Search results for: machine learning techniques

Authors: Hailah Alballaa, Azeddine Chikh

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Several challenges impede the adoption of Recommender Systems for Technology Enhanced Learning (TEL): to collect and identify possible datasets; to select between different recommender approaches; to evaluate their performances. The aim is of this paper is twofold: First, it aims to introduce a survey on the most significant work in this area. Second, it aims at identifying possible research directions.

Keywords: datasets, content-based filtering, recommender systems, TEL

Procedia PDF Downloads 244

Authors: Shuge Lei, Haonan Hu, Dasheng Sun, Huabin Zhang, Kehong Yuan, Jian Dai, Yan Tong

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It remains challenging to measure reliability from classification results from different machine learning models. This paper proposes a reliable soup optimization algorithm based on the model weight fusion algorithm Model Soup, aiming to improve reliability by using dual-channel reliability as the objective function to fuse a series of weights in the breast ultrasound classification models. Experimental results on breast ultrasound clinical datasets demonstrate that reliable soup significantly enhances the reliability of breast ultrasound image classification tasks. The effectiveness of the proposed approach was verified via multicenter trials. The results from five centers indicate that the reliability optimization algorithm can enhance the reliability of the breast ultrasound image classification model and exhibit low multicenter correlation.

Keywords: breast ultrasound image classification, feature attribution, reliability assessment, reliability optimization

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Authors: Yingling Chen

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Most current research has been focused on whether NESTs have advantages over NNESTs in English language Teaching. The purpose of this study was to investigate English learners’ preferences toward native English speaking teachers and non-English speaking teachers in four skills of English language learning. This qualitative study consists of 12 participants. Two open-ended questions were investigated and analyzed. The findings revealed that the participants held an overall preference for NESTs over NNESTs in reading, writing, and listening English skills; nevertheless, they believed both NESTs and NNESTs offered learning experiences strengths, and weaknesses to satisfy students’ need in their English instruction.

Keywords: EFL, instruction, Student Rating of Instructions (SRI), perception

Procedia PDF Downloads 214

Authors: Evgeniia Surkova, Manal Assaad, Hleb Makeyeu, Juho Makio

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The main objective of STIMEY (Science, Technology, Innovation, Mathematics, Engineering for the Young) project is the delivery of a hybrid learning environment that combines multi-level components such as social media concepts, robotic artefacts, and radio, among others. It is based on a well-researched pedagogical framework to attract European youths to STEM (science, technology, engineering, and mathematics) education and careers. To develop and integrate these various components, STIMEY is executed in iterative research cycles leading to progressive improvements. Scrum was the development methodology of choice in the project, as studies indicated its benefits as an agile methodology in global software development, especially of e-learning and integrated learning projects. This paper describes the project partners’ experience with the Scrum framework, discussing the challenges faced in its implementation and the mitigation practices employed. The authors conclude with exploring user experience tools and principles for future research, as a novel direction in supporting the Scrum development team.

Keywords: e-learning, global software development, scrum, STEM education

Procedia PDF Downloads 179

Authors: T. Asefi, J. Faiz, M. A. Khan

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This paper introduces dual rotor axial flux machines with surface mounted and spoke type ferrite permanent magnets with concentrated windings; they are introduced as alternatives to a generator with surface mounted Nd-Fe-B magnets. The output power, voltage, speed and air gap clearance for all the generators are identical. The machine designs are optimized for minimum mass using a population-based algorithm, assuming the same efficiency as the Nd-Fe-B machine. A finite element analysis (FEA) is applied to predict the performance, emf, developed torque, cogging torque, no load losses, leakage flux and efficiency of both ferrite generators and that of the Nd-Fe-B generator. To minimize cogging torque, different rotor pole topologies and different pole arc to pole pitch ratios are investigated by means of 3D FEA. It was found that the surface mounted ferrite generator topology is unable to develop the nominal electromagnetic torque, and has higher torque ripple and is heavier than the spoke type machine. Furthermore, it was shown that the spoke type ferrite permanent magnet generator has favorable performance and could be an alternative to rare-earth permanent magnet generators, particularly in wind energy applications. Finally, the analytical and numerical results are verified using experimental results.

Keywords: axial flux, permanent magnet generator, dual rotor, ferrite permanent magnet generator, finite element analysis, wind turbines, cogging torque, population-based algorithms

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Authors: Aparna Datey

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This paper explores the theoretical frameworks underpinning professional learning, professional development, and academic identity. The focus is on sessional teachers (also called tutors or adjuncts) in architectural design studios, who may be practitioners, masters or doctoral students and academics hired ‘as needed’. Drawing from Schön’s work on reflective practice, learning and developmental theories of Vygotsky (social constructionism and zones of proximal development), informal and workplace learning, this research proposes that sessional teachers not only develop their teaching skills but also shape their identities through their 'everyday' work. Continuing academic staff develop their teaching through a combination of active teaching, self-reflection on teaching, as well as learning to teach from others via formalised programs and informally in the workplace. They are provided professional development and recognised for their teaching efforts through promotion, student citations, and awards for teaching excellence. The teaching experiences of sessional staff, by comparison, may be discontinuous and they generally have fewer opportunities and incentives for teaching development. In the absence of access to formalised programs, sessional teachers develop their teaching informally in workplace settings that may be supportive or unhelpful. Their learning as teachers is embedded in everyday practice applying problem-solving skills in ambiguous and uncertain settings. Depending on their level of expertise, they understand how to teach a subject such that students are stimulated to learn. Adult learning theories posit that adults have different motivations for learning and fall into a matrix of readiness, that an adult’s ability to make sense of their learning is shaped by their values, expectations, beliefs, feelings, attitudes, and judgements, and they are self-directed. The level of expertise of sessional teachers depends on their individual attributes and motivations, as well as on their work environment, the good practices they acquire and enhance through their practice, career training and development, the clarity of their role in the delivery of teaching, and other factors. The architectural design studio is ideal for study due to the historical persistence of the vocational learning or apprenticeship model (learning under the guidance of experts) and a pedagogical format using two key approaches: project-based problem solving and collaborative learning. Hence, investigating the theoretical frameworks underlying academic roles and informal professional learning in the workplace would deepen understanding of their professional development and how they shape their academic identities. This qualitative research is ongoing at a major university in Australia, but the growing trend towards hiring sessional staff to teach core courses in many disciplines is a global one. This research will contribute to including transient sessional teachers in the discourse on institutional quality, effectiveness, and student learning.

