Search results for: Emotional intelligence

Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Jiaxin Shi, Kaifeng Hao, Qingfan An, Zeng Peng

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Due to a lack of data sources and data processing techniques, it has always been difficult to quantify public space quality, which includes urban construction quality and how it is perceived by people, especially in large urban areas. This study proposes a quantitative research method based on the consideration of emotional health and physical health of the built environment. It highlights the low quality of public areas in Tianjin, China, where there are many negative elements. Deep learning technology is then used to measure how effectively people perceive urban areas. First, this work suggests a deep learning model that might simulate how people can perceive the quality of urban construction. Second, we perform semantic segmentation on street images to identify visual elements influencing scene perception. Finally, this study correlated the scene perception score with the proportion of visual elements to determine the surrounding environmental elements that influence scene perception. Using a small-scale labeled Tianjin street view data set based on transfer learning, this study trains five negative spatial discriminant models in order to explore the negative space distribution and quality improvement of urban streets. Then it uses all Tianjin street-level imagery to make predictions and calculate the proportion of negative space. Visualizing the spatial distribution of negative space along the Tianjin Inner Ring Road reveals that the negative elements are mainly found close to the five key districts. The map of Tianjin was combined with the experimental data to perform the visual analysis. Based on the emotional assessment, the distribution of negative materials, and the direction of street guidelines, we suggest guidance content and design strategy points of the negative phenomena in Tianjin street space in the two dimensions of perception and substance. This work demonstrates the utilization of deep learning techniques to understand how people appreciate high-quality urban construction, and it complements both theory and practice in urban planning. It illustrates the connection between human perception and the actual physical public space environment, allowing researchers to make urban interventions.

Keywords: human perception, public space quality, deep learning, negative elements, street images

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Authors: P. Jayashree, S. Rajkumar

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With the trend of virtual and augmented reality environments booming to provide a life like experience, gaming is a major tool in supporting such learning environments. In this work, a variant of Voronoi heuristics, employing supervised learning for the TRON game is proposed. The paper discusses the features that would be really useful when a machine learning bot is to be used as an opponent against a human player. Various game scenarios, nature of the bot and the experimental results are provided for the proposed variant to prove that the approach is better than those that are currently followed.

Keywords: artificial Intelligence, automation, machine learning, TRON game, Voronoi heuristics

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Authors: Nayer Mofidtabatabaei

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Advancements in artificial intelligence (AI) have transformed various fields, including psychology and behavioral sciences. This paper explores the diverse ways in which AI is applied to enhance research, diagnosis, therapy, and understanding of human behavior and mental health. We discuss the potential benefits and challenges associated with AI in these fields, emphasizing the ethical considerations and the need for collaboration between AI researchers and psychological and behavioral science experts. Artificial Intelligence (AI) has gained prominence in recent years, revolutionizing multiple industries, including healthcare, finance, and entertainment. One area where AI holds significant promise is the field of psychology and behavioral sciences. AI applications in this domain range from improving the accuracy of diagnosis and treatment to understanding complex human behavior patterns. This paper aims to provide an overview of the various AI applications in psychological and behavioral sciences, highlighting their potential impact, challenges, and ethical considerations. Mental Health Diagnosis AI-driven tools, such as natural language processing and sentiment analysis, can analyze large datasets of text and speech to detect signs of mental health issues. For example, chatbots and virtual therapists can provide initial assessments and support to individuals suffering from anxiety or depression. Autism Spectrum Disorder (ASD) Diagnosis AI algorithms can assist in early ASD diagnosis by analyzing video and audio recordings of children's behavior. These tools help identify subtle behavioral markers, enabling earlier intervention and treatment. Personalized Therapy AI-based therapy platforms use personalized algorithms to adapt therapeutic interventions based on an individual's progress and needs. These platforms can provide continuous support and resources for patients, making therapy more accessible and effective. Virtual Reality Therapy Virtual reality (VR) combined with AI can create immersive therapeutic environments for treating phobias, PTSD, and social anxiety. AI algorithms can adapt VR scenarios in real-time to suit the patient's progress and comfort level. Data Analysis AI aids researchers in processing vast amounts of data, including survey responses, brain imaging, and genetic information. Privacy Concerns Collecting and analyzing personal data for AI applications in psychology and behavioral sciences raise significant privacy concerns. Researchers must ensure the ethical use and protection of sensitive information. Bias and Fairness AI algorithms can inherit biases present in training data, potentially leading to biased assessments or recommendations. Efforts to mitigate bias and ensure fairness in AI applications are crucial. Transparency and Accountability AI-driven decisions in psychology and behavioral sciences should be transparent and subject to accountability. Patients and practitioners should understand how AI algorithms operate and make decisions. AI applications in psychological and behavioral sciences have the potential to transform the field by enhancing diagnosis, therapy, and research. However, these advancements come with ethical challenges that require careful consideration. Collaboration between AI researchers and psychological and behavioral science experts is essential to harness AI's full potential while upholding ethical standards and privacy protections. The future of AI in psychology and behavioral sciences holds great promise, but it must be navigated with caution and responsibility.

Keywords: artificial intelligence, psychological sciences, behavioral sciences, diagnosis and therapy, ethical considerations

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Authors: Hyojung Shin, Munhee Kweon

