Search results for: electronic learning platform

Authors: Atuhurra Jesse, N'guessan Yves-Roland Douha, Pabitra Lenka

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The detection of diseases within plants has attracted a lot of attention from computer vision enthusiasts. Despite the progress made to detect diseases in many plants, there remains a research gap to train image classifiers to detect the cacao swollen shoot virus disease or CSSVD for short, pertinent to cacao plants. This gap has mainly been due to the unavailability of high quality labeled training data. Moreover, institutions have been hesitant to share their data related to CSSVD. To fill these gaps, image classifiers to detect CSSVD-infected cacao plants are presented in this study. The classifiers are based on VGG16, ResNet50 and Vision Transformer (ViT). The image classifiers are evaluated on a recently released and publicly accessible KaraAgroAI Cocoa dataset. The best performing image classifier, based on ResNet50, achieves 95.39\% precision, 93.75\% recall, 94.34\% F1-score and 94\% accuracy on only 20 epochs. There is a +9.75\% improvement in recall when compared to previous works. These results indicate that the image classifiers learn to identify cacao plants infected with CSSVD.

Keywords: CSSVD, image classification, ResNet50, vision transformer, KaraAgroAI cocoa dataset

Procedia PDF Downloads 108

Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

Procedia PDF Downloads 185

Authors: Duygu Dere, Mert Ergeneci, Kaan Gokcesu

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Google Trends data has gained increasing popularity in the applications of behavioral finance, decision science and risk management. Because of Google’s wide range of use, the Trends statistics provide significant information about the investor sentiment and intention, which can be used as decisive factors for corporate and risk management fields. However, an anomaly, a significant increase or decrease, in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends data, which is modelled as an Additive white Gaussian noise (AWGN). Since through time, the baseline noise power shows a gradual change an adaptive thresholding method is required to track and learn the baseline noise for a correct classification. To this end, we introduce an online method to classify meaningful deviations in Google Trends data. Through extensive experiments, we demonstrate that our method can successfully classify various anomalies for plenty of different data.

Keywords: adaptive data processing, behavioral finance , convex optimization, online learning, soft minimum thresholding

Procedia PDF Downloads 171

Authors: Enrico Dorigatti

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The growing pile of hazardous e-waste produced especially by those developed and wealthy countries gets relentlessly bigger, composed of the EEDs (Electric and Electronic Device) that are often thrown away although still well functioning, mainly due to (programmed) obsolescence. As a consequence, e-waste has taken, over the last years, the shape of a frightful, uncontrollable, and unstoppable phenomenon, mainly fuelled by market policies aiming to maximize sales—and thus profits—at any cost. Against it, governments and organizations put some efforts in developing ambitious frameworks and policies aiming to regulate, in some cases, the whole lifecycle of EEDs—from the design to the recycling. Incidentally, however, such regulations sometimes make the disposal of the devices economically unprofitable, which often translates into growing illegal e-waste trafficking—an activity usually undertaken by criminal organizations. It seems that nothing, at least in the near future, can stop the phenomenon of e-waste production and accumulation. But while, from a practical standpoint, a solution seems hard to find, much can be done regarding people's education, which translates into informing and promoting good practices such as reusing and repurposing. This research argues that circuit bending—an activity rooted in neo-materialist philosophy and post-digital aesthetic, and based on repurposing EEDs into novel music instruments and sound generators—could have a great potential in this. In particular, it asserts that circuit bending could expose ecological, environmental, and social criticalities related to the current market policies and economic model. Not only thanks to its practical side (e.g., sourcing and repurposing devices) but also to the artistic one (e.g., employing bent instruments for ecological-aware installations, performances). Currently, relevant literature and debate lack interest and information about the ecological aspects and implications of the practical and artistic sides of circuit bending. This research, therefore, although still at an early stage, aims to fill in this gap by investigating, on the one side, the ecologic potential of circuit bending and, on the other side, its capacity of sensitizing people, through artistic practice, about e-waste-related issues. The methodology will articulate in three main steps. Firstly, field research will be undertaken—with the purpose of understanding where and how to source, in an ecologic and sustainable way, (discarded) EEDs for circuit bending. Secondly, artistic installations and performances will be organized—to sensitize the audience about environmental concerns through sound art and music derived from bent instruments. Data, such as audiences' feedback, will be collected at this stage. The last step will consist in realising workshops to spread an ecologically-aware circuit bending practice. Additionally, all the data and findings collected will be made available and disseminated as resources.

