Search results for: higher learning institutions

Authors: Mohamad Amin Amini, Mehdi Poursha

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Nonlinear response history analysis (NL-RHA) is a robust analytical tool for estimating the seismic demands of structures responding in the inelastic range. However, because of its conceptual and numerical complications, the nonlinear static procedure (NSP) is being increasingly used as a suitable tool for seismic performance evaluation of structures. The conventional pushover analysis methods presented in various codes (FEMA 356; Eurocode-8; ATC-40), are limited to the first-mode-dominated structures, and cannot take higher modes effect into consideration. Therefore, since more than a decade ago, researchers developed enhanced pushover analysis procedures to take higher modes effect into account. The main objective of this study is to propose an enhanced invariant lateral displacement distribution to take higher modes effect into consideration in performing a displacement-based pushover analysis, whereby a set of laterally applied displacements, rather than forces, is monotonically applied to the structure. For this purpose, the effect of different parameters such as the spectral displacement of ground motion, the modal participation factor, and the effective modal participating mass ratio on the lateral displacement distribution is investigated to find the best distribution. The major simplification of this procedure is that the effect of higher modes is concentrated into a single invariant lateral load distribution. Therefore, only one pushover analysis is sufficient without any need to utilize a modal combination rule for combining the responses. The invariant lateral displacement distribution for pushover analysis is then calculated by combining the modal story displacements using the modal combination rules. The seismic demands resulting from the different procedures are compared to those from the more accurate nonlinear response history analysis (NL-RHA) as a benchmark solution. Two structures of different heights including 10 and 20-story special steel moment resisting frames (MRFs) were selected and evaluated. Twenty ground motion records were used to conduct the NL-RHA. The results show that more accurate responses can be obtained in comparison with the conventional lateral loads when the enhanced modal lateral displacement distributions are used.

Keywords: displacement-based pushover, enhanced lateral load distribution, higher modes effect, nonlinear response history analysis (NL-RHA)

Procedia PDF Downloads 262

Authors: Renae McNair

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Subjective well-being levels were assessed in individuals diagnosed with an autoimmune disease. The current exploratory analysis sought to examine two factors that impact subjective well-being in individuals diagnosed with a chronic health condition. The two factors, resilience, and rumination, were assessed as possible moderators in self-reported levels of subjective well-being were measured. The importance of understanding the psychological state of perceived well-being in an individual diagnosed with an autoimmune disease is important given the impact of the level of subjective well-being on life longevity. In previous research, higher levels of subjective well-being are correlated with longer life longevity, including those individuals who have been diagnosed with an autoimmune disease. Conversely, individuals who report higher levels of negative affect have a shorter length of life longevity. According to the Center for Disease Control (CDC) and a report from the National Health Council, currently, 8-10% of individuals in the United States have been diagnosed with at least one autoimmune disease. Although treatment plans are in place to help manage the physical effects of disease, the psychological state of the person impacts life longevity. Resilience and rumination impact subjective well-being as an outcome in individuals diagnosed with an autoimmune disease. Resilience is the ability to adjust or adapt effectively and positively to unfavorable life conditions or events. Resilience acts as a protective factor in life, allowing those who face adversity to successfully adapt, regardless of the health diagnosis. Rumination is the worry or dwelling on the negative aspects of a given situation. Rumination interrupts the adaptive response, leading to a decrease in well-being. The relationship between resilience and subjective well-being were examined correlated with higher levels of resilience and higher levels of self-reported subjective well-being.

Keywords: subjective well-being, rumination, resilience, autoimmune disease

Procedia PDF Downloads 240

Authors: Wei-Jong Yang, Wei-Hau Du, Pau-Choo Chang, Jar-Ferr Yang, Pi-Hsia Hung

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The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: color moments, visual thing recognition system, SIFT, color SIFT

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Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava

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This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.

Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series

Procedia PDF Downloads 124

Authors: Wei Zhao

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The pandemic crisis disrupted normal classroom practices so that the constraints of the traditional practice became apparent. This turns out to be new opportunities for technology-based learning and teaching. However, how the technology supports the preschool teachers go through this sudden crisis and how preschool teachers conceived of the use of technology, appropriate and design technological artifacts as a mediator of knowledge construction in order to suit young children’s literacy level are rarely explored. This study addresses these issues by looking at the influence of a web-supported teacher community on changes/shifts in preschool teachers’ epistemological beliefs and practices. This teachers’ professional development community was formulated before the pandemic time and developed virtually throughout the home-based learning caused by Covid-19. It served as a virtual and asynchronous community for those teachers to collaboratively plan for and conduct online lessons using the knowledge-building approach for the purpose of sustaining children’s learning curiosity and opening up new learning opportunities during the lock-down period. The knowledge-building approach helps to increase teachers’ collective responsibility to collaboratively work on shared educational goals in the teacher community and awareness of noticing new ideas or innovations in their classroom. Based on the data collected across five months during and after the lock-down period and the activity theory, results show a dynamic interplay between the evolution of the community culture, the growth of teacher community and teachers’ identity transformation and professional development. Technology is useful in this regard not only because it transforms the geographical distance and new gathering guidelines after the outbreak of pandemic into new ways of communal communication and collaboration. More importantly, while teachers selected, monitored and adapted the technology, it acts as a catalyst for changes in teachers’ old teaching practices and epistemological dispositions.

Keywords: activity theory, changes in epistemology and practice, knowledge building, web-based teachers’ professional development community

Procedia PDF Downloads 168

Authors: K. Doi, T. Nishimura, H. Fujimoto

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The number of people who become visually impaired and do not have sufficient tactile experiences has increased by various disease. Especially, many acquired visually impaired persons due to accidents, disorders, and aging cannot adequately read Braille. It is known that learning Braille requires a great deal of time and the acquisition of various skills. In our previous studies, we reported one of the problems in learning Braille. Concretely, the standard Braille size is too small for Braille beginners. And also we are short of the objective data regarding easily readable Braille size. Therefore, it is necessary to conduct various experiments for evaluating Braille size that would make learning easier for beginners. In this study, for the purpose of investigating easy-to-read conditions of vertical and horizontal dot spacing for beginners, we conducted one Braille reading experiment. In this our experiment, we prepared test pieces by use of our original Braille printer with controlling function of Braille size. We specifically considered Braille beginners with acquired visual impairments who were unfamiliar with Braille. Therefore, ten sighted subjects with no experience of reading Braille participated in this experiment. Size of vertical and horizontal dot spacing was following conditions. Each dot spacing was 2.0, 2.3, 2.5, 2.7, 2.9, 3.1mm. The subjects were asked to read one Braille character with controlled Braille size. The results of this experiment reveal that Braille beginners can read Braille accurately and quickly when both vertical and horizontal dot spacing are 3.1 mm or more. This knowledge will be helpful data in considering Braille size for acquired visually impaired persons.

Keywords: paper-based Braille, vertical and horizontal dot spacing, readability, acquired visual impairment, Braille beginner

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Authors: Fawad Azeem

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This paper deals with the comparative analysis of Karachi Electric before and after privatization. Technical as well as financial analysis has been done based on the available KE’s stats for last decade. Karachi Electric has evolved as a better entity in terms of its financial and technical achievements. On the other hand, human resources have been seriously affected due to mass firing of employees from the organizations. Study and analysis show that transparent and unbiased privatization practices on institutions like KE that were in serious trouble can upsurge the standards of the institution. Further, for the betterment of the social circle privatization must not affect the employment opportunities.

