Search results for: online and adaptive learning

Authors: Lassaad Smirani

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In this paper we propose a Neurosymbolic Learning System (NLS) as a channel estimator for Long Term Evolution Advanced (LTE-A) uplink. The proposed system main idea based on Neural Network has modules capable of performing bidirectional information transfer between symbolic module and connectionist module. We demonstrate various strengths of the NLS especially the ability to integrate theoretical knowledge (rules) and experiential knowledge (examples), and to make an initial knowledge base (rules) converted into a connectionist network. Also to use empirical knowledge witch by learning will have the ability to revise the theoretical knowledge and acquire new one and explain it, and finally the ability to improve the performance of symbolic or connectionist systems. Compared with conventional SC-FDMA channel estimation systems, The performance of NLS in terms of complexity and quality is confirmed by theoretical analysis and simulation and shows that this system can make the channel estimation accuracy improved and bit error rate decreased.

Keywords: channel estimation, SC-FDMA, neural network, hybrid system, BER, LTE-A

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Authors: Samatha Ss Sutton, Kaouther Kooli

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In recent years social media influences (SMI) have become integrated into many companies marketing strategies to create buzz, target new and younger markets and further expand social media coverage in business (Lim et al 2017). SMI’s can be defined as online personalities with a substantial number of followers, across one or more social media platforms, with influence on their followers (Lou and Yuan 2018). Recently expenditure on influencer marketing has increased exponentially becoming an important area for marketing opportunities and strategies in the future (Lou and Yuan 2018). In order to market products and brands effectively through SMI’s it is important for business to understand the attributes of SMI that effect purchase intention (Lim et al 2017) of their followers and whether or not these attributes vary across generations so to market effectively to their specific segment or target market. The present study involves quantitative research to understand the attributes by which influence differs across generations namely Generation X and Millennials and its effects on purchase intentions of these generational groups. A survey will be conducted using an online questionnaire. Structural Equation Modelling and Multi group analysis will be applied. The study provides insight to marketers/decision makers on how to use influencers accordingly with their target consumer.

Keywords: social media marketing, social media influencers, attitude towards social media influencers, intention to purchase

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Authors: Hang Yang, Jichao Li, Kewei Yang, Tianyang Lei

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Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis.

Keywords: data mining, industrial system, multivariate time series, anomaly detection

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Authors: Malik Ghulam Behlol, Alison Fox, Faiza Masood, Sabiha Arshad

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This paper explores the barriers to the application of learning-supportive assessment at teaching practicum while investigating the role of university teachers (UT), cooperative teachers (CT), prospective teachers ( PT) and heads of the practicum schools (HPS) in the selected universities of Pakistan and the UK. It is a qualitative case study and data were collected through the lesson observation of UT in the pre-service teacher education setting and PT in practicum schools. Interviews with UT, HPS, and Focus Group Discussions with PT were conducted too. The study has concluded that as compared to the UK counterpart, PT in Pakistan faces significant barriers in applying learning-supportive assessment in the school practicum settings because of large class sizes, lack of institutionalised collaboration between universities and schools, poor modelling of the lesson, ineffective feedback practices, lower order thinking assignments, and limited opportunities to use technology in school settings.

Keywords: assessment, pre-service teacher education, theory-practice gap, teacher education

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Authors: Leigh Ann Wilson, Melanie Borrego

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The purpose of institutional learning outcomes (ILOs) is to assess what students across the university know and what they do not. The issue is gathering this information in a systematic and usable way. This presentation will explain how one institution has engineered this process for both student success and maximum faculty curriculum and course design input. At Brandman University, there are three levels of learning outcomes: course, program, and institutional. Institutional Learning Outcomes (ILOs) are mapped to specific courses. Faculty course developers write the signature assignments (SAs) in alignment with the Institutional Learning Outcomes for each course. These SAs use a specific rubric that is applied consistently by every section and every instructor. Each year, the 12-member General Education Team (GET), as a part of their work, conducts the calibration and assessment of the university-wide SAs and the related rubrics for one or two of the five ILOs. GET members, who are senior faculty and administrators who represent each of the university's schools, lead the calibration meetings. Specifically, calibration is a process designed to ensure the accuracy and reliability of evaluating signature assignments by working with peer faculty to interpret rubrics and compare scoring. These calibration meetings include the full time and adjunct faculty members who teach the course to ensure consensus on the application of the rubric. Each calibration session is chaired by a GET representative as well as the course custodian/contact where the ILO signature assignment resides. The overall calibration process GET follows includes multiple steps, such as: contacting and inviting relevant faculty members to participate; organizing and hosting calibration sessions; and reviewing and discussing at least 10 samples of student work from class sections during the previous academic year, for each applicable signature assignment. Conversely, the commitment for calibration teams consist of attending two virtual meetings lasting up to three hours in duration. The first meeting focuses on interpreting the rubric, and the second meeting involves comparing scores for sample work and sharing feedback about the rubric and assignment. Next, participants are expected to follow all directions provided and participate actively, and respond to scheduling requests and other emails within 72 hours. The virtual meetings are recorded for future institutional use. Adjunct faculty are paid a small stipend after participating in both calibration meetings. Full time faculty can use this work on their annual faculty report for "internal service" credit.