Keywords: circuit bending, ecology, sound art, sustainability

Procedia PDF Downloads 174

Authors: Hyunbin Choi, Jihyeon Noh, Changwon Lim

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In this paper, we propose SiamMask++, an architecture that performs layer-wise aggregation and depth-wise cross-correlation and introduce multi-RPN module and multi-MASK module to improve EAO (Expected Average Overlap), a representative performance evaluation metric for Visual Object Tracking (VOT) challenge. The proposed architecture, SiamMask++, has two versions, namely, bi_SiamMask++, which satisfies the real time (56fps) on systems equipped with GPUs (Titan XP), and rf_SiamMask++, which combines mask refinement modules for EAO improvements. Tests are performed on VOT2016, VOT2018 and VOT2019, the representative datasets of Visual Object Tracking tasks labeled as rotated bounding boxes. SiamMask++ perform better than SiamMask on all the three datasets tested. SiamMask++ is achieved performance of 62.6% accuracy, 26.2% robustness and 39.8% EAO, especially on the VOT2018 dataset. Compared to SiamMask, this is an improvement of 4.18%, 37.17%, 23.99%, respectively. In addition, we do an experimental in-depth analysis of how much the introduction of features and multi modules extracted from the backbone affects the performance of our model in the VOT task.

Keywords: visual object tracking, video, deep learning, layer wise aggregation, Siamese network

Procedia PDF Downloads 165

Authors: Lule Basha, Eralda Gjika

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The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable in one country's competitiveness, trade and current account, inflation, wages, domestic economic activity, and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021, and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables on the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of the Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: exchange rate, random forest, time series, machine learning, prediction

Procedia PDF Downloads 106

Authors: Botirjon Kosimov

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This work sheds light on the reforms towards the independence of the judiciary in Uzbekistan, as well as issues of further ensuring judicial independence in the country based on international values, particularly the legal practice of the United States. In every democratic state infringed human rights are reinstated and violated laws are protected by the help of justice based on the strict principle of judicial independence. The realization of this principle in Uzbekistan has been paid much attention since the proclamation of its independence. In the country, a series of reforms have been implemented in the field of the judiciary in order to actualize the principle of judicial independence. Uzbekistan has been reforming the judiciary considering both international and national values and practice of foreign countries. While forming a democratic state based on civil society, Uzbekistan shares practice with the most developed countries in the world. The United States of America can be a clear example which is worth learning how to establish and ensure an independent judiciary. It seems that although Uzbekistan has reformed the judiciary efficiently, it should further reform considering the legal practice of the United States.

Keywords: dependent judges, independent judges, judicial independence, judicial reforms, judicial life tenure, obstacles to judicial independence

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

Procedia PDF Downloads 266

Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

Procedia PDF Downloads 329

Authors: Turuganti Venkateswarlu, Jagadeesh Anmala

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Excessive runoffs from various non-point source land uses, and other point sources are rapidly contaminating the water quality of streams in the Upper Green River watershed, Kentucky, USA. It is essential to maintain the stream water quality as the river basin is one of the major freshwater sources in this province. It is also important to understand the water quality parameters (WQPs) quantitatively and qualitatively along with their important features as stream water is sensitive to climatic events and land-use practices. In this paper, a model was developed for predicting one of the significant WQPs, Fecal Coliform (FC) from precipitation, temperature, urban land use factor (ULUF), agricultural land use factor (ALUF), and forest land-use factor (FLUF) using Random Forest (RF) algorithm. The RF model, a novel ensemble learning algorithm, can even find out advanced feature importance characteristics from the given model inputs for different combinations. This model’s outcomes showed a good correlation between FC and climate events and land use factors (R2 = 0.94) and precipitation and temperature are the primary influencing factors for FC.