Keywords: Karachi Electric, power, energy, privatization

Procedia PDF Downloads 341

Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Shreya Goyal

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The study aims to build user-friendly software to understand patient arrival patterns and compute the number of potential patients who will visit a particular health facility for a given period by using a machine learning algorithm. The underlying machine learning algorithm used in this study is the Support Vector Machine (SVM). Accurate prediction of patient arrival allows hospitals to operate more effectively, providing timely and efficient care while optimizing resources and improving patient experience. It allows for better allocation of staff, equipment, and other resources. If there's a projected surge in patients, additional staff or resources can be allocated to handle the influx, preventing bottlenecks or delays in care. Understanding patient arrival patterns can also help streamline processes to minimize waiting times for patients and ensure timely access to care for patients in need. Another big advantage of using this software is adhering to strict data protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States as the hospital will not have to share the data with any third party or upload it to the cloud because the software can read data locally from the machine. The data needs to be arranged in. a particular format and the software will be able to read the data and provide meaningful output. Using software that operates locally can facilitate compliance with these regulations by minimizing data exposure. Keeping patient data within the hospital's local systems reduces the risk of unauthorized access or breaches associated with transmitting data over networks or storing it in external servers. This can help maintain the confidentiality and integrity of sensitive patient information. Historical patient data is used in this study. The input variables used to train the model include patient age, time of day, day of the week, seasonal variations, and local events. The algorithm uses a Supervised learning method to optimize the objective function and find the global minima. The algorithm stores the values of the local minima after each iteration and at the end compares all the local minima to find the global minima. The strength of this study is the transfer function used to calculate the number of patients. The model has an output accuracy of >95%. The method proposed in this study could be used for better management planning of personnel and medical resources.

Keywords: machine learning, SVM, HIPAA, data

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Authors: Revoti Prasad Bora, Nikita Katyal

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Promotion is a key element in the retail business. Thus, analysis of promotions to quantify their effectiveness in terms of Revenue and/or Margin is an essential activity in the retail industry. However, measuring the sales/revenue uplift is based on estimations, as the actual sales/revenue without the promotion is not present. Further, the presence of Halo and Cannibalization in a multiple parallel promotions’ scenario complicates the problem. Calculating Baseline by considering inter-brand/competitor items or using Halo and Cannibalization's impact on Revenue calculations by considering Baseline as an interpretation of items’ unit sales in neighboring nonpromotional weeks individually may not capture the overall Revenue uplift in the case of multiple parallel promotions. Hence, this paper proposes a Machine Learning based method for calculating the Revenue uplift by considering the Halo and Cannibalization impact on the Baseline and the Revenue. In the first section of the proposed methodology, Baseline of an item is calculated by incorporating the impact of the promotions on its related items. In the later section, the Revenue of an item is calculated by considering both Halo and Cannibalization impacts. Hence, this methodology enables correct calculation of the overall Revenue uplift due a given promotion.

Keywords: Halo, Cannibalization, promotion, Baseline, temporary price reduction, retail, elasticity, cross price elasticity, machine learning, random forest, linear regression

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Authors: Kai Warsoenke, Maik Mackiewicz

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To achieve a high quality of assembled car body structures, tolerancing is used to ensure a geometric accuracy of the single car body parts. There are two main techniques to determine the required tolerances. The first is tolerance analysis which describes the influence of individually tolerated input values on a required target value. Second is tolerance synthesis to determine the location of individual tolerances to achieve a target value. Both techniques are based on classical statistical methods, which assume certain probability distributions. To ensure competitiveness in both saturated and dynamic markets, production processes in vehicle manufacturing must be flexible and efficient. The dimensional specifications selected for the individual body components and the resulting assemblies have a major influence of the quality of the process. For example, in the manufacturing of forming tools as operating equipment or in the higher level of car body assembly. As part of the metrological process monitoring, manufactured individual parts and assemblies are recorded and the measurement results are stored in databases. They serve as information for the temporary adjustment of the production processes and are interpreted by experts in order to derive suitable adjustments measures. In the production of forming tools, this means that time-consuming and costly changes of the tool surface have to be made, while in the body shop, uncertainties that are difficult to control result in cost-intensive rework. The stored measurement results are not used to intelligently design tolerances in future processes or to support temporary decisions based on real-world geometric data. They offer potential to extend the tolerancing methods through data analysis and machine learning models. The purpose of this paper is to examine real-world measurement data from individual car body components, as well as assemblies, in order to develop an approach for using the data in short-term actions and future projects. For this reason, the measurement data will be analyzed descriptively in the first step in order to characterize their behavior and to determine possible correlations. In the following, a database is created that is suitable for developing machine learning models. The objective is to create an intelligent way to determine the position and number of measurement points as well as the local tolerance range. For this a number of different model types are compared and evaluated. The models with the best result are used to optimize equally distributed measuring points on unknown car body part geometries and to assign tolerance ranges to them. The current results of this investigation are still in progress. However, there are areas of the car body parts which behave more sensitively compared to the overall part and indicate that intelligent tolerancing is useful here in order to design and control preceding and succeeding processes more efficiently.

