Search results for: batch-constrained reinforcement learning

Authors: Terence Soule, Tami Al Ghamdi

Abstract:

To study how the partial knowledge transfer may affect the Genetic Algorithm (GA) performance, we model the Transfer Learning (TL) process using GA as the model solver. The objective of the TL is to transfer the knowledge from one problem to another related problem. This process imitates how humans think in their daily life. In this paper, we proposed to study a case where the knowledge transferred from the S problem has less information than what the T problem needs. We sampled the transferred population using different strategies of TL. The results showed transfer part of the knowledge is helpful and speeds the GA process of finding a solution to the problem.

Keywords: transfer learning, partial transfer, evolutionary computation, genetic algorithm

Procedia PDF Downloads 132

Authors: Wanyi Zhu, Xia Ming, Huafeng Wang, Junda Chen, Lu Liu, Jiangwei Jiang, Guohua Liu

Abstract:

Anomaly detection is a first and imperative step needed to respond to unexpected problems and to assure high performance and security in large data center management. This paper presents an online anomaly detection system through an innovative approach of ensemble machine learning and adaptive differentiation algorithms, and applies them to performance data collected from a continuous monitoring system for multi-tier web applications running in Alibaba data centers. We evaluate the effectiveness and efficiency of this algorithm with production traffic data and compare with the traditional anomaly detection approaches such as a static threshold and other deviation-based detection techniques. The experiment results show that our algorithm correctly identifies the unexpected performance variances of any running application, with an acceptable false positive rate. This proposed approach has already been deployed in real-time production environments to enhance the efficiency and stability in daily data center operations.

Keywords: Alibaba data centers, anomaly detection, big data computation, dynamic ensemble learning

Procedia PDF Downloads 201

Authors: Vania Helena Tonussi Vidal, Ester Eliane Jeunon

Abstract:

This article aims to analyze the change processes of management related bureaucracy and learning organization model. The theoretical framework was based on Beer and Nohria (2001) model, identified as E and O Theory. Based on this theory the empirical research was conducted in connection with six key dimensions: goal, leadership, focus, process, reward systems and consulting. We used a case study of an educational Institution located in Barbacena, Minas Gerais. This traditional center of technical knowledge for long time adopted the bureaucratic way of management. After many changes in a business model, as the creation of graduate and undergraduate courses they decided to make a deep change in management model that is our research focus. The data were collected through semi-structured interviews with director, managers and courses supervisors. The analysis were processed by the procedures of Collective Subject Discourse (CSD) method, develop by Lefèvre & Lefèvre (2000), Results showed the incremental growing of management model toward a learning organization. Many impacts could be seeing. As negative factors we have: people resistance; poor information about the planning and implementation process; old politics inside the new model and so on. Positive impacts are: new procedures in human resources, mainly related to manager skills and empowerment; structure downsizing, open discussions channel; integrated information system. The process is still under construction and now great stimulus is done to managers and employee commitment in the process.

Keywords: bureaucracy, organizational learning, organizational change, E and O theory

Procedia PDF Downloads 434

Authors: Paulo Sérgio Ribeiro de Araújo Bogas

Abstract:

Although several studies have assumed (at least implicitly) that learners' approaches to learning develop into deeper approaches to higher education, there appears to be no clear theoretical basis for this assumption and no empirical evidence. As a scientific contribution to this discussion, a pedagogical intervention of a quasi-experimental nature was developed, with a mixed methodology, evaluating the intervention within a single curricular unit of Marketing, using cases based on real challenges of brands, business simulation, and customer projects. Primary and secondary experiences were incorporated in the intervention: the primary experiences are the experiential activities themselves; the secondary experiences result from the primary experience, such as reflection and discussion in work teams. A diversified learning relationship was encouraged through the various connections between the different members of the learning community. The present study concludes that in the same context, the student's responses can be described as students who reinforce the initial deep approach, students who maintain the initial deep approach level, and others who change from an emphasis on the deep approach to one closer to superficial. This typology did not always confirm studies reported in the literature, namely, whether the initial level of deep processing would influence the superficial and the opposite. The result of this investigation points to the inclusion of pedagogical and didactic activities that integrate different motivations and initial strategies, leading to the possible adoption of deep approaches to learning since it revealed statistically significant differences in the difference in the scores of the deep/superficial approach and the experiential level. In the case of real challenges, the categories of “attribution of meaning and meaning of studied” and the possibility of “contact with an aspirational context” for their future professional stand out. In this category, the dimensions of autonomy that will be required of them were also revealed when comparing the classroom context of real cases and the future professional context and the impact they may have on the world. Regarding the simulated practice, two categories of response stand out: on the one hand, the motivation associated with the possibility of measuring the results of the decisions taken, an awareness of oneself, and, on the other hand, the additional effort that this practice required for some of the students.

