Search results for: emotional intelligence

Authors: Alejandra Sierra, Maria de los Angeles Covarrubias, Guillermo Julian Gonzalez, Noe Alfaro

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The objective of this study was to identify the cultural dimensions of the terms unplanned pregnancy and unwanted pregnancy built by adolescent women, through the focus of the social representations. Two associative methods were used: free listings and the paired comparison. 72 female students between the ages of 15 and 19 were interviewed, from the downtown area of Leon Guanajuato, Mexico. Words related to inducer terms were classified into five thematic categories: facilitators, consequences, reactions, expectations, and lexicon. The results showed that the social representations of unplanned pregnancy highlighted elements related to economic difficulties and negative emotional aspects, while unwanted pregnancy was associated with negative emotional aspects such as anger, anxiety, and sadness. The meanings each person attributes to terms related to pregnancy are culturally constructed and differ between populations; therefore, more attention should be paid to understanding the cultural meanings and attitudes of people in fertility decision-making, including also the views of adolescent men and other types of population, stratified by age groups and social conditions.

Keywords: adolescent, qualitative research, unplanned pregnancy, unwanted pregnancy

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Authors: Chiou-Fang Liou

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Cancer has been one of the most life-threaten diseases worldwide for 30+ years. The influences of cancer illness include symptoms from cancer itself along with its treatments. The quality of life among patients diagnosed with cancer during cancer treatments has been conceptualized within four domains: Functional Well-Being, Social Well-Being, Physical Well-Being, and Emotional Well-Being. Patients with cancer often need to make adjustments to face all the challenges. The middle-range theory of Resourcefulness and Quality of life has been applied to explore factors contributing to cancer patients’ needs. Resourcefulness is defined as sets of skills that can be learned and consisted of Person and Social Resourcefulness. Empirical evidence also supported a possible relationship between Resourcefulness and Quality of Life. However, little is known about the extent to which the two concepts are related to each other. This study, therefore, applied a multivariate technique, Canonical Correlation Analysis, to identify the relationship between the two sets of variables with multi-dimensional measures, the Resourcefulness and Quality of Life in Cancer patients receiving treatments. After IRB approval, this multi-centered study took place at two medical centers in the Central Region of Taiwan. Sample A total of 186 patients with various cancer diagnoses and either receiving radiation therapy or chemotherapy consented to and answered questionnaires. The Import findings of the Generalized F test identified two typical sets with several linear relations and explained a total of 79.1% of the total variance. The first typical set found Personal Resourcefulness negatively related to Social Well-being, Functional being, Emotional Well-being, and Physical, in that order. The second typical set found Social Resourcefulness negatively related to Functional Well-being and Physical-being yet positively related to Social Well-being and Emotional Well-being. Discussion and Conclusion, The results of this presented study supported the statistically significant relationship between two sets of variables that are consistent with the theory. In addition, the results are considerably important in cancer patients receiving cancer treatments.

Keywords: cancer, canonical correlation analysis, quality of life, resourcefulness

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Authors: Kyoung-Jae Kim

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Collaborative Filtering (CF) is a popular technique for the personalization in the E-commerce domain to reduce information overload. In general, CF provides recommending items list based on other similar users’ preferences from the user-item matrix and predicts the focal user’s preference for particular items by using them. Many recommender systems in real-world use CF techniques because it’s excellent accuracy and robustness. However, it has some limitations including sparsity problems and complex dimensionality in a user-item matrix. In addition, traditional CF does not consider the emotional interaction between users. In this study, we propose recommender systems using social network and singular value decomposition (SVD) to alleviate some limitations. The purpose of this study is to reduce the dimensionality of data set using SVD and to improve the performance of CF by using emotional information from social network data of the focal user. In this study, we test the usability of hybrid CF, SVD and social network information model using the real-world data. The experimental results show that the proposed model outperforms conventional CF models.

