Search results for: social learning

Authors: Justin D. Olmanson, Fitsum Abebe, Valerie Jones, Eric Kyle, Xianquan Liu, Katherine Robbins, Guieswende Rouamba

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The history of education technology--and designing, adapting, and adopting technologies for use in educational spaces--is nuanced, complex, and dynamic. Yet, despite a range of continually emerging technologies, the design and development process often yields results that appear quite similar in terms of affordances and interactions. Through this study we (1) verify the extent to which designs have been constrained, (2) consider what might account for it, and (3) offer a way forward in terms of how we might identify and strategically sidestep these influences--thereby increasing the diversity of our designs with a given technology or within a particular learning domain. We begin our inquiry from the perspective that a host of co-influencing elements, fields, and meta narratives converge on the education technology design process to exert a tangible, often homogenizing effect on the resultant designs. We identify several elements that influence design in often implicit or unquestioned ways (e.g. curriculum, learning theory, economics, learning context, pedagogy), we describe our methodology for identifying the elemental positionality embedded in a design, we direct our analysis to a particular subset of technologies in the field of literacy, and unpack our findings. Our early analysis suggests that the majority of education technologies designed for use/used in US public schools are heavily influenced by a handful of mainstream theories and meta narratives. These findings have implications for how we approach the education technology design process--which we use to suggest alternative methods for designing/ developing with emerging technologies. Our analytical process and re conceptualized design process hold the potential to diversify the ways emerging and established technologies get incorporated into our designs.

Keywords: curriculum, design, innovation, meta narratives

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Authors: Marie E. Docken

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At some point in the lifecycle of a country, patterns of political and social unrest of varying degrees are observed. Events involving public disorder or civil disobedience may produce effects that range a wide spectrum of varying outcomes, depending on the level of unrest. Many previous studies, primarily theoretical in nature, have attempted to measure public disorder in answering why or how it occurs in society by examining causal factors or underlying issues in the social or political position of a population. The main objective in doing so is to understand how these activities evolve or seek some predictive capability for the events. In contrast, this research involves the fusion of analytics and social studies to provide more knowledge of the public disorder and civil disobedience intensity in populations. With a greater understanding of the magnitude of these events, it is believed that we may learn how they relate to extreme actions such as mass migration or violence. Upon establishing a model for measuring civil unrest based upon empirical data, a case study on various Latin American countries is performed. Interpretations of historical events are combined with analytical results to provide insights regarding the magnitude and effect of social and political activism.

Keywords: public disorder, civil disobedience, Latin America, metrics, data analysis

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Authors: Michael Brazley

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Virtual Reality (VR) and 3D videos are the most current generation of learning technology today. Virtual Reality and 3D videos are being used in professional offices and Schools now for marketing and education. Technology in the field of design has progress from two dimensional drawings to 3D models, using computers and sophisticated software. Virtual Reality is being used as collaborative means to allow designers and others to meet and communicate inside models or VR platforms using avatars. This research proposes to teach students from different backgrounds how to take a digital model into a 3D video, then into VR, and finally VR with multiple avatars communicating with each other in real time. The next step would be to develop the model where people from three or more different locations can meet as avatars in real time, in the same model and talk to each other. This research is longitudinal, studying the use of 3D videos in graduate design and Virtual Reality in XR (Extended Reality) courses. The research methodology is a combination of quantitative and qualitative methods. The qualitative methods begin with the literature review and case studies. The quantitative methods come by way of student’s 3D videos, survey, and Extended Reality (XR) course work. The end product is to develop a VR platform with multiple avatars being able to communicate in real time. This research is important because it will allow multiple users to remotely enter your model or VR platform from any location in the world and effectively communicate in real time. This research will lead to improved learning and training using Virtual Reality and Avatars; and is generalizable because most Colleges, Universities, and many citizens own VR equipment and computer labs. This research did produce a VR platform with multiple avatars having the ability to move and speak to each other in real time. Major implications of the research include but not limited to improved: learning, teaching, communication, marketing, designing, planning, etc. Both hardware and software played a major role in project success.