Keywords: experiential learning, higher education, mixed methods, reflective learning, marketing

Procedia PDF Downloads 83

Authors: Vineesh Amin, Ananya Agrawal

Abstract:

In a prolonged period of uncertainty and disruptions in the pre-said normal order, non-cognitive skills, especially entrepreneurial mindsets, have become a pillar that can reform the educational models to inform the economy. Dreamverse Learning Lab’s IVR-based storytelling program -Call-a-Kahaani- is an evolving experiment with an aim to kindle entrepreneurial mindsets in the remotest locations of India in an accessible and engaging manner. At the heart of this experiment is the belief that at every phase in our life’s story, we have a choice which brings us closer to achieving our true potential. This interactive program is thus designed using real-time storytelling principles to empower learners, ages 24 and below, to make choices and take decisions as they become more self-aware, practice grit, try new things through stories, guided activities, and interactions, simply over a phone call. This research paper highlights the framework behind an ongoing scalable, data-oriented, low-tech program to kindle entrepreneurial mindsets in media dark zones supported by iterative design and prototyping to reach 13700+ unique learners who made 59000+ calls for 183900+min listening duration to listen to content pieces of around 3 to 4 min, with the last monitored (March 2022) record of 34% serious listenership, within one and a half years of its inception. The paper provides an in-depth account of the technical development, content creation, learning, and assessment frameworks, as well as mobilization models which have been leveraged to build this end-to-end system.

Keywords: non-cognitive skills, entrepreneurial mindsets, speech interface, remote learning, storytelling

Procedia PDF Downloads 209

Authors: Kaine Gulozer

Abstract:

A strong methodological conception in the practice of teaching, content, and language integrated learning (CLIL) is adapted to boost efficiency in the second language (L2) instruction with a range of proficiency levels. This study aims to investigate whether the incorporation of two different mediums of meaningful CLIL reading activities (in-school and out-of-school settings) influence L2 students’ development of comprehension skills differently. CLIL based instructional methodology was adopted and total of 50 preparatory year students (N=50, 25 students for each proficiency level) from two distinct language proficiency learners (elementary and intermediate) majoring in engineering faculties were recruited for the study. Both qualitative and quantitative methods through a post-test design were adopted. Data were collected through a questionnaire, a reading comprehension test and a semi-structured interview addressed to the two proficiency groups. The results show that both settings in relation to the development of reading comprehension are beneficial, whereas the impact of the reading activities conducted in school settings was higher at the elementary language level of students than that of the one conducted out-of-class settings based on the reported interview results. This study suggests that the incorporation of meaningful CLIL reading activities in both settings for both proficiency levels could create students’ self-awareness of their language learning process and the sense of ownership in successful improvements of field-specific reading comprehension. Further potential suggestions and implications of the study were discussed.

Keywords: content and language integrated learning, in-school setting, language proficiency, out-of-school setting, reading comprehension

Procedia PDF Downloads 146

Authors: Neha J. Nandaniya

Abstract:

In the present research paper researcher put focuses on the opinions of pre-service teachers have been taken regarding online language teaching, which was held during the COVID-19 pandemic and is still going on. The researcher developed a three-point rating scale in Google Forms to find out the views of trainees on online language learning, in which 167 B. Ed. trainees having language content and method gave their responses. After scoring the responses obtained by the investigator, the chi-square value was calculated, and the findings were concluded. The major finding of the study is language learning is not as effective as offline teaching mode.