Keywords: recommender systems, collaborative filtering, social network information, singular value decomposition

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Authors: Nir Wittenberg, Moshe Farhi

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This mixed-methods study examines the integration of artificial intelligence (AI) tools in a first-year social work course to assess their potential for enhancing professional knowledge and skills. The incorporation of digital technologies, such as AI, in social work interventions, training, and research has increased, with the expectation that AI will become as commonplace as email and mobile phones. However, policies and ethical guidelines regarding AI, as well as empirical evaluations of its usefulness, are lacking. As AI is gradually being adopted in the field, it is prudent to explore AI thoughtfully in alignment with pedagogical goals. The outcomes assessed include professional identity, course satisfaction, and motivation. AI offers unique reflective learning opportunities through personalized simulations, feedback, and queries to complement face-to-face lessons. For instance, AI simulations provide low-risk practices for situations such as client interactions, enabling students to build skills with less stress. However, it is essential to recognize that AI alone cannot ensure real-world competence or cultural sensitivity. Outcomes related to student learning, experience, and perceptions will help to elucidate the best practices for AI integration, guiding faculty, and advancing pedagogical innovation. This strategic integration of selected AI technologies is expected to diversify course methodology, improve learning outcomes, and generate new evidence on AI’s educational utility. The findings will inform faculty seeking to thoughtfully incorporate AI into teaching and learning.

Keywords: artificial intelligence (AI), social work education, students, developing a professional identity, ethical considerations

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Authors: Japnoor Garcha, Andrew P. Smith, A. James

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There has been a significant increase in the prevalence and estimates of neurodevelopmental disorders, especially autism spectrum disorders, in the last decade. The literature has seen increasing research on understanding wellbeing and mental health. To understand the association and interaction of wellbeing and mental health with autism and ADHD, a survey was given to 560 secondary school students. The survey used the wellbeing process questionnaire, the autism spectrum quotient, the ADHD self-report scale, and the strengths and difficulties questionnaire. The analysis conducted using SPSS showed that there was a significant correlation between anxiety, depression, A.Q., and ADHD. Anxiety and depression were also significantly correlated with all wellbeing and SDQ variables. The regression analysis showed that anxiety was significantly associated with positive wellbeing, negative wellbeing, emotional problems, and prosocial behaviour, whereas depression was significantly associated with positive wellbeing, negative wellbeing, physical health, flourishing, conduct problems, emotional problems and peer problems.

Keywords: ADHD traits, anxiety, autistic traits, depression

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Authors: Prakash Singh, Piet Maphodisa Kgohlo

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Knowledge-worker productivity is one of the biggest leadership challenges facing all organizations in the twenty-first century. It cannot be denied that knowledge-worker productivity affects all organizations. The work and the workforce are both undergoing greater changes currently than at any time, since the beginning of the industrial revolution two centuries ago. Employees welcome collegial teaming (CT) as an innovative way to develop their work-integrated learning competencies. Human resource development policies must evoke the symbiotic relationship between CT and work-integrated learning, seeing that employees need to be endowed with the competence to move from one skill to another, as each one becomes obsolete, and to simultaneously develop their cognitive and emotional intelligence. The outcome of this relationship must culminate in the development of highly productive knowledge-workers. While this study focuses on teachers, the conceptual framework and the findings of this research can be beneficial for any organization, public or private sector, business or non-business. Therefore, in this quantitative study, the benefits of CT are considered in developing human resources to sustain knowledge-worker productivity. The ANOVA p-values reveal that the majority of teachers agree that CT can empower them to overcome the challenges of managing curriculum change. CT can equip them with continuous and sustained learning, growth and improvement, necessary for knowledge-worker productivity. This study, therefore, confirms that CT benefits all workers, immaterial of their age, gender or experience. Hence, this exploratory research provides a new perspective of CT in addressing knowledge-worker productivity when organizational change alters the vision of the organization.