Keywords: virtual reality, avatars, education, XR

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Authors: Catherine Coron

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This paper first tries to scrutinize the diverse meanings and policies of inclusive education in the United Kingdom and France in the recent period thanks to a comparative analysis of the recent literature as well as the various definitions, legislation and good practices of inclusive education. The central question is to find the links between inclusion and economic wellbeing in the economic, social and cultural context of the two countries. The first part questions the economic, social and cultural meaning of the definitions thanks to a comparison between the various perspectives to envisage the notions of inclusion and wellbeing in the two countries in order to better understand the way they are interpreted according to each cultural background. The second part analyses the various policies implemented recently in order to determine the main characteristics, the differences, and the similarities, as well as the economic challenges in terms of wellbeing. The final goal of this paper is to identify the main economic, social and cultural values as regards sustainability in each country.

Keywords: education, inclusion, students with special needs, wellbeing

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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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

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Authors: Narendra Babu Bommenahalli Veerabhadrappa

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Teaching sustainability concepts along with profit maximizing philosophy of business in management education is a challenge. This paper explores and evaluates various learning models to inculcate sustainability concepts in management education. The paper explains about a new pedagogy that was tested in a business management school (Indus Business Academy, Bangalore, India) to teach sustainability. The pedagogy was designed by intertwining concepts related to sustainability with digital marketing concepts. As part of this experimental method, students (in groups) were assigned with various topics of sustainability and were asked to work with concepts of digital marketing and thus market the concepts of sustainability. The paper explains as a case study as to how sustainability was integrated with digital marketing tools and how learning towards sustainability was facilitated. It also explains the outcomes of this pedagogical method, in terms of inculcating sustainability concepts amongst management students as well as marketing and proliferation of sustainability concepts to bring about the behavioral changes amongst target audience towards sustainability.

Keywords: management-education, pedagogy, sustainability, behavior

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Authors: Jaskaran Singh Sidhu

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Aim: The purpose of this study is to find the relationship between wellness among sportsmen and non sportsmen. Methodology: The present study is an experimental study for 80 senior secondary volleyball players of 16-19 years of age from Ludhiana District of Punjab (India), and 80 non-sportsperson were taken from senior secondary school of Ludhiana district. The sample for this study was taken through a random sampling technique. Tools: A five point scale havinf 50 items was used to acess the wellness Statistical Analysis: To find out the relationship among the variables exists or not, a t-test was used to test the significance of the difference between the means. Statistics for each characteristic were calculated; Mean, Standard deviation, Standard error of Mean. Data were analyzed using SPSS (statistical package for the social sciences). Statistical significance was set at p < 0.05. Results: Substantial deviations were noted at p<0.5 in the totality of wellness. Sportsmen show significant differences exist at p<0.5 in three parameters of wellness i.e., physical wellness, mental wellness, and social wellness. In spiritual and emotional wellness attributes, non-sportsmen shows significant difference at p<0.5. Conclusion: From the data interpretation it reflects that overall wellness can be improved by participation in sports. It further noted in study that participation in sports promote the attributes of wellness i.e., physical wellness, mental wellness, emotional wellness and social wellness.

