Search results for: non-formal learning contexts

Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: P. Zarfam, M. Mansouri Baghbaderani

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In seismic design, the proper reaction to the earthquake and the correct and accurate prediction of its subsequent effects on the structure are critical. Choose a proper probability distribution, which gives a more realistic probability of the structure's damage rate, is essential in damage discussions. With the development of design based on performance, analytical method of modal push over as an inexpensive, efficacious, and quick one in the estimation of the structures' seismic response is broadly used in engineering contexts. In this research three concrete frames of 3, 6, and 13 stories are analyzed in non-linear modal push over by 30 different earthquake records by OpenSEES software, then the detriment indexes of roof's displacement and relative displacement ratio of the stories are calculated by two parameters: peak ground acceleration and spectra acceleration. These indexes are used to establish the value of damage relations with log-normal distribution and logistics distribution. Finally the value of these relations is compared and the effect of height on the mentioned damage relations is studied, too.

Keywords: modal pushover analysis, concrete structure, seismic damage, log-normal distribution, logistic distribution

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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Authors: S. Chee Choy, Pauline Swee Choo Goh, Yow Lin Liew

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The purpose of this study is to evaluate the English version and a Malay translation of the 21-item Learner Awareness Questionnaire for its application to assess student learning in higher education. The Learner Awareness Questionnaire, originally written in English, is a quantitative measure of how and why students learn. The questionnaire gives an indication of the process and motives to learn using four scales: survival, establishing stability, approval, and loving to learn. Data in the present study came from 680 university students enrolled in various programs in Malaysia. The Malay version of the questionnaire supported a similar four-factor structure and internal consistency to the English version. The four factors of the Malay version also showed moderate to strong correlations with those of the English versions. The results suggest that the Malay version of the questionnaire is similar to the English version. However, further refinement for the questions is needed to strengthen the correlations between the two questionnaires.

Keywords: student learning, learner awareness, questionnaire development, instrument validation

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Authors: Taksina Bunbut

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Personality and grooming are vital for creating professionalism and safety image for all staffs in the airline industry. Airline Business Program also has an aim to educate students through the subject Personality Development and Grooming in order to elevate the quality of students to meet standard requirements of the airline industry. However, students agree that there are many difficulties that cause unsuccessful learning experience in this subject. The research is to study problems that can afflict students from getting good results in the classroom. Furthermore, exploring possible solutions to overcome challenges are also included in this study. The research sample consists of 140 students who attended the class of Personality Development and Grooming. The employed research instrument is a questionnaire. Statistic for data analysis is t-test and Multiple Regression Analysis. The result found that although students are satisfied with teaching and learning of this subject, they considered that teaching in English and teaching topics in social etiquette in different cultures are difficult for them to understand.

Keywords: personality development, grooming, Airline Business Program, soft skill

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Authors: Subhajit Das, Nirjhar Dhang

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Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data.

Keywords: damage detection, finite element model updating, modal assurance criteria, structural health monitoring, teaching learning based optimization

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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: Nurul Jamilah Binti Rosly

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The typology of motion must be understood as a change from one location to another. But from a conceptual point of view, motion can also occur in non-spatial contexts associated with human and social factors. Therefore, from the conceptual point of view, the concept of non-spatial motion involves the movement of time, ownership, identity, state, and existence. Accordingly, this study will focus on the lexical as shared, accept, be, store, and exist as the study material. The data in this study were extracted from the Database of Languages and Literature Corpus Database, Malaysia, which was analyzed using semantics and syntax concepts using Conceptual Structure Theory - Ray Jackendoff (2002). Semantic representations are represented in the form of conceptual structures in argument functions that include functions [events], [situations], [objects], [paths] and [places]. The findings show that the mapping of these arguments comprises three main stages, namely mapping the argument structure, mapping the tree, and mapping the role of thematic items. Accordingly, this study will show the representation of non- spatial Malay language areas.

Keywords: arguments, concepts, constituencies, events, situations, thematics

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Authors: Sofia Horjales, Florencia Palma