Keywords: assessment, assurance of learning, course design, institutional learning outcomes, rubrics, signature assignments

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Pirada Techaratpong

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This qualitative research has 2 objectives: to study marketing management of the cultural learning center in Suansunandha Rajabhat University and to suggest guidelines to improve its marketing management. This research is based on a case study of the Arts and Culture Office in Suansunandha Rajabhat University, Bangkok. This research found the Art and Culture Office has no formal marketing management. However, the marketing management is partly covered in the overall business plan, strategic plan, and action plan. The process can be divided into 5 stages. The marketing concept has long been introduced to its policy but not apparently put into action due to inflexible system. Some gaps are found in the process. The research suggests the Art and Culture Office implement the concept of marketing orientation, meeting the needs and wants of its target customers and adapt to the changing situation. Minor guidelines for improvement are provided.

Keywords: cultural learning center, marketing, management, museum

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Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

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The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: complex-valued signal processing, synthetic aperture radar, 2-D radar imaging, compressive sensing, sparse Bayesian learning

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Authors: M. Roberts Masillamani

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There is a phenomenal difference, as regard to the teaching methodology adopted at the urban and the rural area colleges. However, bright and talented student may be from rural back ground even. But there is huge dearth of the digitization in the rural areas and lesser developed countries. Today’s students need new skills to compete and successful in the future. Education should be combination of practical, intellectual, and social skills. What does this mean for rural classrooms and how can it be achieved. Rural colleges are not able to hire the best resources, since the best teacher’s aim is to move towards the city. If city is provided everywhere, then there will be no rural area. This is possible by forming university clusters (UC). The University cluster is a group of renowned and accredited universities coming together to bridge this dearth. The UC will deliver the live lectures and allow the students’ from remote areas to actively participate in the classroom. This paper tries to present a plan of action of providing a better live classroom teaching and learning system from the city to the rural and the lesser developed countries. This paper titled “University Clusters using ICT for teaching and learning” provides a true concept of opening live digital classroom windows for rural colleges, where resources are not available, thus reducing the digital divide. This is different from pod casting a lecture or distance learning and eLearning. The live lecture can be streamed through digital equipment to another classroom. The rural students can collaborate with their peers and critiques, be assessed, collect information, acquire different techniques in assessment and learning process. This system will benefit rural students and teachers and develop socio economic status. This will also will increase the degree of confidence of the Rural students and teachers. Thus bringing about the concept of ‘Train the Trainee’ in reality. An educational university cloud for each cluster will be built remote infrastructure facilities (RIF) for the above program. The users may be informed, about the available lecture schedules, through the RIF service. RIF with an educational cloud can be set by the universities under one cluster. This paper talks a little more about University clusters and the methodology to be adopted as well as some extended features like, tutorial classes, library grids, remote laboratory login, research and development.

Keywords: lesser developed countries, digital divide, digital learning, education, e-learning, ICT, library grids, live classroom windows, RIF, rural, university clusters and urban

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Authors: H. J. Joshi, Satyajeet Patil, Parth Dandavate, Mihir Kulkarni, Harshita Agrawal

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As the world looks towards a sustainable future, electric vehicles have become increasingly popular. Millions worldwide are looking to switch to Electric cars over the previously favored combustion engine-powered cars. This demand has seen an increase in Electric Vehicle Charging Stations. The big challenge is that the randomness of electrical energy makes it tough for these charging stations to provide an adequate amount of energy over a specific amount of time. Thus, it has become increasingly crucial to model these patterns and forecast the energy needs of power stations. This paper aims to analyze how different machine learning models perform on Electric Vehicle charging time-series data. The data set consists of authentic Electric Vehicle Data from the Netherlands. It has an overview of ten thousand transactions from public stations operated by EVnetNL.

