Journal articles: 'Personality Computing'

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Vinciarelli, Alessandro, and Gelareh Mohammadi. "More Personality in Personality Computing." IEEE Transactions on Affective Computing 5, no. 3 (July 1, 2014): 297–300. http://dx.doi.org/10.1109/taffc.2014.2341252.

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Vinciarelli, Alessandro, and Gelareh Mohammadi. "A Survey of Personality Computing." IEEE Transactions on Affective Computing 5, no. 3 (July 1, 2014): 273–91. http://dx.doi.org/10.1109/taffc.2014.2330816.

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Johnson, Richard D., Natasha Veltri, and Jason B. Thatcher. "Beliefs and Attributions toward Computing Technology." Journal of Organizational and End User Computing 27, no. 3 (July 2015): 27–54. http://dx.doi.org/10.4018/joeuc.2015070102.

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This study critiques and extends the work of , who investigated the relations between social cues in an interface, user personality, user beliefs about the social role and capabilities of computers, and the attributions of responsibility users made for their interactions and outcomes with a computer. In this study, rather than examining the simple, direct effects investigated previously, we examine the moderating role of social cues in the interface. In addition, building upon recent findings from psychology, the authors assess personality traits individually, rather than aggregating them. To evaluate the theorized relations, 152 individuals participated in a controlled laboratory experiment, where social cues in two computer interfaces were manipulated. Results indicate that social cues moderate the relations between personality, beliefs about the social role of computing, and the attributions made. In addition, the results suggest that disaggregating personality traits is theoretically and practically richer than aggregating them.

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Chuanqin, Zheng. "Computing Personality Trait Based on Multi-source." Journal of Physics: Conference Series 1955, no. 1 (June 1, 2021): 012100. http://dx.doi.org/10.1088/1742-6596/1955/1/012100.

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Wisnubroto, Petrus. "PENGARUH FAKTOR DEMOGRAFI DAN PERSONALITY TERHADAP KEAHLIAN DALAM END USER COMPUTING (Studi Kasus Guru dan Karyawan Administrasi pada Sekolah Menengah Atas Negeri di Kotamadya Yogyakarta)." Conference SENATIK STT Adisutjipto Yogyakarta 1 (December 3, 2013): 1. http://dx.doi.org/10.28989/senatik.v1i0.54.

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This study aims to analyze the influence of demographic factors ( age , gender , education , experience ) and personality factors ( computer anxiety , computer attitude , math anxiety ) to the end user computing expertise of personnel either partially or simultaneously . Models the influence of the demographic variables and personality skills in end user computing research model replicated from Harrison and Rainer (1992 ) and the research model and Gudono Rifa (1998). The study was conducted on teacher and administrative employees of State High School I to XI in Yogyakarta Municipality considering charging adminsitrasinya teacher certification performed with computerized online program for you State , appropriate curriculum in 2013 all teachers in the learning process to educate students using computerized , so too the acceptance of new students each academic year always use the on-line computerized . Thus both teachers and administrative staff are required to have expertise in End user Computing . So the research conducted to analyze the influence of demographic factors and personality to expertise in End User Computing. The results showed that demographic factors (age - gender - experience) has a direct influence on the expertise in end user computing 0 , 001 ; 0 , 027 ; 0,007 . Personality factors of computer anxiety (fear - Anticipation 0.057) ; computer attitude (Intimidation 0.05) has a direct influence on the expertise in end user computing . The results were quite surprising is the demographic factors (educational 0.117) , personality factors of computer attitude (0.760 pessimism - optimism 0.150) ; math anxiety (0.334) is not significant enough to expertise in end user computing . To improve performance in the Public High Schools in Yogyakarta municipality with regard to the introduction of new technology on- line computerized system above , need to be considered in decisions about withdrawal of the human resources , training and implementation of computer education for personnel and equipment. Provision of adequate facilities is expected to increase its expertise in end user computing , which in turn improves the performance of State High School and individual performance

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Nunes, Maria Augusta Silveira Netto. "Psychological Aspects in lifelike synthetic agents: Towards to the Personality Markup Language (A Brief Survey)." RENOTE 7, no. 3 (December 21, 2009): 390–400. http://dx.doi.org/10.22456/1679-1916.13581.

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This paper describes how human psychological aspects have been used in lifelike synthetic agents in order to provide believability during the human-computer interaction. We describe a brief survey of applications where Affective Computing Scientists have applied psychological aspects, like Emotion and Personality. Based on those aspects we describe the effort done by Affective Computing scientists in order to create a Markup Language to express and standardize Emotions. Because they have not yet concentrated their effort on Personality, here, we propose a starting point to create a Markup Language to express Personality.

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Yang, Guo Liang, Jin Hui Zhang, and Hui Sun. "Design of Emotional Interaction System Based on Affective Computing Model." Applied Mechanics and Materials 198-199 (September 2012): 367–73. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.367.

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An emotional interaction system is designed by using the theories about affective computing in this paper, which includes the emotional information capturing, machine emotional model and emotional expression. This paper focuses on the problem of building machine emotional model, not only gives the basic definitions of personality space, mood space, and emotion space, but also establishes the quantitative relationship of personality mood and emotion. At last, this paper builds a machine affective model which can reflect the transformation law of the mood, emotion and personality. Related simulation results show that the model can effectively simulates the change law of human emotion. Finally, this paper designs the software interface of the emotional interaction system.

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Bhushan, Shashi. "An Efficient Soft Computing Approach for Text Identification using Artificial Intelligence Model." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3860–66. http://dx.doi.org/10.22214/ijraset.2021.35766.

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This paper presents an enhanced system in the field of text identification using Soft computing techniques. The model designed in this work analyzes the blogs or input text and classifies the personality into five major categories; Neuroticism, Extraversion, Openness, Conscientiousness and Agreeableness. The blog or text is first passed through POS tagger then a feature vector matrix is generated according to the attributes of the personality chart. Each column of FVM is calculated in its domain that improves the final result of personality identification. The result of the proposed model is improvement over similar work by other researchers [1, 2, 3].

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Board, Editorial. "Well-known Personality in a Technical World and Information System." Global Journal of Enterprise Information System 8, no. 1 (August 9, 2016): 62. http://dx.doi.org/10.18311/gjeis/2016/7294.