Keywords: collegial teaming, human resource development, knowledge-worker productivity, work-integrated learning

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Authors: Min-Ha Hong, Kee-Won Kim, Seung-Hoon Kim

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The character in the story-based content, such as novels and movies, is one of the core elements to understand the story. In particular, the character’s tendency is an important factor to analyze the story-based content, because it has a significant influence on the storyline. If readers have the knowledge of the tendency of characters before reading a novel, it will be helpful to understand the structure of conflict, episode and relationship between characters in the novel. It may therefore help readers to select novel that the reader wants to read. In this paper, we propose a method of extracting the tendency of the characters from a novel written in Korean. In advance, we build the dictionary with pairs of the emotional words in Korean and English since the emotion words in the novel’s sentences express character’s feelings. We rate the degree of polarity (positive or negative) of words in our emotional words dictionary based on SenticNet. Then we extract characters and emotion words from sentences in a novel. Since the polarity of a word grows strong or weak due to sentence features such as quotations and modifiers, our proposed method consider them to calculate the polarity of characters. The information of the extracted character’s polarity can be used in the book search service or book recommendation service.

Keywords: character tendency, data mining, emotion word, Korean novel

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Authors: Samir Hessas

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Modern medicine is witnessing a profound transformation as advanced technology reshapes surgical environments. Hybrid operating rooms, where state-of-the-art medical equipment, advanced imaging solutions, and Artificial Intelligence (AI) converge, are at the forefront of this revolution. In this comprehensive exploration, we scrutinize the multifaceted facets of AI and delve into an array of groundbreaking technologies. We also discuss visionary concepts that hold the potential to revolutionize hybrid operating rooms, making them more efficient and patient-centered. These innovations encompass real-time imaging, surgical simulation, IoT and remote monitoring, 3D printing, telemedicine, quantum computing, and nanotechnology. The outcome of this fusion of technology and imagination is a promising future of surgical precision, individualized patient care, and unprecedented medical advances in hybrid operating rooms.

Keywords: artificial intelligence, hybrid operating rooms, telemedicine, monitoring

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Authors: Yao Chen

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Empathy is crucial for teachers working with teenagers in secondary school. Despite that, little attention was paid to English language teachers’ experiences of empathy in class. Empathy contains cognitive, emotional, and behavioral components that are manifested in the teaching practice. The qualitative study focused on how Chinese ELT teachers expressed empathy in interaction with students in public high schools and private institutions and what factors might lead them to show empathy in different ways. Four participants were invited to attend the individual interviews to share their stories about their empathic experiences. Classroom observation was conducted to investigate teachers’ language use in teaching and non-verbal communication with students to witness their behavior of expressing empathy. Through thematic analysis, three main themes relevant to different types of empathy in teachers’ interaction with students were generated: 1) perspective taking, 2) emotional connections, 3) action taking. Based on the participants’ statements of their personal experiences, the discussion concluded the reasons for their differences in expressing empathy. The result underlined the significance of the role of empathy in building a rapport with students and motivating their language learning. Further implications for the role of empathy in ELT teachers’ professional development are also discussed.

Keywords: teacher empathy, experiences, interaction with students, ELT class

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

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In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

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This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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Authors: Angela Oktavia Suryani, Bernadeth Gloria, Edwin Sutamto, Jessica Kristianty, Ni Made Rai Sapitri, Patricia Catherine Agla, Sitti Arlinda Rochiadi

