Personality Trait Detection via Transfer Learning

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.

Taxation Models and Improvement Proposals for Personal Income Tax in the Sharing Economy

As an innovative economic model,the sharing economy has seen rapid growth globally in recent years.It has not only brought a profound impact on traditional economies but also injected new vitality and momentum into the sustainable development of the social economy.However,for the sharing economy,the process of collecting personal income tax is facing several issues,such as the ambiguity of tax policies regarding personal income,challenges in identifying taxpayers,and difficulties in defining income.To achieve the fairness and efficiency of personal income tax collection in the sharing economy,this study proposes optimized regulatory mechanisms and conducts in-depth discussions on the adjustment of personal income tax policies,innovation in tax management technology,and improvement in the quality of personal income tax services.

Model considering panic emotion and personality traits for crowd evacuation

Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN(openness, conscientiousness,extroversion, agreeableness, neuroticism) model and SIS(susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.

Personality and Nomophobia: A Moderated Mediation Model of Self-Esteem and Non-Self-Determined Solitude

Background:With the development of information technology,mobile phone has brought much convenience to people’s lives but also caused many negative consequences due to excessive use,such as mobile phone addiction and nomophobia.Previous studies have explored the relationship between the Big Five Personality and proble-matic mobile phone use(PMPU).However,they focus on mobile phone addiction.Although there is a correlation between nomophobia and mobile phone addiction,the psychological structure is different.Therefore,it is neces-sary to explore the relationship between personality and nomophobia and the underlying mechanism.This study aims to examine the relationship between Big Five Personality and nomophobia,then construct a moderated mediation model to explore the mediation effect of solitude between Big Five Personality and nomophobia,as well as the moderation effect of self-esteem.Method:Data from 678 college students(351 females,51.77%)were collected.Participants completed the Big Five Personality Inventory,Solitude Behavior Scale,Nomophobia Scale and Self-esteem Scale.Analyses were conducted via mediation and moderated mediation.Results:Structural equation models revealed that solitude mediated the relationship between neuroticism and nomophobia.The results showed that neuroticism positively predicted solitude,which in turn positively predicted nomophobia.Four types of solitude partially mediated the relationship between neuroticism and nomophobia.We also found that self-esteem moderated the association between neuroticism and non-self-determined solitude.It is note-worthy that high self-esteem cannot protect people from negative factors.However,because of its characteristics,it is easy to receive more social information,and people high in neuroticism are sensitive to negative social infor-mation.It may cause maladaptive behavior.Conclusion:Findings demonstrated a process through which neu-roticism relates to nomophobia and a context under which these relationships may have occurred.

Vocational School Students With Five-Factor Model of Personality Determination of Personality Profile

Personality refers to the integration of feelings, thoughts, and behaviors specific to individual. The fact that personality has a distinguishing feature among individuals explains different behaviors of individuals against events and situations. This arises from the fact that personality is specific to individual and is affected by the effects of many factors with which it interacts, and from the integration of them. In this context, this research is based on the hypothesis that there is a relationship between vocs.tionai school students＇ demographic features including their family, socio-culture, geographical environments （gender, age, department, grade, parental education, number of siblings, birth order in the family, and the family＇s income levels）, and the personality profiles. The research was carded out as part of five-factor model of personality in an attempt to determine whether vocational school students＇ personality profiles vary according to demographic variables and to reveal the relationship between them. The research sample consisted of 220 students selected from the students studying in Altmta~ Vocational School in the Spring Term of the 2013-2014 Academic Year. Data were analyzed via SPSS 15.0 statistical package program by performing t-test, analysis of variance and logistic regression analysis. While a significant difference was found between students＇ five-factor personality profiles according to gender, age, department, average, father＇s education, and the number of siblings, no significant difference was found among grade, mother＇s education, birth order in the family, and income levels. Moreover, independent variables affecting students＇ academic averages were determined as a result of the logistic regression analysis.