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Non-suicide Self Injuri (NSSI) can be defined as the act of an individual who does not intend to die directly and intentionally damaging his or her body tissues. According to a study conducted by the Korean Ministry of Education in 2018, the NSSI is widely spreading among teenagers, with 7.9 percent of all middle school students and 6.4 percent of high school students reporting experience in NSSI. As such, it is understood that the first time of the NSSI is in adolescence. However, the NSSI may not start and stop at a certain time, but may last longer. However, despite the widespread prevalence of NSSI among teenagers, little is known about the process and maintenance of NSSI college students on a continuous development basis. Korea's NSSI research trends are mainly focused on individual internal vulnerabilities (high levels of painful emotions/awareness, lack of pain tolerance) and interpersonal vulnerabilities (poor communication skills and social problem solving), and little studies have been done on individuals' unique characteristics and environmental factors such as substrate or environmental vulnerability factors. In particular, environmental factors are associated with the occurrence of NSSI by acting as a vulnerability factor that can interfere with the emotional control of individuals, whereas individual factors play a more direct role by contributing to the maintenance of NSSI, so it is more important to consider this for personal environmental involvement in NSSI. This study focused on the Fight-Flight-Freeze System as a factor in the defensive avoidance system of Reward Sensitivity in individual factors. Also, Environmental factors include the level of expressed emotion in family. Wedig and Nock (2007) said that if parents with a self-critical cognitive style take the form of criticizing their children, the experience of NSSI increases. The high level of parental criticism is related to the increasing frequency of NSSI acts as well as to serious levels of NSSI. If the normal coping mechanism fails to control emotions, people want to overcome emotional difficulties even through NSSI, and emotional disturbances experienced by individuals within an unsupported social relationship increase vulnerability to NSSI. Based on these theories, this study is to find ways to prevent NSSI and intervene in counseling effectively by verifying the differences between the characteristics experienced NSSI persons and non-experienced NSSI persons. Therefore, the purpose of this research was to examine the relationship of Fight-Flight-Freeze System (FFFS), level of expressed emotion in family and emotion regulation difficulties, comparing those who experienced Non-Suicidal Self-Injury (NSSI) with those who did not experienced Non-Suicidal Self-Injury (NSSI). The data were collected from university students in Seoul Korea and Gyeonggi-do province. 99 subjects were experienced student of NSSI, while 375 were non- experienced student of NSSI. The results of this study are as follows. First, the result of t-test indicated that NSSI attempters showed a significant difference in fight-flight-freeze system, level of expressed emotion and emotion regulation difficulties, compared with non-attempters. Second, fight-flight-freeze system, level of expressed emotion in family and emotion regulation difficulties of NSSI attempters showed a significant difference in correlation. The correlation was significant only freeze system of fight-flight-freeze system, Level of expressed emotion in family and emotion regulation difficulties. Third, freeze system and level of expressed emotion in family predicted emotion regulation difficulties of NSSI attempters. Fight-freeze system and level of expressed emotion in family predicted emotion regulation difficulties of non-NSSI attempters. Lastly, Practical implications for counselors and limitations of this study are discussed.

Keywords: fight-flight-freeze system, level of expressed emotion in family, emotion regulation difficulty, non-suicidal self injury

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Authors: Sonia Mastrangelo

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The Miller Umwelt Assessment Scale is a useful tool for obtaining information about the developmental capacities of children on the autism spectrum. The assessment, made up of 19 tasks in the areas of: body organization, contact with surroundings, expressive and receptive communication, representation, and social-emotional development, has been used with much success over the past 40 years. While many assessments are difficult to administer to children on the autism spectrum, the simplicity of the MUAS reveals key strengths and challenges for both low and high functioning children on the spectrum. The results guide parents and clinicians in providing a curriculum and/or home program that moves children up the developmental ladder.

Keywords: autism spectrum disorder, assessment, reading intervention, Miller method

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Authors: E. Didaskalou, C. Roussi-Vergou, E. Andreou, G. Skrzypiec, P. Slee

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There has been growing interest lately, in the study of victimization among adolescent students in Greece and elsewhere with a view to improve school policies concerning anti-bullying practices. Researchers have recently focused on investigating the relationships between the extent of students’ victimization and the distinct mechanisms that they employ for coping with this particular problem. In particular, the emphasis has been placed on exploring the relationship between the coping strategies students use to counteract bullying, their sense of belonging at school, and extent of their victimization. Methods: Within the research framework outlined above, we set out to: a) examine the frequency of self-reported victimization among secondary school students, b) investigate the coping strategies employed by students when confronted with school bullying and c) explore any differences between bullied and non-bullied students with regard to coping strategies and school belongingness. The sample consisted of 860 from fifteen secondary public schools in central Greece. The schools were typical Greek secondary schools and the principals volunteered to participate in this study. Participants’ age ranged from 12 to 16 years. Measures: a) Exposure to Victimization: The frequency of victimization was directly located by asking students the question: ‘Over the last term, how often have you been bullied or harassed by a student or students at this high school?’ b) Coping Strategies: The ‘Living and Learning at School: Bullying at School’ was administered to students, c) School belongingness was assessed by the Psychological Sense of School Membership Scale, that students completed. Results: Regarding the frequency of self-reported victimization, 1.5% of the students reported being victimized every day, 2.8% most days of the week, 2.1% one or more days a week, 2.9% about once a week, 22.6% less than once a week and 68.1% never. The coping strategies that the participants employed for terminating their victimization included: a) adult support seeking, b) emotional coping/keep away from school, c) keeping healthy and fit, d) demonstrating a positive attitude towards the bully, d) peer support seeking, e) emotional out bursting, f) wishful thinking and self-blaming, g) pretending as if it is not happening, h) displaying assertive behaviors and i) getting away from the bullies. Bullied from non-bullied children did not differ as much in coping, as in feelings of being rejected in school. Discussion: The findings are in accordance with accumulated research evidence which points to a strong relationship between student perceptions of school belongingness and their involvement in bullying behaviors. We agree with the view that a positive school climate is likely to serve as a buffer that mitigates wider adverse societal influences and institutional attitudes which favor violence and harassment among peers.