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Our society is currently experiencing a profound transformation, demanding a proactive response from governmental bodies and higher education institutions to empower the next generation as catalysts for change. Environmental sustainability is rooted in the critical need to maintain the equilibrium and integrity of natural ecosystems, ensuring the preservation of precious natural resources and biodiversity for the benefit of both present and future generations. It is an essential cornerstone of sustainable development, complementing social and economic sustainability. In this evolving landscape, active methodologies take a central role, aligning perfectly with the principles of the 2030 Agenda for Sustainable Development and emerging as a pivotal element of teacher education. The emphasis on active learning methods has been driven by the urgent need to nurture sustainability and instill social responsibility in our future leaders. The Universidad Tecnológica of Uruguay (UTEC) is a public, technologically-oriented institution established in 2012. UTEC is dedicated to decentralization, expanding access to higher education throughout Uruguay, and promoting inclusive social development. Operating through Regional Technological Institutes (ITRs) and associated centers spread across the country, UTEC faces the challenge of remote student populations. To address this, UTEC utilizes e-learning for equal opportunities, self-regulated learning, and digital skills development, enhancing communication among students, teachers, and peers through virtual classrooms. The Interdisciplinary Continuing Education Program is part of the Innovation and Entrepreneurship Department of UTEC. The main goal is to strengthen innovation skills through a transversal and multidisciplinary approach. Within this Program, we have developed a Case of Study and Project-Based Learning Virtual Course designed for university students and open to the broader UTEC community. The primary aim of this course is to establish a strong foundation for comprehending and addressing environmental sustainability issues from an interdisciplinary perspective. Upon completing the course, we expect students not only to understand the intricate interactions between social and ecosystem environments but also to utilize their knowledge and innovation skills to develop projects that offer enhancements or solutions to real-world challenges. Our course design centers on innovative learning experiences, rooted in active methodologies. We explore the intersection of these methods with sustainability and social responsibility in the education of university students. A paramount focus lies in gathering student feedback, empowering them to autonomously generate ideas with guidance from instructors, and even defining their own project topics. This approach underscores that when students are genuinely engaged in subjects of their choice, they not only acquire the necessary knowledge and skills but also develop essential attributes like effective communication, critical thinking, and problem-solving abilities. These qualities will benefit them throughout their lifelong learning journey. We are convinced that education serves as the conduit to merge knowledge and cultivate interdisciplinary collaboration, igniting awareness and instigating action for environmental sustainability. While systemic changes are undoubtedly essential for society and the economy, we are making significant progress by shaping perspectives and sparking small, everyday actions within the UTEC community. This approach empowers our students to become engaged global citizens, actively contributing to the creation of a more sustainable future.

Keywords: active learning, environmental education, project-based learning, soft skills development

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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: Adeline Stals

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The transformation of architectural design practices has been underway for almost forty years due to the development and democratization of computer technology. New and more efficient tools are constantly being proposed to architects, amplifying a technological wave that sometimes stimulates them, sometimes overwhelms them, depending essentially on their digital culture and the context (socio-economic, structural, organizational) in which they work on a daily basis. Our focus is on VR, AR, and MR technologies dedicated to architecture. The commercialization of affordable headsets like the Oculus Rift, the HTC Vive or more low-tech like the Google CardBoard, makes it more accessible to benefit from these technologies. In that regard, researchers report the growing interest of these tools for architects, given the new perspectives they open up in terms of workflow, representation, collaboration, and client’s involvement. However, studies rarely mention the consequences of the sample studied on results. Our research provides an overview of VR, AR, and MR researches among a corpus of papers selected from conferences and journals. A closer look at the sample of these research projects highlights the necessity to take into consideration the context of studies in order to develop tools truly dedicated to the real practices of specific architect profiles. This literature review formalizes milestones for future challenges to address. The methodology applied is based on a systematic review of two sources of publications. The first one is the Cumincad database, which regroups publications from conferences exclusively about digital in architecture. Additionally, the second part of the corpus is based on journal publications. Journals have been selected considering their ranking on Scimago. Among the journals in the predefined category ‘architecture’ and in Quartile 1 for 2018 (last update when consulted), we have retained the ones related to the architectural design process: Design Studies, CoDesign, Architectural Science Review, Frontiers of Architectural Research and Archnet-IJAR. Beside those journals, IJAC, not classified in the ‘architecture’ category, is selected by the author for its adequacy with architecture and computing. For all requests, the search terms were ‘virtual reality’, ‘augmented reality’, and ‘mixed reality’ in title and/or keywords for papers published between 2015 and 2019 (included). This frame time is defined considering the fast evolution of these technologies in the past few years. Accordingly, the systematic review covers 202 publications. The literature review on studies about XR technologies establishes the state of the art of the current situation. It highlights that studies are mostly based on experimental contexts with controlled conditions (pedagogical, e.g.) or on practices established in large architectural offices of international renown. However, few studies focus on the strategies and practices developed by offices of smaller size, which represent the largest part of the market. Indeed, a European survey studying the architectural profession in Europe in 2018 reveals that 99% of offices are composed of less than ten people, and 71% of only one person. The study also showed that the number of medium-sized offices is continuously decreasing in favour of smaller structures. In doing so, a frontier seems to remain between the worlds of research and practice, especially for the majority of small architectural practices having a modest use of technology. This paper constitutes a reference for the next step of the research and for further worldwide researches by facilitating their contextualization.