Keywords: forecasting, smart grid, electric vehicle load forecasting, machine learning, time series forecasting

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Authors: Silka Abyadati, Dadi Rusdiana, Enjang Akhmad Juanda

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This study aims to investigate the students’ mastery concepts enhancement between students who are studying by using Integrated Real-Life Video and Animation (IRVA) and students who are studying without using IRVA. The development of IRVA is conducted by five stages: Analyze, Design, Development, Implementation and Evaluation (ADDIE) based on constructivist for Rotational Dynamics material in Physics learning. A constructivist model-based learning used is Interpretation Construction (ICON), which has the following phases: 1) Observation, 2) Construction interpretation, 3) Contextualization prior knowledge, 4) Conflict cognitive, 5) Learning cognitive, 6) Collaboration, 7) Multiple interpretation, 8) Multiple manifestation. The IRVA is developed for the stages of observation, cognitive conflict and cognitive learning. The sample of this study consisted of 32 students experimental group and a control group of 32 students in class XI of the school year 2015/2016 in one of Senior High Schools Bandung. The study was conducted by giving the pretest and posttest in the form of 20 items of multiple choice questions to determine the enhancement of mastery concept of Rotational Dynamics. Hypothesis testing is done by using T-test on the value of N-gain average of mastery concepts. The results showed that there is a significant difference in an enhancement of students’ mastery concepts between students who are studying by using IRVA and students who are studying without IRVA. Students in the experimental group increased by 0.468 while students in the control group increased by 0.207.

Keywords: ADDIE, constructivist learning, Integrated Real-Life Video and Animation, mastery concepts, rotational dynamics

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Authors: Paola Utreras, Yazmina Olmos, Loreto Sanhueza

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This work shows how the learning of physics is enriched with scientific inquiry practices, achieving learning that results in the use of higher-level cognitive skills. The activities, which were carried out with students of the 3rd semester of the courses of the Faculty of Sciences of the Engineering of the Austral University of Chile, focused on the understanding of the nature of the thermodynamic variables and how they relate to each other. This, through the analysis of atmospheric data obtained in the meteorological station Miraflores, located on the campus. The proposed activities consisted of the elaboration of time series, linear analysis of variables, as well as the analysis of frequencies and periods. From their results, the students reached conclusions associated with the nature of the thermodynamic variables studied and the relationships between them, to finally make public their results in a report using scientific writing standards. It is observed that introducing topics that are close to them, interesting and which affect their daily lives allows a better understanding of the subjects, which is reflected in higher levels of approval and motivation for the subject.

Keywords: basic sciences, inquiry-based learning, scientific inquiry, thermodynamics

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Authors: B. Likar, K. Širok

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The paper presents a new method for efficient innovation process management. Even though the innovation management methods, tools and knowledge are well established and documented in literature, most of the companies still do not manage it efficiently. Especially in SMEs the front end of innovation - problem identification, idea creation and selection - is often not optimally performed. Our eMIPS methodology represents a sort of "umbrella methodology"- a well-defined set of procedures, which can be dynamically adapted to the concrete case in a company. In daily practice, various methods (e.g. for problem identification and idea creation) can be applied, depending on the company's needs. It is based on the proactive involvement of the company's employees supported by the appropriate methodology and external experts. The presented phases are performed via a mixture of face-to-face activities (workshops) and online (eLearning) activities taking place in eLearning Moodle environment and using other e-communication channels. One part of the outcomes is an identified set of opportunities and concrete solutions ready for implementation. The other also very important result is connected to innovation competences for the participating employees related with concrete tools and methods for idea management. In addition, the employees get a strong experience for dynamic, efficient and solution oriented managing of the invention process. The eMIPS also represents a way of establishing or improving the innovation culture in the organization. The first results in a pilot company showed excellent results regarding the motivation of participants and also as to the results achieved.