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We are pleased to share profile of Col. Inderjit Singh (Retd). Inderjit is a an experienced Information Systems and Information Security Professional with experience of more than 25 year across a wide spectrum of areas spanning Solution Architecture, Program/Project Management Telecom, IT Infrastructure Management, Info Security Advisory and Architecture, Cyber Security, Cyber Warfare and Cyber Forensics, Data Centers Design and Operations, Cloud Computing and E-Commerce Startup. An experienced Information Systems Professional with experience of more than 25 year across a wide spectrum of areas spanning Solution Architecture, Program/ Project Management in Telecom, IT Infrastructure Management, Information Security Advisory and Architecture, Cyber Security, Cyber Warfare and Cyber Forensics, Data Centers Design and Operations, Cloud Computing and E-Commerce.

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Choong, En Jun, and Kasturi Dewi Varathan. "Predicting judging-perceiving of Myers-Briggs Type Indicator (MBTI) in online social forum." PeerJ 9 (June 23, 2021): e11382. http://dx.doi.org/10.7717/peerj.11382.

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The Myers-Briggs Type Indicator (MBTI) is a well-known personality test that assigns a personality type to a user by using four traits dichotomies. For many years, people have used MBTI as an instrument to develop self-awareness and to guide their personal decisions. Previous researches have good successes in predicting Extraversion-Introversion (E/I), Sensing-Intuition (S/N) and Thinking-Feeling (T/F) dichotomies from textual data but struggled to do so with Judging-Perceiving (J/P) dichotomy. J/P dichotomy in MBTI is a non-separable part of MBTI that have significant inference on human behavior, perception and decision towards their surroundings. It is an assessment on how someone interacts with the world when making decision. This research was set out to evaluate the performance of the individual features and classifiers for J/P dichotomy in personality computing. At the end, data leakage was found in dataset originating from the Personality Forum Café, which was used in recent researches. The results obtained from the previous research on this dataset were suggested to be overly optimistic. Using the same settings, this research managed to outperform previous researches. Five machine learning algorithms were compared, and LightGBM model was recommended for the task of predicting J/P dichotomy in MBTI personality computing.

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Das, Aditi. "Automatic Personality Identification using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3528–34. http://dx.doi.org/10.22214/ijraset.2021.35386.

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Machine Learning has made significant changes in the world making our life more easier and comfortable .One of the most exciting applications is the prediction of Personality automatically using different algorithms. Personality computing and emotive computing, where the popularity of temperament traits is important, have gained increasing interest and a spotlight in several analysis areas recently. These applications can powerfully predict the personality of a Person. The aim of this paper is to use a more rigorous construct Validation system to extend the potential of machine learning approaches to personality assessment. We have reviewed multiple recent applications of Machine Learning to recognize personality, thus providing a broader context of fundamental principles of constructing, validating, and then providing recommendations on how to use Machine Learning to advance the level of our understanding and applying our learnings to develop advanced personality recognition applications. araphrased Text Output text rewrite / rewrite We use deep neural network learning to recognize characteristics independently and, through feature-level fusion of these networks, we obtain final predictions of obvious personalities. We use a previously trained long-term and short-term memory network to integrate time information. We train large-scale models comprised of specific subnetworks- modalities through a two-stage training process. We first train the subnets separately for and then use these trained networks to fit the overall model. We used the ChaLearn First Impressions V2 challenge dataset to evaluate the proposed method. Our method achieves the most effective overall "medium precision" score, with an average score of for 5 personality characteristics, which is compared to the state-of-the-art method.

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Kiciman, Emre. "Workshops." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 1 (August 3, 2021): xxv—xxvi. http://dx.doi.org/10.1609/icwsm.v7i1.14376.

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The workshops presented at ICWSM 2013 included Computational Personality Recognition (Shared Task), Social Computing for Workforce 2.0, Social Media Visualization, and When The City Meets The Citizen.

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Xu, Jia, Weijian Tian, Guoyun Lv, Shiya Liu, and Yangyu Fan. "2.5D Facial Personality Prediction Based on Deep Learning." Journal of Advanced Transportation 2021 (June 30, 2021): 1–12. http://dx.doi.org/10.1155/2021/5581984.

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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.

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Kumar Yadav, Santosh, Shiv Prakash, and Rajat Kumar Jain. "TRI-GUNAS (SATTVA, RAJAS AND TAMAS) AND RISK-TAKING BEHAVIOR AMONG UNDERGRADUATE STUDENTS." International Journal of Research -GRANTHAALAYAH 4, no. 1 (January 31, 2016): 138–45. http://dx.doi.org/10.29121/granthaalayah.v4.i1.2016.2852.

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Tri-Gunas is considered an important personality factors in the eastern philosophy. Objective of the present study is to find out co-relation between Tri-Gunas factors of personality and risk- taking behavior among undergraduate students. Sample consists of 192 under graduate students (94 male & 98 female) selected by adopting random sampling method. The tools used for the study are Personality Assessment Profile by Dr. UpendraDhar, Dr. SapanaParashar and Dr. Santosh Dhar and Risk taking Questionnaire constructed by Verendra Sinha and P.N. Arora. Statistical analysis was done by computing person’s product moment co-relation. Findings of the study are - 1) Sattvic personality and risk-taking behavior are not correlated to each other significantly. 2) Rajsic personality and risk-taking behavior are not correlated to each other significantly. 3) Tamsic personality and risk-taking behavior are not correlated to each other significantly in male students but in case of female students there exists a positive and significant co-relation.

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G.C. Wright, Aidan. "Current Directions in Personality Science and the Potential for Advances through Computing." IEEE Transactions on Affective Computing 5, no. 3 (July 1, 2014): 292–96. http://dx.doi.org/10.1109/taffc.2014.2332331.

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Carducci, Giulio, Giuseppe Rizzo, Diego Monti, Enrico Palumbo, and Maurizio Morisio. "TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning." Information 9, no. 5 (May 18, 2018): 127. http://dx.doi.org/10.3390/info9050127.

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Guo, Ao, and Jianhua Ma. "Archetype-Based Modeling of Persona for Comprehensive Personality Computing from Personal Big Data." Sensors 18, no. 3 (February 25, 2018): 684. http://dx.doi.org/10.3390/s18030684.

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J Sterling, Sabrina. "The Correlation between Temperament, Technology Preference and Proficiency in Middle School Students." Journal of Information Technology Education: Research 15 (2016): 001–18. http://dx.doi.org/10.28945/2333.