Keywords: automotive production, machine learning, process optimization, smart tolerancing

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Authors: Maha Jazouli

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Suicide is one of the most important causes of death in the prison environment, both in Canada and internationally. Rates of attempts of suicide and self-harm have been on the rise in recent years, with hangings being the most frequent method resorted to. The objective of this article is to propose a method to automatically detect in real time suicidal behaviors. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Our proposed system gives us satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.

Keywords: suicide detection, Kinect azure, RGB-D camera, SVM, machine learning, gesture recognition

Procedia PDF Downloads 168

Authors: Alper Akin, Ibrahim Aydogdu

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This study presents a hybrid metaheuristic algorithm to obtain optimum designs for steel space buildings. The optimum design problem of three-dimensional steel frames is mathematically formulated according to provisions of LRFD-AISC (Load and Resistance factor design of American Institute of Steel Construction). Design constraints such as the strength requirements of structural members, the displacement limitations, the inter-story drift and the other structural constraints are derived from LRFD-AISC specification. In this study, a hybrid algorithm by using teaching-learning based optimization (TLBO) and harmony search (HS) algorithms is employed to solve the stated optimum design problem. These algorithms are two of the recent additions to metaheuristic techniques of numerical optimization and have been an efficient tool for solving discrete programming problems. Using these two algorithms in collaboration creates a more powerful tool and mitigates each other’s weaknesses. To demonstrate the powerful performance of presented hybrid algorithm, the optimum design of a large scale steel building is presented and the results are compared to the previously obtained results available in the literature.

Keywords: optimum structural design, hybrid techniques, teaching-learning based optimization, harmony search algorithm, minimum weight, steel space frame

Procedia PDF Downloads 529

Authors: Georgios Charizanos, Haydar Demirhan, Duygu Icen

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Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted.

Keywords: fuzzy logistic regression, fuzzy, logistic, machine learning

Procedia PDF Downloads 54

Authors: Laila Albughayl

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The purpose of this qualitative case study was to explore Saudi females’ leadership journeys as they navigate the labyrinth of leadership in higher education. To gain a better understanding of how these leaders overcame challenges and accessed support as they progressed through the labyrinth to top positions in Saudi higher education. The significance of this research derived from the premise that leaders need to acquire essential leadership competencies such as knowledge, skills, and practices to effectively lead through economic transformation, growing globalism, and rapidly developing technology in an increasingly diverse world. In addition, understanding Saudi women’s challenges in the labyrinth will encourage policymakers to improve the situation under which these women work. The metaphor ‘labyrinth’ for Eagly and Carli (2007) encapsulates the winding paths, dead ends, and maze-like pathways that are full of challenges and supports that women traverse to access and maintain leadership positions was used. In this study, ‘labyrinth’ was used as the conceptual framework to explore women leaders’ challenges and opportunities in leadership in Saudi higher education. A proposed model for efficient navigation of the labyrinth of leadership was used. This model focused on knowledge, skills, and behaviours (KSB) as the analytical framework for examining responses to the research questions. This research was conducted using an interpretivist qualitative approach. A case study was the methodology used. Semi-structured interviews were the main data collection method. Purposive sampling was used to select ten Saudi leaders in three public universities. In coding, the 6-step framework of thematic analysis for Braun and Clarke was used to identify, analyze, and report themes within the data. NVivo software was also used as a tool to assist with managing and organizing the data. The resultant findings showed that the challenges identified by participants in navigating the labyrinth of leadership in Saudi higher education replicated some of those identified in the literature. The onset findings also revealed that the organizational barriers in Saudi higher education came as the top hindrance to women’s advancement in the labyrinth of leadership, followed by societal barriers. The findings also showed that women’s paths in the labyrinth of leadership in higher education were still convoluted and tedious compared to their male counterparts. In addition, the findings revealed that Saudi women leaders use significant strategies to access leadership posts and effectively navigate the labyrinth; this was not indicated in the literature. In addition, the resultant findings revealed that there are keys that assisted Saudi female leaders in effectively navigating the labyrinth of leadership. For example, the findings indicated that spirituality (religion) was a powerful key that enabled Saudi women leaders to pursue and persist in their leadership paths. Based on participants' experiences, a compass for effective navigation of the labyrinth of leadership in higher education was created for current and aspirant Saudi women leaders to follow. Finally, the findings had several significant implications for practice, policy, theory, and future research.