Keywords: creativity, distance learning, front end, innovation, problem

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Authors: Chassel T. Paras, Tryxzy Q. Dela Cruz, Ma. Carmela Lousie V. Goingco, Pauline L. Tolentino, Carmela S. Dizon

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In implementing inclusive education, parental involvement is measured to be an irreplaceable contributing factor. Parental involvement is described as an indispensable aspect of the teaching-learning process and has a remarkable effect on the student's academic performance. However, there are still differences in the viewpoints, expectations, and needs of both parents and teachers that are not yet fully conveyed in their relationship; hence, the perceptions of SNED teachers are essential in their collaboration with parents. This qualitative study explored how SNED teachers perceive the roles of parental involvement in the teaching-learning process of students with special needs. To answer this question, one-on-one face-to-face semi-structured interviews with three SNED teachers in a selected public school in Angeles City, Philippines, that offer special needs education services were conducted. The gathered data are then analyzed using Interpretative Phenomenological Analysis (IPA). The results revealed four superordinate themes, which include: (1) roles of parental involvement, (2) parental involvement opportunities, (3) barriers to parental involvement, and (4) parent-teacher collaboration practices. These results indicate that SNED teachers are aware of the roles and importance of parental involvement; however, despite parent-teacher collaboration, there are still barriers that impede parental involvement. Also, SNED teachers acknowledge the big roles of parents as they serve as main figures in the teaching-learning process of their children with special needs. Lastly, these results can be used as input in developing a school-facilitated parenting involvement framework that encompasses the contribution of SNED teachers in planning, developing, and evaluating parental involvement programs, which future researchers can also use in their studies

Keywords: parental involvement, special needs education, teaching-learning process, teachers’ perceptions, special needs education teachers, interpretative phenomenological analysis

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Authors: Yun-Ju Chiu, Feng-Yi Chen

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Kinematics is the beginning to learn mechanics in physics course. The concept of acceleration plays an important role in learning kinematics. Teachers usually instruct the conception through the formulas and graphs of kinematics and the well-known law F = ma. However, over the past few decades, a lot of researchers reveal numerous students’ difficulties in learning acceleration. One of these difficulties is that students frequently confuse acceleration with velocity and force. Why is the concept of acceleration so difficult to learn? The aim of this study is to understand the conceptual evolution of acceleration through the historical textual analysis. Text analysis and one-to-one interviews with high school students and teachers are used in this study. This study finds the history of science constructed from textbooks is usually quite different from the real evolution of history. For example, most teachers and students believe that the best-known law F = ma was written down by Newton. The expression of the second law is not F = ma in Newton’s best-known book Principia in 1687. Even after more than one hundred years, a famous Cambridge textbook titled An Elementary Treatise on Mechanics by Whewell of Trinity College did not express this law as F = ma. At that time of Whewell, the early mid-nineteenth century Britain, the concept of acceleration was not only ambiguous but also confused with the concept of force. The process of learning the concept of acceleration is analogous to its conceptual development in history. The study from the perspective of historical textual analysis will promote the understanding of the concept learning difficulties, the development of professional physics teaching, and the improvement of the context of physics textbooks.

Keywords: acceleration, textbooks, mechanics, misconception, history of science

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Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

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Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%- 40% compared to a traditional RL model.

Keywords: control system, hydroponics, machine learning, reinforcement learning

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Authors: Ahmad Shukri Mohd Noor, Emma Ahmad Sirajudin, Rabiei Mamat

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Pertaining to a particular Marine interest, the process of data sampling could take years before a study can be concluded. Therefore, the need for a robust backup system for the data is invariably implicit. In recent advancement of Marine applications, more functionalities and tools are integrated to assist the work of the researchers. It is anticipated that this modality will continue as research scope widens and intensifies and at the same to follow suit with current technologies and lifestyles. The convenience to collect and share information these days also applies to the work in Marine research. Therefore, Marine system designers should be aware that high availability is a necessary attribute in Marine repository applications as well as a robust backup system for the data. In this paper, the approach to high availability is related both to hardware and software but the focus is more on software. We consider a NABTIC repository system that is primitively built on a single server and does not have replicated components. First, the system is decomposed into separate modules. The modules are placed on multiple servers to create a distributed system. Redundancy is added by placing the copies of the modules on different servers using Neighbor Caring Environment System(NCES) technique. NCER is utilizing parallel replication components mechanism. A background monitoring is established to check servers’ heartbeats to confirm their aliveness. At the same time, a critical adaptive threshold is maintained to make sure a failure is timely detected using Adaptive Fault Detection (AFD). A confirmed failure will set the recovery mode where a selection process will be done before a fail-over server is instructed. In effect, the Marine repository service is continued as the fail-over masks a recent failure. The performance of the new prototype is tested and is confirmed to be more highly available. Furthermore, the downtime is not noticeable as service is immediately restored automatically. The Marine repository system is said to have achieved fault tolerance.