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It was found that some blogs or personal websites in Indonesia sell standardized intelligence tests (for example, Progressive Matrices (PM), Intelligence Structure Test (IST), and Culture Fair Intelligence Test (CFIT)) and other psychological tests, together with the manual and the key answers for public. Individuals can buy and prepare themselves for selection or recruitment with the real test. This action drives people to lie to the institution (education or company) and also to themselves. It was also found that those tests are old. Some items are not relevant with the current context, for example a question about a diameter of a certain coin that does not exist anymore. These problems motivate us to develop a new intelligence battery test, namely of Multiple Aptitude Battery (MAB). The battery test was built by using Thurstone’s Primary Mental Abilities theory and intended to be used by high schools students, university students, and worker applicants. The battery tests consist of 9 subtests. In the current study we examine six subtests, namely Reading Comprehension, Verbal Analogies, Numerical Inductive Reasoning, Numerical Deductive Reasoning, Mechanical Ability, and Two Dimensional Spatial Reasoning for university students. The study included 1424 data from students recruited by convenience sampling from eight faculties at Atma Jaya Catholic University of Indonesia. Classical and modern test approaches (Item Response Theory) were carried out to identify the item difficulties of the items and confirmatory factor analysis was applied to examine their internal validities. The validity of each subtest was inspected by using convergent–discriminant method, whereas the reliability was examined by implementing Kuder–Richardson formula. The result showed that the majority of the subtests were difficult in medium level, and there was only one subtest categorized as easy, namely Verbal Analogies. The items were found homogenous and valid measuring their constructs; however at the level of subtests, the construct validity examined by convergent-discriminant method indicated that the subtests were not unidimensional. It means they were not only measuring their own constructs but also other construct. Three of the subtests were able to predict academic performance with small effect size, namely Reading Comprehension, Numerical Inductive Reasoning, and Two Dimensional Spatial Reasoning. GPAs in intermediate level (GPAs at third semester and above) were considered as a factor for predictive invalidity. The Kuder-Richardson formula showed that the reliability coefficients for both numerical reasoning subtests and spatial reasoning were superior, in the range 0.84 – 0.87, whereas the reliability coefficient for the other three subtests were relatively below standard for ability test, in the range of 0.65 – 0.71. It can be concluded that some of the subtests are ready to be used, whereas some others are still need some revisions. This study also demonstrated that the convergent-discrimination method is useful to identify the general intelligence of human.

Keywords: intelligence, psychometric examination, multiple aptitude battery, university students

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Authors: Sasha Goodwin

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Sex offenders are often viewed as a homogenous group, but they encompass a diverse range of individuals with varying characteristics and offenses. The principal aim of this study was to ascertain how members of the Australian public perceive and define a sex offender while also investigating the emotional underpinnings associated with these attitudes and definitions. To assess public attitude, this study used the innovative utilization of visual methodologies to assess the public's perception of sex offenders. The study employed the iSquare approach, a visual methodology framework that offers unique viewpoints and insights into public attitudes toward sex offenders. Through the utilization of this approach, this study established an academic foundation for a deeper understanding of the public's perception of sex offenders. The data analysis revealed that most participants associated sex offenders with strong negative emotions, primarily disgust and anger. The findings of this research point towards the potential for fostering a social environment characterized by evidence-based discussions instead of reactionary punitive responses. Promoting a comprehensive understanding of the diverse nature of sexual offenders aims to broaden perceptions, fostering constructive attitudes.

Keywords: visual methodologies, public perception, sex offenders, offender characteristics, emotional attitudes, isquare approach, attitudes

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Authors: Chen Xi, LIU Xuebin, Kuan Sinman, LI Haofeng, Huang Hongming, Zeng Chengyu, Lao Xuerui

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Stray animals are on the rise in mainland China's cities. There are legal reasons for this, namely the lack of protection for domestic pets in mainland China, where only wildlife protection laws exist. At a social level, the ease with which families adopt pets and the lack of a social view of animal nature have led to the frequent abandonment and loss of stray animals. If left unmanaged, conflicts between humans and stray animals can also increase. This project provides an inexpensive and widely applicable management tool for urban management by collecting videos and pictures of stray animals captured by surveillance or transmitted by humans and using artificial intelligence technology (mainly using Yolov5 recognition technology) and recording and managing them in a database.