Keywords: school bullying, school belonging, student coping strategies, victimization

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Authors: Betul Altinok

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The aim of this study is researching servant leadership behaviors of sports managers. For this purpose, Servant Leadership behaviors of Sport Managers (N=69) working as Dean, School Principal and Head of Department in Sport Sciences Faculties, Physical Education and Sport Schools and Departments educating Physical Education and Sport investigated via questionnaires applied to academicians (N=1185) working in these institutions. Servant Leadership Questionnaire sent via e-mail to all Academicians working in Physical Education and Sport educating Faculties, Schools of Universities and Departments in Turkey. 406 survey which is responded and accurately completed by Academicians were evaluated. In this study, Servant Leadership Questionnaire developed and conducted validity and reliability analysis by Barbuto and Wheeler (2006) used to investigate sports managers servant leadership behaviors. Scale translated into Turkish then validity and reliability analysis were conducted. After measurement model of servant leadership questionnaire verified, Shapiro Wilk normality test was applied to obtained data to determine whether has got a normal distribution or not, depending on gender, job title, profession time, department and evaluated manager. Results of practiced normality test showed that data has not got a normal distribution (nonparametric). After normality test, Mann Whitney-U test applied at 0.05 value for determining whether there is a difference between servant leadership scores according to gender and Kruskal Wallis Test applied at 0.05 value for determining whether there is a difference between servant leadership scores according to job title, profession time, department and evaluated manager. Test results showed that there were not differences between Altruistic Calling (p>0.05), Emotional Healing (p>0.05), Wisdom (p>0.05), Persuasive Mapping (p>0.05) and (p>0.05), Organizational Stewardship sub-dimensions according to gender. Test results showed that there were not differences between Altruistic Calling (p>0.05), Emotional Healing (p>0.05), Wisdom (p>0.05), Persuasive Mapping (p>0.05) and (p>0.05), Organizational Stewardship sub-dimensions according to job title, profession time, department and evaluated manager. In the light of study results, it can be said that applied survey is objective and unfurls evaluated managers servant leadership behaviors. Empirical and practical contribution of this study is to test sports managers servant leadership behaviors in Turkey for the very first time.

Keywords: academicians, management, servant leadership, sport

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Authors: Rosa S. Wong, Keith T.S. Tung, Winnie W. Y. Tso, King-Wa Fu, Nirmala Rao, Patrick Ip

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Electronic devices have become an essential part of our lives. Various reports have highlighted the alarming usage of electronic devices at early ages and its long-term developmental consequences. More sedentary screen time was associated with increased adiposity, worse cognitive and motor development, and psychosocial health. Apart from the problems caused by children’s own screen time, parents today are often paying less attention to their children due to hand-held device. Some anecdotes suggest that distracted parenting has negative impact on parent-child relationship. This study examined whether distracted parenting detrimentally affected parent-child activities which may, in turn, impair children’s psychosocial health. In 2018/19, we recruited a cohort of preschoolers from 32 local kindergartens in Tin Shui Wai and Sham Shui Po for a 5-year programme aiming to build stronger foundations for children from disadvantaged backgrounds through an integrated support model involving medical, education and social service sectors. A comprehensive set of questionnaires were used to survey parents on their frequency of being distracted while parenting and their frequency of learning and recreational activities with children. Furthermore, they were asked to report children’s screen time amount and their psychosocial problems. Mediation analyses were performed to test the direct and indirect effects of electronic device-distracted parenting on children’s psychosocial problems. This study recruited 873 children (448 females and 425 males, average age: 3.42±0.35). Longer screen time was associated with more psychosocial difficulties (Adjusted B=0.37, 95%CI: 0.12 to 0.62, p=0.004). Children’s screen time positively correlated with electronic device-distracted parenting (r=0.369, p < 01). We also found that electronic device-distracted parenting was associated with more hyperactive/inattentive problems (Adjusted B=0.66, p < 0.01), fewer prosocial behavior (Adjusted B=-0.74, p < 0.01), and more emotional symptoms (Adjusted B=0.61, p < 0.001) in children. Further analyses showed that electronic device-distracted parenting exerted influences both directly and indirectly through parent-child interactions but to different extent depending upon the outcome under investigation (38.8% for hyperactivity/inattention, 31.3% for prosocial behavior, and 15.6% for emotional symptoms). We found that parents’ use of devices and children’s own screen time both have negative effects on children’s psychosocial health. It is important for parents to set “device-free times” each day so as to ensure enough relaxed downtime for connecting with children and responding to their needs.

Keywords: early childhood, electronic device, psychosocial wellbeing, parenting

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Authors: Aishi Kundu, Elhan Raza

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"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

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Authors: Ani Wilson Uchenna, Ogbonna Chikodi

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This study examined the impact of artificial intelligence driven applications on banks’ performance in West Africa using Nigeria and Ghana as case studies. Specifically, the study examined the extent to which deployment of smart automated teller machine impacts the banks’ net worth within the reference period in Nigeria and Ghana. It ascertained the impact of point of sale on banks’ net worth within the reference period in Nigeria and Ghana. Thirdly, it verified the extent to which webpay services can influence banks’ performance in Nigeria and Ghana and finally, determined the impact of mobile pay services on banks’ performance in Nigeria and Ghana. The study used automated teller machine (ATM), Point of sale services (POS), Mobile pay services (MOP) and Web pay services (WBP) as proxies for explanatory variables while Bank net worth was used as explained variable for the study. The data for this study were sourced from central bank of Nigeria (CBN) Statistical Bulletin as well as Bank of Ghana (BoGH) Statistical Bulletin, Ghana payment systems oversight annual report and world development indicator (WDI). Furthermore, the mixed order of integration observed from the panel unit test result justified the use of autoregressive distributed lag (ARDL) approach to data analysis which the study adopted. While the cointegration test showed the existence of cointegration among the studied variables, bound test result justified the presence of long-run relationship among the series. Again, ARDL error correction estimate established satisfactory (13.92%) speed of adjustment from long run disequilibrium back to short run dynamic relationship. The study found that while Automated teller machine (ATM) had statistically significant impact on bank net worth (BNW) of Nigeria and Ghana, point of sale services application (POS) statistically and significantly impact on bank net worth within the study period, mobile pay services application was statistically significant in impacting the changes in the bank net worth of the countries of study while web pay services (WBP) had no statistically significant impact on bank net worth of the countries of reference. The study concluded that artificial intelligence driven application have significant an positive impact on bank performance with exception of web pay which had negative impact on bank net worth. The study recommended that management of banks both in Nigerian and Ghanaian should encourage more investments in AI-powered smart ATMs aimed towards delivering more secured banking services in order to increase revenue, discourage excessive queuing in the banking hall, reduced fraud and minimize error in processing transaction. Banks within the scope of this study should leverage on modern technologies to checkmate the excesses of the private operators POS in order to build more confidence on potential customers. Government should convert mobile pay services to a counter terrorism tool by ensuring that restrictions on over-the-counter withdrawals to a minimum amount is maintained and place sanctions on withdrawals above that limit.