Keywords: water quality, land use factors, random forest, fecal coliform

Procedia PDF Downloads 200

Authors: Eftah Bte. Moh Hj Abdullah, Izazol Binti Idris, Abd Aziz Bin Abd Shukor

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This study aims to identify the understanding expectations of school administrators concerning school assessment. The researcher utilized a qualitative descriptive study on 19 administrators from three secondary schools in the North Kinta district. The respondents had been interviewed on their understanding expectations of school assessment using the focus group discussion method. Overall findings showed that the administrators’ understanding expectations of school assessment was weak; especially in terms of content focus, articulation across age and grade, transparency and fairness, as well as the pedagogical implications. Findings from interviews indicated that administrators explained their understanding expectations of school assessment from the aspect of school management, and not from the aspect of instructional leadership or specifically as assessment leaders. The study implications from the administrators’ understanding expectations may hint at the difficulty of the administrators to function as assessment leaders, in order to reduce their focus as manager, and move towards their primary role in the process of teaching and learning. The administrator, as assessment leaders, would be able to reach assessment goals via collaboration in identifying and listing teacher assessment competencies, how to construct assessment capacity, how to interpret assessment correctly, the use of assessment and how to use assessment information to communicate confidently and effectively to the public.

Keywords: assessment leaders, assessment goals, instructional leadership, understanding expectation of assessment

Procedia PDF Downloads 460

Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

Procedia PDF Downloads 241

Authors: Gal Friedman-Hauser, Carmit Katz

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Children and youth with disabilities (CWD) are at increased risk of experiencing sexual abuse compared to their peer group. This phenomenon is prevalent in both physical and online spaces, with the latter becoming an increasingly common platform for such abuse due to recent technological advances. Despite their heightened risk, the processes of disclosure and reporting of online child sexual abuse (OCSA) for CWD remain largely unexamined, leaving significant gaps in understanding how to effectively address their needs. The current study seeks to bridge this gap by examining aspects of disclosure and reporting of OCSA among CWD. The study aimed to identify patterns of reporting and disclosure, explore reactions of children's immediate social circle, and critically analyze the content of the reports, emphasizing both addressed and absent aspects. Using a qualitative-critical methodology, the study involved thematic content analysis of 25 case reports from a hotline in Israel dedicated to uncovering online crimes against children. The reports detailed cases of online sexual abuse involving children aged 11–17. Each report was accompanied by a statistical file classifying the child as having a disability. The findings of the study reveal a silenced phenomenon requiring urgent attention. Most cases of abuse were discovered by chance, often by parents, as children hesitated to disclose the abuse due to fears of negative reactions from family and society—fears that were often realized. Reporting was frequently delayed due to three main factors: lack of trust in the validity of the abuse claims attempts to resolve the situation without involving authorities, and a lack of understanding of the severity of the abuse. In addition, the findings have highlighted the reports primarily reflected parents' perspectives, with a limited representation of the children's voices. Moreover, the aspect of disability was mentioned only in general terms, without in-depth consideration. These findings underscore the marginalization of children with disabilities and the pressing need for tailored recognition of their needs in intervention and treatment processes following OCSA. The analysis of these findings provides a comprehensive understanding of the barriers faced by CWD who experience OCSA. To address these challenges, it is crucial to adopt an approach that simultaneously acknowledges the uniqueness of CWD and affirms their right to equal treatment. Such an approach must account for their increased risk of sexual abuse, the prevalence of underreporting, and the negative societal attitudes they often encounter. By recognizing CWD as both equal and different, interventions can more effectively support their specific needs and promote justice and protection in cases of OCSA

Keywords: children with disabilities, disclosure and reporting, online child sexual abuse, qualitative analysis

