Search results for: emotional intelligence
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2846

Search results for: emotional intelligence

596 Potassium-Phosphorus-Nitrogen Detection and Spectral Segmentation Analysis Using Polarized Hyperspectral Imagery and Machine Learning

Authors: Nicholas V. Scott, Jack McCarthy

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Military, law enforcement, and counter terrorism organizations are often tasked with target detection and image characterization of scenes containing explosive materials in various types of environments where light scattering intensity is high. Mitigation of this photonic noise using classical digital filtration and signal processing can be difficult. This is partially due to the lack of robust image processing methods for photonic noise removal, which strongly influence high resolution target detection and machine learning-based pattern recognition. Such analysis is crucial to the delivery of reliable intelligence. Polarization filters are a possible method for ambient glare reduction by allowing only certain modes of the electromagnetic field to be captured, providing strong scene contrast. An experiment was carried out utilizing a polarization lens attached to a hyperspectral imagery camera for the purpose of exploring the degree to which an imaged polarized scene of potassium, phosphorus, and nitrogen mixture allows for improved target detection and image segmentation. Preliminary imagery results based on the application of machine learning algorithms, including competitive leaky learning and distance metric analysis, to polarized hyperspectral imagery, suggest that polarization filters provide a slight advantage in image segmentation. The results of this work have implications for understanding the presence of explosive material in dry, desert areas where reflective glare is a significant impediment to scene characterization.

Keywords: explosive material, hyperspectral imagery, image segmentation, machine learning, polarization

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595 An In-Depth Comparison Study of Canadian and Danish's Entrepreneurship and Education System

Authors: Amna Khaliq

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In this research paper, a comparison study has been undertaken between Canada and Denmark to analyze the education system between the countries in entrepreneurship. Denmark, a land of high wages and high taxes, and Canada, a land of immigrants and opportunities, have seen a positive relationship in entrepreneurs' growth. They are both considered one of the top ten countries to start a business and to have government support globally. However, education is entirely free to Danish students, including university degrees, compared to Canadians, which can further hurdle for Canadian millennials to grow in the business world—the business experience more growth with educated entrepreneurs with international backgrounds in new immigrants. Denmark has seen a gradual increase in female entrepreneurs over the decade but is still lower than OECD countries. Compassionate management and work-life balance are prioritized in Denmark, unlike in Canada. Danish are early adopters of technology and have excellent infrastructure to support the technology industry, whereas Canada is still a service-oriented and manufacturer-based country. 2018 has been the highest number of opening businesses for Canada and Denmark. Some companies offer high wages, hiring bonuses, flexible working hours, wellness, and mental health benefits during Pandemic to keep the companies running and keep their workers' morale high. Pandemic has taught consumers new patterns to shop online. It is essential now to use technology and automation to increase productivity in businesses. Only those companies will survive that are applying this strategy. The Pandemic has ultimately changed entrepreneurs' and employees' behavior in the business world. Along with Ph.D. professors, entrepreneurs should be allowed to teach at learning intuitions. Millennials turn out to be the most entrepreneurial generation in both countries. Entrepreneurship education will only be beneficial when students create businesses and learn from real-life experiences. Managing physical, mental, emotional, and psychological health while dealing with high pressure in entrepreneurship are soft skills learned through practical work.

Keywords: entrepreneurship education, millennials, pandemic, Denmark, Canada

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594 Predicting Expectations of Non-Monogamy in Long-Term Romantic Relationships

Authors: Michelle R. Sullivan

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Positive romantic relationships and marriages offer a buffer against a host of physical and emotional difficulties. Conversely, poor relationship quality and marital discord can have deleterious consequences for individuals and families. Research has described non-monogamy, infidelity, and consensual non-monogamy, as both consequential and causal of relationship difficulty, or as a unique way a couple strives to make a relationship work. Much research on consensual non-monogamy has built on feminist theory and critique. To the author’s best knowledge, to date, no studies have examined the predictive relationship between individual and relationship characteristics and expectations of non-monogamy. The current longitudinal study: 1) estimated the prevalence of expectations of partner non-monogamy and 2) evaluated whether gender, sexual identity, age, education, how a couple met, and relationship quality were predictive expectations of partner non-monogamy. This study utilized the publically available longitudinal dataset, How Couples Meet and Stay Together. Adults aged 18- to 98-years old (n=4002) were surveyed by phone over 5 waves from 2009-2014. Demographics and how a couple met were gathered through self-report in Wave 1, and relationship quality and expectations of partner non-monogamy were gathered through self-report in Waves 4 and 5 (n=1047). The prevalence of expectations of partner non-monogamy (encompassing both infidelity and consensual non-monogamy) was 4.8%. Logistic regression models indicated that sexual identity, gender, education, and relationship quality were significantly predictive of expectations of partner non-monogamy. Specifically, male gender, lower education, identifying as lesbian, gay, or bisexual, and a lower relationship quality scores were predictive of expectations of partner non-monogamy. Male gender was not predictive of expectations of partner non-monogamy in the follow up logistic regression model. Age and whether a couple met online were not associated with expectations of partner non-monogamy. Clinical implications include awareness of the increased likelihood of lesbian, gay, and bisexual individuals to have an expectation of non-monogamy and the sequelae of relationship dissatisfaction that may be related. Future research directions could differentiate between non-monogamy subtypes and the person and relationship variables that lead to the likelihood of consensual non-monogamy and infidelity as separate constructs, as well as explore the relationship between predicting partner behavior and actual partner behavioral outcomes.