Keywords: online language teaching, ICT competency, B. Ed. trainees, COVID-19 pandemic

Procedia PDF Downloads 84

Authors: Danica Maljkovic, Igor Balen, Bojana Dalbelo Basic

Abstract:

Following the regulation of EU Directive on Energy Efficiency, specifically Article 9, individual metering in district heating systems has to be introduced by the end of 2016. These directions have been implemented in member state’s legal framework, Croatia is one of these states. The directive allows installation of both heat metering devices and heat cost allocators. Mainly due to bad communication and PR, the general public false image was created that the heat cost allocators are devices that save energy. Although this notion is wrong, the aim of this work is to develop a model that would precisely express the influence of installation heat cost allocators on potential energy savings in each unit within multifamily buildings. At the same time, in recent years, a science of machine learning has gain larger application in various fields, as it is proven to give good results in cases where large amounts of data are to be processed with an aim to recognize a pattern and correlation of each of the relevant parameter as well as in the cases where the problem is too complex for a human intelligence to solve. A special method of machine learning, decision tree method, has proven an accuracy of over 92% in prediction general building consumption. In this paper, a machine learning algorithms will be used to isolate the sole impact of installation of heat cost allocators on a single building in multifamily houses connected to district heating systems. Special emphasises will be given regression analysis, logistic regression, support vector machines, decision trees and random forest method.

Keywords: district heating, heat cost allocator, energy efficiency, machine learning, decision tree model, regression analysis, logistic regression, support vector machines, decision trees and random forest method

Procedia PDF Downloads 249

Authors: Mehrnaz Mostafavi

Abstract:

The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

Procedia PDF Downloads 101

Authors: Mustafa Alhamdi

Abstract:

Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

Procedia PDF Downloads 150

Authors: Chang Seok Lee, Sang Whan Han, Girbo Ko, Debbie Kim

Abstract:

Existing reinforced concrete structures are designed and built without considering seismic loads. The columns in such buildings generally exhibit widely spaced transverse reinforcements without using seismic hooks. Due to the insufficient reinforcement details in columns, brittle shear failure is expected in columns that may cause pre-mature building collapse mechanism during earthquakes. In order to retrofit those columns, the accurate seismic behavior of the columns needs to be predicted with proper analytical models. In this study, an analytical model is proposed for accurately simulating the cyclic behavior of shear critical columns. The parameters for pinching and cyclic deterioration in strength and stiffness are calibrated using test data of column specimens failed by shear.

Keywords: analytical model, cyclic deterioration, existing reinforced concrete columns, shear failure

Procedia PDF Downloads 265

Authors: Hireni Mankodi

Abstract:

The textile preforms for thermoplastic composite play a key role in providing the mechanical properties and gives the idea about preparing combination of yarn from Glass, Basalt, Carbon as reinforcement and PP, PET, Nylon as thermoplastic matrix at yarn stage for preforms to improve the quality and performance of laminates. The main objectives of this work are to develop the hybrid yarn using different yarn manufacturing process and prepare different performs using hybrid yarns. It has been observed that the glass/pp combination give homogeneous distribution in yarn. The proportion varied to optimize the glass/pp composition. The different preform has been prepared with combination of hybrid yarn, PP, glass combination. Further studies will investigate the effect of glass content in fabric, effect of weave, warps and filling density, number of layer plays significant role in deciding mechanical properties of thermoplastic laminates.

Keywords: thermoplastic, preform, laminates, hybrid yarn, glass

Procedia PDF Downloads 581

Authors: Saba Gachpaz, Hamid Reza Heidari

Abstract:

The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

Procedia PDF Downloads 84

Authors: Nguyen J. M., Lucas G., Brunner M., Ruan S., Antonioli D.

Abstract:

Deep learning based on convolutional neural networks (CNN) is a very powerful technique for classifying information from an image. We propose a new method, DeepNic, to transform each variable of a tabular dataset into an image where each pixel represents a set of conditions that allow the variable to make an error-free prediction. The contrast of each pixel is proportional to its prediction performance and the color of each pixel corresponds to a sub-family of NICs. NICs are probabilities that depend on the number of inputs to each neuron and the range of coefficients of the inputs. Each variable can therefore be expressed as a function of a matrix of 2 vectors corresponding to an image whose pixels express predictive capabilities. Our objective is to transform each variable of tabular data into images into an image that can be analysed by CNNs, unlike other methods which use all the variables to construct an image. We analyse the NIC information of each variable and express it as a function of the number of neurons and the range of coefficients used. The predictive value and the category of the NIC are expressed by the contrast and the color of the pixel. We have developed a pipeline to implement this technology and have successfully applied it to genomic expressions on an Affymetrix chip.