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This study examined the relationship between middle school students’ personality type and their academic performance in the technology courses in which they participated. It also explored the differences in technology use by personality. Most participants identified games as a favorite pastime. However, there were some noted temperamental differences. Students with the analytical personality reported the most varied use of computers, and rated their technology skills significantly higher on the self-perception scales and performed at a higher proficiency level than their peers. The study also investigated the effectiveness of the two computer courses offered at the schools in the study. Students who completed the Computer Literacy course during the school year performed significantly higher than those who took the Explorations Technology course, both courses, or no technology course at all. However, those with the analytical temperament performed better in the Explorations Technology course. Results suggest personality can predict technology use in students. Findings are consistent with similar research in the computing industry.

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Yoro, Rume Elizabeth, Fidelis ObukohwoAghware, Maureen Ifeanyi Akazue, Ayei Egu Ibor, and Arnold Adimabua Ojugo. "Evidence of personality traits on phishing attack menace among selected university undergraduates in Nigerian." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (April 1, 2023): 1943. http://dx.doi.org/10.11591/ijece.v13i2.pp1943-1953.

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Access ease, mobility, portability, and improved speed have continued to ease the adoption of computing devices; while, consequently proliferating phishing attacks. These, in turn, have created mixed feelings in increased adoption and nosedived users’ trust level of devices. The study recruited 480-students, who were exposed to socially-engineered attack directives. Attacks were designed to retrieve personal data and entice participants to access compromised links. We sought to determine the risks of cybercrimes among the undergraduates in selected Nigerian universities, observe students’ responses and explore their attitudes before/after each attack. Participants were primed to remain vigilant to all forms of scams as we sought to investigate attacks’ influence on gender, students’ status, and age to perceived safety on susceptibility to phishing. Results show that contrary to public beliefs, age, status, and gender were not among the factors associated with scam susceptibility and vulnerability rates of the participants. However, the study reports decreased user trust levels in the adoption of these new, mobile computing devices.

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Maulidah, Mawadatul, and Hilman Ferdinandus Pardede. "Prediction Of Myers-Briggs Type Indicator Personality Using Long Short-Term Memory." Jurnal Elektronika dan Telekomunikasi 21, no. 2 (December 31, 2021): 104. http://dx.doi.org/10.14203/jet.v21.104-111.

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Personality is defined as the mix of features and qualities that make up an individual's particular character, including thoughts, feelings, and behaviors. With the rapid development of technology, personality computing is becoming a popular research field by providing users with personalization. Many researchers have used social media data to automatically predict personality. This research uses a public dataset from Kaggle, namely the Myers-Briggs Personality Type Dataset. The purpose of this study is to predict the accuracy and F1-score values so that the performance for predicting and classifying Myers–Briggs Type Indicator (MBTI) personality can work optimally by using attributes from the MBTI dataset, namely posts and types. Predictive accuracy analysis was carried out using the Long Short-Term Memory (LSTM) algorithm with random oversampling technique with the Imblearn library for MBTI personality type prediction and comparing the performance of the method proposed in this study with other popular machine learning algorithms. Experiments show that the LSTM model using the RMSprop optimizer and learning speed of 10-3 provides higher performance in terms of accuracy while for the F1-score the LSTM model using the RMSprop Optimizer and learning speed of 10-2 gives a higher value than the proposed machine learning algorithm so that the model MBTI dataset using LSTM with random oversampling can help in identifying the MBTI personality type.

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Žampachová, Barbora, Eva Landová, and Daniel Frynta. "Methods for measuring mammalian personalities: In which animals and how accurately can we quantify it?" Lynx, new series 48, no. 1 (December 1, 2017): 183–98. http://dx.doi.org/10.2478/lynx-2017-0011.

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Abstract The study of personality, and individual differences in behaviour has experienced a steady rise in popularity in the past years. In this review and meta-analysis, we aim to introduce the concept of personality and related phenomena. A behavioural trait should meet two basic conditions to be considered a personality trait – it should be consistent (1) in time and (2) across contexts. In mammals, the two most common orders in personality studies are primates and rodents. We therefore introduce different approaches to personality testing in these two orders. Primate personality studies are based on psychology studies and often rely on the observer’s ratings. Rodent personality studies originate in the studies of physiology and use an experimental approach. We present a more detailed overview of methodological issues of repeatability as a statistical tool for measuring consistency across time. The classic methods of computing repeatability do not consider habituation and other trends which may become confounding factors and lead to underestimation of repeatability. We also discuss consistency across contexts and different understandings of the context definition. We illustrate the variability of personality studies in mammals with a meta-analysis of repeatability estimates. We found that repeatability of behaviour depends on the methodology of behavioural testing and statistical analyses used, but also the number of test repetitions and differences between the focal behaviours. Repeatability decreased with more repetitions and the tests of aggressiveness and exploratory behaviour yielded lower repeatability estimates than the tests of activity.

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Lee, Seo-young, Gyuho Lee, Soomin Kim, and Joonhwan Lee. "Expressing Personalities of Conversational Agents through Visual and Verbal Feedback." Electronics 8, no. 7 (July 16, 2019): 794. http://dx.doi.org/10.3390/electronics8070794.

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As the uses of conversational agents increase, the affective and social abilities of agents become important with their functional abilities. Agents that lack affective abilities could frustrate users during interaction. This study applied personality to implement the natural feedback of conversational agents referring to the concept of affective computing. Two types of feedback were used to express conversational agents’ personality: (1) visual feedback and (2) verbal cues. For visual feedback, participants (N = 45) watched visual feedback with different colors and motions. For verbal cues, participants (N = 60) heard different conditions of agents’ voices with different scripts. The results indicated that the motions of visual feedback were more significant than colors. Fast motions could express distinct and positive personalities. Different verbal cues were perceived as different personalities. The perceptions of personalities differed according to the vocal gender. This study provided design implications for personality expressions applicable to diverse interfaces.

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Fu, Xiaomeng, Suyan Cheng, Li Zhao, and Jiaguo Lv. "Retweet Prediction Based on Multidimensional Features." Wireless Communications and Mobile Computing 2022 (February 16, 2022): 1–8. http://dx.doi.org/10.1155/2022/1863568.

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With the wide use of artificial intelligence-driven mobile devices, more and more Chinese people take part in the Twitter-like social sites, which makes Weibo an excellent communication platform. In view of the wide application of information diffusion in various fields, Weibo has become one of the most important research issues in mobile social computing. In Weibo, the retweet statuses of tweets of other users are considered to be the key mechanism for spreading information. How to predict whether a tweet will be retweeted by a user has received increasing attention in recent years. Research shows that the users’ retweet behavior is driven by their interest and personality. However, most previous works ignore the roles of users’ personality in their retweet behavior. To this end, a prediction model MDF-RP (multidimensional feature-based retweeting prediction) including personality feature is proposed. The prediction model integrates the features from three dimensions, such as author, tweet, and user. And the personality score is obtained based on the well-known Big Five personality trait model. The experimental results under different classifiers show that the performances of MDF-RP features outperform the basic features. And the experiments of cross-validation also demonstrate the stability of MDF-RP features.