Keywords: artificial intelligence (ai), bank performance, automated teller machines (atm), point of sale (pos)

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Authors: Mohammed Seyam, Faridah Othman, Ahmed El-Shafie

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The early warning of high stream flow events (HSF) and floods is an important aspect in the management of surface water and rivers systems. This process can be performed using either process-based models or data driven-based models such as artificial intelligence (AI) techniques. The main goal of this study is to develop efficient AI-based model for predicting the real-time hourly stream flow (Q) and apply it as early warning tool of HSF and floods in the downstream area of the Selangor River basin, taken here as a paradigm of humid tropical rivers in Southeast Asia. The performance of AI-based models has been improved through the integration of the lag time (Lt) estimation in the modelling process. A total of 8753 patterns of Q, water level, and rainfall hourly records representing one-year period (2011) were utilized in the modelling process. Six hydrological scenarios have been arranged through hypothetical cases of input variables to investigate how the changes in RF intensity in upstream stations can lead formation of floods. The initial SF was changed for each scenario in order to include wide range of hydrological situations in this study. The performance evaluation of the developed AI-based model shows that high correlation coefficient (R) between the observed and predicted Q is achieved. The AI-based model has been successfully employed in early warning throughout the advance detection of the hydrological conditions that could lead to formations of floods and HSF, where represented by three levels of severity (i.e., alert, warning, and danger). Based on the results of the scenarios, reaching the danger level in the downstream area required high RF intensity in at least two upstream areas. According to results of applications, it can be concluded that AI-based models are beneficial tools to the local authorities for flood control and awareness.

Keywords: floods, stream flow, hydrological modelling, hydrology, artificial intelligence

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Authors: Mehrzad Soltani, Pegah Rezaei

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In response to the escalating challenges posed by resource scarcity, urban congestion, and the dearth of green spaces, contemporary urban areas have undergone a remarkable transformation into smart cities. This evolution necessitates a strategic and forward-thinking approach to urban development, with the primary objective of diminishing and eventually eradicating dependence on non-renewable energy sources. This steadfast commitment to sustainable development is geared toward the continual enhancement of our global urban milieu, ensuring a healthier and more prosperous environment for forthcoming generations. This transformative vision has been meticulously shaped by an extensive research framework, incorporating in-depth field studies and investigations conducted at both neighborhood and city levels. Our holistic strategy extends its purview to encompass major cities and states, advocating for the realization of exceptional development firmly rooted in the principles of sustainable intelligence. At its core, this approach places a paramount emphasis on stringent pollution control measures, concurrently safeguarding ecological equilibrium and regional cohesion. Central to the realization of this vision is the widespread adoption of environmentally friendly materials and components, championing the cultivation of plant life and harmonious green spaces, and the seamless integration of intelligent lighting and irrigation systems. These systems, including solar panels and solar energy utilization, are deployed wherever feasible, effectively meeting the essential lighting and irrigation needs of these dynamic urban ecosystems. Overall, the transformation of urban areas into smart cities necessitates a holistic and innovative approach to urban development. By actively embracing sustainable intelligence and adhering to strict environmental standards, these cities pave the way for a brighter and more sustainable future, one that is marked by resilient, thriving, and eco-conscious urban communities.

Keywords: smart city, green urban, sustainability, urban management

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Authors: Stephanie Al Otaiba, Kyle Roberts

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Background and Significance Globally far too many students read below grade level; thus improving literacy outcomes is vital. Research suggests that non-cognitive factors, including Social and Emotional Learning (SEL) are linked to success in literacy outcomes. Converging evidence exists that early interventions are more effective than later remediation; therefore teachers need strategies to support early literacy while developing students’ SEL and their vocabulary, or language, for learning. This presentation describe findings from a US federally-funded project that trained teachers to provide an evidence-based read-aloud program for young children, using commercially available books with multicultural characters and themes to help their students “GROW”. The five GROW SEL themes include: “I can name my feelings”, “I can learn from my mistakes”, “I can persist”, “I can be kind to myself and others”, and “I can work toward and achieve goals”. Examples of GROW vocabulary (from over 100 words taught across the 5 units) include: emotions, improve, resilient, cooperate, accomplish, responsible, compassion, adapt, achieve, analyze. Methodology This study used a mixed methods research design, with qualitative methods to describe data from teacher feedback surveys (regarding satisfaction, feasibility), observations of fidelity of implementation, and with quantitative methods to assess the effect sizes for student vocabulary growth. GROW Intervention and Teacher Training Procedures Researchers trained classroom teachers to implement GROW. Each thematic unit included four books, vocabulary cards with images of the vocabulary words, and scripted lessons. Teacher training included online and in-person training; researchers incorporated virtual reality videos of instructors with child avatars to model lessons. Classroom teachers provided 2-3 20 min lessons per week ranging from short-term (8 weeks) to longer-term trials for up to 16 weeks. Setting and Participants The setting for the study included two large urban charter schools in the South. Data was collected across two years; during the first year, participants included 7 kindergarten teachers and 108 and the second year involved an additional set of 5 kindergarten and first grade teachers and 65 students. Initial Findings The initial qualitative findings indicate teachers reported the lessons to be feasible to implement and they reported that students enjoyed the books. Teachers found the vocabulary words to be challenging and important. They were able to implement lessons with fidelity. Quantitative analyses of growth for each taught word suggest that students’ growth on taught words ranged from large (ES = .75) to small (<.20). Researchers will contrast the effects for more and less successful books within the GROW units. Discussion and Conclusion It is feasible for teachers of young students to effectively teach SEL vocabulary and themes during shared book reading. Teachers and students enjoyed the books and students demonstrated growth on taught vocabulary. Researchers will discuss implications of the study and about the GROW program for researchers in learning sciences, will describe some limitations about research designs that are inherent in school-based research partnerships, and will provide some suggested directions for future research and practice.