Keywords: physical, mental, social, emotional, wellness, spiritual

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Authors: Khalid Alshaikh

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In recent years, leadership, Corporate Governance (CG) and Corporate Social Responsibility (CSR) have been under scrutiny in the Libyan society. Scholars and institutions have commenced investigating the possible resolutions they can arrange to alleviate the economic, social and environmental problems the war has produced. Thus far, these constructs requisite an in-depth reinvestigation, reconceptualization, and analysis to clearly reconstruct their rules and regulations. With the demise of Qaddafi’s regime, levels, degrees, and efforts to apply CG regulations have varied in public and private commercial banks. CSR is a new organizational culture that still designs its route within these financial institutions. Detaching itself from any notion of dictatorship and autocratic traits, leadership counts on transformational and transactional styles. Therefore, this paper investigates the extent to which the Board of Directors and Chief Executive Officers (CEOs) redefine these concepts and how they entrench CSR within the framework of CG. The research methodology used both public and private banks as a case study and qualitative research to interview ten Board of Directors (BoDs) and eleven Chief executive managers to explore how leadership, CG, and CSR are defined and how leadership integrates CSR into CG structures. The findings suggest that the CG framework in Libya still requires great efforts to be developed. Full CG code implementation appears daunting. Also, the CSR is still influenced by the power of religion. Nevertheless, the Islamic perspective is more consistent with the social contract concept of the CSR. The Libyan commercial banks do not solely focus on the economic side of maximizing profits, but also concentrate on its morality. The issue is that CSR activities are not enough to achieve good charity publicly and needs strategies to address major social issues. Moreover, leadership is more transformational and transactional and endeavors to make economic, social and environmental changes, but these changes are curtailed by tradition and traditional values dominating the Libyan social life where religious and tribal practices establish the relationship between leaders and their subordinates. Finally, the findings reveal that transformational and transactional leadership styles encourage the incorporation of CSR into the CG regulations. The boardroom and executive management have such a particular role in flagging up how embedded corporate Social responsibility is in organizational culture across the commercial banks, yet it is still important that the BoDs and CEOs need to do much more to embed corporate social responsibility through their core functions. They need to boost their standing to be more influential and make sure that the right discussions about CSR happen with the right stakeholders involved.

Keywords: board of directors, chief executive officers, corporate governance, corporate social responsibility

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Authors: Doug Specht

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This research project sought to explore the links between digital media, PGIS and social movement organisations in Tolima, Colombia. The primary aim of the research was to examine how knowledge is created and disseminated through digital media and GIS in the region, and whether there exists the infrastructure to allow for this. The second strand was to ascertain if this has had a significant impact on the way grassroots movements work and produce collective actions. The third element is a hypothesis about how digital media and PGIS could play a larger role in activist activities, particularly in reference to the extractive industries. Three theoretical strands have been brought together to provide a basis for this research, namely (a) the politics of knowledge, (b) spatial management and inclusion, and (c) digital media and political engagement. Quantitative data relating to digital media and mobile internet use was collated alongside qualitative data relating to the likelihood of using digital media in activist campaigns, with particular attention being given to grassroots movements working against extractive industries in the Tolima region of Colombia. Through interviews, surveys and GIS analysis it has been possible to build a picture of online activism and the role of PPGIS within protest movement in the region of Tolima, Colombia. Results show a gap between the desires of social movements to use digital media and the skills and finances required to implement programs that utilise it. Maps and GIS are generally reserved for legal cases rather than for informing the lay person. However, it became apparent that the combination of digital/social media and PPGIS could play a significant role in supporting the work of grassroots movements.

Keywords: PGIS, GIS, social media, digital media, mining, colombia, social movements, protest

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Authors: Valmir Moratelli

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This paper discusses the construction of the narrative of television fiction from the point of view of the invisibility of the representation of elderly black characters. Through the appointment of social and contemporary elements, we analyze why the theme of old age of black people is practically non-existent in brazilian soap operas of TV Globo. By raising characteristic aspects of the leaflet narrative, we want to discuss how the cancellation of identity discourses about elderly blacks and their relationship with aspects of social life is constructed.

Keywords: audiovisual, black, erderly, television

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Authors: Camilo Cerda Sarabia, Fernanda Bravo Cornejo, Diego Santibanez Oyarce, Hugo Osses Prado, Esteban Gómez Terán, Belén Diaz Diaz, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community.

Keywords: antibiotic resistance, microbial community, unsupervised machine learning., sequences of AMR gene

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Authors: Oumaima Lahmar

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This paper aims to define a structured topography for finance researchers seeking to navigate the body of knowledge in their extrapolation of finance phenomena. To make sense of the body of knowledge in finance, a probabilistic topic modeling approach is applied on 6000 abstracts of academic articles published in three top journals in finance between 1976 and 2020. This approach combines both machine learning techniques and natural language processing to statistically identify the conjunctions between research articles and their shared topics described each by relevant keywords. The topic modeling analysis reveals 35 coherent topics that can well depict finance literature and provide a comprehensive structure for the ongoing research themes. Comparing the extracted topics to the Journal of Economic Literature (JEL) classification system, a significant similarity was highlighted between the characterizing keywords. On the other hand, we identify other topics that do not match the JEL classification despite being relevant in the finance literature.

