Search results for: machine learning tools and techniques

Authors: O. Moussous, T. Medjadj

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The suitability of a Fricke dosimeter for the measurement of a relative depth dose profile and the peak scatter factors was studied. The measurements were carried out in the secondary standard dosimetry laboratory at CRNA Algiers using a collimated 60Co gamma source teletherapy machine. The measurements were performed for different field sizes at the phantom front face, at a fixed source-to-phantom distance of 80 cm. The dose measurements were performed by first placing the dosimeters free-in-air at the distance-source-detector (DSD) of 80.5 cm from the source. Additional measurements were made with the phantom in place. The water phantom type Med-Tec 40x40x40 cm for vertical beam was used in this work as scattering martial. The phantom was placed on the irradiation bench of the cobalt unit at the SSD of 80 cm from the beam focus and the centre of the field coincided with the geometric centre of the dosimeters placed at the depth in water of 5 mm Relative depth dose profile and Peak scatter factors measurements were carried out using our Fricke system. This was intercompared with similar measurements by ionization chamber under identical conditions. There is a good agreement between the relative percentage depth–dose profiles and the PSF values measured by both systems using a water phantom.

Keywords: Fricke dosimeter, depth–dose profiles, peak scatter factors, DSD

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Authors: Shahroz Khan, Erkan Gunpinar, Kemal Mart

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Recently parametric design techniques became a vital concept in the field of Computer Aided Design (CAD), which helps to provide sophisticated platform to the designer in order to automate the design process in efficient time. In these techniques, design process starts by parameterizing the important features of design models (typically the key dimensions), with the implementation of design constraints. The design constraints help to retain the overall shape of the model while modifying its parameters. However, the process of initializing an appropriate number of design parameters and constraints is the crucial part of parametric design techniques, especially for complex surface models such as yacht hull. This paper introduces a method to create complex surface models in favor of parametric design techniques, a method to define the right number of parameters and respective design constraints, and a system to implement design parameters in contract to design constraints schema. For this, in our proposed approach the design process starts by dividing the yacht hull into three sections. Each section consists of different shape lines, which form the overall shape of yacht hull. The shape lines are created using Cubic Bezier Curves, which allow larger design flexibility. Design parameters and constraints are defined on the shape lines in 3D design space to facilitate the designers for better and individual handling of parameters. Afterwards, shape modifiers are developed, which allow the modification of each parameter while satisfying the respective set of criteria and design constraints. Such as, geometric continuities should be maintained between the shape lines of the three sections, fairness of the hull surfaces should be preserved after modification and while design modification, effect of a single parameter should be negligible on other parameters. The constraints are defined individually on shape lines of each section and mutually between the shape lines of two connecting sections. In order to validate and visualize design results of our shape modifiers, a real time graphic interface is created.

Keywords: design parameter, design constraints, shape modifies, yacht hull

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Authors: Emmanuel Habiyakare

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The COVID-19 pandemic has highlighted the significance of educational technologies in teaching and learning. The global pandemic led to the closure of educational institutions worldwide, prompting the widespread implementation of online learning as a substitute method for delivering curricula. The communication platform is known as WhatsApp has gained widespread adoption and extensive utilisation within the realm of education. The primary aims of this literature review are to examine the utilisation patterns and obstacles linked to the implementation of WhatsApp in the realm of education, assess the advantages and possibilities that students and facilitators can derive from utilising this platform for educational purposes, and comprehend the hindrances and restrictions that arise when employing WhatsApp in an academic environment. The literature was acquired through the utilisation of keywords that are linked to both WhatsApp and education from diverse databases. Having a thorough comprehension of current trends, potential advantages, obstacles, and gains linked to the use of WhatsApp is imperative for lecturers and administrators. Scholarly investigations have revealed a noticeable trend of lecturers and students increasingly utilising WhatsApp as a means of communication and collaboration. The objective of this literature review is to make a noteworthy contribution to the domain of education and technology through an investigation of the potential of WhatsApp as a learning tool. Additionally, this review seeks to offer valuable insights on how to effectively incorporate WhatsApp into pedagogical practices. The article underscores the significance of taking into account privacy and security concerns while utilising WhatsApp for educational objectives and puts forth recommendations for additional investigation.

Keywords: tool, COVID-19, opportunities, challenges, learning, WhatsApp

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Authors: Santiago Moll Lopez, Erica Vega Fleitas, Dolors Rosello Ferragud, Luis Manuel Sanchez Ruiz, Jose Antonio Moraño Fernandez

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Game-based learning (GBL) is becoming popular in education. Learning through games offers students a motivating experience related to the social aspect of games. Among the significant positive outcomes are promoting positive emotions and collaboration, improving the assimilation of concepts, and creating an attractive and dynamic environment standout. This work presents a study of the design and implementation of games created with RPG Maker MZ software with a Sci-Fi storytelling environment for developing specific and transversal skills in a Mathematics subject at the Beng in Aerospace Engineering. Games were applied during regular classes and as a part of a Flip-Teaching methodology to increase the motivation and the assimilation of mathematical concepts in an engaging way. The key features of the games were the introduction of avatar design and the promotion of collaboration among students. Students' opinions and grades obtained in the activities and exams showed increased motivation and a significant improvement in their performance compared with other groups or past students' performances.