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Gjoreski, Martin, Tine Kolenik, Timotej Knez, Mitja Luštrek, Matjaž Gams, Hristijan Gjoreski, and Veljko Pejović. "Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits." Applied Sciences 10, no. 11 (May 31, 2020): 3843. http://dx.doi.org/10.3390/app10113843.

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This study introduces two datasets for multimodal research on cognitive load inference and personality traits. Different to other datasets in Affective Computing, which disregard participants’ personality traits or focus only on emotions, stress, or cognitive load from one specific task, the participants in our experiments performed seven different tasks in total. In the first dataset, 23 participants played a varying difficulty (easy, medium, and hard) game on a smartphone. In the second dataset, 23 participants performed six psychological tasks on a PC, again with varying difficulty. In both experiments, the participants filled personality trait questionnaires and marked their perceived cognitive load using NASA-TLX after each task. Additionally, the participants’ physiological response was recorded using a wrist device measuring heart rate, beat-to-beat intervals, galvanic skin response, skin temperature, and three-axis acceleration. The datasets allow multimodal study of physiological responses of individuals in relation to their personality and cognitive load. Various analyses of relationships between personality traits, subjective cognitive load (i.e., NASA-TLX), and objective cognitive load (i.e., task difficulty) are presented. Additionally, baseline machine learning models for recognizing task difficulty are presented, including a multitask learning (MTL) neural network that outperforms single-task neural network by simultaneously learning from the two datasets. The datasets are publicly available to advance the field of cognitive load inference using commercially available devices.

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Archambault, Daniel, Fabio Celli, Elizabeth M. Daly, Ingrid Erickson, Werner Geyer, Germaine Halegoua, Brian Keegan, David R. Millen, Raz Schwartz, and N. Sadat Shami. "Reports on the 2013 Workshop Program of the Seventh International AAAI Conference on Weblogs and Social Media." AI Magazine 34, no. 4 (September 8, 2013): 116–18. http://dx.doi.org/10.1609/aimag.v34i4.2507.

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The Workshop Program of the Program of the Seventh International AAAI Conference on Weblogs and Social Media was held July 11, 2013, in Cambridge, Massachusetts. The program included four workshops, Computational Personality Recognition (Shared Task) (WS-13-01), Social Computing for Workforce 2.0 (WS-13-02), Social Media Visualization 2 (WS-13-03), and When the City Meets the Citizen (WS-13-04). This report summarizes the activities of the four workshops.

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Schwark, Jeremy D., Igor Dolgov, Daniel Hor, and William Graves. "Gender and Personality Trait Measures Impact Degree of Affect Change in a Hedonic Computing Paradigm." International Journal of Human-Computer Interaction 29, no. 5 (April 2013): 327–37. http://dx.doi.org/10.1080/10447318.2012.711703.

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Wan, Chuan, and Yan Tao Tian. "Emotional Expression System Based on Expression Intensity Recognition and Affective Model." Applied Mechanics and Materials 461 (November 2013): 618–22. http://dx.doi.org/10.4028/www.scientific.net/amm.461.618.

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Affective computing is an indispensable aspect in harmonious human-computer interaction and artificial intelligence. Making computers have the ability of generating emotions is a challenging task of affective computing. Affective Computing and Artificial Psychology are new research fields that involve computer and emotions, they have the same key research aspect, affective modeling. The paper introduces the basic affective elements, and the representation of affections in a computer. And we will describe an emotion generation model for a multimodal virtual human. The relationship among the emotion, mood and personality are discussed, and the PAD emotion space is used to define the emotion and the mood. We obtain the strength information of each expression component through fuzzy recognition of facial expressions based on Ekman six expression classifications, and take this information as a signal motivating emotion under the intensity-based affective model. Finally, a 3D virtual Human head with facial expressions is designed to show the emotion generation outputs. Experimental results demonstrate that the emotion generation intensity-based model works effectively and meets the basic principle of human emotion generation.

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Goyal, Ms Pooja, and Dr Sukhvinder Singh Deora. "Reliability of Trust Management Systems in Cloud Computing." Indian Journal of Cryptography and Network Security 1, no. 3 (May 10, 2022): 1–5. http://dx.doi.org/10.54105/ijcns.c1417.051322.

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Cloud computing is an innovation that conveys administrations like programming, stage, and framework over the web. This computing structure is wide spread and dynamic, which chips away at the compensation per-utilize model and supports virtualization. Distributed computing is expanding quickly among purchasers and has many organizations that offer types of assistance through the web. It gives an adaptable and on-request administration yet at the same time has different security dangers. Its dynamic nature makes it tweaked according to client and supplier’s necessities, subsequently making it an outstanding benefit of distributed computing. However, then again, this additionally makes trust issues and or issues like security, protection, personality, and legitimacy. In this way, the huge test in the cloud climate is selecting a perfect organization. For this, the trust component assumes a critical part, in view of the assessment of QoS and Feedback rating. Nonetheless, different difficulties are as yet present in the trust the board framework for observing and assessing the QoS. This paper talks about the current obstructions present in the trust framework. The objective of this paper is to audit the available trust models. The issues like insufficient trust between the supplier and client have made issues in information sharing likewise tended to here. Besides, it lays the limits and their enhancements to help specialists who mean to investigate this point.

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D’Mello, Sidney K., Louis Tay, and Rosy Southwell. "Psychological Measurement in the Information Age: Machine-Learned Computational Models." Current Directions in Psychological Science 31, no. 1 (February 2022): 76–87. http://dx.doi.org/10.1177/09637214211056906.

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Psychological science can benefit from and contribute to emerging approaches from the computing and information sciences driven by the availability of real-world data and advances in sensing and computing. We focus on one such approach, machine-learned computational models (MLCMs)—computer programs learned from data, typically with human supervision. We introduce MLCMs and discuss how they contrast with traditional computational models and assessment in the psychological sciences. Examples of MLCMs from cognitive and affective science, neuroscience, education, organizational psychology, and personality and social psychology are provided. We consider the accuracy and generalizability of MLCM-based measures, cautioning researchers to consider the underlying context and intended use when interpreting their performance. We conclude that in addition to known data privacy and security concerns, the use of MLCMs entails a reconceptualization of fairness, bias, interpretability, and responsible use.