Keywords: urban planning, urban governance, artificial intelligence, convolutional neural network, machine vision

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Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Preventing abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep, long short-term memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviors far from the expected one depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behavior analysis, LSTM

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Authors: Mehrdad Karimi, Farshad Negintaji

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This study has examined the appearance of identity in the urban landscape and its effects on the natural factors. For this purpose, the components of place identity, emotional attachment, place dependence and social bond which totally constitute place attachment, measures it in three domains of cognitive (place identity), affective (emotional attachment) and behavioral (place dependence and social bond). In order to measure the natural factors, three components of the absolute elements, living entities, natural elements have been measured. The study is descriptive and the statistical population has been Yasouj, a city in Iran. To analyze the data the SPSS software has been used. The results in two level of descriptive and inferential statistics have been investigated. In the inferential statistics, Pearson correlation coefficient test has been used to evaluate the research hypotheses. In this study, the variable of identity is in high level and the natural factors are also in high level. These results indicate a positive relationship between place identity and natural factors. Development of environment and reaching the quality level of the personality or identity will develop the individual and society.

Keywords: identity, place identity, landscape, urban landscape, landscaping

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Authors: Mai Anh Tuan, Wenlong Liu, Meng Li

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Despite warnings and concerns about the use of fast food that has health effects, the fast-food industry is actually a source of profit for the global food industry. Obviously, in the face of such huge economic benefits, investors will not hesitate to continuously add recipes, processing methods, menu diversity, etc., to improve and apply information technology in enhancing the diners' experience; the ultimate goal is still to attract diners to find their brand and give them the fastest, most convenient and enjoyable service. In China, as the achievements of the industrial revolution 4.0, big data and artificial intelligence are reaching new heights day by day, now fast-food diners can instantly pay the bills only by identifying the biometric signature available on the self-ordering kiosk, using their own face without any additional form of confirmation. In this study, the author will evaluate the acceptance level of customers with this new form of payment through a survey of customers who have used and witnessed the use of smart kiosks and biometric payments within the city of Nanjing, China. A total of 200 valid volunteers were collected in order to test the customers' intentions and feelings when choosing and experiencing payment through AI services. 55% think that it bothers them because of the need for personal information, but more than 70% think that smart kiosk brings out many benefits and convenience. According to the data analysis findings, perceived innovativeness has a positive influence on satisfaction which in turn affects behavioral intentions, including reuse and word-of-mouth intentions.

Keywords: artificial intelligence, catering industry, smart kiosks, technology acceptance

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

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During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

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Authors: Lourens Millard, Gerrit Jan Breukelman, Nonkululeko Mathe

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Aim: The research aims to investigate the differences in visio-spatial skills (VSS) between athletes and non-athletes, as well as variations across sports, presenting conflicting findings. Therefore, the objective of this study was to determine if there exist significant differences in visio-spatial intelligence skills between rugby players and netball players, and whether such disparities are present when comparing both groups to non-athletes. Methods: Participants underwent an optometric assessment, followed by an evaluation of VSS using six established tests: the Hart Near Far Rock, saccadic eye movement, evasion, accumulator, flash memory, and ball wall toss tests. Results: The results revealed that rugby players significantly outperformed netball players in speed of recognition, peripheral awareness, and hand-eye coordination (p=.000). Moreover, both rugby players and netball players performed significantly better than non-athletes in five of the six tests (p=.000), with the exception being the visual memory test (p=.809). Conclusion: This discrepancy in performance suggests that certain VSS are superior in athletes compared to non-athletes, highlighting potential implications for theories of vision, test selection, and the development of sport-specific VSS testing batteries. Furthermore, the use of a hand-eye coordination-specific VSS test battery effectively differentiated between different sports. However, this pattern was not consistent across all VSS tests, indicating that further research should explore the training methods employed by both sports, as these factors may contribute to the observed differences.

Keywords: visio-spatial intelligence (VSI), rugby vision, netball vision, visual skills, sport vision.