Keywords: tabular data, deep learning, perfect trees, NICS

Procedia PDF Downloads 90

Authors: Kevin R. Wilson, Roger Mantie

Abstract:

Community arts-based learning and participatory education are pedagogical techniques that serve to be advantageous for students, curriculum development, and local communities. Using an interpretive approach to examine the significance of this arts-informed research in relation to the eight ‘design imperatives’ proposed as the new model for measuring quality in scholarship for Arizona State University as ‘A New American University’, the purpose of this study was to investigate personal, social, and cultural benefits resulting from student engagement in interdisciplinary community-based projects. Students from a graduate level music education class at the ASU Tempe campus (n=7) teamed with students from an undergraduate level community development class at the ASU Downtown Phoenix campus (n=14) to plan, facilitate, and evaluate seven community-based projects in several locations around the Phoenix-metro area. Data was collected using photo evidence, student reports, and evaluative measures designed by the students. The effectiveness of each project was measured in terms of their ability to meet the eight design imperatives to: 1) leverage place; 2) transform society; 3) value entrepreneurship; 4) conduct use-inspired research; 5) enable student success; 6) fuse intellectual disciplines; 7) be socially embedded; and 8) engage globally. Results indicated that this community arts-based project sufficiently captured the essence of each of these eight imperatives. Implications for how the nature of this interdisciplinary initiative allowed for the eight imperatives to manifest are provided, and project success is expounded upon in relation to utility of each imperative. Discussion is also given for how this type of service learning project formatted within the ‘New American University’ model for measuring quality in academia can be a beneficial pedagogical tool in higher education.

Keywords: community arts-based learning, participatory education, pedagogy, service learning

Procedia PDF Downloads 401

Authors: May Ling González-Ruiz, Ana Cristina Solís-Solís

Abstract:

Everyday more people can thrive towards the dream of pursuing a university diploma. This can be more attainable for some than for others who may face different types of limitations. Even though not all limitations come from within the individual but most of the times they come from without it may include the environment, the support of the person’s family, the school – its infrastructure, administrative procedures, and attitudes. This is a qualitative type of research that is developed through a case study. It is based on the experiences of two students who are visually impaired and who have attended a public university in Costa Rica. We enquire about the experiences of these two students in the English as a Foreign Language courses at the university scenario. An in-depth analysis of their lived experiences is presented. Their values, attitudes, and expectations serve as the guiding elements for this research. Findings are presented in light of the Social Justice Approach to inclusive education. Some of the most salient aspects found have to do with the attitudes the students used to face challenges; others point at those elements that may have hindered the learning experience of the persons observed and to those that encouraged them to continue their journey and successfully achieve a diploma.

Keywords: inclusion, case study, visually impaired student, learning experience, social justice approach

Procedia PDF Downloads 138

Authors: Saeed Eskandari, Seyed Rasoul Mehdikhani

Abstract:

Radiation sources have been used in many industries, such as gamma sources in medical imaging. These waves have destructive effects on humans and the environment. It is very important to detect and find the source of these waves because these sources cannot be seen by the eye. A portable robot has been designed and built with the purpose of revealing radiation sources that are able to scan the place from 5 to 20 meters away and shows the location of the sources according to the intensity of the waves on a two-dimensional digital image. The operation of the robot is done by measuring the pixels separately. By increasing the image measurement resolution, we will have a more accurate scan of the environment, and more points will be detected. But this causes a lot of time to be spent on scanning. In this paper, to overcome this challenge, we designed a method that can optimize this time. In this method, a small number of important points of the environment are measured. Hence the remaining pixels are predicted and estimated by regression algorithms in machine learning. The research method is based on comparing the actual values of all pixels. These steps have been repeated with several other radiation sources. The obtained results of the study show that the values estimated by the regression method are very close to the real values.

Keywords: regression, machine learning, scan radiation, robot

Procedia PDF Downloads 79

Authors: I. A. Libata

Abstract:

The present study was carried out to determine the attitudes of Secondary School Students towards Biology in Birnin Kebbi metropolis. The population of the study is 2680 SS 2 Secondary School Students in Birnin Kebbi metropolis. Proportionate random sampling was used in selecting the samples. Oppinnionnaire was the only instrument used in the study. The instrument was subjected to test-retest reliability. The reliability index of the instrument was 0.69. Overall scores of the Students were analyzed and a mean score was determined, the mean score of students was 85. There were no significant differences between the attitudes of male and female students. The results also revealed that there was significant difference between the attitude of science and art students. The results also revealed that there was significant difference between the attitude of public and private school students. The study also reveals that majority of students in Birnin Kebbi Metropolis have positive attitudes towards biology. Based on the findings of this study, the researcher recommended that teachers should motivate students, which they can do through their teaching styles and by showing them the relevance of the learning topics to their everyday lives. Government and the school management should create the learning environment that helps motivate students not only to come to classes but also want to learn and enjoy learning Biology.