Keywords: game-based learning, rol games, mathematics, science fiction

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Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

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An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including adaptive noise cancellation and echo cancellation. Acoustic echo cancellation is a common occurrence in today’s telecommunication systems. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. In this paper, we review different techniques of adaptive filtering to reduce this unwanted echo. In this paper, we see the behavior of techniques and algorithms of adaptive filtering like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Variable Step-Size Least Mean Square (VSLMS), Variable Step-Size Normalized Least Mean Square (VSNLMS), New Varying Step Size LMS Algorithm (NVSSLMS) and Recursive Least Square (RLS) algorithms to reduce this unwanted echo, to increase communication quality.

Keywords: adaptive acoustic, echo cancellation, LMS algorithm, adaptive filter, normalized least mean square (NLMS), variable step-size least mean square (VSLMS)

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Authors: Sarah Alshehri, Messedah Alqahtani

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Reading instruction in EFL classes is still challenging for teachers, and many students are still behind their expected level. Due to the Covid-19 pandemic, there was a shift in teaching English from face-to face to online classes. This paper will discover how the digital shift during and post pandemic has influenced English literacy instruction and what methods seem to be effective or challenging. Specifically, this paper will examine English language teachers' perspectives on the workable approaches and challenges that encountered them when teaching reading using E-Learning platform in Saudi Arabian Secondary and intermediate schools. The study explores public secondary school EFL teachers’ instructional practices and the challenges encountered when teaching reading online. Quantitative data will be collected through a 28 -item Likert type survey that will be administered to Saudi English teachers who work in public secondary and intermediate schools. The quantitative data will be analyzed using SPSS by conducting frequency distributions, descriptive statistics, reliability tests, and one-way ANOVA tests. The potential outcomes of this study will contribute to better understanding of digital literacy and technology integration in language teaching. Findings of this study can provide directions for professionals and policy makers to improve the quality of English teaching and learning. Limitations and results will be discussed, and suggestions for future directions will be offered.

Keywords: EFL reading, E-learning- EFL literacy, EFL workable approaches, EFL reading instruction

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Authors: Carlos Escobar-del Pozo, César Ahumada-Monroy, Azael García-Rebolledo, Alberto Brambila-Solórzano, Gregorio Martínez-Sánchez, Luis Ortiz-Rincón

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Numerical simulations of flow in complex biological structures have gained considerable attention in the last years. However, the major issue is the validation of the results. The present work shows a Particle Image Velocimetry PIV flow visualization technique in complex biological structures, particularly in intracranial aneurysms. A methodology to reconstruct and generate a transparent model has been developed, as well as visualization and particle tracking techniques. The generated transparent models allow visualizing the flow patterns with a regular camera using the visualization techniques. The final goal is to use visualization as a tool to provide more information on the treatment and surgery decisions in aneurysms.

Keywords: aneurysms, PIV, flow visualization, particle tracking

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Authors: Pei Wang

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The concept of social-emotional learning (SEL) is becoming increasingly popular in both research and practical applications worldwide. However, there is a lack of empirical studies and implementation of SEL in China, particularly from the perspective of parents. This qualitative study examined how Chinese parents perceived SEL, how their views on SEL were shaped, and how these views affected their decisions regarding their children’s education programs. Using the Collaborative for Academic Social and Emotional Learning Interactive Wheel framework and Bronfenbrenner's bioecological theory, the study conducted interviews with eight parents whose children attended public, international, and private schools in China. All collected data were conducted a thematic analysis involving three coding phases. The findings revealed that interviewees perceived SEL as significant to children’s development but held diverse understandings and perspectives on SEL at school depending on the amount and the quality of SEL resources available in their children’s schools. Additionally, parents’ attitudes towards the exam-oriented education system and Chinese culture influenced their views on SEL in school. Nevertheless, their socioeconomic status (SES) was the most significant factor in their perspectives on SEL, which significantly impacted their choices in their children's educational programs. High-SES families had more options to pursue SEL resources by sending their children to international schools or Western countries, while lower middle-class SES families had limited SEL resources in public schools. This highlighted educational inequality in China and emphasized the need for greater attention and investment in SEL programs in Chinese public schools.

Keywords: Chinese, inequality, parent, school, social-emotional learning

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Authors: Reena Murali, David Peter S.