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Lazarević, Ljiljana B., Michael Bošnjak, Goran Knežević, Boban Petrović, Danka Purić, Predrag Teovanović, Goran Opačić, and Bojana Bodroža. "Disintegration as an Additional Trait in the Psychobiological Model of Personality." Zeitschrift für Psychologie 224, no. 3 (July 2016): 204–15. http://dx.doi.org/10.1027/2151-2604/a000254.

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Abstract. This meta-analytic study investigates the relations between Disintegration-like phenomena (i.e., various aspects of symptomatology with the prefix “schizo-,” both at the clinical and the subclinical level) and the traits of the Psychobiological Model of Personality (PBMP). The empirically based benchmark for assuming the distinctness of the trait Disintegration was .30. The sample included 26 manuscripts with 30 studies and 424 effect sizes. By computing inverse sampling variance weighted mean correlation coefficients under a random-effects assumption, the following associations were found between Disintegration and Harm Avoidance, Novelty Seeking, Reward Dependence, Persistence, Self-Directedness, Cooperativeness, and Self-Transcendence: .23, .04, −.15, −.02, −.23, −.16, and .17, respectively. Two variables were found to moderate the Disintegration-Self-Transcendence correlation. Despite the theoretical expectation and some empirical evidence that Self-Transcendence (and other character traits) should capture variations in Disintegration-like phenomena, our results suggest that schizo-type phenomena are not adequately covered by the PBMP.

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Cernian, Alexandra, Nicoleta Vasile, and Ioan Stefan Sacala. "Fostering Cyber-Physical Social Systems through an Ontological Approach to Personality Classification Based on Social Media Posts." Sensors 21, no. 19 (October 4, 2021): 6611. http://dx.doi.org/10.3390/s21196611.

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The exponential increase in social networks has led to emergent convergence of cyber-physical systems (CPS) and social computing, accelerating the creation of smart communities and smart organizations and enabling the concept of cyber-physical social systems. Social media platforms have made a significant contribution to what we call human behavior modeling. This paper presents a novel approach to developing a users’ segmentation tool for the Romanian language, based on the four DISC personality types, based on social media statement analysis. We propose and design the ontological modeling approach of the specific vocabulary for each personality and its mapping with text from posts on social networks. This research proposal adds significant value both in terms of scientific and technological contributions (by developing semantic technologies and tools), as well as in terms of business, social and economic impact (by supporting the investigation of smart communities in the context of cyber-physical social systems). For the validation of the model developed we used a dataset of almost 2000 posts retrieved from 10 social medial accounts (Facebook and Twitter) and we have obtained an accuracy of over 90% in identifying the personality profile of the users.

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Kemp, Kathryn C., Jessica A. Kaczorowski, Christopher J. Burgin, Michael L. Raulin, Donald R. Lynam, Chelsea Sleep, Joshua D. Miller, Neus Barrantes-Vidal, and Thomas R. Kwapil. "Association of Multidimensional Schizotypy with PID-5 Domains and Facets." Journal of Personality Disorders 36, no. 6 (December 2022): 680–700. http://dx.doi.org/10.1521/pedi.2022.36.6.680.

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The underlying vulnerability for schizophrenia-spectrum disorders is expressed across a continuum of clinical and subclinical symptoms referred to as schizotypy. Schizotypy is a multidimensional construct with positive, negative, and disorganized dimensions. The present study examined associations of positive, negative, and disorganized schizotypy with pathological personality traits and facets assessed by the Personality Inventory for DSM-5 (PID-5) in 1,342 young adults. As hypothesized, positive schizotypy was associated with the PID-5 psychoticism domain and facets, negative schizotypy was associated with the detachment domain and facets and the restricted affectivity facet, and disorganized schizotypy's strongest associations were with the distractibility and eccentricity facets and the negative affect domain. The PID-5 facets accounted for upwards of two thirds of the variance in each schizotypy dimension. The authors conclude by providing regression-based algorithms for computing positive, negative, and disorganized schizotypy scores based on the PID-5 facets.

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Han, Zhong. "Research on Sports Balanced Development Evaluation System Based on Edge Computing and Balanced Game." Security and Communication Networks 2021 (April 5, 2021): 1–8. http://dx.doi.org/10.1155/2021/5557138.

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Sports can promote physical and mental health and the development of personality. How to build a balanced development evaluation system for sports and find a quality education suitable for the school are particularly important. In this article, we use edge computing technology to design a balanced development framework for sports. The framework will guide students to actively participate in physical exercise and develop sports to a higher, more comprehensive level. Then, the equilibrium game model is used to analyse the evaluation system of the balanced development of college sports. The research results show that the university sports balanced development evaluation system has good application prospects. The empirical analysis results verify its accuracy and reliability.

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Lorenzo-Seva, Urbano, and Pere J. Ferrando. "MSA: The forgotten index for identifying inappropriate items before computing exploratory item factor analysis." Methodology 17, no. 4 (December 17, 2021): 296–306. http://dx.doi.org/10.5964/meth.7185.

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Kaiser’s single-variable measure of sampling adequacy (MSA) is a very useful index for debugging inappropriate items before a factor analysis (FA) solution is fitted to an item-pool dataset for item selection purposes. For reasons discussed in the article, however, MSA is hardly used nowadays in this context. In our view, this is unfortunate. In the present proposal, we first discuss the foundation and rationale of MSA from a ‘modern’ FA view, as well as its usefulness in the item selection process. Second, we embed the index within a robust approach and propose improvements in the preliminary item selection process. Third, we implement the proposal in different statistical programs. Finally, we illustrate its use and advantages with an empirical example in personality measurement.

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Izzo, Federica, and Quirino Picone. "Defining an Integrated and Computed Methodology Approach for Sentiment and Psychographic Analysis in Tourism Research." Journal of Tourism and Services 13, no. 25 (December 20, 2022): 1–21. http://dx.doi.org/10.29036/jots.v13i25.393.

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High-performance computational resources and artificial intelligence-based tools can enhance tourism research and marketing. However, a formal methodological approach using digital technologies in this field is still missing. This research work presents the preliminary results of defining an integrated computational methodology in tourism research and marketing. In addition, the paper aims to provide guidelines for a methodological approach leveraging technological resources and Big Data. The proposed research method is based on online User-Generated Content (UGC) analysis through a psychographic approach based on the Big Five Model, Sentiment Analysis, and Machine Learning techniques. The study is supported by high-performance computing resources, artificial intelligence-based tools, and open-source Python-based software for data collection, text analysis, and psychographic attribution. Results show a remarkable performance of the BFF prediction model and confirm the role of personality in the tourists’ decision-making and appreciation of a site. Future developments of this project involve using the acquired structured dataset labeled with sentiment and psychographic attribution to create a further prediction model on tourist segments and appreciation as part of a marketing strategy in tourism management. Future research should push forward the development of further integrated and performing computer-based methodology in tourism research and marketing, leveraging the massive amount of data and the potential of high-performance computing techniques. The main contribution of this research effort is twofold: the definition of a general-purpose BFF/Sentiment Analysis methodology and the development of a prediction model from online UGC based on the Big Five personality traits in the tourism research scenario.