Keywords: attitudes, students, Birnin-Kebbi, metropolis

Procedia PDF Downloads 402

Authors: Ahmad Saleem

Abstract:

Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.

Keywords: blockchain, Ethereum, cryptocurrency, prediction

Procedia PDF Downloads 97

Authors: Inkeri Jaaskelainen

Abstract:

The aim of this study is to shed light on what is it like to study in apprenticeship training using intermediate (or even lower level) Finnish. The aim is to find out and describe these students' experiences and feelings while acquiring a profession in Finnish as it is important to understand how immigrant background adult learners learn and how their needs could be better taken into account. Many students choose apprenticeships and start vocational training while their language skills in Finnish are still very weak. At work, students should be able to simultaneously learn Finnish and do vocational studies in a noisy, demanding, and stressful environment. Learning and understanding new things is very challenging under these circumstances, and sometimes students get exhausted and experience a lot of stress - which makes learning even more difficult. Students are different from each other, and so are their ways to learn. Both duties at work and school assignments require reasonably good general language skills, and, especially at work, language skills are also a safety issue. The empirical target of this study is a group of students with an immigrant background who studied in various fields with intensive L2 support in 2016–2018 and who by now have completed a vocational upper secondary qualification. The interview material for this narrative study was collected from those who completed apprenticeship training in 2019–2020. The data collection methods used are a structured thematic interview, a questionnaire, and observational data. Interviewees with an immigrant background have an inconsistent cultural and educational background - some have completed an academic degree in their country of origin while others have learned to read and write only in Finland. The analysis of the material utilizes thematic analysis, which is used to examine learning and related experiences. Learning a language at work is very different from traditional classroom teaching. With evolving language skills, at an intermediate level at best, rushing and stressing makes it even more difficult to understand and increases the fear of failure. Constant noise, rapidly changing situations, and uncertainty undermine the learning and well-being of apprentices. According to preliminary results, apprenticeship training is well suited to the needs of an adult immigrant student. In apprenticeship training, students need a lot of support for learning and understanding a new communication and working culture. Stress can result in, e.g., fatigue, frustration, and difficulties in remembering and understanding. Apprenticeship training can be seen as a good path to working life. However, L2 support is a very important part of apprenticeship training, and it indeed helps students to believe that one day they will graduate and even get employed in their new country.

Keywords: apprenticeship training, vocational basic degree, Finnish learning, wee-being

Procedia PDF Downloads 133

Authors: Rohan Roberts

Abstract:

The purpose of this project was to collaborate with a group of high-functioning, more-able students (aged 15-18) to promote STEM Education and a love for science by bringing a cosmic perspective into the classroom and high school environment. This was done using 21st century science of learning, a focus on the latest research on Neuroeducation, and modern pedagogical methods based on Howard Gardner's theory of Multiple Intelligences, Bill Lucas’ theory of New Smarts, and Sir Ken Robinson’s recommendations on encouraging creativity. The result was an increased sense of passion, excitement, and wonder about science in general, and about the marvels of space and the universe in particular. In addition to numerous unique and innovative science-based initiatives, clubs, workshops, and science trips, this project also saw a marked rise in student-teacher collaboration in science learning and in student engagement with the general public through the press, social media, and community-based initiatives. This paper also outlines the practical impact that bringing a cosmic perspective into the classroom has had on the lives, interests, and future career prospects of the students involved in this endeavour.