Keywords: open relationship, polyamory, infidelity, relationship satisfaction

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593 Optimizing Data Integration and Management Strategies for Upstream Oil and Gas Operations

Authors: Deepak Singh, Rail Kuliev

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The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry.

Keywords: master data management, IoT, AI&ML, cloud Computing, data optimization

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592 Cognitive Dissonance in Robots: A Computational Architecture for Emotional Influence on the Belief System

Authors: Nicolas M. Beleski, Gustavo A. G. Lugo

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Robotic agents are taking more and increasingly important roles in society. In order to make these robots and agents more autonomous and efficient, their systems have grown to be considerably complex and convoluted. This growth in complexity has led recent researchers to investigate forms to explain the AI behavior behind these systems in search for more trustworthy interactions. A current problem in explainable AI is the inner workings with the logic inference process and how to conduct a sensibility analysis of the process of valuation and alteration of beliefs. In a social HRI (human-robot interaction) setup, theory of mind is crucial to ease the intentionality gap and to achieve that we should be able to infer over observed human behaviors, such as cases of cognitive dissonance. One specific case inspired in human cognition is the role emotions play on our belief system and the effects caused when observed behavior does not match the expected outcome. In such scenarios emotions can make a person wrongly assume the antecedent P for an observed consequent Q, and as a result, incorrectly assert that P is true. This form of cognitive dissonance where an unproven cause is taken as truth induces changes in the belief base which can directly affect future decisions and actions. If we aim to be inspired by human thoughts in order to apply levels of theory of mind to these artificial agents, we must find the conditions to replicate these observable cognitive mechanisms. To achieve this, a computational architecture is proposed to model the modulation effect emotions have on the belief system and how it affects logic inference process and consequently the decision making of an agent. To validate the model, an experiment based on the prisoner's dilemma is currently under development. The hypothesis to be tested involves two main points: how emotions, modeled as internal argument strength modulators, can alter inference outcomes, and how can explainable outcomes be produced under specific forms of cognitive dissonance.

Keywords: cognitive architecture, cognitive dissonance, explainable ai, sensitivity analysis, theory of mind

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591 Exercise and Social Activities for Elderly with an Impairment Who Are Living Alone in the Community: Effects and Influencing Factors of a Dutch Program

Authors: Renate Verkaik, Mieke Rijken, Hennie Boeije

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Elderly who are living alone and who are having one or more impairments are vulnerable for a loss of wellbeing and institutionalization. Physical exercise and social activities together with peers have the potential to make them more resilient. The Dutch program ‘More Resilience, Longer at Home’ initiated by FNO funded 126 local projects to stimulate vulnerable older citizens to participate in exercise and social activities, and as such to improve wellbeing and independent living. The program evaluation addressed the following questions: (1) what are the effects of the program on older (65+) participants exercise behavior, social activities and what is the relationship with wellbeing?, (2) which factors contribute to successful implementation of the projects and their outcomes? A mixed method approach was used. Effects on participants were assessed with a short survey, containing questions on exercise, social engagement, daily functioning, loneliness and life satisfaction. Results of the participants were compared with those of a reference group from the Dutch national population. Perceived influencing factors were investigated with a questionnaire for project leaders. This questionnaire was based on site visits and interviews with project leaders, volunteers and participating elderly. Preliminary results show that social engagement of the participating elderly rises significantly (p ≤ .05) as do their exercise levels and daily functioning. They experience less social loneliness, but not less emotional loneliness. Additionally, there is a positive association between daily functioning and life satisfaction and between exercise and life satisfaction. Perceived influencing factors that contribute to successful implementation of the projects can be categorized in 4 types: (1) characteristics of the activities; (2) profiles of the involved staff (professionals and volunteers), (3) characteristics of the organization, (4) the social political environment. Conclusions are that local projects have been successful in stimulating older citizens to participate in exercise and social activities. Multiple factors need to be addressed to ensure sustainability and scaling-up of the good practices.

Keywords: elderly living alone in the community, exercise and social activities, resilience, quality of life

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590 Health Challenges Of Unmarried Women Over Thirty In Pakistan: A Public Health Perspective On Nutrition And Well-being