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The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies shows that up regulation of mRNA cause serious diseases like Cancer. So designing effective siRNA with good knockdown effects play an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (ΔG), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.

Keywords: artificial neural network, double stranded RNA, RNA interference, short interfering RNA

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Authors: Emily Cherono Sawe, Elisha Ondieki Makori

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Knowledge and digital economies have brought challenges and potential opportunities for universities to innovate and improve the quality of learning. Disruption technologies and information dynamics continue to transform and change the landscape in teaching, scholarship, and research activities across universities. Digital literacy is a fundamental and imperative element in higher education and training, as witnessed during the new norm. COVID-19 caused unprecedented disruption in universities, where teaching and learning depended on digital innovations and applications. Academic services and activities were provided online, including library information services. Information professionals were forced to adopt various digital platforms in order to provide information services to patrons. University libraries’ roles in fulfilling educational responsibilities continue to evolve in response to changes in pedagogy, technology, economy, society, policies, and strategies of parent institutions. Libraries are currently undergoing considerable transformational change as a result of the inclusion of a digital environment. Academic libraries have been at the forefront of providing online learning resources and online information services, as well as supporting students and staff to develop digital literacy skills via online courses, tutorials, and workshops. Digital literacy transformation and information staff are crucial elements reminiscent of the prioritization of skills and knowledge for lifelong learning. The purpose of this baseline research is to assess the implications of digital literacy transformation in institutions of higher learning in Kenya and share appropriate strategies to leverage and sustain teaching and research. Objectives include examining the leverage and preparedness of the digital literacy environment in streamlining learning in the universities, exploring and benchmarking imperative digital competence for information professionals, establishing the perception of information professionals towards digital literacy skills, and determining lessons, best practices, and strategies to accelerate digital literacy transformation for effective research and learning in the universities. The study will adopt a descriptive research design using questionnaires and document analysis as the instruments for data collection. The targeted population is librarians and information professionals, as well as academics in public and private universities teaching information literacy programmes. Data and information are to be collected through an online structured questionnaire and digital face-to-face interviews. Findings and results will provide promising lessons together with best practices and strategies to transform and change digital literacies in university libraries in Kenya.

Keywords: digital literacy, digital innovations, information professionals, librarians, higher education, university libraries, digital information literacy

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Authors: Xiaoling Dai

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Many Chinese artists have used digital photography to create works with features of Chinese landscape paintings since the 20th century. The ‘New Mountains and Water’ works created by digital techniques reflect the fusion of photographic techniques and traditional Chinese aesthetic thoughts. Borrowing from Chinese landscape paintings in the Song Dynasty, the Chinese photographer Yao Lu uses digital photography to reflect contemporary environmental construction in his series New Landscapes. By portraying a variety of natural environments brought by urbanization in the contemporary period, Lu deconstructs traditional Chinese paintings and reconstructs contemporary photographic practices. The primary object of this study is to investigate how Chinese photographer Yao Lu redefines and re-interprets the relationship between tradition and contemporaneity. In this study, Yao Lu’s series work New Landscapes is used for photo elicitation, which seeks to broaden understanding of the development of Chinese landscape photography. Furthermore, discourse analysis will be used to evaluate how Chinese social developments influence the creation of photographic practices. Through visual and discourse analysis, this study aims to excavate the relationship between tradition and contemporaneity in Lu’s works. According to New Landscapes, the study argues that in Lu’s interpretations of landscapes, tradition and contemporaneity are seen to establish a new relationship. Traditional approaches to creation do not become obsolete over time. On the contrary, traditional notions and styles of creation can shed new light on contemporary issues or techniques.

Keywords: Chinese aesthetics, Yao Lu, new landscapes, tradition, contemporaneity

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Authors: Bowen Ding, Yihao Kuang

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In recent years, with the development of large-scale pre-trained lan-guage models, building vector representations of text through deep neural network models has become a standard practice for natural language processing tasks. From the performance on downstream tasks, we can know that the text representation constructed by these models contains linguistic information, but its encoding mode and extent are unclear. In this work, a structural probe is proposed to detect whether the vector representation produced by a deep neural network is embedded with a syntax tree. The probe is trained with the deep metric learning method, so that the distance between word vectors in the metric space it defines encodes the distance of words on the syntax tree, and the norm of word vectors encodes the depth of words on the syntax tree. The experiment results on ELMo and BERT show that the syntax tree is encoded in their parameters and the word representations they produce.