Keywords: architectural design, literature review, SME, XR technologies

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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: 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: Jacopo Baboni Schilingi

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We here describe the theoretical and philosophical understanding of a long term use and development of algorithmic computer-based tools applied to music composition. The findings of our research lead us to interrogate some specific processes and systems of communication engaged in the discovery of specific cultural artworks: artistic creation in the sono-musical domain. Our hypothesis is that the patterns of auditory learning cannot be only understood in terms of social transmission but would gain to be questioned in the way they rely on various ranges of acoustic stimuli modes of consciousness and how the different types of memories engaged in the percept-action expressive systems of our cultural communities also relies on these shadowy conscious entities we named “Reduced Descriptive Structures”.

Keywords: algorithmic sonic computation, corrected and self-correcting learning patterns in acoustic perception, morphological derivations in sensorial patterns, social unconscious modes of communication

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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: 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: 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: Nikfarid Lida, Maryam Rassouli, Leili Borimnejad, Hamid Alavi Majd

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Purpose: Chronic sorrow is experienced by mothers of children with cancer. It is a multidimensional concept and is experienced by mothers in different ways depends on their various contexts. Little is known about the concept of chronic sorrow in mothers of children with cancer living in Iran. This study aimed to clarify the concept and explain lived experiences of chronic sorrow in Iranian mothers of children with cancer. Methods: In this hermeneutic phenomenological study, 8 mothers of children with cancer participated in semi structured in-depth interviews about their experiences of chronic sorrow. Interviews continued until data saturation was reached. All interviews were recorded, transcribed, analyzed, and interpreted using 7 steps of the Dickelman et al’s phenomenological approach. Results: Three main themes emerged from mothers’ experiences of chronic sorrow related to child’s cancer. These main themes were ‘climbing up shaky rocks,’ ‘fear and hope,’ and ‘continuous role changing.’ Each of these themes consisted of several subthemes. Conclusion: There are similarities in experiencing chronic sorrow by mothers of children with chronic diseases in different societies. However some experiences are unique in Iranian mothers of children with cancer.

Keywords: cancer, children, mothers, Iran, phenomenology

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Authors: Nadeesha Sewwandi, Dilini Shashikala, R. Kanapathy, S. Viyasan, R. M. S. Kumara, Duminda Guruge

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Early childhood development is important to the emotional, social, and physical development of young children and it has a direct effect on their overall development and on the adult they become. Play is so important to optimal child developments including skill development, social development, imagination, creativity and it fulfills a baby’s inborn need to learn. So, health promotion approach empowers people about the development of early childhood. Play area is a new concept and this study focus how this play areas helps to the development of early childhood of children in rural villages in Sri Lanka. This study was conducted with a children society in a rural village called Welankulama in Sri Lanka. Survey was conducted with children society about emotional, social and physical development of young children (Under age eight) in this village using questionnaires. It described most children under eight years age have poor level of emotional, social and physical development in this village. Then children society wanted to find determinants for this problem and among them they prioritized determinants like parental interactions, learning environment and social interaction and address them using an innovative concept called play area. In this village there is a common place as play area under a big tamarind tree. It consists of a playhouse, innovative playing toys, mobile library, etc. Twice a week children, parents, grandparents gather to this nice place. Collective feeding takes place in this area once a week and it was conducted by several mothers groups in this village. Mostly grandparents taught about handicrafts and this is a very nice place to share their experiences with all. Healthy competitions were conducted in this place through playing to motivate the children. Happy calendar (mood of the children) was marked by children before and after coming to the play area. In terms of results qualitative changes got significant place in this study. By learning about colors and counting through playing the thinking and reasoning skills got developed among children. Children were widening their imagination by means of storytelling. We observed there were good developments of fine and gross motor skills of two differently abled children in this village. Children learn to empathize with other people, sharing, collaboration, team work and following of rules. And also children gain knowledge about fairness, through role playing, obtained insight on the right ways of displaying emotions such as stress, fear, anger, frustration, and develops knowledge of how they can manage their feelings. The reading and writing ability of the children got improved by 83% because of the mobile library. The weight of children got increased by 81% in the village. Happiness was increased by 76% among children in the society. Playing is very important for learning during early childhood period of a person. Health promotion interventions play a major role to the development of early childhood and it help children to adjust to the school setting and even to enhance children’s learning readiness, learning behaviors and problem solving skills.