Authors: Anum Obaid, Iman Fatima, Wanisha Feroz, Haleema Imran, Hammad Tariq

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In Pakistan, the health of unmarried women over thirty is an emerging public health concern due to its increasing prevalence. Achieving the Sustainable Development Goals (SDGs) requires addressing nutrition and public health issues. This research investigates these goals through the lens of nutrition and public health, specifically examining the challenges faced by unmarried women over thirty in Faisalabad, Pakistan. According to a recent United Nations report, there are 10 million unmarried women over the age of 35 in Pakistan. The United Nations defines health as "a state of complete physical, mental, and social well-being, and not merely the absence of disease or infirmity." Being unmarried and under constant societal pressure profoundly influences the dietary behaviors and nutritional status of these women, affecting their overall health, including physical, mental, and social well-being. A qualitative research approach was employed, involving interviews with both unmarried and married women over thirty. This research examines how marital status influences dietary practices, nutritional status, mental and social health, and their subsequent impacts. Factors such as physical health, mental and emotional status, societal pressure, social health, economic independence, and decision-making power were analyzed to understand the effect of singleness on overall wellness. Findings indicated that marital status significantly affects the dietary patterns and nutritional practices among women in Faisalabad. It was also revealed that unmarried women experienced more stress and had a less optimistic mindset compared to married women, due to loneliness or the absence of a spouse in their lives. Nutritional knowledge varied across marital status, impacting the overall health triangle, including physical, mental, and social health. Understanding these dynamics is crucial for developing targeted interventions to improve nutritional outcomes and overall health among unmarried women in Faisalabad. This study highlights the importance of fostering supportive environments and raising awareness about the health needs of unmarried women over thirty to enhance their overall well-being.

Keywords: health triangle, unmarried woman over thirty, socio-cultural barriers, women’s health

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589 A Use Case-Oriented Performance Measurement Framework for AI and Big Data Solutions in the Banking Sector

Authors: Yassine Bouzouita, Oumaima Belghith, Cyrine Zitoun, Charles Bonneau

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Performance measurement framework (PMF) is an essential tool in any organization to assess the performance of its processes. It guides businesses to stay on track with their objectives and benchmark themselves from the market. With the growing trend of the digital transformation of business processes, led by innovations in artificial intelligence (AI) & Big Data applications, developing a mature system capable of capturing the impact of digital solutions across different industries became a necessity. Based on the conducted research, no such system has been developed in academia nor the industry. In this context, this paper covers a variety of methodologies on performance measurement, overviews the major AI and big data applications in the banking sector, and covers an exhaustive list of relevant metrics. Consequently, this paper is of interest to both researchers and practitioners. From an academic perspective, it offers a comparative analysis of the reviewed performance measurement frameworks. From an industry perspective, it offers exhaustive research, from market leaders, of the major applications of AI and Big Data technologies, across the different departments of an organization. Moreover, it suggests a standardized classification model with a well-defined structure of intelligent digital solutions. The aforementioned classification is mapped to a centralized library that contains an indexed collection of potential metrics for each application. This library is arranged in a manner that facilitates the rapid search and retrieval of relevant metrics. This proposed framework is meant to guide professionals in identifying the most appropriate AI and big data applications that should be adopted. Furthermore, it will help them meet their business objectives through understanding the potential impact of such solutions on the entire organization.

Keywords: AI and Big Data applications, impact assessment, metrics, performance measurement

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588 The Effect of Transactional Analysis Group Training on Self-Knowledge and Its Ego States (The Child, Parent, and Adult): A Quasi-Experimental Study Applied to Counselors of Tehran

Authors: Mehravar Javid, Sadrieh Khajavi Mazanderani, Kelly Gleischman, Zoe Andris

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The present study was conducted with the aim of investigating the effectiveness of transactional analysis group training on self-knowledge and Its dimensions (self, child, and adult) in counselors working in public and private high schools in Tehran. Counseling has become an important job for society, and there is a need for consultants in organizations. Providing better and more efficient counseling is one of the goals of the education system. The personal characteristics of counselors are important for the success of the therapy. In TA, humans have three ego states, which are named parent, adult, and child, and the main concept in the transactional analysis is self-state, which means a stable feeling and pattern of thinking related to behavioral patterns. Self-knowledge, considered a prerequisite to effective communication, fosters psychological growth, and recognizing it, is pivotal for emotional development, leading to profound insights. The research sample included 30 working counselors (22 women and 8 men) in the academic year 2019-2020 who achieved the lowest scores on the self-knowledge questionnaire. The research method was quasi-experimental with a control group (15 people in the experimental group and 15 people in the control group). The research tool was a self-awareness questionnaire with 29 questions and three subscales (child, parent, and adult Ego state). The experimental group was exposed to transactional analysis training for 10 once-weekly 2-hour sessions; the questionnaire was implemented in both groups (post-test). Multivariate covariance analysis was used to analyze the data. The data showed that the level of self-awareness of counselors who received transactional analysis training is higher than that of counselors who did not receive any training (p<0.01). The result obtained from this analysis shows that transactional analysis training is an effective therapy for enhancing self-knowledge and its subscales (Adult ego state, Parent ego state, and Child ego state). Teaching transactional analysis increases self-knowledge, and self-realization and helps people to achieve independence and remove irresponsibility to improve intra-personal and interpersonal relationships.

Keywords: ego state, group, transactional analysis, self-knowledge

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587 Recent Developments in the Application of Deep Learning to Stock Market Prediction

Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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586 Study and Simulation of a Dynamic System Using Digital Twin

Authors: J.P. Henriques, E. R. Neto, G. Almeida, G. Ribeiro, J.V. Coutinho, A.B. Lugli