Keywords: women, leadership, labyrinth, higher education

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Authors: Sebastianus Menggo

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The aim of communicative language teaching is to enable learners to communicate in the target language. Each learner is required to perform the micro and macro components in each utterance produced. Utterances produced must be in line with the understanding of competence and performance of each speaker. These are inter-depended. Competence and performance are obliged to be appeared proportionally in creating the utterances. The representative of competence and performance reflects the linguistics identity of a speaker in providing sentences in each certain language community. Each lexicon spoken may lead that interlocutor in comprehending the intentions utterances given. However proportional performance of both components in an utterance needed to be further elaborated. Finding appropriate gap between competence and performance components in a communicative competence must be supported positive response given by the learners.The learners’ inability to keep communicative competence proportionally is caused by inside and outside factors. The inside factors are certain lacks such as lack of self-confidence and lack of motivation which could make students feel ashamed to produce utterances, scared to make mistakes, and have no enough confidence. Knowing learner’s English learning motivation is an urgent variable to be considered in creating conducive atmosphere classroom which will raise the learners to do more toward the achievement of communicative competence. Meanwhile, the outside factor is related with the teacher. The teacher should be able to recognize the students’ problem in creating conducive atmosphere in the classroom that will raise the students’ ability to be an English speaker qualified. Moreover, the aim of this research is to know and describe the English learning motivation affecting students’ communicative competence of 48 students of XI grade of science program at catholic senior of Saint Ignasius Loyola Labuan Bajo, West Flores, Indonesia. Correlation design with purposive procedure applied in this research. Data were collected through questionnaire, interview, and students’ speaking achievement document. Result shows the description of motivation significantly affecting students’ communicative competence.

Keywords: communicative, competence, English, learning, motivation

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Authors: Muhammad Saqib Manzoor, Camille Dickson-Deane, Prashan Karunaratne

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Cobb-Douglas production (utility) function is a fundamental function widely used in economics teaching and research. The key reason is the function's characteristics to describe the actual production using inputs like labour and capital. The characteristics of the function like returns to scale, marginal, and diminishing marginal productivities are covered in the introductory units in both microeconomics and macroeconomics with a 2-dimensional static visualisation of the function. However, less insight is provided regarding three-dimensional surface, changes in the curvature properties due to returns to scale, the linkage of the short-run production function with its long-run counterpart and marginal productivities, the level curves, and the constraint optimisation. Since (freshman) learners have diverse prior knowledge and cognitive skills, the existing “one size fits all” approach is not very helpful. The aim of this study is to bridge this gap by introducing technological intervention with interactive animations of the three-dimensional surface and sequential unveiling of the characteristics mentioned above using Python software. A small classroom intervention has helped students enhance their analytical and visualisation skills towards active and authentic learning of this topic. However, to authenticate the strength of our approach, a quasi-Delphi study will be conducted to ask domain-specific experts, “What value to the learning process in economics is there using a 2-dimensional static visualisation compared to using a 3-dimensional dynamic visualisation?’ Here three perspectives of the intervention were reviewed by a panel comprising of novice students, experienced students, novice instructors, and experienced instructors in an effort to determine the learnings from each type of visualisations within a specific domain of knowledge. The value of this approach is key to suggesting different pedagogical methods which can enhance learning outcomes.