Keywords: early literacy, learning science, language and vocabulary, social and emotional learning, multi-cultural

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Authors: Timothy Kephart

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Many law enforcement officials believe that gangs communicate messages to both the community and to rival gangs through graffiti. Some social scientists have documented this as well, however no recent research has examined gang graffiti for its underlying meaning. Empirical research on gang graffiti and gang communication through graffiti is limited. This research can be described as an exploratory effort to better understand how, and perhaps why, gangs employ this medium for communication. Furthermore this research showcases how law enforcement agencies can utilize this hidden form of communication to better direct resources and impact gang violence.

Keywords: gangs, graffiti, juvenile justice, policing

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Authors: Ezgi Şendil

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Information extracted from social media has received great attention since it has become an effective alternative for collecting people’s opinions and emotions based on specific experiences in a faster and easier way. The paper aims to put data in a meaningful way to analyze users’ posts and get a result in terms of the experiences and opinions of the users during and after natural disasters. The posts collected from Reddit are classified into nine different categories, including injured/dead people, infrastructure and utility damage, missing/found people, donation needs/offers, caution/advice, and emotional support, identified by using labelled Twitter data and four different machine learning (ML) classifiers.

Keywords: disaster, NLP, postdisaster management, sentiment analysis

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Authors: Nawras Kurzom, Avi Mendelsohn

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The effects of background music on learning and memory are inconsistent, partly due to the intrinsic complexity and variety of music and partly to individual differences in music perception and preference. A prominent musical feature that is known to elicit strong emotional responses is musical tension. Musical tension can be brought about by building anticipation of rhythm, harmony, melody, and dynamics. Delaying the resolution of dominant-to-tonic chord progressions, as well as using dissonant harmonics, can elicit feelings of tension, which can, in turn, affect memory formation of concomitant information. The aim of the presented studies was to explore how forming declarative memory is influenced by musical tension, brought about within continuous music as well as in the form of isolated chords with varying degrees of dissonance/consonance. The effects of musical tension on long-term memory of declarative information were studied in two ways: 1) by evoking tension within continuous music pieces by delaying the release of harmonic progressions from dominant to tonic chords, and 2) by using isolated single complex chords with various degrees of dissonance/roughness. Musical tension was validated through subjective reports of tension, as well as physiological measurements of skin conductance response (SCR) and pupil dilation responses to the chords. In addition, music information retrieval (MIR) was used to quantify musical properties associated with tension and its release. Each experiment included an encoding phase, wherein individuals studied stimuli (words or images) with different musical conditions. Memory for the studied stimuli was tested 24 hours later via recognition tasks. In three separate experiments, we found positive relationships between tension perception and physiological measurements of SCR and pupil dilation. As for memory performance, we found that background music, in general, led to superior memory performance as compared to silence. We detected a trade-off effect between tension perception and memory, such that individuals who perceived musical tension as such displayed reduced memory performance for images encoded during musical tension, whereas tense music benefited memory for those who were less sensitive to the perception of musical tension. Musical tension exerts complex interactions with perception, emotional responses, and cognitive performance on individuals with and without musical training. Delineating the conditions and mechanisms that underlie the interactions between musical tension and memory can benefit our understanding of musical perception at large and the diverse effects that music has on ongoing processing of declarative information.

Keywords: musical tension, declarative memory, learning and memory, musical perception

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Authors: Yunbin Deng

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Most existing applications of automatic speech recognition relies on cooperative subjects at a short distance to a microphone. Standoff speech recognition using microphone arrays can extend the subject to sensor distance somewhat, but it is still limited to only a few feet. As such, most deployed applications of standoff speech recognitions are limited to indoor use at short range. Moreover, these applications require air passway between the subject and the sensor to achieve reasonable signal to noise ratio. This study reports long range (50 feet) automatic speech recognition experiments using a Laser Doppler Vibrometer (LDV) sensor. This study shows that the LDV sensor modality can extend the speech acquisition standoff distance far beyond microphone arrays to hundreds of feet. In addition, LDV enables 'listening' through the windows for uncooperative subjects. This enables new capabilities in automatic audio and speech intelligence, surveillance, and reconnaissance (ISR) for law enforcement, homeland security and counter terrorism applications. The Polytec LDV model OFV-505 is used in this study. To investigate the impact of different vibrating materials, five parallel LDV speech corpora, each consisting of 630 speakers, are collected from the vibrations of a glass window, a metal plate, a plastic box, a wood slate, and a concrete wall. These are the common materials the application could encounter in a daily life. These data were compared with the microphone counterpart to manifest the impact of various materials on the spectrum of the LDV speech signal. State of the art deep neural network modeling approaches is used to conduct continuous speaker independent speech recognition on these LDV speech datasets. Preliminary phoneme recognition results using time-delay neural network, bi-directional long short term memory, and model fusion shows great promise of using LDV for long range speech recognition. To author’s best knowledge, this is the first time an LDV is reported for long distance speech recognition application.

Keywords: covert speech acquisition, distant speech recognition, DSR, laser Doppler vibrometer, LDV, speech intelligence surveillance and reconnaissance, ISR

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Authors: Warnakulasuriya Arachichige Malisha Ann Rosary Fernando, Udalamatta Gamage Omila Chalanka Jinadasa, Bihini Pabasara Amandi Amarasinghe, Manul Thisuraka Mandalawatta, Uthpala Samarakoon, Manori Gamage

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The surge in sexually transmitted diseases (STDs) has become a critical public health crisis demanding urgent attention and action. Like many other nations, Sri Lanka is grappling with a significant increase in STDs due to a lack of education and awareness regarding their dangers. Presently, the available applications for tracking and managing STDs cover only a limited number of easily detectable infections, resulting in a significant gap in effectively controlling their spread. To address this gap and combat the rising STD rates, it is essential to leverage technology and data. Employing technology to enhance the tracking and management of STDs is vital to prevent their further propagation and to enable early intervention and treatment. This requires adopting a comprehensive approach that involves raising public awareness about the perils of STDs, improving access to affordable healthcare services for early detection and treatment, and utilizing advanced technology and data analysis. The proposed mobile application aims to cater to a broad range of users, including STD patients, recovered individuals, and those unaware of their STD status. By harnessing cutting-edge technologies like image detection, symptom-based identification, prevention methods, doctor and clinic recommendations, and virtual counselor chat, the application offers a holistic approach to STD management. In conclusion, the escalating STD rates in Sri Lanka and across the globe require immediate action. The integration of technology-driven solutions, along with comprehensive education and healthcare accessibility, is the key to curbing the spread of STDs and promoting better overall public health.