MAIPFE:An Efficient Multimodal Approach Integrating Pre-Emptive Analysis,Personalized Feature Selection,and Explainable AI

Medical Internet of Things(IoT)devices are becoming more and more common in healthcare.This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way.Existing methods,while useful,have limitations in predictive accuracy,delay,personalization,and user interpretability,requiring a more comprehensive and efficient approach to harness modern medical IoT devices.MAIPFE is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease detection.By using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be transformed.The Multimodal Approach Integrating Pre-emptive Analysis,Personalized Feature Selection,and Explainable AI(MAIPFE)framework,which combines Firefly Optimizer,Recurrent Neural Network(RNN),Fuzzy C Means(FCM),and Explainable AI,improves disease detection precision over existing methods.Comprehensive metrics show the model’s superiority in real-time health analysis.The proposed framework outperformed existing models by 8.3%in disease detection classification precision,8.5%in accuracy,5.5%in recall,2.9%in specificity,4.5%in AUC(Area Under the Curve),and 4.9%in delay reduction.Disease prediction precision increased by 4.5%,accuracy by 3.9%,recall by 2.5%,specificity by 3.5%,AUC by 1.9%,and delay levels decreased by 9.4%.MAIPFE can revolutionize healthcare with preemptive analysis,personalized health insights,and actionable recommendations.The research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.

Perspectives and Experiences of Education Stakeholders: A Quantitative Study on the Adoption of Artificial Intelligence in Executive Training Using Structural Equation Modeling

The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).

A Novel Recommendation Service Method Based on Cloud Model and User Personality

The number of Internet Web services has become increasingly large recently.Cloud services consumers face a critical challenge in selecting services from abundant candidates.Due to the uncertainty of Web service QoS and the diversity of user characteristics,this paper proposes a Web service recommendation method based on cloud model and user personality(WSRCP),which employs cloud model similarity method to analyze the similarity of QoS feedback data among different users,to identify the user with high similarity to the potential user.Based on the QoS data of the users’feedback,Finally,user characteristic attribute Web service recommendation is implemented by personalized collaborative filtering algorithm.The experimental results on the WS-Dream dataset show that our approach not only solves the drawbacks of the sparse user service,but also improves the recommend accuracy.

Path analysis of relationship among personality, perceived stress, coping, social support, and psychological outcomes

AIM: To provide a structural model of the relationship between personality traits, perceived stress, coping strategies, social support, and psychological outcomes in the general population.METHODS: This is a cross sectional study in which the study group was selected using multistage cluster and convenience sampling among a population of 4 million. For data collection, a total of 4763 individuals were asked to complete a questionnaire on demographics, personality traits, life events, coping with stress, social support, and psychological outcomes such as anxiety and depression. To evaluate the comprehensive relation-ship between the variables, a path model was fitted.RESULTS: The standard electronic modules showed that personality traits and perceived stress are important determinants of psychological outcomes. Social support and coping strategies were demonstrated to reduce the increasing cumulative positive effects of neuroticism and perceived stress on the psychological outcomes and enhance the protective effect of extraversion through decreasing the positive effect of perceived stress on the psychological outcomes. CONCLUSION: Personal resources play an important role in reduction and prevention of anxiety and depression. In order to improve the psychological health, it is necessary to train and reinforce the adaptive coping strategies and social support, and thus, to moderate negative personality traits.

Research on the User Interest Modeling of Personalized Search Engine

At present, how to enable Search Engine to construct user personal interest model initially, master user＇s personalized information timely and provide personalized services accurately have become the hotspot in the research of Search Engine area. Aiming at the problems of user model＇s construction and combining techniques of manual customization modeling and automatic analytical modeling, a User Interest Model （UIM） is proposed in the paper. On the basis of it, the corresponding establishment and update algorithms of User lnterest Profile （UIP） are presented subsequently. Simulation tests proved that the UIM proposed and corresponding algorithms could enhance the retrieval precision effectively and have superior adaptability.

