Search results for: academic learning integration

Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: Safdar Muhammad Usman, Youji Kohda, Katsuhiro Umemoto

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Electronic market place plays an important intermediary role for connecting dealers and retail customers. The main aim of this paper is to design a value co-creation model in used-car auctions. More specifically, the study has been designed in order to describe the process of value co-creation in used-car auctions, to explore the co-created values in used-car auctions, and finally conclude the paper indicating the future research directions. Our analysis shows that economic values as well as non-economic values are co-created in used-car auctions. In addition, this paper contributes to the academic society broadening the view of value co-creation in service science.

Keywords: value co-creation, used-car auctions, non-financial values, service science

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Authors: Hasna Mekkaoui Alaoui, Mariem Mekkaoui Alaoui

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In a context marked by a growing and pressing demand for online training within Moroccan universities, massive open online courses (Moocs) are undergoing constant evolution, amplified by the widespread use of digital technology and accentuated by the Coronavirus pandemic. However, despite their growing popularity and expansion, these courses are still lacking in terms of tools, enabling teachers and researchers to carry out a fine-grained analysis of the learning processes taking place within them. What's more, the circulation and sharing of knowledge within these environments is becoming increasingly important. The crucial aspect of traceability emerges here, as MOOCs record and generate traces from the most minute to the most visible. This leads us to consider traceability as a valuable approach in the field of educational research, where the trace is envisaged as a research tool in its own right. In this exploratory research project, we are looking at aspects of community knowledge sharing based on traces observed in the "Communication des organisations" Mooc. Focusing in particular on the mediating trace and its impact in identifying knowledge circulation processes in this learning space, we have mobilized the traces of video capsules as an index of knowledge circulation in the Mooc device. Our study uses a methodological approach based on thematic analysis, and although the results show that learners reproduce knowledge from different video vignettes in almost identical ways, they do not limit themselves to the knowledge provided to them. This research offers concrete perspectives for improving the dynamics of online devices, with a potentially positive impact on the quality of online university teaching.

Keywords: circulation, index, digital environments, mediation., trace

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Authors: Lavanya Annadi, Fahimeh Jafari

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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.

Keywords: road safety, crash prediction, exploratory analysis, machine learning

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Authors: Amal S. Al-Badi, Ali H. Al-Badi

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Many organizations are opting to adopt Open Source Software (OSS) as it is the current trend to rely on each other rather than on companies (Software vendors). It is a clear shift from organizations to individuals, the concept being to rely on collective participation rather than companies/vendors. The main objectives of this research are 1) to identify the current level of OSS usage in Sultan Qaboos University; 2) to identify the potential benefits of using OSS in educational institutes; 3) to identify the OSS applications that are most likely to be used within an educational institute; 4) to identify the existing and potential barriers to the successful adoption of OSS in education. To achieve these objectives a two-stage research method was conducted. First a rigorous literature review of previously published material was performed (interpretive/descriptive approach), and then a set of interviews were conducted with the IT professionals at Sultan Qaboos University in Oman in order to explore the extent and nature of their usage of OSS.

Keywords: open source software, social software, e-learning 2.0, Web 2.0, connectivism, personal learning environment (PLE), OpenCourseWare

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: The Danh Phan

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House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

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Authors: Li-Ching Hung

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The lack of empirical research on portfolio assessment in aboriginal EFL English classes of junior high schools in Taiwan may inhibit EFL teachers from appreciating the utility of this alternative assessment approach. This study addressed the following research questions: 1) understand how aboriginal EFL students and instructors of junior high schools in Taiwan perceive portfolio assessment, and 2) how portfolio assessment affects Taiwanese aboriginal EFL students’ learning outcomes. Ten classes of five junior high schools in Taiwan (from different regions of Taiwan) participated in this study. Two classes from each school joined the study, and each class was randomly assigned as a control group, and one was the experimental group. These five junior high schools consisted of at least 50% of aboriginal students. A mixed research design was utilized. The instructor of each class implemented a portfolio assessment for 15 weeks of the 2015 Fall Semester. At the beginning of the semester, all participants took a GEPT test (pretest), and in the 15th week, all participants took the same level of GEPT test (post-test). Scores of students’ GEPT tests were checked by the researcher as supplemental data in order to understand each student’s performance. In addition, each instructor was interviewed to provide qualitative data concerning students’ general learning performance and their perception of implementing portfolio assessments in their English classes. The results of this study were used to provide suggestions for EFL instructors while modifying their lesson plans regarding assessment. In addition, the empirical data were used as references for EFL instructors implementing portfolio assessments in their classes effectively.