Keywords: STD, machine learning, NLP, artificial intelligence

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Authors: Hamda Ajmal, Karen Young, Ruth Melia, John Bogue, Mary O'Sullivan, Jim Duggan, Hannah Wood

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The Covid-19 pandemic led to a range of public health mitigation strategies in order to suppress the SARS-CoV-2 virus. The drastic changes in everyday life due to lockdowns had the potential for a significant negative impact on public mental health, and a key public health goal is to now assess the evidence from available Irish datasets to provide useful insights on this issue. Text-50808 is an online text-based mental health support service, established in Ireland in 2020, and can provide a measure of revealed distress and mental health concerns across the population. The aim of this study is to explore statistical associations between public mental health in Ireland and the Covid-19 pandemic. Uniquely, this study combines two measures of emotional wellbeing in Ireland: (1) weekly text volume at Text-50808, and (2) emotional wellbeing indicators reported by respondents of the Amárach public opinion survey, carried out on behalf of the Department of Health, Ireland. For this analysis, a multivariate graphical exploratory data analysis (EDA) was performed on the Text-50808 dataset dated from 15th June 2020 to 30th June 2021. This was followed by time-series analysis of key mental health indicators including: (1) the percentage of daily/weekly texts at Text-50808 that mention Covid-19 related issues; (2) the weekly percentage of people experiencing anxiety, boredom, enjoyment, happiness, worry, fear and stress in Amárach survey; and Covid-19 related factors: (3) daily new Covid-19 case numbers; (4) daily stringency index capturing the effect of government non-pharmaceutical interventions (NPIs) in Ireland. The cross-correlation function was applied to measure the relationship between the different time series. EDA of the Text-50808 dataset reveals significant peaks in the volume of texts on days prior to level 3 lockdown and level 5 lockdown in October 2020, and full level 5 lockdown in December 2020. A significantly high positive correlation was observed between the percentage of texts at Text-50808 that reported Covid-19 related issues and the percentage of respondents experiencing anxiety, worry and boredom (at a lag of 1 week) in Amárach survey data. There is a significant negative correlation between percentage of texts with Covid-19 related issues and percentage of respondents experiencing happiness in Amárach survey. Daily percentage of texts at Text-50808 that reported Covid-19 related issues to have a weak positive correlation with daily new Covid-19 cases in Ireland at a lag of 10 days and with daily stringency index of NPIs in Ireland at a lag of 2 days. The sudden peaks in text volume at Text-50808 immediately prior to new restrictions in Ireland indicate an association between a rise in mental health concerns following the announcement of new restrictions. There is also a high correlation between emotional wellbeing variables in the Amárach dataset and the number of weekly texts at Text-50808, and this confirms that Text-50808 reflects overall public sentiment. This analysis confirms the benefits of the texting service as a community surveillance tool for mental health in the population. This initial EDA will be extended to use multivariate modeling to predict the effect of additional Covid-19 related factors on public mental health in Ireland.

Keywords: COVID-19 pandemic, data analysis, digital health, mental health, public health, digital health

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Authors: Chyong-Ru Liu, Wen-Shiung Huang, Wan-Ching Tang, Shan-Pei Chen

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Two of the biggest challenges companies involved in sports and exercise information services face are how to strengthen participation in virtual sports/exercise communities and how to increase the ongoing participatoriness of those communities. In the past, relatively little research has explored mechanisms for strengthening alignment practice and community response from the perspective of self-construal, and as such this study seeks to explore the self-construal of virtual sports/exercise communities, the role it plays in the emotional commitment of forming communities, and the factor that can strengthen alignment practice. Moreover, which factor of the emotional commitment of forming virtual communities have the effect of strengthening interference in the process of transforming customer citizenship behaviors? This study collected 625 responses from the two leading websites in terms of fan numbers in the provision of information on road race and marathon events in Taiwan, with model testing conducted through linear structural equation modelling and the bootstrapping technique to test the proposed hypotheses. The results proved independent construal had a stronger positive direct effect on affective commitment to fellow customers than did interdependent construal, and the influences of affective commitment to fellow customers in enhancing customer citizenship behavior. Public self-consciousness moderates the relationships among independent self-construal and interdependent self-construal on effective commitment to fellow customers. Perceived playfulness moderates the relationships between effective commitment to fellow customers and customer citizenship behavior. The findings of this study provide significant insights for the researchers and related organizations. From the theoretical perspective, this is empirical research that investigated the self-construal theory and responses (i.e., affective commitment to fellow customers, customer citizenship behavior) in virtual sports/exercise communities. We further explore how to govern virtual sports/exercise community participants’ heterogeneity through public self-consciousness mechanism to align participants’ affective commitment. Moreover, perceived playfulness has the effect of strengthening effective commitment to fellow customers with customer citizenship behaviors. The results of this study can provide a foundation for the construction of future theories and can be provided to related organizations for reference in their planning of virtual communities.