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Industry 4.0, or the Fourth Industrial Revolution, is transforming the relationship between people and machines. In this scenario, some technologies such as Cloud Computing, Internet of Things, Augmented Reality, Artificial Intelligence, Additive Manufacturing, among others, are making industries and devices increasingly intelligent. One of the most powerful technologies of this new revolution is the Digital Twin, which allows the virtualization of a real system or process. In this context, the present paper addresses the linear and nonlinear dynamic study of a didactic level plant using Digital Twin. In the first part of the work, the level plant is identified at a fixed point of operation, BY using the existing method of least squares means. The linearized model is embedded in a Digital Twin using Automation Studio® from Famous Technologies. Finally, in order to validate the usage of the Digital Twin in the linearized study of the plant, the dynamic response of the real system is compared to the Digital Twin. Furthermore, in order to develop the nonlinear model on a Digital Twin, the didactic level plant is identified by using the method proposed by Hammerstein. Different steps are applied to the plant, and from the Hammerstein algorithm, the nonlinear model is obtained for all operating ranges of the plant. As for the linear approach, the nonlinear model is embedded in the Digital Twin, and the dynamic response is compared to the real system in different points of operation. Finally, yet importantly, from the practical results obtained, one can conclude that the usage of Digital Twin to study the dynamic systems is extremely useful in the industrial environment, taking into account that it is possible to develop and tune controllers BY using the virtual model of the real systems.

Keywords: industry 4.0, digital twin, system identification, linear and nonlinear models

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585 Studying Second Language Development from a Complex Dynamic Systems Perspective

Authors: L. Freeborn

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This paper discusses the application of complex dynamic system theory (DST) to the study of individual differences in second language development. This transdisciplinary framework allows researchers to view the trajectory of language development as a dynamic, non-linear process. A DST approach views language as multi-componential, consisting of multiple complex systems and nested layers. These multiple components and systems continuously interact and influence each other at both the macro- and micro-level. Dynamic systems theory aims to explain and describe the development of the language system, rather than make predictions about its trajectory. Such a holistic and ecological approach to second language development allows researchers to include various research methods from neurological, cognitive, and social perspectives. A DST perspective would involve in-depth analyses as well as mixed methods research. To illustrate, a neurobiological approach to second language development could include non-invasive neuroimaging techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to investigate areas of brain activation during language-related tasks. A cognitive framework would further include behavioural research methods to assess the influence of intelligence and personality traits, as well as individual differences in foreign language aptitude, such as phonetic coding ability and working memory capacity. Exploring second language development from a DST approach would also benefit from including perspectives from the field of applied linguistics, regarding the teaching context, second language input, and the role of affective factors such as motivation. In this way, applying mixed research methods from neurobiological, cognitive, and social approaches would enable researchers to have a more holistic view of the dynamic and complex processes of second language development.

Keywords: dynamic systems theory, mixed methods, research design, second language development

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584 The Role of Parental Stress and Emotion Regulation in Responding to Children’s Expression of Negative Emotion

Authors: Lizel Bertie, Kim Johnston

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Parental emotion regulation plays a central role in the socialisation of emotion, especially when teaching young children to cope with negative emotions. Despite evidence which shows non-supportive parental responses to children’s expression of negative emotions has implications for the social and emotional development of the child, few studies have investigated risk factors which impact parental emotion socialisation processes. The current study aimed to explore the extent to which parental stress contributes to both difficulties in parental emotion regulation and non-supportive parental responses to children’s expression of negative emotions. In addition, the study examined whether parental use of expressive suppression as an emotion regulation strategy facilitates the influence of parental stress on non-supportive responses by testing the relations in a mediation model. A sample of 140 Australian adults, who identified as parents with children aged 5 to 10 years, completed an online questionnaire. The measures explored recent symptoms of depression, anxiety, and stress, the use of expressive suppression as an emotion regulation strategy, and hypothetical parental responses to scenarios related to children’s expression of negative emotions. A mediated regression indicated that parents who reported higher levels of stress also reported higher levels of expressive suppression as an emotion regulation strategy and increased use of non-supportive responses in relation to young children’s expression of negative emotions. These findings suggest that parents who experience heightened symptoms of stress are more likely to both suppress their emotions in parent-child interaction and engage in non-supportive responses. Furthermore, higher use of expressive suppression strongly predicted the use of non-supportive responses, despite the presence of parental stress. Contrary to expectation, no indirect effect of stress on non-supportive responses was observed via expressive suppression. The findings from the study suggest that parental stress may become a more salient manifestation of psychological distress in a sub-clinical population of parents while contributing to impaired parental responses. As such, the study offers support for targeting overarching factors such as difficulties in parental emotion regulation and stress management, not only as an intervention for parental psychological distress, but also the detection and prevention of maladaptive parenting practices.

Keywords: emotion regulation, emotion socialisation, expressive suppression, non-supportive responses, parental stress

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583 Reducing the Imbalance Penalty Through Artificial Intelligence Methods Geothermal Production Forecasting: A Case Study for Turkey

Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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582 The Representation of Women in Iraq: Gender Wage Gap and the Position of Women within Iraqi Society