Keywords: assessment, portfolio assessment, qualitative design, aboriginal ESL students

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Authors: Abdelbasit Gadour

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A great number of children in mainstream schools across Libya are currently living with emotional, behavioral difficulties. This study aims to explore teachers’ perceptions of children’s emotional and behavioral difficulties (EBD) and their attributions of the causes of EBD. The relevance of this area of study to current educational practice is illustrated in the fact that primary school teachers in Libya find classroom behavior problems one of the major difficulties they face. The information presented in this study was gathered from 182 teachers that responded back to the survey, of whom 27 teachers were later interviewed. In general, teachers’ perceptions of EBD reflect personal experience, training, and attitudes. Teachers appear from this study to use words such as indifferent, frightened, withdrawn, aggressive, disobedient, hyperactive, less ambitious, lacking concentration, and academically weak to describe pupils with emotional and behavioral difficulties (EBD). The implications of this study are envisaged as being extremely important to support teachers addressing children’s EBD and shed light on the contributing factors to EBD for a successful teaching-learning process in Libyan primary schools.

Keywords: children, emotional and behavior difficulties, learning, teachers'

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Authors: Sheelagh Heugh, Michael Upton, Kriya Kalidas, Stephen Breen

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This article aims to create awareness of the opportunities and issues the artificial intelligence (AI) tool GPT-3 (Generative Pre-trained Transformer-3) brings to higher education. Technological disruptors have featured in higher education (HE) since Konrad Klaus developed the first functional programmable automatic digital computer. The flurry of technological advances, such as personal computers, smartphones, the world wide web, search engines, and artificial intelligence (AI), have regularly caused disruption and discourse across the educational landscape around harnessing the change for the good. Accepting AI influences are inevitable; we took mixed methods through participatory action research and evaluation approach. Joining HE communities, reviewing the literature, and conducting our own research around Chat GPT-3, we reviewed our institutional approach to changing our current practices and developing policy linked to assessments and the use of Chat GPT-3. We review the impact of GPT-3, a high-powered natural language processing (NLP) system first seen in 2020 on HE. Historically HE has flexed and adapted with each technological advancement, and the latest debates for educationalists are focusing on the issues around this version of AI which creates natural human language text from prompts and other forms that can generate code and images. This paper explores how Chat GPT-3 affects the current educational landscape: we debate current views around plagiarism, research misconduct, and the credibility of assessment and determine the tool's value in developing skills for the workplace and enhancing critical analysis skills. These questions led us to review our institutional policy and explore the effects on our current assessments and the development of new assessments. Conclusions: After exploring the pros and cons of Chat GTP-3, it is evident that this form of AI cannot be un-invented. Technology needs to be harnessed for positive outcomes in higher education. We have observed that materials developed through AI and potential effects on our development of future assessments and teaching methods. Materials developed through Chat GPT-3 can still aid student learning but lead to redeveloping our institutional policy around plagiarism and academic integrity.

Keywords: artificial intelligence, Chat GPT-3, intellectual property, plagiarism, research misconduct

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Authors: Desplanches Maxime

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Accurate estimation of the remaining useful life of lithium-ion batteries for electronic devices is crucial. Data-driven methodologies encounter challenges related to data volume and acquisition protocols, particularly in capturing a comprehensive range of aging indicators. To address these limitations, we propose a hybrid approach that integrates an electrochemical model with state-of-the-art data analysis techniques, yielding a comprehensive database. Our methodology involves infusing an aging phenomenon into a Newman model, leading to the creation of an extensive database capturing various aging states based on non-destructive parameters. This database serves as a robust foundation for subsequent analysis. Leveraging advanced data analysis techniques, notably principal component analysis and t-Distributed Stochastic Neighbor Embedding, we extract pivotal information from the data. This information is harnessed to construct a regression function using either random forest or support vector machine algorithms. The resulting predictor demonstrates a 5% error margin in estimating remaining battery life, providing actionable insights for optimizing usage. Furthermore, the database was built from the Newman model calibrated for aging and performance using data from a European project called Teesmat. The model was then initialized numerous times with different aging values, for instance, with varying thicknesses of SEI (Solid Electrolyte Interphase). This comprehensive approach ensures a thorough exploration of battery aging dynamics, enhancing the accuracy and reliability of our predictive model. Of particular importance is our reliance on the database generated through the integration of the electrochemical model. This database serves as a crucial asset in advancing our understanding of aging states. Beyond its capability for precise remaining life predictions, this database-driven approach offers valuable insights for optimizing battery usage and adapting the predictor to various scenarios. This underscores the practical significance of our method in facilitating better decision-making regarding lithium-ion battery management.