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Authors: Lina S. Calucag

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Integration of research and teaching in higher education can provide valuable ways of enhancing the student learning experience, but establishing such integrative links can be complex and problematic, given different practices and levels of understanding. This study contributes to the pedagogical literature in drawing on findings from students’ survey exploring perceptions of research-informed teaching to examine how links between research and teaching can be suitably strengthened. The study employed a descriptive research design limited to the undergraduate students taking thesis/capstone courses in the tertiary levels private or public colleges and universities across the globe as respondents of the study. The findings noted that the students’ responses from different disciplines: engineering, science, education, business-related, and computer on the nexus between research and teaching is remarkable in fostering student expectations of research-informed teaching approaches. Students’ expectations on research-led, research-oriented, research-based, and research-tutored are enablers in linking research and teaching. It is recommended that experimental studies should be conducted using the four different research-informed teaching approaches in the classroom, namely: research-led, research-oriented, research-based, and research-tutored.

Keywords: research-led, research-informed teaching, research-oriented teaching, research-tutored, research-based

Procedia PDF Downloads 165

Authors: S. Gordon

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Dual language immersion is growing fast in language teaching today. This study provides an overview and evaluation of the different models of Dual language immersion programs in US K-12 schools. First, the paper provides a brief current literature review on the theory of Dual Language Immersion (DLI) in Second Language Acquisition (SLA) studies. Second, examples of several types of DLI language teaching models in US K-12 public schools are presented (including 50/50 models, 90/10 models, etc.). Third, we focus on the unique example of DLI education in the state of Utah, a successful, growing program in K-12 schools that includes: French, Chinese, Spanish, and Portuguese. The project investigates the theory and practice particularly of the case of public elementary and secondary school children that study half their school day in the L1 and the other half in the chosen L2, from kindergarten (age 5-6) through high school (age 17-18). Finally, the project takes the observations of Utah French DLI elementary through secondary programs as a case study. To conclude, we look at the principal challenges, pedagogical objectives and outcomes, and important implications for other US states and other countries (such as France currently) that are in the process of developing similar language learning programs.

Keywords: dual language immersion, second language acquisition, language teaching, pedagogy, teaching, French

Procedia PDF Downloads 181

Authors: Surendar Vaddepalli

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Management education plays a crucial role to enable industry to cope with emerging challenges. It has spread in the last fifteen-twenty years in India and gained popularity as it was aimed at imbibing versatility and multi-tasking abilities in student community. Several management institutions started looking at upgrading their competencies in terms of faculty, research and industry interaction. The competitive business environment has been one of the drivers that paved the way for growing demand for management graduates in the employment market. Industry expects their executives to be engaged in a constant learning process. The ever-increasing demand for managers has led to establish more management institutions; however, the growth was not in line with the expectations from the industry. While top Business Schools are continuously changing the contents and delivery methodologies, academic standards of most of the other Business Schools are not up to the mark and quality of service provided by these institutes has opened various issues for discussion. On this back ground it is important to address the concerns of Indian management education experiencing with time and we have to rethink about the management education and efforts should be made to create a dynamic environment. This paper ties to study the current trends and tries to find out need for value based management education in India to rejuvenate it.

Keywords: management education, management, value based management education, business school, India

Procedia PDF Downloads 381

Authors: Monika Paleczna, Barbara Szmigielska

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The period of adolescence is a time when young people are becoming more and more active and conscious users of the digital world. One of the most frequently undertaken activities by them is computer games. Young players can choose from a wide range of games, including action, adventure, strategy, and logic games. The main aim of this study is to answer the question about the motivation of teenage players. The basic question is what motivates young players to play computer games and what motivates them to play a particular game. Fifty adolescents aged 15-17 participated in the study. They completed a questionnaire in which they determined what motivates them to play, how often they play computer games, and what type of computer games they play most often. It was found that entertainment and learning English are among the most important motives. The most important specific features related to a given game are the knowledge of its previous parts and the ability to play for free. The motives chosen by the players will be described in relation to the concepts of internal and external as well as approach and avoidance motivation. An additional purpose of this study is to present data concerning preferences regarding the type of games and the amount of time they spend playing.