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Chen, Houn-Gee, and Robert P. Vecchio. "Nested IF-THEN-ELSE constructs in end-user computing: personality and aptitude as predictors of programming ability." International Journal of Man-Machine Studies 36, no. 6 (June 1992): 843–59. http://dx.doi.org/10.1016/0020-7373(92)90076-w.

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Farrokhi, N., and S. Ghahari. "The Purpose of this Research was Standardizing the Questionnaire of Personality Disorder Cluster A." European Psychiatry 41, S1 (April 2017): S256. http://dx.doi.org/10.1016/j.eurpsy.2017.02.055.

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IntroductionAs more or less stable personality traits of the person, temperament, intellect and body is what makes an individual unique compatibility with the environment.ObjectiveThe purpose of this research was standardizing the questionnaire of personality disorder cluster A. On the basis of realizing criterion standard, DSM- 5.Method1303 people from universities of Tehran and Alborz provinces (753 females and 550 males) were examined by using the randomized sampling method. The questions of the questionnaire were conformed Dr. ShahramVaziri on the basis of Iran s population and culture. Then the reliability was tested and accomplished simultaneously Millon(MCMI-III) questionnaire.ResultAfter computing the correlation scales of Millon test with each of the questions, 20 questions that showed the highest correlation and diagnosis coefficient were chosen and scored again in next stage.ConclusionsInvestigating the psychometric component of three scales (Paranoid 60%, Schizoid 66%, Schizotypal 59%) shows that they are reliable and defensibly valid. It can be said that questions related to all three measures paranoid, schizoid and schizotypal of acceptable psychometric properties and reliability are desirable.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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Fucci, Davide, and Hidetake Uwano. "Fifth International Workshop on Emotion Awareness in Software Engineering (SEmotion2020)." ACM SIGSOFT Software Engineering Notes 46, no. 1 (February 2021): 28–29. http://dx.doi.org/10.1145/3437479.3437487.

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A ective computing studies and develops both systems and de- vices to recognize, interpret, process, and simulate human a ect| i.e., the experience of feelings or emotions. Software engineering involves people in a broad range of activities (from requirements to validation) where personality, moods, and emotions play a crucial role. Recently, researchers have started to study the role of af- fective computing and a ective states in software engineering but contributions on this topic are presented and discussed in diverse conferences and workshops. The SEmotion workshop follows on the fourth edition held at ICSE 2019, towards the consolidation of an international, sustainable forum for researchers and practition- ers interested in the role of a ects in software engineering to meet, present, and discuss their work-in-progress. SEmotion showcases contributions about empirical methods for emotions detection in software engineering, theoretical models inspired by neighboring elds, as well as ad-hoc tools for supporting emotion awareness in software development. This paper presents and overview of the fth edition of the workshop.

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Varina, Hanna B., Viacheslav V. Osadchyi, Kateryna P. Osadcha, Svetlana V. Shevchenko, and Svitlana H. Lytvynova. "Peculiarities of cloud computing use in the process of the first-year students’ adaptive potential development." CTE Workshop Proceedings 8 (March 19, 2021): 521–38. http://dx.doi.org/10.55056/cte.305.

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Technologies based on cloud computing is one of the demanded and actively developing areas of the modern information world. Cloud computing refers to an innovative technology that allows you to combine IT resources of various hardware platforms into a single whole and provide the user with access to them via a local network or the global Internet. Cloud services from various providers offer users access to their resources via the Internet via free or shareware cloud applications, the hardware and software requirements of which do not imply that the user has high-performance and resource-consuming computers. Cloud technologies represent a new way of organizing the educational process and offers an alternative to traditional methods of organizing the educational process, creates an opportunity for personal learning, collective teaching, interactive classes, and the organization of psychological support. The scientific article is devoted to the problem of integrating cloud technologies not only in the process of training highly qualified specialists, but also in the formation of professionally important personality traits. The article describes the experience of introducing cloud technologies into the process of forming the adaptive potential of students in conditions of social constraints caused by the COVID-19 pandemic.

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Hu, Xiao, Fanjie Li, and Ruilun Liu. "Detecting Music-Induced Emotion Based on Acoustic Analysis and Physiological Sensing: A Multimodal Approach." Applied Sciences 12, no. 18 (September 18, 2022): 9354. http://dx.doi.org/10.3390/app12189354.

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The subjectivity of listeners’ emotional responses to music is at the crux of optimizing emotion-aware music recommendation. To address this challenge, we constructed a new multimodal dataset (“HKU956”) with aligned peripheral physiological signals (i.e., heart rate, skin conductance, blood volume pulse, skin temperature) and self-reported emotion collected from 30 participants, as well as original audio of 956 music pieces listened to by the participants. A comprehensive set of features was extracted from physiological signals using methods in physiological computing. This study then compared performances of three feature sets (i.e., acoustic, physiological, and combined) on the task of classifying music-induced emotion. Moreover, the classifiers were also trained on subgroups of users with different Big-Five personality traits for further customized modeling. The results reveal that (1) physiological features contribute to improving performance on valence classification with statistical significance; (2) classification models built for users in different personality groups could sometimes further improve arousal prediction; and (3) the multimodal classifier outperformed single-modality ones on valence classification for most user groups. This study contributes to designing music retrieval systems which incorporate user physiological data and model listeners’ emotional responses to music in a customized manner.

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Murphy, Brett A., Thomas H. Costello, Ashley L. Watts, Yuk Fai Cheong, Joanna M. Berg, and Scott O. Lilienfeld. "Strengths and Weaknesses of Two Empathy Measures: A Comparison of the Measurement Precision, Construct Validity, and Incremental Validity of Two Multidimensional Indices." Assessment 27, no. 2 (May 31, 2018): 246–60. http://dx.doi.org/10.1177/1073191118777636.