Keywords: availability, fault detection, replication, fault tolerance, marine application

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Authors: Elham Azimzadeh, Hamidollah Hassanlouei, Hadi Nobari, Georgian Badicu, Jorge Pérez-Gómez, Luca Paolo Ardigò

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Children with cerebral palsy (CP) have weak physical abilities and their limitations may have an effect on performing everyday motor activities. One of the most important and common debilitating factors in CP is the malfunction in the upper extremities to perform motor skills and there is strong evidence that task-specific training may lead to improve general upper limb function among this population. However, augmented feedback enhances the acquisition and learning of a motor task. Practice conditions may alter the difficulty, e.g., the reduced frequency of PG could be more challenging for this population to learn a motor task. So, the purpose of this study was to investigate the effect of physical guidance (PG) on learning a tracking task in children with cerebral palsy (CP). Twenty-five independently ambulant children with spastic hemiplegic CP aged 7-15 years were assigned randomly to five groups. After the pre-test, experimental groups participated in an intervention for eight sessions, 12 trials during each session. The 0% PG group received no PG; the 25% PG group received PG for three trials; the 50% PG group received PG for six trials; the 75% PG group received PG for nine trials; and the 100% PG group, received PG for all 12 trials. PG consisted of placing the experimenter's hand around the children's hand, guiding them to stay on track and complete the task. Learning was inferred by acquisition and delayed retention tests. The tests involved two blocks of 12 trials of the tracking task without any PG being performed by all participants. They were asked to make the movement as accurate as possible (i.e., fewer errors) and the number of total touches (errors) in 24 trials was calculated as the scores of the tests. The results showed that the higher frequency of PG led to more accurate performance during the practice phase. However, the group that received 75% PG had significantly better performance compared to the other groups in the retention phase. It is concluded that the optimal frequency of PG played a critical role in learning a tracking task in children with CP and likely this population may benefit from an optimal level of PG to get the appropriate amount of information confirming the challenge point framework (CPF), which state that too much or too little information will retard learning a motor skill. Therefore, an optimum level of PG may help these children to identify appropriate patterns of motor skill using extrinsic information they receive through PG and improve learning by activating the intrinsic feedback mechanisms.

Keywords: cerebral palsy, challenge point framework, motor learning, physical guidance, tracking task

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Terefe Alemu

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This study is intended to examine Ethiopia’s trade flow determinants and trade potential with G20 countries whether it was overtraded or there is/are trade potential by using trade gravity model. The sources of panel data used were IMF, WDI, United Nations population division, The Heritage Foundation, Washington's No. 1 think tank online website database, online distance calculator, and others for the duration of 2010 to 2019 for 10 consecutive years. The empirical data analyzing tool used was Random effect model (REM), which is effective in estimation of time-invariant data. The empirical data analyzed using STATA software result indicates that Ethiopia has a trade potential with seven countries of G20, whereas Ethiopia overtrade with 12 countries and EU region. The Ethiopia’s and G20 countries/region bilateral trade flow statistically significant/ p<0.05/determinants were the population of G20 countries, growth domestic products of G20 countries, growth domestic products of Ethiopia, geographical distance between Ethiopia and G20 countries. The top five G20 countries exported to Ethiopia were china, United State of America, European Union, India, and South Africa, whereas the top five G20 countries imported from Ethiopia were EU, China, United State of America, Saudi Arabia, and Germany, respectively. Finally, the policy implication were Ethiopia has to Keep the consistence of trade flow with overtraded countries and improve with under traded countries through trade policy revision, and secondly, focusing on the trade determinants to improve trade flow is recommended.

Keywords: trade gravity model, trade determinants, G20, international trade, trade potential

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Authors: Xabier Olaz Moratinos

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Drone navigation is critical, particularly during the initial phases, such as the initial ascension, where pilots may fail due to strong external interferences that could potentially lead to a crash. In this ongoing work, a drone has been successfully trained to perform an ascent of up to 6 meters at speeds with external disturbances pushing it up to 24 mph, with the DQN algorithm managing external forces affecting the system. It has been demonstrated that the system can control its height, position, and stability in all three axes (roll, pitch, and yaw) throughout the process. The learning process is carried out in the Gazebo simulator, which emulates interferences, while ROS is used to communicate with the agent.