Keywords: deep metric learning, syntax tree probing, natural language processing, word representations

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Authors: Mehmet Oren, Susan Pedersen, Sevket C. Cetin

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Despite the frequently criticized disadvantages of the traditional used paper and pencil assessment, it is the most frequently used method in our schools. Although assessments do an acceptable measurement, they are not capable of measuring all the aspects and the richness of learning and knowledge. Also, many assessments used in schools decontextualize the assessment from the learning, and they focus on learners’ standing on a particular topic but do not concentrate on how student learning changes over time. For these reasons, many scholars advocate that using simulations and games (S&G) as a tool for assessment has significant potentials to overcome the problems in traditionally used methods. S&G can benefit from the change in technology and provide a contextualized medium for assessment and teaching. Furthermore, S&G can serve as an instructional tool rather than a method to test students’ learning at a particular time point. To investigate the potentials of using educational games as an assessment and teaching tool, this study presents the implementation and the validation of an automated embedded assessment (AEA), which can constantly monitor student learning in the game and assess their performance without intervening their learning. The experiment was conducted on an undergraduate level engineering course (Digital Circuit Design) with 99 participant students over a period of five weeks in Spring 2016 school semester. The purpose of this research study is to examine if the proposed method of AEA is valid to assess student learning in a 3D Educational game and present the implementation steps. To address this question, this study inspects three aspects of the AEA for the validation. First, the evidence-centered design model was used to lay out the design and measurement steps of the assessment. Then, a confirmatory factor analysis was conducted to test if the assessment can measure the targeted latent constructs. Finally, the scores of the assessment were compared with an external measure (a validated test measuring student learning on digital circuit design) to evaluate the convergent validity of the assessment. The results of the confirmatory factor analysis showed that the fit of the model with three latent factors with one higher order factor was acceptable (RMSEA < 0.00, CFI =1, TLI=1.013, WRMR=0.390). All of the observed variables significantly loaded to the latent factors in the latent factor model. In the second analysis, a multiple regression analysis was used to test if the external measure significantly predicts students’ performance in the game. The results of the regression indicated the two predictors explained 36.3% of the variance (R2=.36, F(2,96)=27.42.56, p<.00). It was found that students’ posttest scores significantly predicted game performance (β = .60, p < .000). The statistical results of the analyses show that the AEA can distinctly measure three major components of the digital circuit design course. It was aimed that this study can help researchers understand how to design an AEA, and showcase an implementation by providing an example methodology to validate this type of assessment.

Keywords: educational video games, automated embedded assessment, assessment validation, game-based assessment, assessment design

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Authors: Emmanuel Okechukwu Nwafor, Folake Olubunmi Akintayo, Denis Alcides Rezende

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Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks, which can be related to cities, whether physical or digital. The aim of this research is to assess how well decision tree algorithms can predict trip purposes in Makurdi, Nigeria, while also exploring their connection to the strategic digital city initiative. The research methodology involves formalizing household demographic and trips information datasets obtained from extensive survey process. Modelling and Prediction were achieved using Python Programming Language and the evaluation metrics like R-squared and mean absolute error were used to assess the decision tree algorithm's performance. The results indicate that the model performed well, with accuracies of 84% and 68%, and low MAE values of 0.188 and 0.314, on training and validation data, respectively. This suggests the model can be relied upon for future prediction. The conclusion reiterates that This model will assist decision-makers, including urban planners, transportation engineers, government officials, and commuters, in making informed decisions on transportation planning and management within the framework of a strategic digital city. Its application will enhance the efficiency, sustainability, and overall quality of transportation services in Makurdi, Nigeria.

Keywords: decision tree algorithm, trip purpose, intelligent transport, strategic digital city, travel pattern, sustainable transport

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Authors: C. von Essen

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This study outlines and evaluates a constructivist design approach to the creation of educational video on postgraduate business degree programmes. Many online courses are tapping into the educational affordances of video, as this form of online learning has the potential to create rich, multimodal experiences. And yet, in many learning contexts video is still being used to transmit instruction to passive learners, rather than promote learner engagement and knowledge creation. Constructivism posits the notion that learning is shaped as students make connections between their experiences and ideas. This paper pivots on the following research question: how can we design educational video in ways which promote constructivist learning and stimulate analytic viewing? By exploring and categorizing over two thousand educational videos created since 2014 for over thirty postgraduate courses in business, economics, mathematics and statistics, this paper presents and critically reflects on a taxonomy of video styles and features. It links the pedagogical intent of video – be it concept explanation, skill demonstration, feedback, real-world application of ideas, community creation, or the cultivation of course narrative – to specific presentational characteristics such as visual effects including diagrammatic and real-life graphics and aminations, commentary and sound options, chronological sequencing, interactive elements, and presenter set-up. The findings of this study inform a framework which captures the pedagogical, technological and production considerations instructional designers and educational media specialists should be conscious of when planning and preparing the video. More broadly, the paper demonstrates how learning theory and technology can coalesce to produce informed and pedagogical grounded instructional design choices. This paper reveals how crafting video in a more conscious and critical manner can produce powerful, new educational design.