Keywords: finance literature, textual analysis, topic modeling, perplexity

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Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

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Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

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Authors: Esmaeil Zabihi, Seyed Hossein Alavi

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The purpose of this study is the review and prioritizes of sport-centered social capital on Islamic Azad university of Garmsar student’s tendency in sports activities, especially public sports based on Bourdieu viewpoint. 365 students at of 6254 students (3140 male and 3114 female) of Islamic Azad university of Garmsar branch were selected randomly, Morgan also used. 272 person answer questionnaire (with the help of specific questionnaires moeinaldini, 1391) it has 30 questions. Researchers have confirmed the validity of this questionnaire justifiability. Reliability was achieved through Cronbach's alpha 84%. The analysis of data done by, SPSS software and independent t-tests, friedman, u Mann Whitney, Kruskal Wallis test. friedman test showed below things : environmental dimension (MR=4.71) social and economic factors (MR=3.57) and family (MR=3.47). These factors have significant role on sports activities of students (P≤1.0). Also, comparison of these dimensions showed that the men believe to university (t-4.30) in increasing students participation. These results show that environment like (such as) governmental organization, going to the stadium and life environment is the most important factor which can effect on sport and body activities of the students.

Keywords: cultural capital, social capital, Bourdieu viewpoint, sport for all

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Authors: Bharatendra Rai

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The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning.

Keywords: long short-term memory networks, convolutional recurrent networks, text classification, hyperparameter tuning, Tukey honest significant differences

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Authors: Jana Teplá

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This text focuses on the visual construction of youth in press photographs in socialist Czechoslovakia. It deals with photographs in a magazine for young readers, Mladý svět, published by the Socialist Union of Youth of Czechoslovakia. The aim of this study was to develop a methodological tool for uncovering the values and the ideological messages in the strategies used in the visual construction of reality in the socialist press. Two methods of visual analysis were applied to the photographs, a quantitative content analysis and a social semiotic analysis. The social semiotic analysis focused on images representing youth in their free time. The study shows that the meaning of a socialist press photograph is a result of a struggle for ideological power between formal and informal ideologies. This struggle takes place within the process of production of the photograph and also within the process of interpretation of the photograph.

Keywords: ideology, press photography, socialist regime, social semiotics, youth

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Authors: Genziana Lay

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A wide majority of older children and adolescents who enter the juvenile court system present with an array of problematic symptoms and behaviors including anxiety, depression, aggressive acting out, detachment, and substance abuse. Attachment theory offers a framework for understanding normative and pathological functioning, which during development is influenced by emotional, social and cognitive elements. There is clear evidence that children and adolescents with the highest risk of developing adaptation problems present an insecure attachment profile. Most offending minors have experienced dysfunctional family relationships as well as social and/or economic deprivation. Their maladaptive attachment develops not only through their relationship with caregivers but with the environment at large. Activation of their faulty attachment system leads them to feel emotionally overwhelmed and engage in destructive behaviors and decision-making. A psycho-social intervention with this population conceptualized as attachment therapy is a multi-faceted, practical approach that has shown excellent results in terms of increased psychological well-being and drastically reduced rates of re-offense/ destructive behavior. Through several; components including psychotherapy, monitoring, volunteering, meditation and socialization, the program focuses on seven dimensions: self-efficacy, responsibility, empathy/reparation, autonomy/security, containment/structure, insight building, and relational health. This paper presents the program and illustrates how the framework of attachment theory practically applied to psycho-social intervention has great therapeutic and social reparation potential. Preliminary evidence drawn from the Sassari Juvenile Court is very promising; this paper will illustrate these results and propose an even more comprehensive, applicable approach to psycho-social reparative intervention that leads to greater psychological health and reduced recidivism in the child and adolescent population.