Keywords: Li-ion battery, aging, diagnostics, data analysis, prediction, machine learning, electrochemical model, regression

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Authors: Gebremeskel Hagos Gebremedhin, Feng Chong, Heyan Huang

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Software system is the combination of architecture and organized components to accomplish a specific function or set of functions. A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture. The main aim of this paper is to analyze prove architecture design and automatic re-architecting using machine learning. Intelligence software architecture and automatic re-architecting process is reorganizing in to more suitable one of the software organizational structure system using the user access dataset for creating relationship among the components of the system. The 3-step approach of data mining was used to analyze effective recovery, transformation and implantation with the use of clustering algorithm. Therefore, automatic re-architecting without changing the source code is possible to solve the software complexity problem and system software reuse.

Keywords: intelligence, software architecture, re-architecting, software reuse, High level design

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Authors: Jacqueline Żammit

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Recent research indicates that the utilization of right-brain strategies holds significant implications for the acquisition of language skills. Nevertheless, the utilization of visual culture as a means to stimulate these strategies and amplify language retention among adults engaging in second language (L2) learning remains a relatively unexplored area. This investigation delves into the impact of visual culture on activating right-brain processes during the initial stages of language acquisition, particularly in the context of teaching Maltese as a second language (ML2) to adult learners. By employing a qualitative research approach, this study convenes a focus group comprising twenty-seven educators to delve into a range of visual culture techniques integrated within language instruction. The collected data is subjected to thematic analysis using NVivo software. The findings underscore a variety of impactful visual culture techniques, encompassing activities such as drawing, sketching, interactive matching games, orthographic mapping, memory palace strategies, wordless picture books, picture-centered learning methodologies, infographics, Face Memory Game, Spot the Difference, Word Search Puzzles, the Hidden Object Game, educational videos, the Shadow Matching technique, Find the Differences exercises, and color-coded methodologies. These identified techniques hold potential for application within ML2 classes for adult learners. Consequently, this study not only provides insights into optimizing language learning through specific visual culture strategies but also furnishes practical recommendations for enhancing language competencies and skills.

Keywords: visual culture, right-brain strategies, second language acquisition, maltese as a second language, visual aids, language-based activities

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Authors: Dejenie Birile Gemeda, Wilhelm Stork

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The power system is one of the most important systems in modern society. The existing power system is approaching the critical operating limits as views of several power system operators. With the increase of load demand, high capacity and long transmission networks are widely used to meet the requirement. With the integration of renewable energies such as wind and solar, the uncertainty, intermittence bring bigger challenges to the operation of power systems. These dynamic uncertainties in the power system lead to power disturbances. The disturbances in a heavily stressed power system cause distance relays to mal-operation or false alarms during post fault power oscillations. This unintended operation of these relays may propagate and trigger cascaded trappings leading to total power system blackout. This is due to relays inability to take an appropriate tripping decision based on ensuing power swing. According to the N-1 criterion, electric power systems are generally designed to withstand a single failure without causing the violation of any operating limit. As a result, some overloaded components such as overhead transmission lines can still work for several hours under overload conditions. However, when a large power swing happens in the power system, the settings of the distance relay of zone 3 may trip the transmission line with a short time delay, and they will be acting so quickly that the system operator has no time to respond and stop the cascading. Misfiring of relays in absence of fault due to power swing may have a significant loss in economic performance, thus a loss in revenue for power companies. This research paper proposes a method to distinguish stable power swing from unstable using dynamic line rating (DLR) in response to power swing or disturbances. As opposed to static line rating (SLR), dynamic line rating support effective mitigation actions against propagating cascading outages in a power grid. Effective utilization of existing transmission lines capacity using machine learning DLR predictions will improve the operating point of distance relay protection, thus reducing unintended power outages due to power swing.