Keywords: machine learning, DQN, Gazebo, navigation

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Authors: Marie Alexandre, Jean Bernatchez

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This paper focuses on the complexity of the face-to-face-to-distance learning transition process. Our research action aims to support the process of transition from classroom to distance learning for teachers in higher education with regard to pedagogical practices that can meet the various needs of students using digital learning environments. In Quebec and elsewhere in the world, the advent of digital education is helping to transform teaching, which is significantly changing the role of teachers. While distance education implies a dissociation of teaching and learning to a variable degree in space and time, distance education (DE) is becoming more and increasingly becoming a preferred option for maintaining the delivery of certain programs and providing access to programs and to provide access to quality activities throughout Quebec. Given the impact of teaching practices on educational success, this paper reports on the results of three research objectives: 1) To document teachers' knowledge of teaching in distance education through the design, experimentation and production of a repertoire of the determinants of pedagogical practices in response to students' needs. 2) Explain, according to a gendered logic, the adequacy between the pedagogical practices implemented in distance learning and the response to the profiles and needs expressed by students using digital learning environments; 3) Produce a model of a support approach during the process of transition from classroom to distance learning at the college level. A mixed methodology, i.e., a quantitative component (questionnaire survey) and a qualitative component (explanatory interviews and living lab) was used in cycles that were part of an ongoing validation process. The intervention includes the establishment of a professional collaboration group, webinars training webinars for the participating teachers on the didactic issue of knowledge-teaching in FAD, the didactic use of technologies, and the differentiated socialization models of educational success in college education. All of the tools developed will be used by partners in the target environment as well as by all teacher educators, students in initial teacher training, practicing teachers, and the general public. The results show that access to training leading to qualifications and commitment to educational success reflects the existing links between the people in the educational community. The relational stakes of being present in distance education take on multiple configurations and different dimensions of learning testify to needs and realities that are sometimes distinct depending on the life cycle. This project will be of interest to partners in the targeted field as well as to all teacher trainers, students in initial teacher training, practicing college teachers, and to university professors. The entire educational community will benefit from digital resources in education. The scientific knowledge resulting from this action research will benefit researchers in the fields of pedagogy, didactics, teacher training and pedagogy in higher education in a digital context.

Keywords: action research, didactics, digital learning environment, distance learning, higher education, pedagogy technological, pedagogical content knowledge

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Authors: Xiuying Wang, Changliang Li, Bo Xu

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A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

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Authors: Malavika Sundararajan

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This study introduces the use of mindfulness techniques in the classroom to make individuals aware of how the creative thinking process works, resulting in more constructive learning and application. Case observation method was utilized within a classroom setting in a graduate class in the Business School. It entailed, briefing the student participants about the use of a template called the dots and depths map, and having them complete it for themselves, compare it to their team members and reflect on the outputs. Finally, they were debriefed about the use of the template and its value to their learning and creative application process. The major finding is the increase in awareness levels of the participants following the use of the template, leading to a subsequent pursuit of diverse knowledge and acquisition of relevant information and not jumping to solutions directly, which increased their overall creative outputs for the given assignment. The significant value of this study is that it can be applied to any classroom on any subject as a powerful mindfulness tool which increases creative problem solving through constructive knowledge building.

Keywords: connecting dots, mindful awareness, constructive knowledge building, learning creatively

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Authors: Panagiota A. Kotsoni, George S. Ypsilandis