Neuroticism personality trait is associated with Quality of Life in patients with Chronic Heart Failure

AIM: To evaluate Quality of life(QoL) in chronic heart failure(CHF) in relation to Neuroticism personality trait and CHF severity.METHODS: Thirty six consecutive, outpatients with Chronic Heart Failure(6 females and 30 males, mean age: 54 ± 12 years), with a left ventricular ejection fraction ≤ 45% at optimal medical treatment at the time of inclusion, were asked to answer the Kansas City Cardiomyopathy Questionnaire(KCCQ) for Quality ofLife assessment and the NEO Five-Factor Personality Inventory for personality assessment. All patients un-derwent a symptom limited cardiopulmonary exercise testing on a cycle-ergometer, in order to access CHF severity. A multivariate linear regression analysis us-ing simultaneous entry of predictors was performed to examine which of the CHF variables and of the person-ality variables were correlated independently to QoL scores in the two summary scales of the KCCQ, namely the Overall Summary Scale and the Clinical Summary Scale.RESULTS: The Neuroticism personality trait score had a significant inverse correlation with the Clinical Sum-mary Score and Overall Summary Score of the KCCQ(r =-0.621, P < 0.05 and r =-0.543, P < 0.001, respec-tively). KCCQ summary scales did not show significant correlations with the personality traits of Extraversion, Openness, Conscientiousness and Agreeableness. Mul-tivariate linear regression analysis using simultaneous entry of predictors was also conducted to determine the best linear combination of statistically significant univari-ate predictors such as Neuroticism, VE/VCO2 slope and VO2 peak, for predicting KCCQ Clinical Summary Score. The results show Neuroticism(β =-0.37, P < 0.05), VE/VCO2 slope(β =-0.31, P < 0.05) and VO2 peak(β = 0.37, P < 0.05) to be independent predictors of QoL. In multivariate regression analysis Neuroticism(b =-0.37, P < 0.05), the slope of ventilatory equivalent for carbon dioxide output during exercise,(VE/VCO2 slope)(b =-0.31, P < 0.05) and peak oxygen uptake(VO2 peak),(b = 0.37, P < 0.05) were independent predictors of QoL(adjusted R2 = 0.64; F = 18.89, P < 0.001).CONCLUSION: Neuroticism is independently associat-ed with QoL in CHF. QoL in CHF is not only determined by disease severity but also by the Neuroticism person-ality trait.

Personality Traits and Facebook Use: The Combined/Interactive Effect of Extraversion, Neuroticism and Conscientiousness

This study examined the influence of Big Five personality traits on Facebook usage and examined the interactions of traits in this context based on Torgersen’s (1995) typological approach. The effect of self-esteem, narcissism, loneliness, shyness and boredom proneness on Facebook usage was also investigated. The sample included both student (N = 190) and (N = 184) non-student samples. Narcissism was the strongest predictor of time spent on Facebook per day for both students and non-students. Narcissism was also the strongest predictor of number of daily logins for non-students, however, agreeableness was the strongest predictor of logins for students. Extraversion was the strongest predictor of number of Facebook friends for both students and non- students, however the interaction of Extraversion and Neuroticism was also a predictor of Facebook friends for students, and the interaction of Extraversion and Conscientiousness for non-students. Future research should consider the combined effect of personality traits on overall Facebook use.

Personality Traits and Cognitive Determinants——An Empirical Investigation of the Use of Smartphone Security Measures

In the last years, increasing smartphones’ capabilities have caused a paradigm shift in the way of users’ view and using mobile devices. Although researchers have started to focus on behavioral models to explain and predict human behavior, there is limited empirical research about the influence of smartphone users’ individual differences on the usage of security measures. The aim of this study is to examine the influence of individual differences on cognitive determinants of behavioral intention to use security measures. Individual differences are measured by the Five-Factor Model;cognitive determinants of behavioral intention are adapted from the validated behavioral models theory of planned behavior and technology acceptance model. An explorative, quantitative survey of 435 smartphone users is served as data basis. The results suggest that multiple facets of smartphone user’s personalities significantly affect the cognitive determinants, which indicate the behavioral intention to use security measures. From these findings, practical and theoretical implications for companies, organizations, and researchers are derived and discussed.

Online RGB-D person re-identification based on metric model update

Person re-identification （re-id） on robot platform is an important application for human-robot- interaction （HRI）, which aims at making the robot recognize the around persons in varying scenes. Although many effective methods have been proposed for surveillance re-id in recent years, re-id on robot platform is still a novel unsolved problem. Most existing methods adapt the supervised metric learning offline to improve the accuracy. However, these methods can not adapt to unknown scenes. To solve this problem, an online re-id framework is proposed. Considering that robotics can afford to use high-resolution RGB-D sensors and clear human face may be captured, face information is used to update the metric model. Firstly, the metric model is pre-trained offline using labeled data. Then during the online stage, we use face information to mine incorrect body matching pairs which are collected to update the metric model online. In addition, to make full use of both appearance and skeleton information provided by RGB-D sensors, a novel feature funnel model （FFM） is proposed. Comparison studies show our approach is more effective and adaptable to varying environments.