Keywords: self-construal theory, public self-consciousness, affective commitment, customer citizenship behavior

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Authors: Ghassan Kbar, Shady Aly, Ibrahim Alsharawy, Akshay Bhatia, Nur Alhasan, Ronaldo Enriquez

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The Smart Help for persons with disability (PWD) is a part of the project SMARTDISABLE which aims to develop relevant solution for PWD that target to provide an adequate workplace environment for them. It would support PWD needs smartly through smart help to allow them access to relevant information and communicate with other effectively and flexibly, and smart editor that assist them in their daily work. It will assist PWD in knowledge processing and creation as well as being able to be productive at the work place. The technical work of the project involves design of a technological scenario for the Ambient Intelligence (AmI) - based assistive technologies at the workplace consisting of an integrated universal smart solution that suits many different impairment conditions and will be designed to empower the Physically disabled persons (PDP) with the capability to access and effectively utilize the ICTs in order to execute knowledge rich working tasks with minimum efforts and with sufficient comfort level. The proposed technology solution for PWD will support voice recognition along with normal keyboard and mouse to control the smart help and smart editor with dynamic auto display interface that satisfies the requirements for different PWD group. In addition, a smart help will provide intelligent intervention based on the behavior of PWD to guide them and warn them about possible misbehavior. PWD can communicate with others using Voice over IP controlled by voice recognition. Moreover, Auto Emergency Help Response would be supported to assist PWD in case of emergency. This proposed technology solution intended to make PWD very effective at the work environment and flexible using voice to conduct their tasks at the work environment. The proposed solution aims to provide favorable outcomes that assist PWD at the work place, with the opportunity to participate in PWD assistive technology innovation market which is still small and rapidly growing as well as upgrading their quality of life to become similar to the normal people at the workplace. Finally, the proposed smart help solution is applicable in all workplace setting, including offices, manufacturing, hospital, etc.

Keywords: ambient intelligence, ICT, persons with disability PWD, smart application, SHW

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Authors: Ekrem Aksoy, Nihat Adar, Selçuk Canbek

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Ambient Computing or Ambient Intelligence (AmI) is emerging area in computer science aiming to create intelligently connected environments and Internet of Things. In this paper, we propose communication middleware architecture for AmI. This middleware architecture addresses problems of communication, networking, and abstraction of applications, although there are other aspects (e.g. HCI and Security) within general AmI framework. Within this middleware architecture, any application developer might address HCI and Security issues with extensibility features of this platform.

Keywords: AmI, ambient computing, middleware, distributed-systems, software-defined networking

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Authors: Reshma Parvin Nuri, Heather Michelle Aldersey, Setareh Ghahari

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Objectives: Worldwide, it is well known that taking care of children with disabilities (CWD) can have a significant impact on the entire family unit. Over the last few decades, an increased number of studies have been conducted on families of CWD in higher income countries, and much of this research has identified family needs and strategies to meet those needs. However, family needs are incredibly under-studied in developing countries. Therefore, the aims of this study were to: (a) explore the needs of families of children with cerebral palsy (CP) in Bangladesh; (b) investigate how some of the family needs have been met and (c) identify the sources of supports that might help the families to meet their needs in the future. Methods: A face to face, semi-structured in-depth interview was conducted with 20 family members (12 mothers, 4 fathers, 1 sister, 2 grandmothers, and 1 aunt) who visited the Centre for the Rehabilitation of the Paralysed (CRP), Bangladesh between June and August 2016. Constant comparison method of grounded theory approach within the broader spectrum of qualitative study was used to analyze the data. Results: Participants identified five categories of needs: (a) financial needs, (b) access to disability-related services, (c) family and community cohesion, (d) informational needs, and (e) emotional needs. Participants overwhelmingly reported that financial need is their greatest family need. Participants noted that families encountered additional financial expenses for a child with CP, beyond what they would typically pay for their other children. Participants were seeing education as their non-primary need as they had no hope that their children would be physically able to go to school. Some participants also shared their needs for social inclusion and participation and receiving emotional support. Participants further expressed needs to receive information related to the child’s health condition and availability/accessibility of governmental support programs. Besides unmet needs, participants also highlighted that some of their needs have been met through formal and informal support systems. Formal support systems were mainly institution-based and run by non-governmental organizations, whereas participants identified informal support coming from family, friends and community members. Participants overwhelmingly reported that they receive little to no support from the government. However, participants identified the government as the key stakeholder who can play vital role in meeting their unmet needs. Conclusions: In the next phase of this research, the plan is to understand how the Government of the People’s Republic of Bangladesh is working to meet the needs of families of CWD. There is also need for further study on needs of families of children with conditions other than CP and those who live in the community and do not have access to the CRP Services. There is clear need to investigate ways to enable children with CP have better access to education in Bangladesh.

Keywords: Bangladesh, children with cerebral palsy, family needs, support

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Authors: Somnath Bhattachryya, Montree Bunruangses, Somchat Sonasang, Preecha Yupapin

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In this study and research on meditation and insight, the design and experiment with electronic circuits to manipulate the meditators' mental circles that call the chakras to have the same size is proposed. The shape of the circuit is 4-ports, called an add-drop multiplexer, that studies the meditation structure called the four-mindfulness foundation, then uses an AC power signal as an input instead of the meditation time function, where various behaviors with the method of re-filtering the signal (successive filtering), like eight noble paths. Start by inputting a signal at a frequency that causes the velocity of the wave on the perimeter of the circuit to cause particles to have the speed of light in a vacuum. The signal changes from electromagnetic waves and matter waves according to the velocity (frequency) until it reaches the point of the relativistic limit. The electromagnetic waves are transformed into photons with properties of wave-particle overcoming the limits of the speed of light. As for the matter wave, it will travel to the other side and cannot pass through the relativistic limit, called a shadow signal (echo) that can have power from increasing speed but cannot create speed faster than light or insight. In the experiment, the only the side where the velocity is positive, only where the speed above light or the corresponding frequency indicates intelligence. Other side(echo) can be done by changing the input signal to the other side of the circuit to get the same result. But there is no intelligence or speed beyond light. It is also used to study the stretching, contraction of time and wormholes that can be applied for teleporting, Bose-Einstein condensate and teleprinting, quantum telephone. The teleporting can happen throughout the system with wave-particle and echo, which is when the speed of the particle is faster than the stretching or contraction of time, the particle will submerge in the wormhole, when the destination and time are determined, will travel through the wormhole. In a wormhole, time can determine in the future and the past. The experimental results using the microstrip circuit have been found to be by the principle of quantum relativity, which can be further developed for both tools and meditation practitioners for quantum technology.