Authors: Hanaa Sameen Ameen Bajilan

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Human rights should be protected and promoted without regard to race, ethnicity, religion, political philosophy, or sexual orientation, following our firm convictions. Thus, any infringement of these rights or disdain for; any use of violence against women undermines the principles and human values of equality and endangers the entire society, including its potential to live in peace and to make growth and development. This paper represents the condition of the new Iraqi women regarding issues such as the gender wage gap, education, health, and violence against women. The study aims to determine the impact of traditions and customs on the legal position of Iraqi women. First, it seeks to assess the effects of culture as a historical agency on the legal status of Iraqi women. Second, the influence of cultural developments in the later part of the twentieth century on Iraqi women's legal standing, and third, the importance of cultural variety as a progressive cultural component in women's legal position. Finally, the study highlights the representation of women in Iraq: Gender wage Gap, Women's liberation between culture and law, and the role of women within Iraqi society based on an Iraqi novel named (Orange Light) in Arabic: برتقالو ضو. in her book, the Iraqi writer Nadia Al-Abru succeeds in portraying the post-war society's devotion to the sexual, emotional and mental marginalization of women in terms of the value of attendance. Since the study of Iraqi women's literature in Arabic-English translation is a new avenue of research that contributes to all three areas, this investigation aims to establish critical lines of engagement between contemporary Iraqi women's literature in English translation and feminist translation conceptual frameworks, and this is accomplished by first focusing on why analyzing Iraqi women writers' novels in Arabic-English translation is a timeline of inquiry that contributes to existing and emerging knowledge fields concerning Iraqi women writers' contemporary critical contexts and scholarship on Arab women's literature in Arabic-English translation.

Keywords: women in İraq, equality, violence, gender wage gap, Nadia Al-Abru, (orange light), women's liberation, İraqi women's literature,

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581 Non-Invasive Data Extraction from Machine Display Units Using Video Analytics

Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

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Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: human machine interface, industrial internet of things, internet of things, optical character recognition, video analytics

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580 The Impact of Artificial Intelligence on Pharmacy and Pharmacology

Authors: Mamdouh Milad Adly Morkos

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Despite having the greatest rates of mortality and morbidity in the world, low- and middle-income (LMIC) nations trail high-income nations in terms of the number of clinical trials, the number of qualified researchers, and the amount of research information specific to their people. Health inequities and the use of precision medicine may be hampered by a lack of local genomic data, clinical pharmacology and pharmacometrics competence, and training opportunities. These issues can be solved by carrying out health care infrastructure development, which includes data gathering and well-designed clinical pharmacology training in LMICs. It will be advantageous if there is international cooperation focused at enhancing education and infrastructure and promoting locally motivated clinical trials and research. This paper outlines various instances where clinical pharmacology knowledge could be put to use, including pharmacogenomic opportunities that could lead to better clinical guideline recommendations. Examples of how clinical pharmacology training can be successfully implemented in LMICs are also provided, including clinical pharmacology and pharmacometrics training programmes in Africa and a Tanzanian researcher's personal experience while on a training sabbatical in the United States. These training initiatives will profit from advocacy for clinical pharmacologists' employment prospects and career development pathways, which are gradually becoming acknowledged and established in LMICs. The advancement of training and research infrastructure to increase clinical pharmacologists' knowledge in LMICs would be extremely beneficial because they have a significant role to play in global health

Keywords: electromagnetic solar system, nano-material, nano pharmacology, pharmacovigilance, quantum theoryclinical simulation, education, pharmacology, simulation, virtual learning low- and middle-income, clinical pharmacology, pharmacometrics, career development pathways

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579 A Methodology to Integrate Data in the Company Based on the Semantic Standard in the Context of Industry 4.0

Authors: Chang Qin, Daham Mustafa, Abderrahmane Khiat, Pierre Bienert, Paulo Zanini

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Nowadays, companies are facing lots of challenges in the process of digital transformation, which can be a complex and costly undertaking. Digital transformation involves the collection and analysis of large amounts of data, which can create challenges around data management and governance. Furthermore, it is also challenged to integrate data from multiple systems and technologies. Although with these pains, companies are still pursuing digitalization because by embracing advanced technologies, companies can improve efficiency, quality, decision-making, and customer experience while also creating different business models and revenue streams. In this paper, the issue that data is stored in data silos with different schema and structures is focused. The conventional approaches to addressing this issue involve utilizing data warehousing, data integration tools, data standardization, and business intelligence tools. However, these approaches primarily focus on the grammar and structure of the data and neglect the importance of semantic modeling and semantic standardization, which are essential for achieving data interoperability. In this session, the challenge of data silos in Industry 4.0 is addressed by developing a semantic modeling approach compliant with Asset Administration Shell (AAS) models as an efficient standard for communication in Industry 4.0. The paper highlights how our approach can facilitate the data mapping process and semantic lifting according to existing industry standards such as ECLASS and other industrial dictionaries. It also incorporates the Asset Administration Shell technology to model and map the company’s data and utilize a knowledge graph for data storage and exploration.

Keywords: data interoperability in industry 4.0, digital integration, industrial dictionary, semantic modeling

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578 Tracing Syrian Refugees Urban Mobilities: The Case of Egypt and Canada