Keywords: educational video, constructivism, instructional design, business education

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Authors: Noga Bregman

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Accurate and efficient earthquake detection and phase picking are crucial for seismic hazard assessment and emergency response. This study introduces EQMamba, a deep-learning method that combines the strengths of the Earthquake Transformer and the Mamba model for simultaneous earthquake detection and phase picking. EQMamba leverages the computational efficiency of Mamba layers to process longer seismic sequences while maintaining a manageable model size. The proposed architecture integrates convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and Mamba blocks. The model employs an encoder composed of convolutional layers and max pooling operations, followed by residual CNN blocks for feature extraction. Mamba blocks are applied to the outputs of BiLSTM blocks, efficiently capturing long-range dependencies in seismic data. Separate decoders are used for earthquake detection, P-wave picking, and S-wave picking. We trained and evaluated EQMamba using a subset of the STEAD dataset, a comprehensive collection of labeled seismic waveforms. The model was trained using a weighted combination of binary cross-entropy loss functions for each task, with the Adam optimizer and a scheduled learning rate. Data augmentation techniques were employed to enhance the model's robustness. Performance comparisons were conducted between EQMamba and the EQTransformer over 20 epochs on this modest-sized STEAD subset. Results demonstrate that EQMamba achieves superior performance, with higher F1 scores and faster convergence compared to EQTransformer. EQMamba reached F1 scores of 0.8 by epoch 5 and maintained higher scores throughout training. The model also exhibited more stable validation performance, indicating good generalization capabilities. While both models showed lower accuracy in phase-picking tasks compared to detection, EQMamba's overall performance suggests significant potential for improving seismic data analysis. The rapid convergence and superior F1 scores of EQMamba, even on a modest-sized dataset, indicate promising scalability for larger datasets. This study contributes to the field of earthquake engineering by presenting a computationally efficient and accurate method for simultaneous earthquake detection and phase picking. Future work will focus on incorporating Mamba layers into the P and S pickers and further optimizing the architecture for seismic data specifics. The EQMamba method holds the potential for enhancing real-time earthquake monitoring systems and improving our understanding of seismic events.

Keywords: earthquake, detection, phase picking, s waves, p waves, transformer, deep learning, seismic waves

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Authors: C. A. Barros, Ana P. Barroso

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Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: automotive Industry, industry 4.0, Internet of Things, IATF 16949:2016, measurement system analysis

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Authors: Rita Chattopadhyay, Vivek Anand Thoutam

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Speech is the most acceptable means of communication where we can quickly exchange our feelings and thoughts. Quite often, people can communicate orally but cannot interact or work with computers or devices. It’s easy and quick to give speech commands than typing commands to computers. In the same way, it’s easy listening to audio played from a device than extract output from computers or devices. Especially with Robotics being an emerging market with applications in warehouses, the hospitality industry, consumer electronics, assistive technology, etc., speech-based human-machine interaction is emerging as a lucrative feature for robot manufacturers. Considering this factor, the objective of this paper is to design the “Audio-Visual Co-Data Processing Pipeline.” This pipeline is an integrated version of Automatic speech recognition, a Natural language model for text understanding, object detection, and text-to-speech modules. There are many Deep Learning models for each type of the modules mentioned above, but OpenVINO Model Zoo models are used because the OpenVINO toolkit covers both computer vision and non-computer vision workloads across Intel hardware and maximizes performance, and accelerates application development. A speech command is given as input that has information about target objects to be detected and start and end times to extract the required interval from the video. Speech is converted to text using the Automatic speech recognition QuartzNet model. The summary is extracted from text using a natural language model Generative Pre-Trained Transformer-3 (GPT-3). Based on the summary, essential frames from the video are extracted, and the You Only Look Once (YOLO) object detection model detects You Only Look Once (YOLO) objects on these extracted frames. Frame numbers that have target objects (specified objects in the speech command) are saved as text. Finally, this text (frame numbers) is converted to speech using text to speech model and will be played from the device. This project is developed for 80 You Only Look Once (YOLO) labels, and the user can extract frames based on only one or two target labels. This pipeline can be extended for more than two target labels easily by making appropriate changes in the object detection module. This project is developed for four different speech command formats by including sample examples in the prompt used by Generative Pre-Trained Transformer-3 (GPT-3) model. Based on user preference, one can come up with a new speech command format by including some examples of the respective format in the prompt used by the Generative Pre-Trained Transformer-3 (GPT-3) model. This pipeline can be used in many projects like human-machine interface, human-robot interaction, and surveillance through speech commands. All object detection projects can be upgraded using this pipeline so that one can give speech commands and output is played from the device.