Keywords: cobb-douglas production function, quasi-Delphi method, effective teaching and learning, 3D-visualisations

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Authors: Martha Wilson

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With the peak of Computer-Assisted Language Learning slowly coming to pass and Mobile-Assisted Language Learning, at times, a bit lacking in the communicative department, we are now faced with a challenging question: How can we engage the interest of our digital native students and, most importantly, sustain it? As previously mentioned, our classrooms are now experiencing an influx of “digital natives” – people who have grown up using and having unlimited access to technology. While modernizing our curriculum and digitalizing our classrooms are necessary in order to accommodate this new learning style, it is a huge financial burden and a massive undertaking for language institutes. Instead, opting for a more compact, simple, yet multidimensional pedagogical tool may be the solution to the issue at hand. This paper aims to give a brief overview into an existing device referred to as Student Response Systems (SRS) and to expand on this notion to include a new prototype of response system that will be designed as a mobile application to eliminate the need for costly hardware and software. Additionally, an analysis into recent attempts by other institutes to develop the Mobile Response System (MRS) and customer reviews of the existing MRSs will be provided, as well as the lessons learned from those projects. Finally, while the new model of MRS is still in its infancy stage, this paper will discuss the implications of incorporating such an application as a tool to support and to enrich traditional techniques and also offer practical classroom applications with the existing response systems that are immediately available on the market.

Keywords: app, clickers, mobile app, mobile response system, student response system

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Authors: Okechukwu Uzoma Nkwocha, Edward Uche Omeire

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Information and communication technologies (ICT) undeniably, have expanded the sphere of arts and creativity. It proves to be an important tool for production, preservation, sharing and utilization of arts and cultural heritage. While art and heritage institutions around the globe are increasingly utilizing ICT for the promotion and sharing of their collections, the story seems different in most part of Africa. In this paper, we will examine the prospects and problems of utilizing ICT in promotion, preservation and sharing of arts and cultural heritage.

Keywords: arts, cultural heritage, digitalization, ICT

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Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

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We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: daily activity recognition, healthcare, IoT sensors, transfer learning

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Authors: Ahcene Habbi, Yassine Boudouaoui

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This paper deals with the problem of automatic rule generation for fuzzy systems design. The proposed approach is based on hybrid artificial bee colony (ABC) optimization and weighted least squares (LS) method and aims to find the structure and parameters of fuzzy systems simultaneously. More precisely, two ABC based fuzzy modeling strategies are presented and compared. The first strategy uses global optimization to learn fuzzy models, the second one hybridizes ABC and weighted least squares estimate method. The performances of the proposed ABC and ABC-LS fuzzy modeling strategies are evaluated on complex modeling problems and compared to other advanced modeling methods.

Keywords: automatic design, learning, fuzzy rules, hybrid, swarm optimization

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Authors: Luís Rocha, Adam Gąska, Enrico Savio, Michael Marxer, Christoph Battaglia

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The changes observed in the last years in the field of manufacturing and production engineering, popularly known as "Fourth Industry Revolution", utilizes the achievements in the different areas of computer sciences, introducing new solutions at almost every stage of the production process, just to mention such concepts as mass customization, cloud computing, knowledge-based engineering, virtual reality, rapid prototyping, or virtual models of measuring systems. To effectively speed up the production process and make it more flexible, it is necessary to tighten the bonds connecting individual stages of the production process and to raise the awareness and knowledge of employees of individual sectors about the nature and specificity of work in other stages. It is important to discover and develop a suitable education method adapted to the specificities of each stage of the production process, becoming an extremely crucial issue to exploit the potential of the fourth industrial revolution properly. Because of it, the project “Train4Dim” (T4D) intends to develop complex training material for digital manufacturing, including content for design, manufacturing, and quality control, with a focus on coordinate metrology and portable measuring systems. In this paper, the authors present an approach to using an active learning methodology for digital manufacturing. T4D main objective is to develop a multi-degree (apprenticeship up to master’s degree studies) and educational approach that can be adapted to different teaching levels. It’s also described the process of creating the underneath methodology. The paper will share the steps to achieve the aims of the project (training model for digital manufacturing): 1) surveying the stakeholders, 2) Defining the learning aims, 3) producing all contents and curriculum, 4) training for tutors, and 5) Pilot courses test and improvements.