Authors: N. Elgendy, N. Hussein

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The current Syrian crisis has caused unprecedented practices of global mobility. The process of forced eviction and the resettlement of refugees could be seen through the insights of the “new mobilities paradigm”. The mobility of refugees in terms of meaning and practice is a subject that calls for further studies. There is a need for the development of an approach to human mobility to understand a practice that is turning into a phenomenon in the 21st century. This paper aims at studying, from a qualitative point of view, the process of movement within the six constituents of mobility defined as the first phase of the journey of a refugee. The second phase would include the process of settling in and re-defining the host country as new “home” to refugees. The change in the refugee state of mind and crossing the physical and mental borders from a “foreigner” to a citizen is encouraged by both the governmental policies and the local communities’ efforts to embrace these newcomers. The paper would focus on these policies of social and economic integration. The concept of integration connotes the idea that refugees would enjoy the opportunities, rights and services available to the citizens of the refugee’s new community. So, this paper examines this concept through showcasing the two hosting countries of Canada and Egypt, as they provide two contrasting situations in terms of cultural, geographical, economic and political backgrounds. The analysis would highlight the specific policies defined towards the refugees including the mass communication, media calls, and access to employment. This research is part of a qualitative research project on the process of Urban Mobility practiced by the Syrian Refugees, drawing on conversational interviews with new-settlers who have moved to the different hosting countries, from their home in Syria. It explores these immigrants’ practical and emotional relationships with the process of movement and settlement. It uses the conversational interviews as a tool to document analysis and draw relationships in an attempt to establish an understanding of the factors that contribute to the new-settlers feeling of home and integration within the new community.

Keywords: integration, mobility, policy, refugees

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577 A Study on the Effects of a Mindfulness Training on Managers: The Case of the Malian Company for the Development of Textile

Authors: Aboubacar Garba Konte, Wei Jun, Li Xiaohui

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Nowadays companies are facing increasing pressure. The market environment changes more frequently than ever. Therefore, managers have to develop their agility, their performance and their capacity for innovation. Most companies look for managerial innovations to develop in their employees qualities such as motivation, commitment, creativity, autonomy or even the ability to adapt to change and manage intensive pressure. On a more collective level, companies are looking for teams that are able to organize, communicate and develop a form of collective intelligence based on cooperation and solidarity. Among the many managerial innovations that are currently developing, mindfulness (or mindfulness) is drawing the attention of a growing number of companies (Google, Apple, Sony, ING ...), These companies have implemented programs based on mindfulness. Although the concept of mindfulness and its effects have been the subject of in-depth research in the psychological field, research on mindfulness in the field of management is still in its infancy and it is necessary to evaluate its contribution to organizations. The purpose of this research is to evaluate the effects of a mindfulness training among the managers of a Malian textile company (CMDT). We conducted a case study on their experience and their managerial practices. In addition, we discuss the innovative nature of mindfulness in terms of managerial practice The results show significant positive effects on two major skills identified by managers that raise significant difficulties in their daily lives: their ability to supervise a team of employees with all that this implies in terms of interpersonal skills and their ability to organize and prioritize their activities. In addition, the research methodology sheds light on the innovative nature of mindfulness in a favorable organizational environment.

Keywords: mindfulness, manager, managerial innovation, relational skills, organization and prioritization

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576 Solving a Micromouse Maze Using an Ant-Inspired Algorithm

Authors: Rolando Barradas, Salviano Soares, António Valente, José Alberto Lencastre, Paulo Oliveira

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This article reviews the Ant Colony Optimization, a nature-inspired algorithm, and its implementation in the Scratch/m-Block programming environment. The Ant Colony Optimization is a part of Swarm Intelligence-based algorithms and is a subset of biological-inspired algorithms. Starting with a problem in which one has a maze and needs to find its path to the center and return to the starting position. This is similar to an ant looking for a path to a food source and returning to its nest. Starting with the implementation of a simple wall follower simulator, the proposed solution uses a dynamic graphical interface that allows young students to observe the ants’ movement while the algorithm optimizes the routes to the maze’s center. Things like interface usability, Data structures, and the conversion of algorithmic language to Scratch syntax were some of the details addressed during this implementation. This gives young students an easier way to understand the computational concepts of sequences, loops, parallelism, data, events, and conditionals, as they are used through all the implemented algorithms. Future work includes the simulation results with real contest mazes and two different pheromone update methods and the comparison with the optimized results of the winners of each one of the editions of the contest. It will also include the creation of a Digital Twin relating the virtual simulator with a real micromouse in a full-size maze. The first test results show that the algorithm found the same optimized solutions that were found by the winners of each one of the editions of the Micromouse contest making this a good solution for maze pathfinding.

Keywords: nature inspired algorithms, scratch, micromouse, problem-solving, computational thinking

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575 The ReliVR Project: Feasibility of a Virtual Reality Intervention in the Psychotherapy of Depression

Authors: Kyra Kannen, Sonja D. Roelen, Sebastian Schnieder, Jarek Krajewski, Steffen Holsteg, André Karger, Johanna Askeridis, Celina Slawik, Philip Mildner, Jens Piesk, Ruslan David, Holger Kürten, Benjamin Oster, Robert Malzan, Mike Ludemann

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Virtual Reality (VR) is increasingly recognized for its potential in transforming mental disorder treatment, offering advantages such as cost-effectiveness, time efficiency, accessibility, reduced stigma, and scalability. While the application of VR in the context of anxiety disorders has been extensively evaluated and demonstrated to be effective, the utilization of VR as a therapeutic treatment for depression remains under-investigated. Our goal is to pioneer immersive VR therapy modules for treating major depression, alongside a web-based system for home use. We develop a modular digital therapy platform grounded in psychodynamic therapy interventions which addresses stress reduction, exploration of social situations and relationship support, social skill training, avoidance behavior analysis, and psychoeducation. In addition, an automated depression monitoring system, based on acoustic voice analysis, is implemented in the form of a speech-based diary to track the affective state of the user and depression severity. The use of immersive VR facilitates patient immersion into complex and realistic interpersonal interactions with high emotional engagement, which may contribute to positive treatment acceptance and satisfaction. In a proof-of-concept study, 45 depressed patients were assigned to VR or web-platform modules, evaluating user experience, usability and additional metrics including depression severity, mindfulness, interpersonal problems, and treatment satisfaction. The findings provide valuable insights into the effectiveness and user-friendliness of VR and web modules for depression therapy and contribute to the refinement of more tailored digital interventions to improve mental health.