Keywords: OpenVINO, automatic speech recognition, natural language processing, object detection, text to speech

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Authors: P. Senthil Kumari

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Main concerns in data mining investigation are social controls of data mining for handling ambiguity, noise, or incompleteness on text data. We describe an innovative approach for unplanned text data detection of community networks achieved by classification mechanism. In a tangible domain claim with humble secrecy backgrounds provided by community network for evading annoying content is presented on consumer message partition. To avoid this, mining methodology provides the capability to unswervingly switch the messages and similarly recover the superiority of ordering. Here we designated learning-centered mining approaches with pre-processing technique to complete this effort. Our involvement of work compact with rule-based personalization for automatic text categorization which was appropriate in many dissimilar frameworks and offers tolerance value for permits the background of comments conferring to a variety of conditions associated with the policy or rule arrangements processed by learning algorithm. Remarkably, we find that the choice of classifier has predicted the class labels for control of the inadequate documents on community network with great value of effect.

Keywords: text mining, data classification, community network, learning algorithm

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Authors: Yuet Wah Echo Yeung

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Working with people from minority ethnic groups, refugees and asylum seeking communities who have limited proficiency in the language of the host country often presents a major challenge for social workers. Because of language differences, social workers need to work with interpreters to ensure accurate information is collected for their assessment and intervention. Drawing from social learning theory, this paper discusses how role play was used as an experiential learning exercise in a training session to help social work students develop skills when working with interpreters. Social learning theory posits that learning is a cognitive process that takes place in a social context when people observe, imitate and model others’ behaviours. The roleplay also helped students understand the role of the interpreter and the challenges they may face when they rely on interpreters to communicate with service users and their family. The first part of the session involved role play. A tutor played the role of social worker and deliberately behaved in an unprofessional manner and used inappropriate body language when working alongside the interpreter during a home visit. The purpose of the roleplay is not to provide a positive role model for students to ‘imitate’ social worker’s behaviours. Rather it aims to active and provoke internal thinking process and encourages students to critically consider the impacts of poor practice on relationship building and the intervention process. Having critically reflected on the implications for poor practice, students were then asked to play the role of social worker and demonstrate what good practice should look like. At the end of the session, students remarked that they learnt a lot by observing the good and bad example; it showed them what not to do. The exercise served to remind students how practitioners can easily slip into bad habits and of the importance of respect for the cultural difference when working with people from different cultural backgrounds.

Keywords: role play, social learning theory, social work practice, working with interpreters

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Authors: Salman Hameed

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In this paper, a self-tuning fuzzy PI controller (STFPIC) is proposed for thyristor controlled series capacitor (TCSC) to improve power system dynamic performance. In a STFPIC controller, the output scaling factor is adjusted on-line by an updating factor (α). The value of α is determined from a fuzzy rule-base defined on error (e) and change of error (Δe) of the controlled variable. The proposed self-tuning controller is designed using a very simple control rule-base and the most natural and unbiased membership functions (MFs) (symmetric triangles with equal base and 50% overlap with neighboring MFs). The comparative performances of the proposed STFPIC and the standard fuzzy PI controller (FPIC) have been investigated on a multi-machine power system (namely, 4 machine two area system) through detailed non-linear simulation studies using MATLAB/SIMULINK. From the simulation studies it has been found out that for damping oscillations, the performance of the proposed STFPIC is better than that obtained by the standard FPIC. Moreover, the proposed STFPIC as well as the FPIC have been found to be quite effective in damping oscillations over a wide range of operating conditions and are quite effective in enhancing the power carrying capability of the power system significantly.

Keywords: genetic algorithm, power system stability, self-tuning fuzzy controller, thyristor controlled series capacitor

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Shane-Anthony Smith

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Teachers across grade levels and content areas face a myriad of challenges in the classroom. From inconsistent attendance to disruptive behaviors, these challenges can have a dire impact on the educational space, untimely leading to a loss of instructional time and student disenfranchisement from learning. While these challenges are not new to the educational landscape, the post-COVID classroom has, in many instances, been more severely impacted by behaviors that are not conducive to learning. Despite the mounting challenges, the role of the teacher remains unchanged - that is, to create and maintain a safe environment that is conducive to learning and promotes successful learning outcomes. Accomplishing this feat is no easy task. Yet, there are steps teachers can - indeed, must - take to better set themselves and their students up for success. The key to achieving this success is effective classroom transitions. This paper presents a four-step approach for teachers to engage in successful classroom transitions to promote meaningful student engagement and active positive learning outcomes. The transition strategy I will explore is called PREP (Pause, Reset, Establish Expectations, and Proceed). I developed this strategy in my work as a Residency Director for my university’s teacher residency program. In this role, I am tasked with coaching emerging teachers and their in-service teaching mentors in the field, as well as providing mentorship to special education resident teachers pursuing teaching degrees in the program. As a teacher educator, being in Middle and High school classrooms provides an intricate and critical understanding of the challenges, opportunities, and possibilities in the classroom. For this paper, I will explore how teachers can optimize the opportunities PREP provides to keep students engaged and, thus, improve student achievement. I will describe the approach, explain its use, and provide case-study examples of its classroom application.