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Authors: Rodrigo Lentini, Beatrice Ionascu, Friederike A. Eyssel, Scandar Copti, Mohamad Eid

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The haptic modality has brought a new dimension to human computer interaction by engaging the human sense of touch. However, designing appropriate haptic stimuli, and in particular tactile stimuli, for various applications is still challenging. To tackle this issue, we present an intuitive system that facilitates the authoring of tactile gestures for various applications. The system transforms a hand gesture into a tactile gesture that can be rendering using a home-made haptic jacket. A case study is presented to demonstrate the ability of the system to develop tactile gestures that are recognizable by human subjects. Four tactile gestures are identified and tested to intensify the following four emotional responses: high valence – high arousal, high valence – low arousal, low valence – high arousal, and low valence – low arousal. A usability study with 20 participants demonstrated high correlation between the selected tactile gestures and the intended emotional reaction. Results from this study can be used in a wide spectrum of applications ranging from gaming to interpersonal communication and multimodal simulations.

Keywords: tactile stimulation, tactile gesture, emotion reactions, arousal, valence

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Authors: Seba Quqandi

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This study investigates the bond that people form using their assistive devices and the tactics applied during the product design process to help improve the user experience leading to a long-term product relationship. The aim is to develop a conceptual framework with which to describe and analyze the bond people form with their assistive devices and to integrate human emotions as a factor during the development of the product design process. The focus will be on the assistive technology market, namely, the Aid-For-Daily-Living market for situational impairments, to increase the quality of wellbeing. Findings will help us better understand the real issues of the product experience concerning people’s interaction throughout the product performance, establish awareness of the emotional effects in the daily interaction that fosters the product attachment, and help product developers and future designers create a connection between users and their assistive devices. The research concludes by discussing the implications of these findings for professionals and academics in the form of experiments in order to identify new areas that can stimulate new /or developed design directions.

Keywords: experience design, interaction design, emotion, design psychology, assistive tools, customization, userentred design

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Authors: Mehravar Javid, Stacey Dershewitz, Shohreh Hashemighoochani, Artin Yousefi

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One of the most common reasons for destroying relationships is infidelity. This study was conducted to understand children's experience of infidelity in Iran. In this qualitative research, the interpretive phenomenological approach and theoretical (purposive) sampling method were used, and the data was saturated by conducting semi-structured interviews with eight offspring of families affected by infidelity. All interviews were audio recorded and analyzed on paper after implementation. Acceptability and homogeneity of data were confirmed by different methods. Eight main themes were identified from the participants' statements: description of the family atmosphere, lack of security and distrust, emotional reactions, behavioral reactions, worry about the future, financial problems, mother's physical-psychological state, and mother's coping strategies and offspring's proposed solutions. The children's experiences showed that in addition to causing irreparable harm to the wife, infidelity affected the whole family and caused the children to suffer crucial consequences such as emotional, behavioral, educational, and economic consequences and mistrust.

Keywords: consequences and hurt, infidelity, offspring, phenomenology, qualitative research

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Authors: Ramón Cruzat, Sergio F. Ochoa, Ignacio Casas, Luis A. Guerrero, José Bravo

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Patients under health treatments that involve long stays at a hospital or health centre (e.g. cancer, organ transplants and severe burns), tend to get bored or depressed because of the lack of social interaction with family and friends. Such a situation also affects the evolution and effectiveness of their treatments. In many cases, the solution to this problem involves extra challenges, since many patients need to rest quietly (or remain in bed) to their being contagious. Considering the weak health condition in which usually are these kinds, keeping them motivated and quiet represents an important challenge for nurses and caregivers. This article presents a mobile ubiquitous game called MagicRace, which allows hospitalized kinds to interact socially with one another without putting to risk their sensitive health conditions. The game does not require a communication infrastructure at the hospital, but instead, it uses a mobile ad hoc network composed of the handheld devices used by the kids to play. The usability and performance of this application was tested in two different sessions. The preliminary results show that users experienced positive feelings from this experience.