Keywords: quantu meditation, insight picture, quantum circuit, absolute time, teleportation

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Authors: László Schmidt

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Technological and digital innovation is constantly and dynamically evolving, which poses an enormous challenge to both lawmaking and law enforcement. To legislation because artificial intelligence permeates many areas of people’s daily lives that the legislator must regulate. it can see how challenging it is to regulate e.g. self-driving cars/taxis/camions etc. Not to mention cryptocurrencies and Chat GPT, the use of which also requires legislative intervention. Artificial intelligence also poses an extraordinary challenge to law enforcement. In criminal cases, police and prosecutors can make great use of AI in investigations, e.g. in forensics, DNA samples, reconstruction, identification, etc. But it can also be of great help in the detection of crimes committed in cyberspace. In the case of cybercrime, on the one hand, it can be viewed as a new type of crime that can only be committed with the help of information systems, and that has a specific protected legal object, such as an information system or data. On the other hand, it also includes traditional crimes that are much easier to commit with the help of new tools. According to Hungarian Criminal Code section 375 (1), any person who, for unlawful financial gain, introduces data into an information system, or alters or deletes data processed therein, or renders data inaccessible, or otherwise interferes with the functioning of the information system, and thereby causes damage, is guilty of a felony punishable by imprisonment not exceeding three years. The Covid-19 coronavirus epidemic has had a significant impact on our lives and our daily lives. It was no different in the world of crime. With people staying at home for months, schools, restaurants, theatres, cinemas closed, and no travel, criminals have had to change their ways. Criminals were committing crimes online in even greater numbers than before. These crimes were very diverse, ranging from false fundraising, the collection and misuse of personal data, extortion to fraud on various online marketplaces. The most vulnerable age groups (minors and elderly) could be made more aware and prevented from becoming victims of this type of crime through targeted programmes. The aim of the study is to show the Hungarian judicial practice in relation to cybercrime and possible preventive solutions.

Keywords: cybercrime, COVID-19, Hungary, criminal law

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Yanting Cao, Kazumitsu Nawata

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As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained.

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

Procedia PDF Downloads 141

Authors: Noor Ismael, Lisa Mische Lawson, Lauren Little, Murad Moqbel

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Background/Significance: Caregivers of children with Autism Spectrum Disorders (ASD) develop coping mechanisms to overcome daily challenges to successfully parent their child. There is variability in coping strategies used among caregivers of children with ASD. Capturing homogeneity among such variable groups may help elucidate targeted intervention approaches for caregivers of children with ASD. Study Purpose: This study aimed to identify groups of caregivers of children with ASD based on coping mechanisms, and to examine whether there are differences among these groups in terms of strain level. Methods: This study utilized a secondary data analysis, and included survey responses of 273 caregivers of children with ASD. Measures consisted of the COPE Inventory and the Caregiver Strain Questionnaire. Data analyses consisted of cluster analysis to group caregiver coping strategies, and analysis of variance to compare the caregiver coping groups on strain level. Results: Cluster analysis results showed four distinct groups with different combinations of coping strategies: Social-Supported/Planning (group one), Spontaneous/Reactive (group two), Self-Supporting/Reappraisal (group three), and Religious/Expressive (group four). Caregivers in group one (Social-Supported/Planning) demonstrated significantly higher levels than the remaining three groups in the use of the following coping strategies: planning, use of instrumental social support, and use of emotional social support, relative to the other three groups. Caregivers in group two (Spontaneous/Reactive) used less restraint relative to the other three groups, and less suppression of competing activities relative to the other three groups as coping strategies. Also, group two showed significantly lower levels of religious coping as compared to the other three groups. In contrast to group one, caregivers in group three (Self-Supporting/Reappraisal) demonstrated significantly lower levels of the use of instrumental social support and the use of emotional social support relative to the other three groups. Additionally, caregivers in group three showed more acceptance, positive reinterpretation and growth coping strategies. Caregivers in group four (Religious/Expressive) demonstrated significantly higher levels of religious coping relative to the other three groups and utilized more venting of emotions strategies. Analysis of Variance results showed no significant differences between the four groups on the strain scores. Conclusions: There are four distinct groups with different combinations of coping strategies: Social-Supported/Planning, Spontaneous/Reactive, Self-Supporting/Reappraisal, and Religious/Expressive. Each caregiver group engaged in a combination of coping strategies to overcome the strain of caregiving.

Keywords: autism, caregivers, cluster analysis, coping strategies

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Authors: Veslava Osinska, Adam Szalach, Dominik Piotrowski

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Contemporary VR technologies allow users to explore virtual 3D spaces where they can work, socialize, learn, and play. User's interaction with GUI and the pictures displayed implicate perceptual and also cognitive processes which can be monitored due to neuroadaptive technologies. These modalities provide valuable information about the users' intentions, situational interpretations, and emotional states, to adapt an application or interface accordingly. Virtual galleries outfitted by specialized assets have been designed using the Unity engine BITSCOPE project in the frame of CHIST-ERA IV program. Users interaction with gallery objects implies the questions about his/her visual interests in art works and styles. Moreover, an attention, curiosity, and other emotional states are possible to be monitored and analyzed. Natural gaze behavior data and eye position were recorded by built-in eye-tracking module within HTC Vive headset gogle for VR. Eye gaze results are grouped due to various users’ behavior schemes and the appropriate perpetual-cognitive styles are recognized. Parallelly usability tests and surveys were adapted to identify the basic features of a user-centered interface for the virtual environments across most of the timeline of the project. A total of sixty participants were selected from the distinct faculties of University and secondary schools. Users’ primary knowledge about art and was evaluated during pretest and this way the level of art sensitivity was described. Data were collected during two months. Each participant gave written informed consent before participation. In data analysis reducing the high-dimensional data into a relatively low-dimensional subspace ta non linear algorithms were used such as multidimensional scaling and novel technique technique t-Stochastic Neighbor Embedding. This way it can classify digital art objects by multi modal time characteristics of eye tracking measures and reveal signatures describing selected artworks. Current research establishes the optimal place on aesthetic-utility scale because contemporary interfaces of most applications require to be designed in both functional and aesthetical ways. The study concerns also an analysis of visual experience for subsamples of visitors, differentiated, e.g., in terms of frequency of museum visits, cultural interests. Eye tracking data may also show how to better allocate artefacts and paintings or increase their visibility when possible.

Keywords: eye tracking, VR, UX, visual art, virtual gallery, visual communication

Procedia PDF Downloads 42