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The quality of empathy research, and clinical assessment, hinges on the validity and proper interpretation of the measures used to assess the construct. This study investigates, in an online sample of 401 adult community participants, the construct validity of the Affective and Cognitive Measure of Empathy (ACME) relative to that of the Interpersonal Reactivity Index (IRI), the most widely used multidimensional empathy research measure. We investigated the factor structures of both measures, as well as their measurement precision across varying trait levels. We also examined them both in relation to convergent and discriminant criteria, including broadband personality dimensions, general emotionality, personality disorder features, and interpersonal malignancy. Our findings suggest that the ACME possesses incremental validity beyond the IRI for most constructs related to interpersonal malignancy. Our results further indicate that the IRI Personal Distress scale is severely deficient in construct validity, raising serious concerns regarding past findings that have included it when computing total empathy scores. Finally, our results indicate that both questionnaires display poor measurement precision at high trait levels, emphasizing the need for future researchers to develop indices that can reliably measure high levels of empathy.

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Zhang, Guolei, Jia Li, and Li Hao. "Cloud Computing and Its Application in Big Data Processing of Distance Higher Education." International Journal of Emerging Technologies in Learning (iJET) 10, no. 8 (December 14, 2015): 55. http://dx.doi.org/10.3991/ijet.v10i8.5280.

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In the development of information technology the development of scientific theory has brought the progress of science and technology. The progress of science and technology has an impact on the educational field, which changes the way of education. The arrival of the era of big data for the promotion and dissemination of educational resources has played an important role, it makes more and more people benefit. Modern distance education relies on the background of big data and cloud computing, which is composed of a series of tools to support a variety of teaching mode. Clustering algorithm can provide an effective evaluation method for students' personality characteristics and learning status in distance education. However, the traditional K-means clustering algorithm has the characteristics of randomness, uncertainty, high time complexity, and it does not meet the requirements of large data processing. In this paper, we study the parallel K-means clustering algorithm based on cloud computing platform Hadoop, and give the design and strategy of the algorithm. Then, we carry out experiments on several different sizes of data sets, and compare the performance of the proposed method with the general clustering method. Experimental results show that the proposed algorithm which is accelerated has good speed up and low cost. It is suitable for the analysis and mining of large data in the distance higher education.

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Su, Ja-Hwung, Yi-Wen Liao, Jia-Zhen Xu, and Yu-Wei Zhao. "A Personality-Driven Recommender System for Cross-Domain Learning Based on Holland Code Assessments." Sustainability 13, no. 7 (April 2, 2021): 3936. http://dx.doi.org/10.3390/su13073936.

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Over the past few decades, AI has been widely used in the field of education. However, very little attention has been paid to the use of AI for enhancing the quality of cross-domain learning. College/university students are often interested in different domains of knowledge but may be unaware of how to choose relevant cross-domain courses. Therefore, this paper presents a personality-driven recommender system that suggests cross-domain courses and related jobs by computing personality similarities and probable course grades. In this study, 710 students from 12 departments in a Taiwanese university conducted Holland code assessments. Based on the assessments, a comprehensive empirical study, including objective and subjective evaluations, was performed. The results reveal that (1) the recommender system shows very promising performances in predicting course grades (objective evaluations), (2) most of the student testers had encountered difficulties in selecting cross-domain courses and needed the further support of a recommender system, and (3) most of the student testers positively rated the proposed system (subjective evaluations). In summary, Holland code assessments are useful for connecting personalities, interests and learning styles, and the proposed system provides helpful information that supports good decision-making when choosing cross-domain courses.

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Demianenko, V. B. "Trends in the use of cloud computing technologies to create personalized learning trajectories for students of the Minor Academy of Sciences of Ukraine." CTE Workshop Proceedings 1 (March 21, 2013): 84–86. http://dx.doi.org/10.55056/cte.139.

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In recent years, reform trends have been noticeable in the system of extracurricular education: the goal is to create a new system of education that promotes personal professional self-determination of children, their adaptation to life in a dynamic information society, development of creative abilities, and involvement in culture. Personality-oriented education is not the formation of a personality with predetermined properties, but the creation of favorable conditions for the full identification and development of personal functions of the student. Among the most fruitful applications of the computer Y. I. Mashbitz notes the importance of implementing problem-based learning; forming creative thinking of schoolchildren, their readiness for creative work. M.I. Zhaldak stresses that the use of ICTs in the learning process "should not be concerned only with the study of certain curriculum material, but above all with the all-round and harmonious development of students' personalities, their creative abilities". V. Yu. Bykov notes that in recent years, further dynamic development acquire means and technologies of information and communication networks, in particular, the Internet, forming a computer and technological platform of the educational environment of modern education, especially open. On this basis, the subject-technological organization of information educational space is carried out, the processes of accumulation and storage of various subject collections of electronic computing resources are put in order, equal access to them for students is provided, ICT-support of learning processes, scientific research and education management is significantly improved.

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Youn, Jeong-Jin, KyoungEun Kim, MI-Seung Yun, and Jae-jin Jang. "Research on Creativity and Personality Convergence Education to Enhance University Students' Future Competencies." Korean Association of General Education 15, no. 3 (June 30, 2021): 11–28. http://dx.doi.org/10.46392/kjge.2021.15.3.11.

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This study proposes some of the elements of competencies that university students must have for coping with life in a society of the future. Furthermore, it proposes a particular teaching method to effectively strengthen these future competencies. In order to carry out this research, a review of previous studies, as well as an analysis of pertinent literature, were conducted in order to ascertain the competencies in question. In addition, three Delphi surveys were conducted by 10 experts in each major field involved. The results showed that future university students would require the following competencies: creativity and personality competency (problem solving ability, curiosity, a challenging spirit, cognitive flexibility, responsibility, leadership, etc.), a common good competency (common good sensitivity, inclusiveness, empathy and communication ability, etc.), and convergence competency (humanities literacy, engineering literacy, aesthetic literacy, etc.), and digital technology application competency (digital literacy, big data utilization, computing thinking, etc.). The results of this study can be used to develop a plan for the advancement of university education-one that meets the various needs of students living in any given future society. Moreover, it can also provide some ideas for the development of educational content by applying certain future competencies and future teaching methods.

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Wang, Ying. "The Intervention of Music Education on Students’ Mental Health Based on Fuzzy Computing." Mathematical Problems in Engineering 2022 (August 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/5632481.