Keywords: attachment, child, adolescent, crime, juvenile, psychosocial

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Authors: S. Odabasi Cimer, A. Cimer

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Recently, the poor quality of education, low achieving students, low international exam performances and little or no effect of the education reforms on the teaching in the classrooms are the main problems of education discussed in Turkey. Research showed that the quality of an education system can not exceed the quality of its teachers and teaching. Therefore, in-service training (INSET) courses are important to improve teacher quality, thereby, the quality of education. However, according to the research conducted on the evaluation of the INSET courses in Turkey, they are not effective in improving the quality of teaching in the classroom. The main reason for this result is because INSET courses are conducted and delivered in limited time and presented theoretically, which does not meet the needs of teachers and as a result, the knowledge and skills taught are not used in the classrooms. Recently, developed countries have been using Critical Friends Groups (CFGs) successfully for the purpose of school-based training of teachers. CFGs are the learning groups which contain 6-10 teachers aimed at fostering their capacities to undertake instructional and personal improvement and schoolwide reform. CFGs have been recognized as a critical feature in school reform, improving teaching practice and improving student achievement. In addition, in the USA, teachers have named CFGs one of the most powerful professional development activities in which they have ever participated. Whereas, in Turkey, the concept is new. This study aimed to investigate the implications of application, evaluation, and promotion of CFGs which has the potential to contribute to teacher development and student learning in schools in Turkey. For this purpose, the study employed a qualitative approach and case study methodology to implement the model in high schools. The research was conducted in two schools and 13 teachers working in these schools participated. The study lasted two years and the data were collected through various data collection tools including interviews, meeting transcripts, questionnaires, portfolios, and diaries. The results of the study showed that CFGs contributed professional development of teachers and their students’ learning. It also contributed to a culture of collaborative work in schools. A number of barriers and challenges which prevent effective implementation were also determined.

Keywords: critical friends group, education reform, science learning, teacher education

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Authors: Cristina Maria Mendoza Sanchez, Maria Angeles Navarro Perán

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Introduction: Nursing as a profession, from its initial formation and after its development in practice, has been built and identified mainly from its technical competence and professionalization within the positivist approach of the XIX century that provides a conception of the disease built on the basis of to the biomedical paradigm, where the care provided is more focused on the physiological processes and the disease than on the suffering person understood as a whole. The main issue that is in need of study here is a review of the nursing profession's history to get to know how the nursing profession was before the XIX century. It is unclear if there were organizations or people with knowledge about looking after others or if many people survived by chance. The holistic care, in which the appearance of the disease directly affects all its dimensions: physical, emotional, cognitive, social and spiritual. It is not a concept from the 21st century. It is common practice, most probably since established life in this world, with the final purpose of covering all these perspectives through quality care. Objective: In this paper, we describe and analyze the history of education in nursing learning in terms of reviewing and analysing theoretical foundations of clinical teaching and learning in nursing, with the final purpose of determining and describing the development of the nursing profession along the history. Method: We have done a descriptive systematic review study, doing a systematically searched of manuscripts and articles in the following health science databases: Pubmed, Scopus, Web of Science, Temperamentvm and CINAHL. The selection of articles has been made according to PRISMA criteria, doing a critical reading of the full text using the CASPe method. A compliment to this, we have read a range of historical and contemporary sources to support the review, such as manuals of Florence Nightingale and John of God as primary manuscripts to establish the origin of modern nursing and her professionalization. We have considered and applied ethical considerations of data processing. Results: After applying inclusion and exclusion criteria in our search, in Pubmed, Scopus, Web of Science, Temperamentvm and CINAHL, we have obtained 51 research articles. We have analyzed them in such a way that we have distinguished them by year of publication and the type of study. With the articles obtained, we can see the importance of our background as a profession before modern times in public health and as a review of our past to face challenges in the near future. Discussion: The important influence of key figures other than Nightingale has been overlooked and it emerges that nursing management and development of the professional body has a longer and more complex history than is generally accepted. Conclusions: There is a paucity of studies on the subject of the review to be able to extract very precise evidence and recommendations about nursing before modern times. But even so, as more representative data, an increase in research about nursing history has been observed. In light of the aspects analyzed, the need for new research in the history of nursing emerges from this perspective; in order to germinate studies of the historical construction of care before the XIX century and theories created then. We can assure that pieces of knowledge and ways of care were taught before the XIX century, but they were not called theories, as these concepts were created in modern times.