Keywords: ubiquitous game, children's emotional support, social isolation, mobile collaborative interactions

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Authors: Lien Fa Lin, Yung Ming Li, Hsin Chen Hsieh

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The rise of smartphones brings new concept So-Lo-Mo (social-local-mobile) in mobile commerce area in recent years. However, current So-Lo-Mo services only focus on individual users but not a group of users, and the development of group commerce is not enough to satisfy the demand of real-time group buying and less to think about the social relationship between customers. In this research, we integrate mobile intelligence with group commerce and consider customers' preference, real-time context, and social influence as components in the mechanism. With the support of this mechanism, customers are able to gather near customers with the same potential purchase willingness through mobile devices when he/she wants to purchase products or services to have a real-time group-buying. By matching the demand and supply of mobile group-buying market, this research improves the business value of mobile commerce and group commerce further.

Keywords: group formation, group commerce, mobile commerce, So-Lo-Mo, social influence

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Authors: Walid Moudani, Ahmad Shahin

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This research presents a study to handle the recruitment services system. It aims to enhance a business intelligence system by embedding data mining in its core engine and to facilitate the link between job searchers and recruiters companies. The purpose of this study is to present an intelligent management system for supporting recruitment services based on data mining methods. It consists to apply segmentation on the extracted job postings offered by the different recruiters. The details of the job postings are associated to a set of relevant features that are extracted from the web and which are based on critical criterion in order to define consistent clusters. Thereafter, we assign the job searchers to the best cluster while providing a ranking according to the job postings of the selected cluster. The performance of the proposed model used is analyzed, based on a real case study, with the clustered job postings dataset and classified job searchers dataset by using some metrics.

Keywords: job postings, job searchers, clustering, genetic algorithms, business intelligence

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Authors: Abdulmalek Ahmed

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Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).

Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)

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Authors: Wendy de Gomez

Abstract:

Standardization is a critical element in the ability of a society to reduce uncertainty, subjectivity, misrepresentation, and interpretation while simultaneously contributing to innovation. Technological standardization is critical to codify specific operationalization through legal instruments that provide rules of development, expectation, and use. In the current emerging technology landscape Artificial Intelligence (AI) hardware as a general use technology has seen incredible growth as evidenced from AI technology patents between 2012 and 2018 in the United States Patent Trademark Office (USPTO) AI dataset. However, as outlined in the 2023 United States Government National Standards Strategy for Critical and Emerging Technology the codification through standardization of emerging technologies such as AI has not kept pace with its actual technological proliferation. This gap has the potential to cause significant divergent possibilities for the downstream outcomes of AI in both the short and long term. This original empirical research provides an overview of the standardization efforts around AI in different geographies and provides a background to standardization law. It quantifies the longitudinal trend of Artificial Intelligence hardware patents through the USPTO AI dataset. It seeks evidence of existing Standard Essential Patents from these AI hardware patents through a text analysis of the Statement of patent history and the Field of the invention of these patents in Patent Vector and examines their determination as a Standard Essential Patent and their inclusion in existing AI technology standards across the four main AI standards bodies- European Telecommunications Standards Institute (ETSI); International Telecommunication Union (ITU)/ Telecommunication Standardization Sector (-T); Institute of Electrical and Electronics Engineers (IEEE); and the International Organization for Standardization (ISO). Once the analysis is complete the paper will discuss both the theoretical and operational implications of F/Rand Licensing Agreements for the owners of these Standard Essential Patents in the United States Court and Administrative system. It will conclude with an evaluation of how Standard Setting Organizations (SSOs) can work with SEP owners more effectively through various forms of Intellectual Property mechanisms such as patent pools.