Keywords: computer games, motivation, game preferences, adolescence

Procedia PDF Downloads 187

Authors: Widyani Fatwa Dewi, Subroto Athor

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In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

Procedia PDF Downloads 167

Authors: Guerriche Amina

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The recent trends in foreign language teaching -influenced widely by the process of globalization, interculturalism, and global flows and migration- are leaning towards adopting an intercultural perspective to help in developing students who are global citizens able to effectively function across diverse boundaries (cultural, social, geographical). Researchers call for intercultural learning and teaching perspective that would foster and increase intercultural awareness and understanding (e.g., Guilherme, 2002; Byram et al., 2002). The present research aims at unfolding whether including the cultural dimension in foreign language instruction can help in developing students’ intercultural understanding and awareness. In doing so, a cultural pedagogical experiment was designed and conducted for the period of one year at the level of the university. Data were collected qualitatively and analyzed thematically. Results help in drawing important implications for educational institutions, foreign language teachers, and syllabus designers about the importance and effectiveness of perceiving foreign language instruction as a social activity that can nurture interculturally competent individuals who adequately respond to the demands of today’s intercultural and globalized societies.

Keywords: foreign language teaching, intercultural awareness, language and culture, intercultural understanding

Procedia PDF Downloads 136

Authors: Daniel James Pitchforth, Timothy James Rogers, Ulf Tyge Tygesen, Elizabeth Jane Cross

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Wave loading is a primary cause of fatigue within offshore structures and its quantification presents a challenging and important subtask within the SHM framework. The accurate representation of physics in such environments is difficult, however, driving the development of data-driven techniques in recent years. Within many industrial applications, empirical laws remain the preferred method of wave loading prediction due to their low computational cost and ease of implementation. This paper aims to develop an approach that combines data-driven Gaussian process models with physical empirical solutions for wave loading, including Morison’s Equation. The aim here is to incorporate physics directly into the covariance function (kernel) of the Gaussian process, enforcing derived behaviors whilst still allowing enough flexibility to account for phenomena such as vortex shedding, which may not be represented within the empirical laws. The combined approach has a number of advantages, including improved performance over either component used independently and interpretable hyperparameters.

Keywords: offshore structures, Gaussian processes, Physics informed machine learning, Kernel design

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Authors: Muhammad Iqbal, Edi Istiyono

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Misconceptions are one of the problems in physics learning where students' understanding is not in line with scientific theory. The aim of this research is to find diagnostic tools to identify misconceptions and how to remediate physics misconceptions. In this research, the articles that will be reviewed come from the Scopus database related to physics misconceptions from 2013-2023. The articles obtained from the Scopus database were then selected according to the Prisma model, so 29 articles were obtained that focused on discussing physics misconceptions, especially regarding diagnostic tools and remediation methods. Currently, the most widely used diagnostic tool is the four-tier test, which is able to measure students' misconceptions in depth by knowing whether students are guessing or not and from then on, there is also a trend toward five-tier diagnostic tests with additional sources of information obtained. So that the origin of students' misconceptions is known. There are several ways to remediate student misconceptions, namely 11 ways and one of the methods used is digital practicum so that abstract things can be visualized into real ones. This research is limited to knowing what tools are used to diagnose and remediate misconceptions, so it is not yet known how big the effect of remediation methods is on misconceptions. The researcher recommends that in the future further research can be carried out to find out the most appropriate remediation method for remediating student misconceptions.