Keywords: nursing history, nursing theory, Saint John of God, Florence Nightingale, learning, nursing education

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Authors: Kwizera Mireille, Kharesh Ahmed Al-Khadher

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This paper explores the critical issue of girls' and women's right to education, exploring the challenges they face in accessing and benefiting from quality education. Gender disparities in education have persisted globally, hindering social progress and sustainable development. The fundamental importance of education in empowering individuals and promoting gender equality is acknowledged, making it imperative to address the disparities that hinder girls' and women's educational opportunities. The paper discusses various factors contributing to these disparities, including cultural norms(common in third-world countries), socio-economic constraints, and systemic biases. Drawing on a wide range of scholarly sources, empirical studies, and reports from international organizations, this paper highlights the broader societal benefits of educating girls and women, ranging from improved health outcomes to enhanced economic development and greater social and political participation. The paper further outlines strategies and initiatives aimed at overcoming these challenges. These include policy interventions, community-based programs, and international collaborations that work towards eliminating gender-based discrimination in educational settings. The paper emphasizes the significance of not only ensuring access but also fostering an inclusive and safe learning environment that encourages girls and women to thrive academically and personally. By analyzing successful case studies and best practices from around the world, the paper offers insights into effective approaches that can be adopted to enhance girls' and women's right to education globally. Furthermore, it emphasizes the importance of raising awareness of girl's and women's education. In conclusion, this paper underscores the urgency of prioritizing and protecting the educational rights of girls and women's right to education as a fundamental human right and catalyst for gender equality. It calls for a concerted effort from governments, NGOs, educational institutions, and society as a whole to create an equitable and empowering educational landscape that contributes to gender equality and sustainable development.

Keywords: empowerment, gender equality, inclusive education, right to education

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Authors: Zzibu Mark Brian

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As software systems become increasingly complex, ensuring code security is a growing concern. Traditional vulnerability detection methods often rely on manual code reviews or static analysis tools, which can be time-consuming and prone to errors. This paper presents a distinct approach to enhancing code security by leveraging artificial intelligence (AI) and machine learning (ML) techniques. Our proposed system utilizes a combination of natural language processing (NLP) and deep learning algorithms to identify and classify vulnerabilities in real-world codebases. By analyzing vast amounts of open-source code data, our AI-powered tool learns to recognize patterns and anomalies indicative of security weaknesses. We evaluated our system on a dataset of over 10,000 open-source projects, achieving an accuracy rate of 92% in detecting known vulnerabilities. Furthermore, our tool identified previously unknown vulnerabilities in popular libraries and frameworks, demonstrating its potential for improving software security.

Keywords: AI, machine language, cord security, machine leaning

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Authors: Jennifer E Simpson

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Background and purpose: The ubiquitous use of the Internet and social media by children and young people has had a dual effect. The first is to open a world of possibilities and promise that is characterized by the ability to consume and create content, connect with friends, explore and experiment. The second relates to risks such as unsolicited requests, sexual exploitation, cyberbullying and commercial exploitation. This duality poses significant difficulties for a generation of foster carers and social workers who have no childhood experience to draw on in terms of growing up using the Internet, social media and digital devices. This presentation is concerned with the findings of a small qualitative study about the use of digital devices and the Internet by care-experienced young people to stay in touch with their families and the way this was managed by foster carers and social workers using specific parental mediation strategies. The findings highlight that restrictive strategies were used by foster carers and endorsed by social workers. An argument is made for an approach that develops a series of balanced solutions that move foster carers from such restrictive approaches to those that are grounded in co-use and are interpretive in nature. Methods: Using a purposive sampling strategy, 12 triads consisting of care-experienced young people (aged 13-18 years), their foster carers and allocated social workers were recruited. All respondents undertook a semi-structured interview, with the young people detailing what social media apps and other devices they used to contact their families via an Ecomap. The foster carers and social workers shared details of the methods and approaches they used to manage digital devices and the Internet in general. Data analysis was performed using a Framework analytic method to explore the various attitudes, as well as complementary and contradictory perspectives of the young people, their foster carers and allocated social workers. Findings: The majority of foster carers made use of parental mediation strategies that erred on the side of typologies that included setting rules and regulations (restrictive), ad-hoc checking of a young person’s behavior and device (monitoring), and software used to limit or block access to inappropriate websites (technical). It was noted that minimal use was made by foster carers of parental mediation strategies that included talking about content (active/interpretive) or sharing Internet activities (co-use). Amongst the majority of the social workers, they also had a strong preference for restrictive approaches. Conclusions and implications: Trepidations on the part of both foster carers and social workers about the use of digital devices and the Internet meant that the parental strategies used were weighted more towards restriction, with little use made of approaches such as co-use and interpretative. This lack of balance calls for solutions that are grounded in co-use and an interpretive approach, both of which can be achieved through training and support, as well as wider policy change.