Keywords: virtual reality therapy, digital health, depression, psychotherapy

Procedia PDF Downloads 35
574 Experience of Inpatient Life in Korean Complex Regional Pain Syndrome: A Phenomenological Study

Authors: Se-Hwa Park, En-Kyung Han, Jae-Young Lim, Hye-Jung Ahn

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Purpose: The objective of this study is to provide basic data for understanding the substance of inpatient life with CRPS (Complex Regional Pain Syndrome) and developing efficient and effective nursing intervention. Methods: From September 2018 to November, we have interviewed 10 CRPS patients about inpatient experiences. To understand the implication of inpatient life experiences with CRPS and intrinsic structure, we have used the question: 'How about the inpatient experiences with CRPS'. For data analysis, the method suggested by Colaizzi was applied as a phenomenological method. Results: According to the analysis, the study participants' inpatient life process was structured in six categories: (a) breakthrough pain experience (b) the limitation of pain treatment, (c) worsen factors of pain during inpatient period, (d) treat method for pain, (e) positive experience for inpatient period, (f) requirements for medical team, family and people in hospital room. Conclusion: Inpatient with CRPS have experienced the breakthrough pain. They had expected immediate treatment for breakthrough pain, but they experienced severe pain because immediate treatment was not implemented. Pain-worsening factors which patients with CRPS are as follows: personal factors from negative emotions such as insomnia, stress, sensitive character, pain part touch or vibration stimulus on the bed, physical factors from high threshold or rapid speed during fast transfer, conflict with other people, climate factors such as humidity or low temperature, noise, smell, lack of space because of many visitors. Patients actively manage the pain committing into another tasks or diversion. And also, patients passively manage the pain, just suppress, give-up. They think positively about rehabilitation treatment. And they require the understanding and sympathy for other people, and emotional support, immediate intervention for medical team. Based on the results of this study, we suppose the guideline of systematic breakthrough pain management for the relaxation of sudden pain, using notice of informing caution for touch or vibration. And we need to develop non-medicine pain management nursing intervention.

Keywords: breakthrough pain, CRPS, complex regional pain syndrome, inpatient life experiences, phenomenological method

Procedia PDF Downloads 111
573 Applications of Evolutionary Optimization Methods in Reinforcement Learning

Authors: Rahul Paul, Kedar Nath Das

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The paradigm of Reinforcement Learning (RL) has become prominent in training intelligent agents to make decisions in environments that are both dynamic and uncertain. The primary objective of RL is to optimize the policy of an agent in order to maximize the cumulative reward it receives throughout a given period. Nevertheless, the process of optimization presents notable difficulties as a result of the inherent trade-off between exploration and exploitation, the presence of extensive state-action spaces, and the intricate nature of the dynamics involved. Evolutionary Optimization Methods (EOMs) have garnered considerable attention as a supplementary approach to tackle these challenges, providing distinct capabilities for optimizing RL policies and value functions. The ongoing advancement of research in both RL and EOMs presents an opportunity for significant advancements in autonomous decision-making systems. The convergence of these two fields has the potential to have a transformative impact on various domains of artificial intelligence (AI) applications. This article highlights the considerable influence of EOMs in enhancing the capabilities of RL. Taking advantage of evolutionary principles enables RL algorithms to effectively traverse extensive action spaces and discover optimal solutions within intricate environments. Moreover, this paper emphasizes the practical implementations of EOMs in the field of RL, specifically in areas such as robotic control, autonomous systems, inventory problems, and multi-agent scenarios. The article highlights the utilization of EOMs in facilitating RL agents to effectively adapt, evolve, and uncover proficient strategies for complex tasks that may pose challenges for conventional RL approaches.

Keywords: machine learning, reinforcement learning, loss function, optimization techniques, evolutionary optimization methods

Procedia PDF Downloads 56
572 Deep Learning-Based Approach to Automatic Abstractive Summarization of Patent Documents

Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

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A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

Procedia PDF Downloads 104
571 An Approach to Building a Recommendation Engine for Travel Applications Using Genetic Algorithms and Neural Networks

Authors: Adrian Ionita, Ana-Maria Ghimes

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The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application.