Keywords: blackout, cascading outages, dynamic line rating, power swing, overhead transmission lines

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Authors: Janneth Gonzalez, Andrés Pinzon Velasco, Maria Angarita

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Astrocytes play an important role in various processes in the brain, including pathological conditions such as neurodegenerative diseases. Recent studies have shown that the increase in saturated fatty acids such as palmitic acid (PA) triggers pro-inflammatorypathways in the brain. The use of synthetic neurosteroids such as tibolone has demonstrated neuro-protective mechanisms. However, broad studies with a systemic point of view on the neurodegenerative role of PA and the neuro-protective mechanisms of tibolone are lacking. In this study, we performed the integration of multi-omic data (transcriptome and proteome) into a human astrocyte genomic scale metabolic model to study the astrocytic response during palmitate treatment. We evaluated metabolic fluxes in three scenarios (healthy, induced inflammation by PA, and tibolone treatment under PA inflammation). We also applied a control theory approach to identify those reactions that exert more control in the astrocytic system. Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid β‐oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA‐mediated cellular regulation, 9 of which were critical only in the inflammatory scenario but not in the protective tibolone one. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a fundamental role in the (de)regulation of metabolic pathways that may increase neurotoxicity and represent potential treatment targets. Finally, the overall framework of our approach facilitates the understanding of complex metabolic regulation, and it can be used for in silico exploration of the mechanisms of astrocytic cell regulation, directing a more complex future experimental work in neurodegenerative diseases.

Keywords: astrocytes, data integration, palmitic acid, computational model, multi-omics

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Authors: Salihu Mohammed Umar

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Perceived physical exercise benefits among staff of tertiary institutions in Adamawa State was investigated as a basis for formulating proper exercise intervention strategies. The study utilized descriptive survey design. The purpose of the study was to determine perceived exercise benefits among staff of tertiary institutions in Adamawa state, Nigeria. The instrument used for data collection was a questionnaire adapted from Exercise Benefit/Barrier Scale (EBBS) developed by Sechrist, Walker and Pender (1985) which was validated by five experts. Three hundred and thirty (330) copies of the questionnaire were distributed among study participants in six institutions of higher learning in Adamawa state. The scale comprised two components; Benefits and Barriers dimensions. To achieve this purpose, three research questions were posed. The instrument had a four response forced-choice Likert-type format with responses ranging from 4 = strongly agree (SA), 3 = Agree (A), 2 = Disagree (D) and 1 = Strongly Disagree (SD). The findings of the study revealed that both male and female staff in institutions of higher learning in Adamawa state perceived exercise as highly beneficial. However, male staff had higher perceived benefits score than their female counterparts. (Male: x̄ = 95.02. SD = 3.08) > female: x̄ = 94.04, SD = 4.35. There was also no significant difference in perceived exercise barriers between staff and students of tertiary institutions in Adamawa state. Based on the finding of the study, it was concluded that staff of tertiary institutions perceived exercise as highly beneficial. It was recommended that since staff of institutions of higher learning in Adamawa State irrespective of gender and religious affiliations have basic knowledge of perceived benefits of exercise, there is the need to explore programmes that will enable staff across the sub-groups to overcome barriers that could discourage physical exercise participation.

Keywords: perception, physical exercise, staff, benefits

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Authors: Lyne Chantal Boudreau, Claudine Auger, Arline Laforest

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As an English-speaking country, Canada faces challenges in French-language education. During both editions of a provincial congress on education planned and conducted under shared cultural leadership, three organizers created a Francophone space where, for the first time in the province of New Brunswick (the only officially bilingual province in Canada), a group of stakeholders from the school, post-secondary and community sectors have succeeded in contributing to reflections on specific topics by sharing winning practices to meet the challenges of learning in a minority Francophone environment. Shared cultural leadership is a hybrid between theories of leadership styles in minority communities and theories of shared leadership. Through shared cultural leadership, the goal is simply to guide leadership and to set up all minority leaderships in minority context through shared leadership. This leadership style requires leaders to transition from a hierarchical to a horizontal approach, that is, to an approach where each individual is at the same level. In this exploratory research, it has been demonstrated that shared leadership exercised under the T-learning model best fosters the mobilization of all partners in advancing in-depth knowledge in a particular field while simultaneously allowing learning of the elements related to the domain in question. This session will present how it is possible to mobilize the whole community through leaders who continually develop their knowledge and skills in their specific field but also in related fields. Leaders in this style of management associated to shared cultural leadership acquire the ability to consider solutions to problems from a holistic perspective and to develop a collective power derived from the leadership of each and everyone in a space where all are rallied to promote the ultimate advancement of society.