Keywords: parental mediation strategies, risk, children in state care, online safety

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Authors: O. F. Elkommos

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“Humour studies” is an interdisciplinary research area that is relatively recent. It interests researchers from the disciplines of psychology, sociology, medicine, nursing, in the work place, gender studies, among others, and certainly teaching, language learning, linguistics, and literature. Linguistic theories of humour research are numerous; some of which are of interest to the present study. In spite of the fact that humour courses are now taught in universities around the world in the Egyptian context it is not included. The purpose of the present study is two-fold: to review the state of arts and to show how linguistic theories of humour can be possibly used as an art and craft of teaching and of learning in EFL literature classes. In the present study linguistic theories of humour were applied to selected literary texts to interpret humour as an intrinsic artistic communicative competence challenge. Humour in the area of linguistics was seen as a fifth component of communicative competence of the second language leaner. In literature it was studied as satire, irony, wit, or comedy. Linguistic theories of humour now describe its linguistic structure, mechanism, function, and linguistic deviance. Semantic Script Theory of Verbal Humor (SSTH), General Theory of Verbal Humor (GTVH), Audience Based Theory of Humor (ABTH), and their extensions and subcategories as well as the pragmatic perspective were employed in the analyses. This research analysed the linguistic semantic structure of humour, its mechanism, and how the audience reader (teacher or learner) becomes an interactive interpreter of the humour. This promotes humour competence together with the linguistic, social, cultural, and discourse communicative competence. Studying humour as part of the literary texts and the perception of its function in the work also brings its positive association in class for educational purposes. Humour is by default a provoking/laughter-generated device. Incongruity recognition, perception and resolving it, is a cognitive mastery. This cognitive process involves a humour experience that lightens up the classroom and the mind. It establishes connections necessary for the learning process. In this context the study examined selected narratives to exemplify the application of the theories. It is, therefore, recommended that the theories would be taught and applied to literary texts for a better understanding of the language. Students will then develop their language competence. Teachers in EFL/ESL classes will teach the theories, assist students apply them and interpret text and in the process will also use humour. This is thus easing students' acquisition of the second language, making the classroom an enjoyable, cheerful, self-assuring, and self-illuminating experience for both themselves and their students. It is further recommended that courses of humour research studies should become an integral part of higher education curricula in Egypt.

Keywords: ABTH, deviance, disjuncture, episodic, GTVH, humour competence, humour comprehension, humour in the classroom, humour in the literary texts, humour research linguistic theories, incongruity-resolution, isotopy-disjunction, jab line, longer text joke, narrative story line (macro-micro), punch line, six knowledge resource, SSTH, stacks, strands, teaching linguistics, teaching literature, TEFL, TESL

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Authors: Carmel Sofer, Dan Vilenchik, Ron Dotsch, Galia Avidan

