scholarly journals 2.5D Facial Personality Prediction Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jia Xu ◽  
Weijian Tian ◽  
Guoyun Lv ◽  
Shiya Liu ◽  
Yangyu Fan
Keyword(s):  
Personality Traits ◽  
Real Life ◽  
Previous Method ◽  
Personality Characteristics ◽  
Facial Contour ◽  
Related Information ◽  
New Findings ◽  
Positive Images ◽  
Personality Prediction ◽  
Personality Computing

The assessment of personality traits is now a key part of many important social activities, such as job hunting, accident prevention in transportation, disease treatment, policing, and interpersonal interactions. In a previous study, we predicted personality based on positive images of college students. Although this method achieved a high accuracy, the reliance on positive images alone results in the loss of much personality-related information. Our new findings show that using real-life 2.5D static facial contour images, it is possible to make statistically significant predictions about a wider range of personality traits for both men and women. We address the objective of comprehensive understanding of a person’s personality traits by developing a multiperspective 2.5D hybrid personality-computing model to evaluate the potential correlation between static facial contour images and personality characteristics. Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images.

scholarly journals Predicting Most Productive Requirements Elicitation Teams using MBTI Personality Traits Model

2019 ◽  
Vol 9 (1) ◽  
pp. 3809-3814
Keyword(s):  
Personality Traits ◽  
Personality Assessment ◽  
Soft Skills ◽  
Requirements Elicitation ◽  
Assessment Model ◽  
Personality Characteristics ◽  
Job Descriptions ◽  
The Social ◽  
Elicitation Process ◽  
Personality Prediction

The social and collaborative nature of requirements elicitation process bases its core dependency on aptitude, attitudes, and personality characteristics of its participants. The participant’s personality characteristics are directly related with their personality traits, which can be categorized using different personality assessment models. The MBTI personality assessment model has been used successfully for the assessment of personality of software engineers since last few decades. In this article, the personality traits for requirements elicitation teams have been predicted using MBTI personality assessment model, on the basis of their industry demanded job descriptions/tasks and major soft skills. The article presents a complete personality prediction process using a systematic approach based on major soft skills mapping with job descriptions, personality attributes and personality traits. The obtained results show that extraversion and feeling personality traits are the most suitable personality traits for requirements analysts/engineers who are assigned the task of requirements elicitation. The obtained results are very much aligned with the already published scholar’s work for software engineer’s personality assessment and development team composition.

scholarly journals Assessing the Big Five personality traits using real-life static facial images

2020 ◽  
Author(s):  
Alexander Kachur ◽  
Evgeny Osin ◽  
Denis Davydov ◽  
Konstantin Shutilov ◽  
Alexey Novokshonov
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Real Life ◽  
Big Five Personality Traits ◽  
Self Report ◽  
Big Five Personality ◽  
Personality Profiles ◽  
Ample Evidence ◽  
New Findings ◽  
Facial Images

There is ample evidence that a human face provides signals of human personality and behaviour. Previous studies have found associations between the features of artificial composite facial images and attributions of personality traits by human experts. We present new findings demonstrating the statistically significant prediction of a wider set of personality features (all the Big Five personality traits) for both men and women using real-life static facial images. Volunteer participants (N = 12,447) provided their face photographs (31,367 images) and completed a self-report measure of the Big Five traits. We trained a cascade of artificial neural networks (ANNs) on a large labelled dataset to predict self-reported Big Five scores. The highest correlations were found for conscientiousness (.360 for men and .335 for women), exceeding the results obtained in prior studies. The findings provide strong support for the hypothesis that it is possible to predict multidimensional personality profiles from static facial images using ANNs trained on large labelled datasets.

Intelligence and Personality

2020 ◽  
Vol 41 (3) ◽  
pp. 124-132
Author(s):  
Marc-André Bédard ◽  
Yann Le Corff
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Real Life ◽  
Big Five Personality Traits ◽  
Original Study ◽  
Life Assessment ◽  
Big Five Personality ◽  
Verbal Intelligence ◽  
Nonverbal Intelligence ◽  
French Canadian

Abstract. This replication and extension of DeYoung, Quilty, Peterson, and Gray’s (2014) study aimed to assess the unique variance of each of the 10 aspects of the Big Five personality traits ( DeYoung, Quilty, & Peterson, 2007 ) associated with intelligence and its dimensions. Personality aspects and intelligence were assessed in a sample of French-Canadian adults from real-life assessment settings ( n = 213). Results showed that the Intellect aspect was independently associated with g, verbal, and nonverbal intelligence while its counterpart Openness was independently related to verbal intelligence only, thus replicating the results of the original study. Independent associations were also found between Withdrawal, Industriousness and Assertiveness aspects and verbal intelligence, as well as between Withdrawal and Politeness aspects and nonverbal intelligence. Possible explanations for these associations are discussed.