Keywords: education, minority context, shared leadership, t-leaning

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Authors: Luisa Carrer, Lorenzo De Masi

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In this paper, we exploit the massive amount of information from Facebook to build a measure of gender attitudes in Italy at a previously impossible resolution—the municipal level. We construct our index via a machine learning method to replicate a benchmark region-level measure. Interestingly, we find that most of the variation in our Gender Norms Index (GNI) is across towns within narrowly defined geographical areas rather than across regions or provinces. In a second step, we show how this local variation in norms can be leveraged for identification purposes. In particular, we use our index to investigate whether these differences in norms carry over to the policy activity of politicians elected in the Italian Parliament. We document that females are more likely to sit in parliamentary committees focused on gender-sensitive matters, labor, and social issues, but not if they come from a relatively conservative town. These effects are robust to conditioning the legislative term and electoral district, suggesting the importance of social norms in shaping legislators’ policy activity.

Keywords: gender equality, gender norms index, Facebook, machine learning, politics

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Authors: Liav Orgad

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This article challenges one of the most fundamental propositions in the democratic theory that the majority culture is protected merely by the forces of democracy and thus needs no special legal protection. By describing changes in the patterns of migration to Europe, in the face of the European society, and in the world as a whole, the Article demonstrates that the majority culture is no longer automatically protected by the forces of democracy. It claims that the changing reality is not adequately addressed by political theory and human rights law and advances the promotion of a new concept—'cultural majority rights'.

Keywords: European migration, European demography, democratic theory, majority rights, integration

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Authors: Yasmine Abu Adla, Racha Soubra, Milana Kasab, Mohamad O. Diab, Aly Chkeir

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Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals, out of which 11 were chosen based on their intraclass correlation coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, 5 features were introduced to the linear discriminant analysis classifier, and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90%, respectively.

Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification

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Authors: Sadeq Al Yaari, Muhammad Alkhunayn, Sajedah Al Yaari, Adham Al Yaari, Ayman Al Yaari, Montaha Al Yaari, Ayah Al Yaari, Fatehi Eissa

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Dysgraphia is a neurological disorder of written expression that impairs writing ability and fine motor skills, resulting primarily in problems relating not only to handwriting but also to writing coherence and cohesion. We investigate the properties of smart writing technology to highlight some unique features of the effects they cause on the academic performance of pupils with dysgraphia. In Amis, dysgraphics undergo writing problems to express their ideas due to ordinary writing aids, as the default strategy. The Amis data suggests a possible connection between available writing aids and pupils’ writing improvement; therefore, texts’ expression and comprehension. A group of thirteen dysgraphic pupils were placed in a regular classroom of primary school, with twenty-one pupils being recruited in the study as a control group. To ensure validity, reliability and accountability to the research, both groups studied writing courses for two semesters, of which the first was equipped with smart writing aids while the second took place in an ordinary classroom. Two pre-tests were undertaken at the beginning of the first two semesters, and two post-tests were administered at the end of both semesters. Tests examined pupils’ ability to write coherent, cohesive and expressive texts. The dysgraphic group received the treatment of a writing course in the first semester in classes with smart technology and produced significantly greater increases in writing expression than in an ordinary classroom, and their performance was better than that of the control group in the second semester. The current study concludes that using smart teaching aids is a ‘MUST’, both for teaching and learning dysgraphia. Furthermore, it is demonstrated that for young dysgraphia, expressive tasks are more challenging than coherent and cohesive tasks. The study, therefore, supports the literature suggesting a role for smart educational aids in writing and that smart writing techniques may be an efficient addition to regular educational practices, notably in special educational institutions and speech-language therapeutic facilities. However, further research is needed to prompt the adults with dysgraphia more often than is done to the older adults without dysgraphia in order to get them to finish the other productive and/or written skills tasks.