Keywords: early childhood development, health promotion approach, play and learning, working with children

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Authors: Malak Alqaydhi

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A problem in the Saudi Arabia education system is that there is a lack of curriculum- based Social, emotional learning (SEL) teaching practices with the pedagogical concept of SEL yet to be practiced in the Kingdom of Saudi Arabia (KSA). Furthermore, voices of teachers and parents have not been captured regarding the use of SEL, particularly in preschools. The importance of this research is to help determine, with the input of teachers and mothers of preschoolers, the efficacy of a culturally adapted SEL program. The purpose of this research is to determine the most appropriate SEL intervention method to appropriately apply in the cultural context of the Saudi preschool classroom setting. The study will use a mixed method exploratory sequential research design, applying qualitative and quantitative approaches including semi-structured interviews with teachers and parents of preschoolers and an experimental research approach. The research will proceed in four phases beginning with a series of interviews with Saudi preschool teachers and mothers, whose voices and perceptions will help guide the second phase of selection and adaptation of a suitable SEL preschool program. The third phase will be the implementation of the intervention by the researcher in the preschool classroom environment, which will be facilitated by the researcher’s cultural proficiency and practical experience in Saudi Arabia. The fourth and final phase will be an evaluation to assess the effectiveness of the trialled SEL among the preschool student participants. The significance of this research stems from its contribution to knowledge about SEL in culturally appropriate Saudi preschools and the opportunity to support initiatives for Saudi early childhood educators to consider implementing SEL programs. The findings from the study may be useful to inform the Saudi Ministry of Education and its curriculum designers about SEL programs, which could be beneficial to trial more widely in the Saudi preschool curriculum.

Keywords: social emotional learning, preschool children, saudi Arabia, child behavior

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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: Nouzha Yasmina Soulimane-Benhabib

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Language is a medium of communication and a means of expression that is why today the learning of foreign languages especially the English language has become a basic necessity for every student who is ambitious. It is known that culture and language are inseparable and complementary, however, in the process of teaching a foreign language, teachers used to focus mainly on preparing adequate syllabi for ESP students, yet, some parameters should be considered. For instance; the culture of the target language may play an important role since students attitudes towards a foreign language enhance their learning or vice versa. The aim of this study is to analyse how culture could influence the teaching of a foreign language, we have taken the example of the English language as it is considered as the second foreign language in Algeria after French. The study is conducted at the Preparatory School of Sciences and Techniques, Tlemcen where twenty-five students participated in this research. The reasons behind learning the English language are various, and since English is the most widely-spoken language in the world, it is the language of research and education and it is used in many other fields, we have to take into consideration one important factor which is the social distance between the culture of the Algerian learner and the culture of the target language, this gap may lead to a culture shock. Two steps are followed in this research: The first one is to collect data from those students who are studying at the Preparatory School under the form of questionnaire and an interview is submitted to six of them in order to reinforce our research and get effective and precise results, and the second step is to analyse these data taking into consideration the diversity of the learners within this institution. The results obtained show that learners’ attitudes towards the English community and culture are mixed and it may influence their curiosity and attention to learn. Despite of big variance between Algerian and European cultures, some of the students focused mainly on the benefits of the English language since they need it in their studies, research and a future carrier, however, the others manifest their reluctance towards this language and this is mainly due to the profound impact of the English culture which is different from the Algerian one.

Keywords: Algeria, culture, English, impact

Procedia PDF Downloads 388

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

Procedia PDF Downloads 457

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

Procedia PDF Downloads 118

Authors: Supannee Pinmanee

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This article focuses on the strategies in the translation of terms of address for both referential and vocative functions from Thai to English from a cultural perspective. The discussion concerns the culture-specific ways in which Thai people use address terms that depend largely on social and conventional contexts, including pragmatic factors, for example, relationships between people, levels of formality, and attitudes. Examples used to illustrate the problems and proposed solutions were drawn from the media, the internet, the novels and the language used by Thai native speakers in expressing Thai address terms. The terms used in this area show very well not only the differences in language but also the different cultures and world views of the speakers of Thai and those of English. Thai has developed its own set of address terms, particularly kinship terms for non-relatives and the Thai royal terms. Some of Newmark’s procedures (1995) are used in the article to illustrate the task of translating Thai terms into English, a language that embodies a very different culture with its own set of address terms. However, no one strategy can be applied to serve all purposes and to translate all the intended senses. One particular term can be translated by several strategies, and which strategy to choose depends largely on one’s purposes and what requirement one needs to fulfill.

Keywords: translation, terms of address, Thai-English translation, Thai culture-specific terms of address, translation strategies

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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

Procedia PDF Downloads 404