for displaying. During the process, we also make improvements to speed up the system for human face reconstruction. The system is very convenient for real-world applications, since it can run very quickly and be easily operated.

scholarly journals Kernel Partial Least Square Regression with High Resistance to Multiple Outliers and Bad Leverage Points on Near-Infrared Spectral Data Analysis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Divo Dharma Silalahi ◽  
Habshah Midi ◽  
Jayanthi Arasan ◽  
Mohd Shafie Mustafa ◽  
Jean-Pierre Caliman
Keyword(s):  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Data Matrix ◽  
High Dimensional ◽  
Leverage Points ◽  
Multiple Outliers ◽  
Data Space ◽  
Least Square Regression

Multivariate statistical analysis such as partial least square regression (PLSR) is the common data processing technique used to handle high-dimensional data space on near-infrared (NIR) spectral datasets. The PLSR is useful to tackle the multicollinearity and heteroscedasticity problem that can be commonly found in such data space. With the problem of the nonlinear structure in the original input space, the use of the classical PLSR model might not be appropriate. In addition, the contamination of multiple outliers and high leverage points (HLPs) in the dataset could further damage the model. Generally, HLPs contain both good leverage points (GLPs) and bad leverage points (BLPs); therefore, in this case, removing the BLPs seems relevant since it has a significant impact on the parameter estimates and can slow down the convergence process. On the other hand, the GLPs provide a good efficiency in the model calibration process; thus, they should not be eliminated. In this study, robust alternatives to the existing kernel partial least square (KPLS) regression, which are called the kernel partial robust GM6-estimator (KPRGM6) regression and the kernel partial robust modified GM6-estimator (KPRMGM6) regression are introduced. The nonlinear solution on PLSR was handled through kernel-based learning by nonlinearly projecting the original input data matrix into a high-dimensional feature mapping that corresponded to the reproducing kernel Hilbert spaces (RKHS). To increase the robustness, the improvements on GM6 estimators are presented with the nonlinear PLSR. Based on the investigation using several artificial dataset scenarios from Monte Carlo simulations and two sets from the near-infrared (NIR) spectral dataset, the proposed robust KPRMGM6 is found to be superior to the robust KPRGM6 and non-robust KPLS.

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