Keywords: smart technology, writing aids, pupils with dysgraphia, hands’ movement

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Authors: Samuel Ntsanwisi

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This study investigates the transformative role of Artificial Intelligence (AI) and Virtual Reality (VR) in the preservation of endangered African languages. The study is contextualized within the film and television industry, highlighting disparities in screen representation for certain languages in South Africa, underscoring the need for increased visibility and preservation efforts; with globalization and cultural shifts posing significant threats to linguistic diversity, this research explores approaches to language preservation. By leveraging AI technologies, such as speech recognition, translation, and adaptive learning applications, and integrating VR for immersive and interactive experiences, the study aims to create a framework for teaching and passing on endangered African languages. Through digital documentation, interactive language learning applications, storytelling, and community engagement, the research demonstrates how these technologies can empower communities to revitalize their linguistic heritage. This study employs a dual-method approach, combining a rigorous literature review to analyse existing research on the convergence of AI, VR, and language preservation with primary data collection through interviews and surveys with ten filmmakers. The literature review establishes a solid foundation for understanding the current landscape, while interviews with filmmakers provide crucial real-world insights, enriching the study's depth. This balanced methodology ensures a comprehensive exploration of the intersection between AI, VR, and language preservation, offering both theoretical insights and practical perspectives from industry professionals.

Keywords: language preservation, endangered languages, artificial intelligence, virtual reality, interactive learning

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Authors: Ziying Wu, Danfeng Yan

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Multi-Access Edge Computing (MEC) is one of the key technologies of the future 5G network. By deploying edge computing centers at the edge of wireless access network, the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios. Meanwhile, with the development of IOV (Internet of Vehicles) technology, various delay-sensitive and compute-intensive in-vehicle applications continue to appear. Compared with traditional internet business, these computation tasks have higher processing priority and lower delay requirements. In this paper, we design a 5G-based Vehicle-Aware Multi-Access Edge Computing Network (VAMECN) and propose a joint optimization problem of minimizing total system cost. In view of the problem, a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed, considering the influences of multiple factors such as concurrent multiple computation tasks, system computing resources distribution, and network communication bandwidth. And, the mixed integer nonlinear programming problem is described as a Markov Decision Process. Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption, optimize computing offloading and resource allocation schemes, and improve system resource utilization, compared with other computing offloading policies.

Keywords: multi-access edge computing, computation offloading, 5th generation, vehicle-aware, deep reinforcement learning, deep q-network

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Authors: Adhi Susilo

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This paper addresses the roles of distance education in the agricultural extension workers’ education. Agriculture plays an important role in both poverty reduction and economic growth. The technology of agriculture in the developing world should change continuously to keep pace with rising populations and rapidly changing social, economic, and environmental conditions. Therefore, agricultural extension workers should have several competencies in order to carry out their duties properly. One of the essential competencies that they must possess is the professional competency that is directly related to their duties in carrying out extension activities. Such competency can be acquired through studying at Universitas Terbuka (UT). With its distance learning system, agricultural extension workers can study at UT without leaving their duties. This paper presenting sociological analysis and lessons learnt from the specific context of Indonesia. Diversities in geographic, demographic, social cultural and economic conditions of the country provide specific challenges for its distance education practice and the process of social transformation to which distance education can contribute. Extension officers used distance education for personal benefits and increased professional productivity. An increase in awareness is important for the further adoption of distance learning for extension purposes. Organizations in both the public and private sector must work to increase knowledge of ICTs for the benefit of stakeholders. The use of ICTs can increase productivity for extensions officers and expand educational opportunities for learners. The use of distance education by extension to disseminate educational materials around the world is widespread. Increasing awareness and use of distance learning can lead to more productive relationships between extension officers and agricultural stakeholders.

Keywords: agricultural extension, demographic and geographic condition, distance education, ICTs

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Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

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Authors: Aadityaa Singla

Abstract:

The big five personality traits are as follows: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In order to get an insight into their personality, many flocks to these categories, which each have different meanings/characteristics. This information is important not only to individuals but also to career professionals and psychologists who can use this information for candidate assessment or job recruitment. The links between AI and psychology have been well studied in cognitive science, but it is still a rather novel development. It is possible for various AI classification models to accurately predict a personality question via ten input questions. This would contrast with the hundred questions that normal humans have to answer to gain a complete picture of their five personality traits. In order to approach this problem, various AI classification models were used on a dataset to predict what a user may answer. From there, the model's prediction was compared to its actual response. Normally, there are five answer choices (a 20% chance of correct guess), and the models exceed that value to different degrees, proving their significance. By utilizing an MLP classifier, decision tree, linear model, and K-nearest neighbors, they were able to obtain a test accuracy of 86.643, 54.625, 47.875, and 52.125, respectively. These approaches display that there is potential in the future for more nuanced predictions to be made regarding personality.