scholarly journals CAD integrated automatic recognition of weld paths

2021 ◽  
Author(s):  
Tuan Anh Tran ◽  
Andrei Lobov ◽  
Tord Hansen Kaasa ◽  
Morten Bjelland ◽  
Ole Terje Midling
Keyword(s):  
Life Cycle ◽  
Spatial Analysis ◽  
Product Life Cycle ◽  
Real Life ◽  
Automatic Recognition ◽  
Integrated Method ◽  
Weld Joints ◽  
Related Information ◽  
Almost All ◽  
Sequential Assembly

AbstractIn this paper, a CAD integrated method is proposed for automatic recognition of potential weld locations in large assembly structures predominantly comprised of weld joints. The intention is to reduce the total man-hours spent on manually locating, assigning, and maintaining weld-related information throughout the product life cycle. The method utilizes spatial analysis of extracted stereolithographic data in combination with available CAD functions to determine whether the accessibility surrounding a given intersection edge is sufficient for welding. To demonstrate the method, a system is developed in Siemens NX using their NXOpen Python API. The paper presents the application of the method to real-life use cases in varying complexity in cooperation with industrial partners. The system is able to correctly recognize almost all weld lines for the parts considered within a few minutes. Some exceptions are known for particular intersection lines located deep within notched joints and geometries weldable through sequential assembly, which are left as a subject to further works.

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

Personality Traits in Childhood and Adolescent Headache

Cephalalgia ◽  
2001 ◽  
Vol 21 (1) ◽  
pp. 53-60 ◽  
Author(s):  
G Lanzi ◽  
CA Zambrino ◽  
O Ferrari-Ginevra ◽  
C Termine ◽  
S D'Arrigo ◽  
...  
Keyword(s):  
Clinical Study ◽  
Personality Traits ◽  
Test Anxiety ◽  
Migraine Without Aura ◽  
Tension Type Headache ◽  
Personality Characteristics ◽  
Anxiety And Depression ◽  
Sociodemographic Data ◽  
Type Headache ◽  
Adolescent Headache

We evaluate personality traits, anxiety and depression in a population of paediatric and adolescent patients, correlating personality characteristics with headache and sociodemographic variables. The clinical features of headache include specific personality traits. We report a clinical study of 57 patients (age 8–18 years), divided up as follows: 12 migraine with aura, 29 migraine without aura and 16 tension-type headache. One of Cattel's tests was administered to every patient; the Children's Depression Inventory test was administered to 53 patients and the Test Anxiety Inventory test to 43 subjects. The scores obtained by every patient in each test were correlated with the characteristics of headache and with sociodemographic data. We found that patients affected by idiopathic headache share some personality traits, mainly emotional rigidity and tendency to repress anger and aggression. These traits do not seem to be correlated with sociodemographic data and the duration of headache: we considered these as characteristic of migrainous patients.

scholarly journals Inter-UAV Routing Scheme Testbeds

Drones ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 2
Author(s):  
Georgios Amponis ◽  
Thomas Lagkas ◽  
Panagiotis Sarigiannidis ◽  
Vasileios Vitsas ◽  
Panagiotis Fouliras
Keyword(s):  
Signal To Noise Ratio ◽  
Real Life ◽  
Simulation Software ◽  
Propagation Models ◽  
Routing Schemes ◽  
Routing Scheme ◽  
Related Information ◽  
Efficient Control ◽  
Communication Architectures ◽  
Scheme Evaluation

With the development of more advanced and efficient control algorithms and communication architectures, UAVs and networks thereof (swarms) now find applications in nearly all possible environments and scenarios. There exist numerous schemes which accommodate routing for such networks, many of which are specifically designed for distinct use-cases. Validation and evaluation of routing schemes is implemented for the most part using simulation software. This approach is however incapable of considering real-life noise, radio propagation models, channel bit error rate and signal-to-noise ratio. Most importantly, existing frameworks or simulation software cannot sense physical-layer related information regarding power consumption which an increasing number of routing protocols utilize as a metric. The work presented in this paper contributes to the analysis of already existing routing scheme evaluation frameworks and testbeds and proposes an efficient, universal and standardized hardware testbed. Additionally, three interface modes aimed at evaluation under different scenarios are provided.

scholarly journals What are psychological constructs? On the nature and statistical modeling of emotions, intelligence, personality traits and mental disorders.