Keywords: misconception, remediation, systematic review, tools

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Authors: Haofei Li

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To support academic growth and foster love of learning, MathMates has been introduced for grade 6-8 students at Andrew Hamilton public school in 2022. The program is targeted at students from diverse backgrounds, particularly those underperforming in Pennsylvania System of School Assessment (PSSA) exams. Then, this study aims to evaluate the efficacy of MathMates by comparing student performance on the PSSA test, before and after the intervention. Through a randomized control trial, the study will collect associated costs using the ingredients method and measure the effectiveness for cost-effectiveness analysis. Text messages will be sent to parents/guardians as a reminder of the program and to encourage student participation. The findings of this study will provide valuable insights for funding organizations seeking to understand the impact and costs of math tutoring interventions on student academic achievement, which also emphasizes the importance of the collaborative efforts between higher education and local public schools.

Keywords: mathematics education, mathematics tutoring, college-community initiative, middle schools, Philadelphia public schools, after-school program, PSSA

Procedia PDF Downloads 93

Authors: Yao Chen

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

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

Procedia PDF Downloads 67

Authors: Henry Acurio, Alvaro Corral, Juan Fonseca

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In Ecuador, subventions on fossil fuels for the road transportation sector have always been part of its economy throughout time, mainly because of demagogy and populism from political leaders. It is clearly seen that the government cannot maintain the subsidies anymore due to its commercial balance and its general state budget; subsidies are a key barrier to implementing the use of cleaner technologies. However, during the last few months, the elimination of subsidies has been done gradually with the purpose of reaching international prices. It is expected that with this measure, the population will opt for other means of transportation, and in a certain way, it will promote the use of private electric vehicles and public, e.g., taxis and buses (urban transport). Considering the three main elements of sustainable development, an analysis of the social, economic, and environmental impacts of eliminating subsidies will be generated at the country level. To achieve this, four scenarios will be developed in order to determine how the subsidies will contribute to the promotion of electro-mobility: 1) A Business as Usual (BAU) scenario; 2) the introduction of 10 000 electric vehicles by 2025; 3) the introduction of 100 000 electric vehicles by 2030; 4) the introduction of 750 000 electric vehicles by 2040 (for all the scenarios, buses, taxis, lightweight duty vehicles, and private vehicles will be introduced, as it is established in the National Electro Mobility Strategy for Ecuador). The Low Emissions Analysis Platform (LEAP) will be used, and it will be suitable to determine the cost for the government in terms of importing derivatives for fossil fuels and the cost of electricity to power the electric fleet that can be changed. The elimination of subventions generates fiscal resources for the state that can be used to develop other kinds of projects that will benefit Ecuadorian society. It will definitely change the energy matrix, and it will provide energy security for the country; it will be an opportunity for the government to incentivize a greater introduction of renewable energies, e.g., solar, wind, and geothermal. At the same time, it will also reduce greenhouse gas emissions (GHG) from the transportation sector, considering its mitigation potential, which as a result, will ameliorate the inhabitant quality of life by improving the quality of air, therefore reducing respiratory diseases associated with exhaust emissions, consequently, achieving sustainability, the Sustainable Development Goals (SDGs), and complying with the agreements established in the Paris Agreement COP 21 in 2015. Electro-mobility in Latin America and the Caribbean can only be achieved by the implementation of the right policies by the central government, which need to be accompanied by a National Urban Mobility Policy (NUMP), and can encompass a greater vision to develop holistic, sustainable transport systems at local governments.

Keywords: electro mobility, energy, policy, sustainable transportation

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Authors: Aayushi Somani, Siba P. Samal