The Tragedy of Unbalanced Personality——The Common Personality Psychological Reasons of the Destruction of Emily and Usher

A Rose for Emily is the masterpiece of William Faulkner, who is regarded as the founder of Southern literature. The Fall of the House of Usher is one of the most popular short stories written by the great American writer Edgar Allan Poe. They have common ends that the main characters of the two novels—Emily and Usher are all destruction of their unbalanced personality.This thesis will set a new point from a branch view of psychology—personality psychology to analyze the two characters Emily and Usher, how they change from pathological personality to abnormal personality, and finally destruct from flesh to soul. The deep meaning is to announce the human inner world will distract by the outside world.

Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation system.Several contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning system.In this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are surveyed.DEMATEL(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among them.The acquiring methods of these contexts are also defined.On the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system.In a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.

Study on the Recommended Model Based on Personal Information System

This paper classifies the scenario elements which affect the real-time information needs of mobile commerce users, and proposes a nomination model that integrates the user’s personalized context elements. In this model, the top K scenarios that have the greatest impact on each user’s instant information demands are calculated from the user’s current scenario and historical data, thereby constructing a user personalized situation and improving it as an input condition that existing scenario-based multi-dimensional information recommendation algorithm is used for project nomination. Result/Conclusion: The improved algorithm and other three algorithms were compared by Movie lens and MBook Crossing dataset. The experimental results show that the model has higher prediction accuracy and can effectively improve user satisfaction and more effectively and solve personalized nomination issues in a mobile commerce environment.

Establishment of Person and Enterprise Process Model in Product Data Management System

Before the implementation of product data management (PDM) system, person model and enterprise process model (EPM) must be firstly established. For the convenience of project management, all the related users must be allocated to the “Person User Role Group” net. Based on the person model and the direction of information flow, the EPM is established subsequently. The EPM consists of several release levels, in which the access controls are defined. The EPM procedure shows the blueprint of the workflow process structure. The establishment of person model and EPM in an enterprise has been instanced at the end of this paper.

Personalized Privacy Protecting Model in Mobile Social Network

With the rapid development of the new generation of information technology,the analysis of mobile social network big data is getting deeper and deeper.At the same time,the risk of privacy disclosure in social network is also very obvious.In this paper,we summarize the main access control model in mobile social network,analyze their contribution and point out their disadvantages.On this basis,a practical privacy policy is defined through authorization model supporting personalized privacy preferences.Experiments have been conducted on synthetic data sets.The result shows that the proposed privacy protecting model could improve the security of the mobile social network while keeping high execution efficiency.

Research on the Influencing Factors of Personal Credit Based on a Risk Management Model in the Background of Big Data

Between states, between enterprises and enterprises, between people, it can be stated that credit is full of every corner of our lives. But the current lack of social credit is fundamental. Credit risk is particularly prominent. In the extensive data generation today, the information on personal credit statistics is very large, but still lack the data system processing and screening. Through the information retrieval of 200 credit information reports, this paper constructs the evaluation system of personal credit by using the basic information of the individual. The basic information of these individuals has great convenience in information collection and information statistics, and this basic information covers all aspects that are likely to result in the breach of contract. Through the use of single factor analysis and logistic model to solve the index system, you can not only find the impact of individual indicators on the degree of personal credit, but also see the overall impact of indicators on the degree of credit, that is, the weight of the indicators. Finally, four different credit ratings are divided by assigning the indicators to the scores. Credit rating can clearly measure the respective credit situation. Through the classification of these levels, measuring the credit line when a person in the individual credit operation, at the same time, it can provide reference and proval to administrative departments, which is benefit for managing credit risks. It has a substantial meaning and value in use. The solution to the rating system cannot only be applied to individuals, but also to the enterprises, with a wide range of versatility.