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Learning an L2 is a demanding process for all students and in particular for those with learning disabilities (LD) who demonstrate an inability to catch up with their classmates’ progress in a given period of time. This area of study, i.e. examining children with learning disabilities in L2 has not (yet) attracted the growing interest that is registered in L1 and thus remains comparatively neglected. It is this scientific field that this study wishes to contribute to. The longitudinal purpose of this study is to locate effective Supportive Feedback Strategies (SFS) and add to the quality of learning in second language vocabulary in both typically developing (TD) and LD children. Specifically, this study aims at investigating and comparing the performance of TD with LD children on two different types of SFSs related to vocabulary short and long-term retention. In this study two different SFSs have been examined to a total of ten (10) unknown vocabulary items. Both strategies provided morphosyntactic clarifications upon new contextualized vocabulary items. The traditional SFS (direct) provided the information only in one hypertext page with a selection on the relevant item. The experimental SFS (engaging) provided the exact same split information in three successive hypertext pages in the form of a hybrid dialogue asking from the subjects to move on to the next page by selecting the relevant link. It was hypothesized that this way the subjects would engage in their own learning process by actively asking for more information which would further lead to their better retention. The participants were fifty-two (52) foreign language learners (33 TD and 19 LD) aged from 9 to 12, attending an English language school at the level of A1 (CEFR). The design of the study followed a typical pre-post-post test procedure after an hour and after a week. The results indicated statistically significant group differences with TD children performing significantly better than the LD group in both short and long-term memory measurements and in both SFSs. As regards the effectiveness of one SFS over another the initial hypothesis was not supported by the evidence as the traditional SFS was more effective compared to the experimental one in both TD and LD children. This difference proved to be statistically significant only in the long-term memory measurement and only in the TD group. It may be concluded that the human brain seems to adapt to different SFS although it shows a small preference when information is provided in a direct manner.

Keywords: learning disabilities, memory, second/foreign language acquisition, supportive feedback

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Authors: Misrak Assefa

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The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

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Authors: Chad Goldsworthy, B. Rajeswari Matam

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The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.

Keywords: convolutional neural networks, data imbalance, deep learning, focal loss, species classification, wildlife conservation

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Authors: Lufungula Osembe

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The field of education has witnessed advances in technology and digital transformation. The methods of teaching have undergone significant changes in recent years, resulting in effects on various areas such as pedagogies, curriculum design, personalized teaching, gamification, data analytics, cloud-based learning applications, artificial intelligence tools, advanced plug-ins in LMS, and the emergence of multimedia creation and design. The field of education has not been immune to the changes brought about by digital innovation in recent years, similar to other fields such as engineering, health, science, and technology. There is a need to look at the variables/elements that digital innovation brings to education and develop a framework for higher institutions of learning to assess their readiness to create a viable environment for digital innovation to be successfully adopted. Given the potential benefits of digital innovation in education, it is essential to develop a framework that can assist academics and policymakers in higher institutions of learning to evaluate the effectiveness of adopting and adapting to the evolving landscape of digital innovation in education. The primary research question addressed in this study is to establish the preparedness of higher institutions of learning to adopt and adapt to the evolving landscape of digital innovation. This study follows a Design Science Research (DSR) paradigm to develop a framework for academics and policymakers in higher institutions of learning to evaluate the readiness of their institutions to adopt digital innovation in education. The Design Science Research paradigm is proposed to aid in developing a readiness framework for digital innovation in education. This study intends to follow the Design Science Research (DSR) methodology, which includes problem awareness, suggestion, development, evaluation, and conclusion. One of the major contributions of this study will be the development of the framework for digital innovation in education. Given the various opportunities offered by digital innovation in recent years, the need to create a readiness framework for digital innovation will play a crucial role in guiding academics and policymakers in their quest to align with emerging technologies facilitated by digital innovation in education.

Keywords: digital innovation, DSR, education, opportunities, research

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Authors: Kemal Efe Eseller, Göktuğ Yazici

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Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.

Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing

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Authors: Jason D. Lin, Christine Sim

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This paper examines the nature of transnational media flows in East Asia, specifically expounding on the popularity of Korean dramas in China and Taiwan. Situated in interdisciplinary academic work from cultural studies, media studies, and linguistics, this project locates the significance of certain genres and regions in determining why some are subject to flow while others remain within domestic borders. Moreover, transnational flows can take one of two routes –official translations and adaptations by media corporations and subtitles written by fans in online communities. The work of 'fan-subbing' has allowed for a more democratized showcase of what bilingual fans consume and are invested in sharing, rather than what major media companies deem relevant and monetizable. This reflects a culture of relatability driven by audiences rather than by corporate direction. Of course, a variety of technological, political, and economic factors play imperative roles in how both professional and fan-made subtitles flowed across borders and between nations. While fan-subbed media may be subject to criticism because of a lack of formal regulation, these limitations can, in some cases, be overcome by the agency afforded to audiences in the digital landscape. Finally, this paper offers a critical lens for deliberating the lasting impact of fan involvement on both professional practices and the flows of mainstream media throughout East Asia.

Keywords: audience studies, bilingual, cultural proximity, fan-subbing, online communities, subtitles

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