Keywords: artificial intelligence, big data, cloud computing, DNA profile, genetic algorithms, machine learning, neural networks, optimization, recommendation system, user profiling

Procedia PDF Downloads 143
570 A Framework for Auditing Multilevel Models Using Explainability Methods

Authors: Debarati Bhaumik, Diptish Dey

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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

Procedia PDF Downloads 68
569 Cognitive Science Based Scheduling in Grid Environment

Authors: N. D. Iswarya, M. A. Maluk Mohamed, N. Vijaya

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Grid is infrastructure that allows the deployment of distributed data in large size from multiple locations to reach a common goal. Scheduling data intensive applications becomes challenging as the size of data sets are very huge in size. Only two solutions exist in order to tackle this challenging issue. First, computation which requires huge data sets to be processed can be transferred to the data site. Second, the required data sets can be transferred to the computation site. In the former scenario, the computation cannot be transferred since the servers are storage/data servers with little or no computational capability. Hence, the second scenario can be considered for further exploration. During scheduling, transferring huge data sets from one site to another site requires more network bandwidth. In order to mitigate this issue, this work focuses on incorporating cognitive science in scheduling. Cognitive Science is the study of human brain and its related activities. Current researches are mainly focused on to incorporate cognitive science in various computational modeling techniques. In this work, the problem solving approach of human brain is studied and incorporated during the data intensive scheduling in grid environments. Here, a cognitive engine is designed and deployed in various grid sites. The intelligent agents present in CE will help in analyzing the request and creating the knowledge base. Depending upon the link capacity, decision will be taken whether to transfer data sets or to partition the data sets. Prediction of next request is made by the agents to serve the requesting site with data sets in advance. This will reduce the data availability time and data transfer time. Replica catalog and Meta data catalog created by the agents assist in decision making process.

Keywords: data grid, grid workflow scheduling, cognitive artificial intelligence

Procedia PDF Downloads 372
568 Identifying Factors of Wellbeing in Russian Orphans

Authors: Alexandra Telitsyna, Galina Semya, Elvira Garifulina

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Introduction: Starting from 2012 Russia conducts deinstitutionalization policy and now the main indicator of success is the number of children living in institutions. Active family placement process has resulted in residents of the institution now mainly consists of adolescents with behavioral and emotional problems, children with disabilities and groups of siblings. Purpose of science research: The purpose of science research is to identify factors for child’s wellbeing while temporary stay in an orphanage and the subjective assessment of children's level of well-being (psychological well-being). Methods: The data used for this project was collected by the questionnaire of 72 indicators, a tool for monitoring the behavior of children and caregivers, an additional questionnaire for children; well-being assessment questionnaire containing 10 scales for three age groups from preschool to older adolescents. In 2016-2018, the research was conducted in 1873 institution in 85 regions of Russia. In each region a team of academics, specialists from Non-profits, independent experts was created. Training was conducted for team members through a series of webinars prior to undertaking the assessment. The results: To ensure the well-being of the children, the following conditions are necessary: 1- Life of children in institution is organised according to the principles of family care (including the creation of conditions for attachment to be formed); 2- Contribution to find family-based placement for children (including reintegration into the primary family); 3- Work with parents of children, who are placed in an organization at the request of parents; 4- Children attend schools according to their needs; 5- Training of staff and volunteers; 6- Special environment and services for children with special needs and children with disabilities; 7- Cooperation with NGOs; 8 - Openness and accessibility of the organization. Conclusion: A study of the psychological well-being of children showed that the most emotionally stressful for children were questions about the presence and frequency of contact with relatives, and the level of well-being is higher in the presence of a trusted adult and respect for rights. The greatest contribution to the trouble is made by the time the child is in the orphanage, the lack of contact with parents and relatives, the uncertainty of the future.

Keywords: identifying factors, orphans, Russia, wellbeing

Procedia PDF Downloads 112
567 The Implications of Kinship Terms in Newspaper Accident Reports

Authors: Tharwat El-Sakran

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The linguistic choices accident news reporters make when reporting killing cases within family circles aid in augmenting the wrath readers feel towards the perpetrators. Undoubtedly, when killers or murderers are labelled with particular words, prospective readers will associate them with the cultural connotations and emotions, whether positive or negative, attached to those words. One of these strategies is the use of kinship terms to anaphorically or cataphorically refer to the defendants. While some articles opt for using the killer’s name, others make use of other kinship labels such as “the mother,” “the father,” “the step-father, and “the step-mother.” The preference for proper nouns over kinship terms and vice versa can be indicative of some of the underlying implications that the article writer may be trying to make about either the status of the killer or the overall incident circumstances. This research examines how the use of referential kinship labels could point to hidden and shared connotations between writers and their prospective readers. This study examined seventy newspaper articles from English-medium publications based in the United Arab Emirates (UAE), the USA, and several other countries. Some of these articles make use of proper nouns referring to the individual directly by name, whereas others refer to individuals based on their kinship relation with the victim or by their occupational status. Furthermore, information was collected from two hundred fifty-one students at several UAE-based universities by asking them what certain kinship words meant to them. The survey questions allowed for real insight into some of the most prevalent interpretations attached to kinship labels and the possible implications for preferring kinship terms over occupational labels and persons’ proper names. Results indicate that newspaper writers employ kinship labels to inspire an emotion in their future readers’ reactions that may not be achieved through the use of the person’s proper name(s). Additionally, respondents to the survey believe that the use of kinship nouns like “mother,” “father,” “step-mother,” and “step-father” tends to inspire a stronger emotional response, as they are almost always associated with particular behavioral cultural codes and conventions. The study concludes with recommendations for teaching the grammar of English words to EFL and mass communication students and with suggestions for translation theorists and further research.

Keywords: kinship terms, accident reports, cultural connotations, translation of kinship terms

Procedia PDF Downloads 108