Keywords: machine learning, personally, big five personality traits, cognitive science

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Authors: Mikaela Kutrolli, Noah S. Pereira, Vanessa Scanlon, Mohamadmahdi Samandari, Ali Tamayol

Abstract:

Bone tissue engineering has drawn significant attention and various biomaterials have been tested. Polymers such as polycaprolactone (PCL) offer excellent biocompatibility, reasonable mechanical properties, and biodegradability. However, PCL scaffolds suffer a critical drawback: a lack of micro/mesoporosity, affecting cell attachment, tissue integration, and mineralization. It also results in a slow degradation rate. While 3D-printing has addressed the issue of macroporosity through CAD-guided fabrication, PCL scaffolds still exhibit poor smaller-scale porosity. To overcome this, we generated composites of PCL, hydroxyapatite (HA), and powdered sucrose (PS). The latter serves as a sacrificial material to generate porous particles after sucrose dissolution. Additionally, we have incorporated dexamethasone (DEX) to boost the PCL osteogenic properties. The resulting scaffolds maintain controlled macroporosity from the lattice print structure but also develop micro/mesoporosity within PCL fibers when exposed to aqueous environments. The study involved mixing PS into solvent-dissolved PCL in different weight ratios of PS to PCL (70:30, 50:50, and 30:70 wt%). The resulting composite was used for 3D printing of scaffolds at room temperature. Printability was optimized by adjusting pressure, speed, and layer height through filament collapse and fusion test. Enzymatic degradation, porogen leaching, and DEX release profiles were characterized. Physical properties were assessed using wettability, SEM, and micro-CT to quantify the porosity (percentage, pore size, and interconnectivity). Raman spectroscopy was used to verify the absence of sugar after leaching. Mechanical characteristics were evaluated via compression testing before and after porogen leaching. Bone marrow stromal cells (BMSCs) behavior in the printed scaffolds was studied by assessing viability, metabolic activity, osteo-differentiation, and mineralization. The scaffolds with a 70% sugar concentration exhibited superior printability and reached the highest porosity of 80%, but performed poorly during mechanical testing. A 50% PS concentration demonstrated a 70% porosity, with an average pore size of 25 µm, favoring cell attachment. No trace of sucrose was found in Raman after leaching the sugar for 8 hours. Water contact angle results show improved hydrophilicity as the sugar concentration increased, making the scaffolds more conductive to cell adhesion. The behavior of bone marrow stromal cells (BMSCs) showed positive viability and proliferation results with an increasing trend of mineralization and osteo-differentiation as the sucrose concentration increased. The addition of HA and DEX also promoted mineralization and osteo-differentiation in the cultures. The integration of PS as porogen at a concentration of 50%wt within PCL scaffolds presents a promising approach to address the poor cell attachment and tissue integration issues of PCL in bone tissue engineering. The method allows for the fabrication of scaffolds with tunable porosity and mechanical properties, suitable for various applications. The addition of HA and DEX further enhanced the scaffolds. Future studies will apply the scaffolds in an in-vivo model to thoroughly investigate their performance.

Keywords: bone, PCL, 3D printing, tissue engineering

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Authors: Kausik Halder, Nabendu Chaki, Ranjan Dasgupta

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We present a modelling framework that supports the engineering of early requirements specifications for design of learner centric dynamic Learning Management System. The framework is based on i* modelling tool and Means End Analysis, that adopts primitive concepts for modelling early requirements (such as actor, goal, and strategic dependency). We show how pedagogical and computational requirements for designing a learner centric Learning Management system can be adapted for the automatic early requirement engineering specifications. Finally, we presented a model on a Learner Quanta based adaptive Courseware. Our early requirement analysis shows that how means end analysis reveals gaps and inconsistencies in early requirements specifications that are by no means trivial to discover without the help of formal analysis tool.