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Studies investigating emotional facial expressions typically reveal consensus among observes regarding the meaning of basic expressions, whose number ranges between 6 to 15 emotional states. Given this limited number of discrete expressions, how is it that the human vocabulary of emotional states is so rich? The present study argues that perceivers use sequences of these discrete expressions as the basis for a much richer vocabulary of emotional states. Such mechanisms, in which a relatively small number of basic components is expanded to a much larger number of possible combinations of meanings, exist in other human communications modalities, such as spoken language and music. In these modalities, letters and notes, which serve as basic components of spoken language and music respectively, are temporally linked, resulting in the richness of expressions. In the current study, in each trial participants were presented with sequences of two images containing facial expression in different combinations sampled out of the eight static basic expressions (total 64; 8X8). In each trial, using single word participants were required to judge the 'state of mind' portrayed by the person whose face was presented. Utilizing word embedding methods (Global Vectors for Word Representation), employed in the field of Natural Language Processing, and relying on machine learning computational methods, it was found that the perceived meanings of the sequences of facial expressions were a weighted average of the single expressions comprising them, resulting in 22 new emotional states, in addition to the eight, classic basic expressions. An interaction between the first and the second expression in each sequence indicated that every single facial expression modulated the effect of the other facial expression thus leading to a different interpretation ascribed to the sequence as a whole. These findings suggest that the vocabulary of emotional states conveyed by facial expressions is not restricted to the (small) number of discrete facial expressions. Rather, the vocabulary is rich, as it results from combinations of these expressions. In addition, present research suggests that using word embedding in social perception studies, can be a powerful, accurate and efficient tool, to capture explicit and implicit perceptions and intentions. Acknowledgment: The study was supported by a grant from the Ministry of Defense in Israel to GA and CS. CS is also supported by the ABC initiative in Ben-Gurion University of the Negev.

Keywords: Glove, face perception, facial expression perception. , facial expression production, machine learning, word embedding, word2vec

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Authors: Amani Hamdan

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A post-workshop of higher-order thinking skills (HOTS), for faculty from diverse academic disciplines, was conducted and the researcher surveyed the participants’ intentions and plans to include HOTS as a goal, as learning and teaching task in their practices. Follow-up interviews with a random sample of participants were used to determine if they fulfilled their intentions three 3 months after the workshop. The degree of planned and enacted HOTS then was analyzed against the post-workshop HOT ability and knowledge. This is one topic that has not been adequately explored in faculty professional development literature where measuring the effect of learning on their ability to use what they learned. This qualitative method study explored a group of male and female faculty members (n=85) enrolled in HOTS 2 day workshop. The results showed that 89% of faculty members although were mostly enthused to apply what they learned after a 3 months period they were caught up with routine presentations and lecturing.

Keywords: higher education, faculty development, Saudi Arabia, higher order thinking skills

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Authors: Aber Aboalgasm, Rupert Ward, Ruth Taylor, Jonathan Glazzard

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Teaching art by digital means is a big challenge for the majority of teachers of art and artistic design courses in primary education schools. These courses can clearly identify relationships between art, technology, and creativity in the classroom .The aim of this article is to present a modern way of teaching art, using digital tools in the art classroom in order to improve creative ability in pupils aged between 9 and 11 years; it also presents a conceptual model for creativity based on digital art. The model could be useful for pupils interested in learning drawing and using an e-drawing package, and for teachers who are interested in teaching their students modern digital art, and improving children’s creativity. This model is designed to show the strategy of teaching art through technology, in order for children to learn how to be creative. This will also help education providers to make suitable choices about which technological approaches they should choose to teach students and enhance their creative ability. It is also expected that use of this model will help to develop social interactive qualities that may improve intellectual ability.

Keywords: digital tools, motivation, creative activity, education

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Authors: Sara Blanc, José V. Benlloch-Dualde, Laura Grindei, Ana C. Torres, Angélica Monteiro

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This paper presents a study that leads a new experience by introducing digital learning applied to a case study focused on primary and secondary school garden-based education. The approach represents an example of interaction among different education and research agents at different countries and levels, such as universities, public and private research, and schools, to get involved in the implementation of education for sustainable development that will make students become more sensible to natural environment, more responsible for their consumption, more aware about waste reduction and recycling, more conscious of the sustainable use of natural resources and, at the same time, more ‘digitally competent’. The experience was designed attending to the European digital education context and OECD directives in transversal skills education. The paper presents the methodology carried out in the study as well as outcomes obtained from experience.

Keywords: school gardens, primary education, secondary education, science technology and innovation in education, digital learning, sustainable development goals, university, knowledge transference

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: neural networks, pattern learning, security, wireless sensor networks

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