Keywords: patents, artifical intelligence, standards, F/Rand agreements

Procedia PDF Downloads 88

Authors: Serge Lacroix

Abstract:

The proposed study looks at the psychoeducational assessment of bilingual students, in English and French in this case. It will be the opportunity to look at language of assessment and specifically how certain tests can be administered in one language and others in another language. It is also a look into the questioning of the validity of the test scores that are obtained as well as the quality and generalizability of the conclusions that can be drawn. Bilingualism and multiculturalism, although in constant expansion, is not considered in norms development and remains a poorly understood factor when it is at play in the context of a psychoeducational assessment. Student placement, diagnoses, accurate measures of intelligence and achievement are all impacted by the quality of the assessment procedure. The same is true for questionnaires administered to parents and self-reports completed by bilingual students who, more often than not, are assessed in a language that is not their primary one or are compared to monolinguals not dealing with the same challenges or the same skills. Results show that students, when offered to work in a bilingual fashion, chooses to do so in a significant proportion. Recommendations will be offered to support educators aiming at expanding their skills when confronted with multilingual students in an assessment context.

Keywords: psychoeducational assessment, bilingualism, multiculturalism, intelligence, achievement

Procedia PDF Downloads 455

Authors: Jae Ni Jang, Young Uk Kim

Abstract:

Background: Chat Generative Pre-training Transformer-3 (ChatGPT; San Francisco, California, Open Artificial Intelligence) is an artificial intelligence chatbot based on a large language model designed to generate human-like text. As the usage of ChatGPT is increasing among less knowledgeable patients, medical students, and anesthesia and pain medicine residents or trainees, we aimed to evaluate the accuracy of ChatGPT-3 responses to questions about the American Society of Anesthesiologists (ASA) classification based on patients’ underlying diseases and assess the quality of the generated responses. Methods: A total of 47 questions were submitted to ChatGPT using textual prompts. The questions were designed for ChatGPT-3 to provide answers regarding ASA classification in response to common underlying diseases frequently observed in adult patients. In addition, we created 18 questions regarding the ASA classification for pediatric patients and pregnant women. The accuracy of ChatGPT’s responses was evaluated by cross-referencing with Miller’s Anesthesia, Morgan & Mikhail’s Clinical Anesthesiology, and the American Society of Anesthesiologists’ ASA Physical Status Classification System (2020). Results: Out of the 47 questions pertaining to adults, ChatGPT -3 provided correct answers for only 23, resulting in an accuracy rate of 48.9%. Furthermore, the responses provided by ChatGPT-3 regarding children and pregnant women were mostly inaccurate, as indicated by a 28% accuracy rate (5 out of 18). Conclusions: ChatGPT provided correct responses to questions relevant to the daily clinical routine of anesthesiologists in approximately half of the cases, while the remaining responses contained errors. Therefore, caution is advised when using ChatGPT to retrieve anesthesia-related information. Although ChatGPT may not yet be suitable for clinical settings, we anticipate significant improvements in ChatGPT and other large language models in the near future. Regular assessments of ChatGPT's ASA classification accuracy are essential due to the evolving nature of ChatGPT as an artificial intelligence entity. This is especially important because ChatGPT has a clinically unacceptable rate of error and hallucination, particularly in pediatric patients and pregnant women. The methodology established in this study may be used to continue evaluating ChatGPT.

Keywords: American Society of Anesthesiologists, artificial intelligence, Chat Generative Pre-training Transformer-3, ChatGPT

Procedia PDF Downloads 50

Authors: Saleit Ron

Abstract:

The Mitar program was established to drive a shift in middle school mathematics education, emphasizing the connection of math to real-life situations, exploring mathematical modeling and literacy, and integrating social and emotional learning (SEL) components for enhanced excellence. The program envisions math coordinators as catalysts for change, equipping them to create educational materials, strengthen leadership skills, and develop SEL competencies within coordinator communities. These skills are then employed to lead transformative efforts within their respective schools. The program engaged 90 participants across six math coordinator communities during 2022-2023, involving 30-60 hours of annual learning. The process includes formative and summative evaluations through questionnaires and interviews, revealing participants' high contentment and successful integration of acquired skills into their schools. Reflections from participants highlighted the need for enhanced change leadership processes, often seeking more personalized mentoring to navigate challenges effectively.

Keywords: math coordinators, mathematical literacy, mathematical modeling, SEL competencies

Procedia PDF Downloads 52