Keywords: cosmic perspective, gifted and talented, neuro-education, STEM education

Procedia PDF Downloads 334

Authors: Sara Honarparast, Omar Chaallal

Abstract:

Reinforced concrete (RC) coupled shear walls (CSWs) are very effective structural systems in resisting lateral loads due to winds and earthquakes and are particularly used in medium- to high-rise RC buildings. However, most of existing old RC structures were designed for gravity loads or lateral loads well below the loads specified in the current modern seismic international codes. These structures may behave in non-ductile manner due to poorly designed joints, insufficient shear reinforcement and inadequate anchorage length of the reinforcing bars. This has been the main impetus to investigate an appropriate strengthening method to address or attenuate the deficiencies of these structures. The objective of this paper is to twofold: (i) evaluate the seismic performance of existing reinforced concrete coupled shear walls under reversed cyclic loading; and (ii) investigate the seismic performance of RC CSWs strengthened with externally bonded (EB) carbon fiber reinforced polymer (CFRP) sheets. To this end, two CSWs were considered as follows: (a) the first one is representative of old CSWs and therefore was designed according to the 1941 National Building Code of Canada (NBCC, 1941) with conventionally reinforced coupling beams; and (b) the second one, representative of new CSWs, was designed according to modern NBCC 2015 and CSA/A23.3 2014 requirements with diagonally reinforced coupling beam. Both CSWs were simulated using ANSYS software. Nonlinear behavior of concrete is modeled using multilinear isotropic hardening through a multilinear stress strain curve. The elastic-perfectly plastic stress-strain curve is used to simulate the steel material. Bond stress–slip is modeled between concrete and steel reinforcement in conventional coupling beam rather than considering perfect bond to better represent the slip of the steel bars observed in the coupling beams of these CSWs. The old-designed CSW was strengthened using CFRP sheets bonded to the concrete substrate and the interface was modeled using an adhesive layer. The behavior of CFRP material is considered linear elastic up to failure. After simulating the loading and boundary conditions, the specimens are analyzed under reversed cyclic loading. The comparison of results obtained for the two unstrengthened CSWs and the one retrofitted with EB CFRP sheets reveals that the strengthening method improves the seismic performance in terms of strength, ductility, and energy dissipation capacity.

Keywords: carbon fiber reinforced polymer, coupled shear wall, coupling beam, finite element analysis, modern code, old code, strengthening

Procedia PDF Downloads 197

Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

Abstract:

Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

Procedia PDF Downloads 145

Authors: Ava Dodd, Jonathan Adams

Abstract:

The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools, and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represents another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

Procedia PDF Downloads 225

Authors: Akash K. Walunj, Dipendu Bhunia, Samarth Gupta, Prabhat Gupta

Abstract:

Coupled shear walls consist of two shear walls connected intermittently by beams along the height. The behavior of coupled shear walls is mainly governed by the coupling beams. The coupling beams are designed for ductile inelastic behavior in order to dissipate energy. The base of the shear walls may be designed for elastic or ductile inelastic behavior. The amount of energy dissipation depends on the yield moment capacity and plastic rotation capacity of the coupling beams. In this paper, an analytical model of coupling beam was developed to calculate the rotations and moment capacities of coupling beam with conventional reinforcement.

Keywords: design studies, computational model(s), case study/studies, modelling, coupling beam

Procedia PDF Downloads 476

Authors: Colleen Cleveland, W. Adam Baldowski

Abstract:

In the dynamic realm of online education, the integration of games and entertainment has emerged as a powerful strategy to captivate learners, drive active participation, and cultivate meaningful learning experiences. This abstract presents an overview of the upcoming conference, "Game On," dedicated to exploring the transformative impact of gamification, interactive simulations, and multimedia content in the digital learning landscape. Introduction: The conference aims to blur the traditional boundaries between education and entertainment, inspiring learners of diverse ages and backgrounds to actively engage in their online learning journeys. By leveraging the captivating elements of games and entertainment, educators can enhance motivation, retention, and deep understanding among virtual classroom participants. Conference Highlights: Commencing with an exploration of theoretical foundations drawing from educational psychology, instructional design, and the latest pedagogical research, participants will gain valuable insights into the ways gamified elements elevate the quality of online education. Attendees can expect interactive sessions, workshops, and case studies showcasing best practices and innovative strategies, including game-based assessments and virtual reality simulations. Inclusivity and Diversity: The conference places a strong emphasis on inclusivity, accessibility, and diversity in the integration of games and entertainment for educational purposes. Discussions will revolve around accommodating diverse learning styles, overcoming potential challenges, and ensuring equitable access to engaging educational content for all learners. Educational Transformation: Educators, instructional designers, and e-learning professionals attending "Game On" will acquire practical techniques to elevate the quality of their online courses. The conference promises a stimulating and informative exploration of blending education with entertainment, unlocking the untapped potential of games and entertainment in online education. Conclusion: "Game On" invites participants to embark on a journey that transforms online education by harnessing the power of entertainment. This event promises to be a cornerstone in the evolution of virtual learning, offering valuable insights for those seeking to create a more engaging and effective online educational experience. Join us as we explore new horizons, pushing the boundaries of online education through the fusion of games and entertainment.