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Music is an important way for people to express and communicate their thoughts and feelings. It not only has the functions of cultivating sentiment, developing intelligence, and promoting personality development but also has an energetic function on the spiritual fitness of pupils. As an important part of quality education in schools, music education has become a pioneer in the implementation of psychological intervention for students. Music education can cultivate sentiment and lively and optimistic mood by emotional images conveyed through various music and has a special role that cannot be replaced by other disciplines. However, the current music education in schools only focuses on the dissemination of music knowledge and there is no research on which type of music can effectively interfere with students’ mental health. In order to be able to choose music that is effective for students’ mental health intervention in music education, this paper will study the intervention research of music education on students’ mental health based on fuzzy computing. This paper extracts the musical features such as average pitch, average pitch intensity, melody direction, pitch stability value, rhythm intensity, and beat, uses fuzzy computing to classify music, determines which types of music can improve students’ mental health, and uses experiments to verify the validity of this research. The consequences of the research show that choosing effective music to intervene in students’ mental health can greatly improve students’ mental health problems. The scores of students’ psychological status after the intervention are 0.73 times of those before the intervention. It demonstrates the validity of the study.

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Hazer-Rau, Dilana, Sascha Meudt, Andreas Daucher, Jennifer Spohrs, Holger Hoffmann, Friedhelm Schwenker, and Harald C. Traue. "The uulmMAC Database—A Multimodal Affective Corpus for Affective Computing in Human-Computer Interaction." Sensors 20, no. 8 (April 17, 2020): 2308. http://dx.doi.org/10.3390/s20082308.

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In this paper, we present a multimodal dataset for affective computing research acquired in a human-computer interaction (HCI) setting. An experimental mobile and interactive scenario was designed and implemented based on a gamified generic paradigm for the induction of dialog-based HCI relevant emotional and cognitive load states. It consists of six experimental sequences, inducing Interest, Overload, Normal, Easy, Underload, and Frustration. Each sequence is followed by subjective feedbacks to validate the induction, a respiration baseline to level off the physiological reactions, and a summary of results. Further, prior to the experiment, three questionnaires related to emotion regulation (ERQ), emotional control (TEIQue-SF), and personality traits (TIPI) were collected from each subject to evaluate the stability of the induction paradigm. Based on this HCI scenario, the University of Ulm Multimodal Affective Corpus (uulmMAC), consisting of two homogenous samples of 60 participants and 100 recording sessions was generated. We recorded 16 sensor modalities including 4 × video, 3 × audio, and 7 × biophysiological, depth, and pose streams. Further, additional labels and annotations were also collected. After recording, all data were post-processed and checked for technical and signal quality, resulting in the final uulmMAC dataset of 57 subjects and 95 recording sessions. The evaluation of the reported subjective feedbacks shows significant differences between the sequences, well consistent with the induced states, and the analysis of the questionnaires shows stable results. In summary, our uulmMAC database is a valuable contribution for the field of affective computing and multimodal data analysis: Acquired in a mobile interactive scenario close to real HCI, it consists of a large number of subjects and allows transtemporal investigations. Validated via subjective feedbacks and checked for quality issues, it can be used for affective computing and machine learning applications.

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Garg, Shruti, Soumyajit Behera, K. Rahul Patro, and Ashwani Garg. "Deep Neural Network for Electroencephalogram based Emotion Recognition." IOP Conference Series: Materials Science and Engineering 1187, no. 1 (September 1, 2021): 012012. http://dx.doi.org/10.1088/1757-899x/1187/1/012012.

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Abstract Emotion recognition using electroencephalogram (EEG) signals is an aspect of affective computing. The EEG refers to recording brain responses via electrical signals by showing external stimuli to the participants. This paper proposes the prediction of valence, arousal, dominance and liking for EEG signals using a deep neural network (DNN). The EEG data is obtained from the AMIGOS dataset, a publicly available dataset for mood and personality research. Two features, normalized and power and normalized wavelet energy, are extracted using Fourier and wavelet transform, respectively. A DNN with three different activation functions (exponential linear unit, rectified linear unit [ReLU] and leaky ReLU) has been applied for single and combined features. The result of combined features with leaky ReLU is found to be the best, with a classification accuracy of 85.47, 81.87, 84.04 and 86.63 for valence, arousal, dominance and liking, respectively.

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Ben-Zeev, D. "Technology-based interventions for psychiatric illnesses: improving care, one patient at a time." Epidemiology and Psychiatric Sciences 23, no. 4 (July 21, 2014): 317–21. http://dx.doi.org/10.1017/s2045796014000432.

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Worldwide, individuals with severe psychiatric illnesses struggle to receive evidence-based care. While science has made remarkably slow progress in the development and implementation of effective psychiatric treatments, we have witnessed enormous progress in the emergence and global penetration of personal computing technology. The present paper examines how digital resources that are already widespread (e.g., smartphones, laptop computers), can be leveraged to support psychiatric care. These instruments and implementation strategies can increase patient access to evidenced-based care, help individuals overcome the barriers associated with the stigma of mental illness, and facilitate new treatment paradigms that harness wireless communication, sensors and the Internet, to enhance treatment potency. Innovative digital treatment programmes that have been used successfully with a range of conditions (i.e., schizophrenia, posttraumatic stress disorder and borderline personality disorder) are presented in the paper to demonstrate the utility and potential impact of technology-based interventions in the years ahead.

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Sunnatilla, A. Z., E. S. Nurakhov, and A. A. Myngzhassar. "IDENTIFICATION OF MBTI (MYERS-BRIGGS TYPE INDEX) HUMAN TYPE USING TEXT ON SOCIAL NETWORKS BASED MACHINE LEARNING." Bulletin of Kazakh National Women's Teacher Training University, no. 2 (July 16, 2021): 136–44. http://dx.doi.org/10.52512/2306-5079-2021-86-2-136-144.

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This study aims to create a classifier using machine learning methods that determine the psychological type of people based on the text published on social networks according to the Myers-Briggs Type Index classification. The article is based on the implementation of automation of the task of determining the personality type using machine learning, with an explanation for determining the characteristics of a person using the MBTI personality indicator. The methods of logistic regression, random forest and support vector machines were used, and a literary analysis of similar works was carried out. The article presents the progress of research work and the results of each classifier, as well as an analysis of the approaches used. In the context of the current quarantine restrictions, such studies can be of great help in the selection of personnel in companies due to the transition of people to an online format of work, since the study involves determining the personal qualities of people based on their posts in social networks. In this paper, the most effective machine learning algorithms for the Kazakh language, which are simple to use and do not require a lot of computing power, were used and, accordingly, the results of the work for each method were presented, among these methods, the accuracy and reliability of the classifier for the Kazakh language by the method of support vectors were at a good level.

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Journal articles on the topic 'Personality Computing'

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