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Three-dimensional (3D) meshes are data structures, which store geometric information of an object or scene, generally in the form of vertices and edges. Current technology in laser scanning and other geometric data acquisition technologies acquire high resolution sampling which leads to high resolution meshes. While high resolution meshes give better quality rendering and hence is used often, the processing, as well as storage of 3D meshes, is currently resource-intensive. At the same time, web applications for data processing have become ubiquitous owing to their accessibility. For 3D meshes, the advancement of 3D web technologies, such as WebGL, WebVR, has enabled high fidelity rendering of huge meshes. However, there exists a gap in ability to stream huge meshes to a native client and browser application due to high network latency. Also, there is an inherent delay of loading WebGL pages due to large and complex models. The focus of our work is to identify the challenges faced when such meshes are streamed into and processed on hand-held devices, owing to its limited resources. One of the solutions that are conventionally used in the graphics community to alleviate resource limitations is mesh compression. Our approach deals with a two-step approach for random accessible progressive compression and its parallel implementation. The first step includes partition of the original mesh to multiple sub-meshes, and then we invoke data parallelism on these sub-meshes for its compression. Subsequent threaded decompression logic is implemented inside the Web Browser Engine with modification of WebGL implementation in Chromium open source engine. This concept can be used to completely revolutionize the way e-commerce and Virtual Reality technology works for consumer electronic devices. These objects can be compressed in the server and can be transmitted over the network. The progressive decompression can be performed on the client device and rendered. Multiple views currently used in e-commerce sites for viewing the same product from different angles can be replaced by a single progressive model for better UX and smoother user experience. Can also be used in WebVR for commonly and most widely used activities like virtual reality shopping, watching movies and playing games. Our experiments and comparison with existing techniques show encouraging results in terms of latency (compressed size is ~10-15% of the original mesh), processing time (20-22% increase over serial implementation) and quality of user experience in web browser.

Keywords: 3D compression, 3D mesh, 3D web, chromium, client-server architecture, e-commerce, level of details, parallelization, progressive compression, WebGL, WebVR

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Authors: Javier Barrachina, Piedad Garrido, Manuel Fogue, Julio A. Sanguesa, Francisco J. Martinez

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It is very common to observe, especially in Computer Science studies that students have difficulties to correctly understand how some mechanisms based on Artificial Intelligence work. In addition, the scope and limitations of most of these mechanisms are usually presented by professors only in a theoretical way, which does not help students to understand them adequately. In this work, we focus on the problems found when teaching Evolutionary Algorithms (EAs), which imitate the principles of natural evolution, as a method to solve parameter optimization problems. Although this kind of algorithms can be very powerful to solve relatively complex problems, students often have difficulties to understand how they work, and how to apply them to solve problems in real cases. In this paper, we present two interactive graphical applications which have been specially designed with the aim of making Evolutionary Algorithms easy to be understood by students. Specifically, we present: (i) TSPS, an application able to solve the ”Traveling Salesman Problem”, and (ii) FotEvol, an application able to reconstruct a given image by using Evolution Strategies. The main objective is that students learn how these techniques can be implemented, and the great possibilities they offer.

Keywords: education, evolutionary algorithms, evolution strategies, interactive learning applications

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Authors: Md. Kamal Uddin

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Bangladesh is trying to achieve Sustainable Development Goals (SDGs) by 2030. Environmental Education (EE) is very vital to reaching the agenda of SDGs. However, a lack of environmental awareness and gaps in theoretical knowledge and its practices still exists in Bangladesh. Therefore, this research aims to understand the students’ perceptions of whether and how their behaviour is environment-friendly to achieve SDGs. It also addresses teachers’ perceptions of what are the shortcomings of environmental education in Bangladesh. It uses the qualitative and quantitative techniques of data collection and analysis based on in-depth interviews, surveys among different categories of participants and classroom observation. The paper finds that the level of EE and students’ awareness of the environment is inadequate. Some teachers believe that the EE is not better in Bangladesh due to the absence of practical learning of EE, lack of the motivations and actions, institutional weakness, inadequate policies, poor implementation, and cultural and traditional beliefs. Thus, this paper argues that Bangladeshi EE is not adequate to change the behaviour of the students towards the environment, which makes it difficult for the country to ensure SD. Thus, this research suggests that there is a need to revise the environmental education policy to change the behaviour and structure of the country for sustainable development.