Keywords: adaptive courseware, early requirement engineering, means end analysis, organizational modelling, requirement modelling

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Authors: Waleed Al-Nuaimy, Luke Anastassiou, Manjinder Kainth

Abstract:

As the process of teaching has evolved with the advent of new technologies over the ages, so has the process of learning. Educators have perpetually found themselves on the lookout for new technology-enhanced methods of teaching in order to increase learning efficiency and decrease ever expanding workloads. Shortly after the invention of the internet, web-based learning started to pick up in the late 1990s and educators quickly found that the process of providing learning material and marking assignments could change thanks to the connectivity offered by the internet. With the creation of early web-based virtual learning environments (VLEs) such as SPIDER and Blackboard, it soon became apparent that VLEs resulted in higher reported computer self-efficacy among students, but at the cost of students being less satisfied with the learning process . It may be argued that the impersonal nature of VLEs, and their limited functionality may have been the leading factors contributing to this reported dissatisfaction. To this day, often faced with the prospects of assigning colossal engineering cohorts their homework and assessments, educators may frequently choose optimally curated assessment formats, such as multiple-choice quizzes and numerical answer input boxes, so that automated grading software embedded in the VLEs can save time and mark student submissions instantaneously. A crucial skill that is meant to be learnt during most science and engineering undergraduate degrees is gaining the confidence in using, solving and deriving mathematical equations. Equations underpin a significant portion of the topics taught in many STEM subjects, and it is in homework assignments and assessments that this understanding is tested. It is not hard to see that this can become challenging if the majority of assignment formats students are engaging with are multiple-choice questions, and educators end up with a reduced perspective of their students’ ability to manipulate equations. Artificial intelligence (AI) has in recent times been shown to be an important consideration for many technologies. In our paper, we explore the use of new AI based software designed to work in conjunction with current VLEs. Using our experience with the software, we discuss its potential to solve a selection of problems ranging from impersonality to the reduction of educator workloads by speeding up the marking process. We examine the software’s potential to increase learning efficiency through its features which claim to allow more customized and higher-quality feedback. We investigate the usability of features allowing students to input equation derivations in a range of different forms, and discuss relevant observations associated with these input methods. Furthermore, we make ethical considerations and discuss potential drawbacks to the software, including the extent to which optical character recognition (OCR) could play a part in the perpetuation of errors and create disagreements between student intent and their submitted assignment answers. It is the intention of the authors that this study will be useful as an example of the implementation of AI in a practical assessment scenario insofar as serving as a springboard for further considerations and studies that utilise AI in the setting and marking of science and engineering assignments.

Keywords: engineering education, assessment, artificial intelligence, optical character recognition (OCR)

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Authors: Marilei Amadeu Sabino

Abstract:

The aim of this study is to establish differences between the terms ‘false cognates’, ‘false friends’ and ‘deceptive cognates’, usually considered to be synonyms. It will be shown they are not synonyms, since they do not designate the same linguistic process or phenomenon. Despite their differences in meaning, many pairs of formally similar words in two (or more) different languages are true cognates, although they are usually known as ‘false’ cognates – such as, for instance, the English and Italian lexical items ‘assist x assistere’; ‘attend x attendere’; ‘argument x argomento’; ‘apology x apologia’; ‘camera x camera’; ‘cucumber x cocomero’; ‘fabric x fabbrica’; ‘factory x fattoria’; ‘firm x firma’; ‘journal x giornale’; ‘library x libreria’; ‘magazine x magazzino’; ‘parent x parente’; ‘preservative x preservativo’; ‘pretend x pretendere’; ‘vacancy x vacanza’, to name but a few examples. Thus, one of the theoretical objectives of this paper is firstly to elaborate definitions establishing a distinction between the words that are definitely ‘false cognates’ (derived from different etyma) and those that are just ‘deceptive cognates’ (derived from the same etymon). Secondly, based on Set Theory and on the concepts of equal sets, subsets, intersection of sets and disjoint sets, this study is intended to elaborate some theoretical and practical questions that will be useful in identifying more precisely similarities and differences between cognate words of different languages, and according to graphic interpretation of sets it will be possible to classify them and provide discernment about the processes of semantic changes. Therefore, these issues might be helpful not only to the Learning of Second and Foreign Languages, but they could also give insights into Foreign and Second Language Teaching Methodology. Acknowledgements: FAPESP – São Paulo State Research Support Foundation – the financial support offered (proc. n° 2017/02064-7).

Keywords: deceptive cognates, false cognates, foreign language learning, teaching methodology

Procedia PDF Downloads 335