Keywords: learning, Industry 4.0, active learning, digital manufacturing

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Authors: José María Agugusto-landa, Inmaculada García-Martínez, Lara Checa Domene, Óscar Gavín Chocano

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Self-concept is the perception that a person has of himself, of his abilities, skills, traits, and values. Self-concept is composed of different dimensions, such as academic self-concept, physical self-concept, social self-concept, emotional self-concept, and family self-concept. The relationship between the dimensions of self-concept and mental health and academic performance among future teachers is a topic of interest for educational psychology. Some studies have found that: (i) There is a positive relationship between general self-concept, academic self-concept and academic performance, that is, students who have a more positive image of themselves tend to get better grades and be more motivated to learn. (ii) There is a positive relationship between emotional intelligence, physical self-concept and healthy habits, that is, students who regulate and understand their emotions better have a higher satisfaction with their physical appearance and follow a more balanced diet and a higher physical activity. As for gender differences in the dimensions of self-concept among future teachers, some studies have found that: (i) Girls tend to have a higher self-concept in the social, family and verbal dimensions, that is, they perceive themselves as more capable of relating to others, communicating effectively and receiving support from their family. (ii) Boys tend to have a higher self-concept in the physical, emotional and mathematical dimensions, that is, they perceive themselves as more capable of performing physical activities, controlling their emotions and solving mathematical problems. (iii) There are no significant differences between general self-concept and academic self-concept according to gender, that is, both girls and boys have a similar perception of their global worth and academic competence.

Keywords: preservice teachers, self-concept, academic performance, mental wellbeing

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Authors: Amanda Abbott-Jones

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Adult students with dyslexia can receive support for cognitive needs but may also experience anxiety, which is less understood. This study aims to test the hypothesis that dyslexic learners in higher education have a higher prevalence of academic and social anxiety than their non-dyslexic peers and explores wider emotional consequences of studying with dyslexia and the ways that adults with dyslexia cope cognitively and emotionally. A mixed-method approach was used in two stages. Stage one compared survey responses from students with dyslexia (N = 102) and students without dyslexia (N = 72) after completion of an anxiety inventory. Stage two explored the emotional consequences of studying with dyslexia and the types of coping strategies used through semi-structured interviews with 20 dyslexic students. Results revealed a statistically significant effect for academic anxiety but not for social anxiety. Findings for stage two showed that: (1) students’ emotional consequences were characterised by a mixture of negative and positive responses, yet negative responses were more frequent in response to questions about academic tasks than positive responses; (2) participants had less to say on coping emotionally, than coping cognitively.

Keywords: dyslexia, higher education, anxiety, emotion

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Authors: Sobia Rana

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The study intends to explain the problems of English collocational use faced by college students in Rawalpindi, Pakistan and recommends some authentic ways that will help in removing the learning barriers in light of the concerning methodological issues. It will not only help the students to improve their knowledge of the phenomena but will also enlighten the target teachers about the significance of authentic collocational use and how it naturalizes both written and spoken expressions. Data from both the students and teachers have been collected with the help of open/close-ended questionnaires to unearth the genuine cause/s and supplement them with the required solutions rooted in the actual problems. The students fail to use authentic collocations owing to multiple reasons: lack of awareness about English collocational use, improper teaching methodologies, and inexpert teachers.