Keywords: environmental education, sustainable development, environmental practice, environmental behaviour, Bangladesh

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Authors: Yi Meng, YunGao

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Having been studied and worked in North America and Europe, we, two Chinese art educators, have been enormously influenced by eastern and western cultures. Thus, we aim to enhance students’ learning experiences by exploring and amalgamating both cultures for art creating. This text draws on our action research study of students’ visual literacy practices in a foundation sketching course in a major Chinese university, exploring art forms by cross-utilizing various cultural aspects. Instead of relying on the predominant western observational drawing skills in our classroom, we taught students about ancient Chinese art in the provincial museum, using Fu Hao owl-shaped vessel, a Shang Dynasty national treasure, as the final sketch project of this course. We took up multimodal literacy, which emphasized students’ critical use of creativity to exploit the semiotic potentials of communicative modes to address diverse cultural issues through their multimodal design. We used the Hong Kong-based artist Tik Ka’s artworks to demonstrate the cultural amalgamation of Chinese traditions and western pop cultures. Collectively, these approaches create a dialogical space for students to experience, analyze, and negotiate with complex modes and potentially transform their understanding of both cultures by redesigning Fu Hao.

Keywords: Chinese traditions, western pop cultures, Fu Hao, arts education, design sketch

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Authors: Elizabeth M. Seabrook, Nikki S. Rickard

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Examining time-dependent measures of emotion such as variability, instability, and inertia, provide critical and complementary insights into mental health status. Observing changes in the pattern of emotional expression over time could act as a tool to identify meaningful shifts between psychological well- and ill-being. From a practical standpoint, however, examining emotion dynamics day-to-day is likely to be burdensome and invasive. Utilizing social media data as a facet of lived experience can provide real-world, temporally specific access to emotional expression. Emotional language on social media may provide accurate and sensitive insights into individual and community mental health and well-being, particularly with focus placed on the within-person dynamics of online emotion expression. The objective of the current study was to examine the dynamics of emotional expression on the social network platform Facebook for active users and their relationship with psychological well- and ill-being. It was expected that greater positive and negative emotion variability, instability, and inertia would be associated with poorer psychological well-being and greater depression symptoms. Data were collected using a smartphone app, MoodPrism, which delivered demographic questionnaires, psychological inventories assessing depression symptoms and psychological well-being, and collected the Status Updates of consenting participants. MoodPrism also delivered an experience sampling methodology where participants completed items assessing positive affect, negative affect, and arousal, daily for a 30-day period. The number of positive and negative words in posts was extracted and automatically collated by MoodPrism. The relative proportion of positive and negative words from the total words written in posts was then calculated. Preliminary analyses have been conducted with the data of 9 participants. While these analyses are underpowered due to sample size, they have revealed trends that greater variability in the emotion valence expressed in posts is positively associated with greater depression symptoms (r(9) = .56, p = .12), as is greater instability in emotion valence (r(9) = .58, p = .099). Full data analysis utilizing time-series techniques to explore the Facebook data set will be presented at the conference. Identifying the features of emotion dynamics (variability, instability, inertia) that are relevant to mental health in social media emotional expression is a fundamental step in creating automated screening tools for mental health that are temporally sensitive, unobtrusive, and accurate. The current findings show how monitoring basic social network characteristics over time can provide greater depth in predicting risk and changes in depression and positive well-being.

Keywords: emotion, experience sampling methods, mental health, social media

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Authors: Nicole Pyle, Daniel Pyle, Christa Haring, Marty Hougen

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Effective special education teachers utilize evidence-based practices for adolescent reading instruction and target the skills needed to improve the reading of older struggling readers. High-Leverage Practices (HLPs) are critical to helping students with disabilities learn important content. Therefore, special education teachers are encouraged to implement HLPs to maximize the learning of students with disabilities, including students with reading difficulties. Teachers’ implementation of HLPs in reading comprehension instruction should aim to develop adolescents’ understanding of grade-level narrative texts and informational texts, including content area texts. Instruction High-Leverage Practices (11-22) that ensure effective implementation of evidence-based practice in reading comprehension instruction for adolescents are presented. Effective reading comprehension activities within the 12 Instruction HLPs are illustrated.

Keywords: high-leverage practices, adolescent, instructional activities, students with disabilities

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