Keywords: classroom management, teaching strategies, student engagement, classroom transition

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Authors: Dennis Infante

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This paper examined the reflections of teachers on curriculum reforms, the implementation of the K-12 Basic Education Program in the Philippines. The results revealed that problems and concerns raised by teachers could be classified into curriculum materials and design; competence, readiness and motivation of the teachers; the learning environment, and support systems; readiness, competence and motivation of students; and other relevant factors. The best features of the K-12 curriculum reforms included (1) the components, curriculum materials; (2) the design, structure and delivery of the lessons; (3) the framework and theoretical approach; (3) the qualities of the teaching-learning activities; (4) and other relevant features. With the demanding task of implementing the new curriculum, the teachers expressed their needs which included (1) making the curriculum materials available to achieve the goals of the curriculum reforms; (2) enrichment of the learning environments; (3) motivating and encouraging the teachers to embrace change; (4) providing appropriate support systems; (5) re-tooling, and empowering teachers to implement the curriculum reforms; and (6) other relevant factors. The research concluded with a synthesis that provided a paradigm for implementing curriculum reforms which recognizes the needs of the teachers and the features of the new curriculum.

Keywords: curriculum reforms, K-12, teachers' reflections, implementing curriculum change

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Authors: Twarakavi Venkata Suresh Kumar, Sailaja Kumar, B. Eswara Reddy

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In a world of tremendous technical developments, effective and efficient higher education has always been a major challenge. The rising number of educational institutions have made it mandatory for healthy competitions among the institutions. To evaluate the qualitative competence of these educations institutions in engineering and technology and related disciplines, an efficient assessment technique in internal and external quality has to be followed. To achieve this, the curriculum is to be developed into courses, and each course has to be presented in the form teaching lesson plan consisting of topics and session outcome known as Course Outcomes (COs), that easily map into different Programme Outcomes (POs). The major objective of these methodologies is to provide quality technical education to its students. Detailed clear weightage in CO-PO mapping helps in proper measurable COs and to devise the POs attainment is an important issue. This ensures in assisting the achievement of the POs with proper weightage to POs, and also improves the successive curriculum development. In this paper, we presented a methodology for mapping CO and PO considering the key elements supported by each PO. This approach is useful in evaluating the attainment of POs which is based on the attainment of COs using the existing data from students' marks taken from various test items. Such direct assessment tools are used to measure the degree to which each student has achieved each course learning outcome by the completion of the course. Hence, these results are also useful in measuring the PO attainment for improving the programme vision and mission.

Keywords: attainment, course outcomes, programme outcomes, educational institutions

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Authors: Suffian Hadi Ayub, Mohammad Rezal Hamzah, Nor Hafizah Abdullah, Sharipah Nur Mursalina Syed Azmy, Hishamuddin Salim

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The liberalisation of the education industry has exposed the institute of higher learning (IHL) in Malaysia to the financial challenges. Without good financial standing, public institution will rely on the government funding. Ostensibly, this contradicts with the government’s aspiration to make universities self-sufficient. With stiff competition from private institutes of higher learning, IHL need to be prepared at the forefront level. The corporate identity itself is the entrance to the world of higher learning and it is in this uniqueness, it will be able to distinguish itself from competitors. This paper examined the perception of the stakeholders at one of the public universities in the east coast region in Malaysia on the perceived reputation and how the university communicate its preparedness for self-sustainability through corporate identity. The findings indicated while the stakeholders embraced the challenges in facing the stiff competition and struggling market conditions, most of them felt the university should put more efforts in mobilising the corporate identity to its constituencies.

Keywords: communication, corporate identity, market conditions, universities

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Authors: Emma K. Sales

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Durian is the flagship fruit of Mindanao and there is an abundance of several cultivars with many confusing identities/ names. The project was conducted to develop procedure for reliable and rapid detection and sorting of durian planting materials. Moreover, it is also aimed to establish specific genetic or DNA markers for routine testing and authentication of durian cultivars in question. The project developed molecular procedures for routine testing. SSR primers were also screened and identified for their utility in discriminating durian cultivars collected. Results of the study showed the following accomplishments; 1. Twenty (29) SSR primers were selected and identified based on their ability to discriminate durian cultivars, 2. Optimized and established standard procedure for identification and authentication of Durian cultivars 3. Genetic profile of durian is now available at Biotech Unit. Our results demonstrate the relevance of using molecular techniques in evaluating and identifying durian clones. The most polymorphic primers tested in this study could be useful tools for detecting variation even at the early stage of the plant especially for commercial purposes. The process developed combines the efficiency of the microsatellites development process with the optimization of non-radioactive detection process resulting in a user-friendly protocol that can be performed in two (2) weeks and easily incorporated into laboratories about to start microsatellite development projects. This can be of great importance to extend microsatellite analyses to other crop species where minimal genetic information is currently available. With this, the University can now be a service laboratory for routine testing and authentication of durian clones.