Keywords: online education, games, entertainment, psychology, therapy, pop culture

Procedia PDF Downloads 51

Authors: Inezia Aurelia, Soebowo Musa

Abstract:

Many firms still fail to develop organizational agility. There are more than 40% of organizations think that they are low/not agile in facing market change. Organizational culture plays an important role in developing the organizations to be adaptive in order to manage the VUCA effectively. This study examines the relationships of organizational culture towards participative leadership, employee satisfaction, employee work motivation, organizational learning, and absorptive capacity in developing organizational agility in managing the VUCA environment. 263 employees located from international chemical-based company offices across the globe who have worked for more than three years were the respondents in this study. This study showed that organizational clan culture promotes the development of participative leadership, which it has an empowering effect on people in the organization resulting in employee satisfaction. The study also confirms the role of organizational culture in creating organizational behavior within the organization that fosters organizational learning, absorptive capacity, and organizational agility, while the study also found that the relationship between participative leadership and employee work motivation is not significant.

Keywords: absorptive capacity, employee satisfaction, employee work motivation, organizational agility, organizational culture, organizational learning, participative leadership

Procedia PDF Downloads 123

Authors: Shaan Khosla, Jon Krohn

Abstract:

In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

Procedia PDF Downloads 84

Authors: Hirokatsu Kawashima

Abstract:

In an attempt to elucidate relationships between listening to music and pronunciation learning, a classroom-based investigation was conducted with Japanese EFL learners (n=45). The subjects were instructed to listen to English songs they liked on YouTube, especially paying attention to phonologically similar vowel and consonant minimal pair words (e.g., live and leave). This kind of activity, which included taking notes, was regularly carried out in the classroom, and the same kind of task was given to the subjects as homework in order to reinforce the in-class activity. The duration of these activities was eight weeks, after which the program was evaluated on a 9-point scale (1: the lowest and 9: the highest) by learners’ self-evaluation. The main questions for this evaluation included 1) how good the learners had been at pronouncing vowel and consonant minimal pair words originally, 2) how often they had listened to songs good for pronouncing vowel and consonant minimal pair words, 3) how frequently they had moved their mouths to vowel and consonant minimal pair words of English songs, and 4) how much they thought the program would support and enhance their pronunciation learning of phonologically similar vowel and consonant minimal pair words. It has been found, for example, A) that the evaluation of this program is by no means low (Mean: 6.51 and SD: 1.23), suggesting that listening to music may support and enhance pronunciation learning, and B) that listening to consonant minimal pair words in English songs and moving the mouth to them are more related to the program’s evaluation (r =.69, p=.00 and r =.55, p=.00, respectively) than listening to vowel minimal pair words in English songs and moving the mouth to them (r =.45, p=.00 and r =.39, p=.01, respectively).

Keywords: minimal pair, music, pronunciation, song

Procedia PDF Downloads 319

Authors: Saifunnahar

Abstract:

As a former English colony, the relationship of Bangladesh with the English language goes a long way back. English is taught as a compulsory subject in Bangladesh from an early age starting from grade 1 and continuing through the 12th, yet, students are not competent enough to communicate in English proficiently. To improve students’ English language competency, the Bangladesh Ministry of Education introduced communicative language teaching (CLT) methods in English classrooms in the 1990s. It has been decades since this effort was taken, but the students’ level of proficiency is still not satisfactory. The main reason behind this failure is that CLT-based teaching-learning methods have not been effectively implemented. Very little research has been conducted to address the issues English as a foreign language (EFL) classrooms are facing to carry out CLT methodologies in secondary schools (grades 6 to 10) in Bangladesh. Though the secondary level is crucial for students’ language learning and retention, EFL classrooms are marked with various issues that make teaching-learning harder for teachers and students. This study provides an overview of the status of CLT in EFL classrooms and the reasons behind failing to implement CLT in secondary schools in Bangladesh through an analysis of the qualitative data collected from different literature. Based on the findings, effective approaches have been recommended to employ CLT in EFL classrooms.

Keywords: Bangladesh, communicative language teaching, English as a foreign language, secondary schools, pedagogy

Procedia PDF Downloads 155