Keywords: English collocational use, teaching methodologies, English learning barriers, vocabulary acquisition, college students of Rawalpindi

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Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

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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 144

Authors: M. Tomaschko

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The integration of technology in educational settings has gained a lot of interest. Especially the use of mobile devices and accompanying mobile applications can offer great potentials to complement traditional education with new technologies and enrich students’ learning in various ways. Nevertheless, the usability of the deployed mathematics application is an indicative factor to exploit the full potential of technology enhanced learning because directing cognitive load toward using an application will likely inhibit effective learning. For this reason, the purpose of this research study is the identification of possible usability issues of the mobile GeoGebra Graphing Calculator application. Therefore, eye tracking in combination with task scenarios, think aloud method, and a SUS questionnaire were used. Based on the revealed usability issues, the mobile application was iteratively redesigned and assessed in order to verify the success of the usability improvements. In this paper, the identified usability issues are presented, and recommendations on how to overcome these concerns are provided. The main findings relate to the conception of a mathematics keyboard and the interaction design in relation to an equation editor, as well as the representation of geometrical construction tools. In total, 12 recommendations were formed to improve the usability of a mobile graphing calculator application. The benefit to be gained from this research study is not only the improvement of the usability of the existing GeoGebra Graphing Calculator application but also to provide helpful hints that could be considered from designers and developers of mobile math applications.

Keywords: GeoGebra, graphing calculator, math education, smartphone, usability

Procedia PDF Downloads 118

Authors: Esperanza C. Santos

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Japanese Language was traditionally considered as a means of imparting culture and training aesthetic experience in students and therefore as something beyond the practical aims of language teaching and learning. Due to the complexity of foreign languages, lots of language learners and teachers shared deep reservations about the potentials of foreign language in enhancing the communication skills of the students. In spite of the arguments against the use of Foreign Language (Nihon-go) in the classroom, the researcher strongly support the use of Nihon-go in teaching communication skills as the researcher believes that Nihon-go is a valuable resource to be exploited in the classroom in order to help the students explore the language in an interesting and challenging way. The focus of this research is to find out the relationship between the preferences, opinions, and perceptions with the communication skills. This study also identifies the significance of the relationship between preferences, opinions and perceptions and communications skills in the activities employed in Foreign language (Nihon-go) among the junior and senior students in Foreign Language 2 at the Imus Institute, Imus Cavite during the academic year 2013-2014. The results of the study are expected to encourage further studies that particularly focused on the communication skills as brought about by the identified factors namely: preferences, opinions, and perceptions on the benefits factor namely the language acquisition; access to Japanese culture and students' interpretative ability. Therefore, this research is in its quest for the issues and concerns on how to effectively teach different learning activities in a Nihon-go class.

Keywords: preferences, opinions, perceptions, language acquisition

Procedia PDF Downloads 299

Authors: Shogo Nagano, Yusuke Tanigaki, Hirokazu Fukuda

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Vegetables harvested early in the morning or late in the afternoon are valued in plant production, and so the time of harvest is important. The biological functions known as circadian clocks have a significant effect on this harvest timing. The purpose of this study was to non-destructively estimate the circadian clock and so construct a method for determining a suitable harvest time. We took eight samples of green busil (Perilla frutescens var. crispa) every 4 hours, six times for 1 day and analyzed all samples at the same time. A hyperspectral camera was used to collect spectrum intensities at 141 different wavelengths (350–1050 nm). Calculation of correlations between spectrum intensity of each wavelength and harvest time suggested the suitability of the hyperspectral camera for non-destructive estimation. However, even the highest correlated wavelength had a weak correlation, so we used machine learning to raise the accuracy of estimation and constructed a machine learning model to estimate the internal time of the circadian clock. Artificial neural networks (ANN) were used for machine learning because this is an effective analysis method for large amounts of data. Using the estimation model resulted in an error between estimated and real times of 3 min. The estimations were made in less than 2 hours. Thus, we successfully demonstrated this method of non-destructively estimating internal time.

Keywords: artificial neural network (ANN), circadian clock, green busil, hyperspectral camera, non-destructive evaluation

Procedia PDF Downloads 285