Keywords: DNA, SSR analysis, genotype, genetic diversity, cultivars

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Authors: Juha Kettunen

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The purpose of this study is to develop a framework for the assessment of research and development projects. The assessment map is developed in this study based on the strategy map of the balanced scorecard approach. The assessment map is applied in a project that aims to reduce the inequality and risk of exclusion of young people from disadvantaged social groups. The assessment map denotes that not only funding but also necessary skills and qualifications should be carefully assessed in the implementation of the project plans so as to achieve the objectives of projects and the desired impact. The results of this study are useful for those who want to develop the implementation of the Erasmus+ Programme and the project teams of research and development projects.

Keywords: non-formal learning, youth work, social inclusion, innovation

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Authors: Didik Sugiyanto

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Conditions that often occur in the framework of a particular vehicle at a car is a collision or collision with another object, an example of such damage is to the frame or chassis for the required design framework that is able to absorb impact energy. Characteristics of the material are influenced by the value of the stiffness of the material that need to be considered in choosing the material properties of the material. To obtain material properties that can be adapted to the experimental conditions tested the tensile and compression testing. In this study focused on the chassis at an angle of 150, 300, and 450. It is based on field studies that vehicle primarily for freight cars have a point of order light between 150 to 450. Research methods include design tools, design framework, procurement of materials and experimental tools, tool-making, the manufacture of the test framework, and the testing process, experiment is testing the power of the press to know the order. From this test obtained the maximum force on the corner of 150 was 569.76 kg at a distance of 16 mm, angle 300 is 370.3 kg at a distance of 15 mm, angle 450 is 391.71 kg at a distance of 28 mm. After reaching the maximum force the order will occur collapse, followed by a decrease in the next distance. It can be concluded that the greatest strain energy occurs at an angle of 150. So it is known that the frame at an angle of 150 produces the best level of security.

Keywords: buckling, collapse, body frame, vehicle

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Authors: Shilpa Murthy, Hazlina Binti Abu Bakar, Juliet Mathew, Chandrashekhar Thummala Hlly Sreerama Reddy, Pathiyil Ravi Shankar

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Simulation is defined as a technique that replaces or expands real experiences with guided experiences that interactively imitate real-world processes or systems. Simulation enables learners to train in a safe and non-threatening environment. For decades, simulation has been considered an integral part of clinical teaching and learning strategy in medical education. The several types of simulation used in medical education and the clinical environment can be applied to several models, including full-body mannequins, task trainers, standardized simulated patients, virtual or computer-generated simulation, or Hybrid simulation that can be used to facilitate learning. Simulation allows healthcare practitioners to acquire skills and experience while taking care of patient safety. The recent COVID pandemic has also led to an increase in simulation use, as there were limitations on medical student placements in hospitals and clinics. The learning is tailored according to the educational needs of students to make the learning experience more valuable. Simulation in the pre-clinical years has challenges with resource constraints, effective curricular integration, student engagement and motivation, and evidence of educational impact, to mention a few. As instructors, we may have more reliance on the use of simulation for pre-clinical students while the students’ confidence levels and perceived competence are to be evaluated. Our research question was whether the implementation of simulation-based learning positively influences preclinical medical students' confidence levels and perceived competence. This study was done to align the teaching activities with the student’s learning experience to introduce more low-fidelity simulation-based teaching sessions for pre-clinical years and to obtain students’ input into the curriculum development as part of inclusivity. The study was carried out at International Medical University, involving pre-clinical year (Medical) students who were started with low-fidelity simulation-based medical education from their first semester and were gradually introduced to medium fidelity, too. The Student Satisfaction and Self-Confidence in Learning Scale questionnaire from the National League of Nursing was employed to collect the responses. The internal consistency reliability for the survey items was tested with Cronbach’s alpha using an Excel file. IBM SPSS for Windows version 28.0 was used to analyze the data. Spearman’s rank correlation was used to analyze the correlation between students’ satisfaction and self-confidence in learning. The significance level was set at p value less than 0.05. The results from this study have prompted the researchers to undertake a larger-scale evaluation, which is currently underway. The current results show that 70% of students agreed that the teaching methods used in the simulation were helpful and effective. The sessions are dependent on the learning materials that are provided and how the facilitators engage the students and make the session more enjoyable. The feedback provided inputs on the following areas to focus on while designing simulations for pre-clinical students. There are quality learning materials, an interactive environment, motivating content, skills and knowledge of the facilitator, and effective feedback.

Keywords: low-fidelity simulation, pre-clinical simulation, students satisfaction, self-confidence

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