2017 ◽  
Author(s):  
Eiko I Fried
Keyword(s):  
Mental Disorders ◽  
Personality Traits ◽  
Statistical Modeling ◽  
Statistical Models ◽  
Personality Characteristics ◽  
Psychological Constructs

Many scholars have raised two related questions: what are psychological constructs such as cognitions, emotions, attitudes, personality characteristics, and intelligence? And how are they best modeled statistically? This paper provides (1) an overview of common theories and statistical models, (2) connects these two domains, and (3) discusses how the recently proposed framework pragmatic nihilism (Peters & Crutzen, 2017) fits in.

Big Five personality traits in simulated negotiation settings

2018 ◽  
Vol 13 (2) ◽  
pp. 201-213 ◽  
Author(s):  
Pedro Fontes Falcão ◽  
Manuel Saraiva ◽  
Eduardo Santos ◽  
Miguel Pina e Cunha
Keyword(s):  
Personality Traits ◽  
Factor Model ◽  
Five Factor Model ◽  
Big Five Personality ◽  
Content Type ◽  
Situational Variables ◽  
Integrative Negotiation ◽  
Negotiation Behavior ◽  
New Findings ◽  
And Behavior

Purpose After a hiatus in the research on individual differences in negotiation, there has been a surge of renewed interest in recent years followed by several new findings. The purpose of this paper is to explore the effects that personality, as structured by the five-factor model, have over negotiation behavior and decision making in order to create new knowledge and prescribe advice to negotiators. Design/methodology/approach This study replicates observations from earlier studies but with the innovation of using a different methodology, as data from a sample of volunteer participants were collected in regard to their personality and behavior during two computerized negotiation simulations, one with the potential for joint gains and the other following a more traditional bargaining scenario. Findings Significant results for both settings were found, with the personality dimensions of agreeableness, conscientiousness, and extraversion systematically reoccurring as the most statistically relevant, although expressing different roles according to the type of negotiation and measure being registered. The findings thus suggest a multidimensional relationship between personality and situational variables in which specific traits can either become liabilities or assets depending upon whether the potential for value creation is present or not. Originality/value The new findings on the impacts of personality traits on both distributive and integrative negotiations allow negotiators to improve their performance and to adapt to specific distributive or integrative negotiation situations.

Personality Traits That Distinguish Special Operations Female Aircrew

2021 ◽  
Vol 92 (4) ◽  
pp. 240-247
Author(s):  
Wayne Chappelle ◽  
Anne H. Shadle ◽  
Rachael N. Martinez ◽  
Laura E. Reardon ◽  
Tanya Goodman ◽  
...  
Keyword(s):  
Personality Traits ◽  
Air Force ◽  
Openness To Experience ◽  
Personality Characteristics ◽  
High Demand ◽  
Special Operations ◽  
Neo Personality Inventory ◽  
Care And Support ◽  
Computer Based ◽  
Insight Into

INTRODUCTION: U.S. Air Force Special Operations Command (AFSOC) female aircrew represent a small group of military personnel in challenging high-risk, high-demand professions. Personality characteristics may play a key role in distinguishing those women who pursue a career as a special operations aircrew member and succeed in this pursuit. Having access to normative personality data can potentially support psychologists in assessing AFSOC female aircrew and subsequently making informed recommendations to leadership.METHODS: A total of 586 AFSOC aircrew trainees58 (9.9%) women and 528 (90.1%) mencompleted a series of computer-based psychological tests to assess cognitive ability and personality traits.RESULTS: Results indicated significant differences between female AFSOC aircrew and female civilians on four of the five NEO Personality Inventory domains: Neuroticism (M 74.9 vs. M 87.1), Extraversion (M 123.7 vs. M 112.8), Openness to Experience (M 122.6 vs. M 111.0), and Conscientiousness (M 136.0 vs. M 120.6), respectively. The comparison between female AFSOC aircrew and male AFSOC aircrew revealed significant differences across three of the five domains: Neuroticism (M 74.9 vs. M 65.1), Openness to Experience (M 122.6 vs. M 115.0), and Agreeableness (M 119.6 vs. M 112.7), respectively.DISCUSSION: Implications for assessment and interpretation of psychological testing are discussed. This paper provides a unique perspective and insight into those who pursue and excel in this career field. Identifying specific personality traits in our AFSOC female aircrew allows for tailored care and support when evaluating readiness in special operations aircrew for optimizing performance.Chappelle W, Shadle AH, Martinez RN, Reardon LE, Goodman T, Spencer H, Thompson W. Personality traits that distinguish special operations female aircrew. Aerosp Med Hum Perform. 2021; 92(4):240247.

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