Keywords: academic identity, architectural design learning, pedagogy, teaching and learning, sessional teachers

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Authors: Sadiq J. Abou-Loukh, Hanan M. Habbi

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This paper presents a steganography to hide the transmitted information without excite suspicious and also illustrates the level of secrecy that can be increased by using cryptography techniques. The proposed system has been implemented firstly by encrypted image file one time pad key and secondly encrypted message that hidden to perform encryption followed by image embedding. Then the new image file will be created from the original image by using four triangles operation, the new image is processed by one of two image processing techniques. The proposed two processing techniques are thresholding and differential predictive coding (DPC). Afterwards, encryption or decryption keys are generated by functional key generator. The generator key is used one time only. Encrypted text will be hidden in the places that are not used for image processing and key generation system has high embedding rate (0.1875 character/pixel) for true color image (24 bit depth).

Keywords: encryption, thresholding, differential predictive coding, four triangles operation

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Authors: Rahul Srivastava, Priti Pundir, Y. R. Sood, Rajnish Shrivastava

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For the interpretation of the signatures of sweep frequency response analysis(SFRA) of transformer different types of statistical techniques serves as an effective tool for doing either phase to phase comparison or sister unit comparison. In this paper with the discussion on SFRA several statistics techniques like cross correlation coefficient (CCF), root square error (RSQ), comparative standard deviation (CSD), Absolute difference, mean square error(MSE),Min-Max ratio(MM) are presented through several case studies. These methods require sample data size and spot frequencies of SFRA signatures that are being compared. The techniques used are based on power signal processing tools that can simplify result and limits can be created for the severity of the fault occurring in the transformer due to several short circuit forces or due to ageing. The advantages of using statistics techniques for analyzing of SFRA result are being indicated through several case studies and hence the results are obtained which determines the state of the transformer.

Keywords: absolute difference (DABS), cross correlation coefficient (CCF), mean square error (MSE), min-max ratio (MM-ratio), root square error (RSQ), standard deviation (CSD), sweep frequency response analysis (SFRA)

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Authors: Mohammad R. Aghakashkooli, Milutin G. Jovanovic

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The brushless doubly-fed reluctance generator (BDFRG) is an emerging, medium-speed alternative to a conventional wound rotor slip-ring doubly-fed induction generator (DFIG) in wind energy conversion systems (WECS). It can provide competitive overall performance and similar low failure rates of a typically 30% rated back-to-back power electronics converter in 2:1 speed ranges but with the following important reliability and cost advantages over DFIG: the maintenance-free operation afforded by its brushless structure, 50% synchronous speed with the same number of rotor poles (allowing the use of a more compact, and more efficient two-stage gearbox instead of a vulnerable three-stage one), and superior grid integration properties including simpler protection for the low voltage ride through compliance of the fractional converter due to the comparatively higher leakage inductances and lower fault currents. Vector controlled pulse-width-modulated converters generally feature a much lower total harmonic distortion relative to hysteresis counterparts with variable switching rates and as such have been a predominant choice for BDFRG (and DFIG) wind turbines. Eliminating a shaft position sensor, which is often required for control implementation in this case, would be desirable to address the associated reliability issues. This fact has largely motivated the recent growing research of sensorless methods and developments of various rotor position and/or speed estimation techniques for this purpose. The main limitation of all the observer-based control approaches for grid-connected wind power applications of the BDFRG reported in the open literature is the requirement for pre-commissioning procedures and prior knowledge of the machine inductances, which are usually difficult to accurately identify by off-line testing. A model reference adaptive system (MRAS) based sensor-less vector control scheme to be presented will overcome this shortcoming. The true machine parameter independence of the proposed field-oriented algorithm, offering robust, inherently decoupled real and reactive power control of the grid-connected winding, is achieved by on-line estimation of the inductance ratio, the underlying rotor angular velocity and position MRAS observer being reliant upon. Such an observer configuration will be more practical to implement and clearly preferable to the existing machine parameter dependent solutions, and especially bearing in mind that with very little modifications it can be adapted for commercial DFIGs with immediately obvious further industrial benefits and prospects of this work. The excellent encoder-less controller performance with maximum power point tracking in the base speed region will be demonstrated by realistic simulation studies using large-scale BDFRG design data and verified by experimental results on a small laboratory prototype of the WECS emulation facility.

Keywords: brushless doubly fed reluctance generator, model reference adaptive system, sensorless vector control, wind energy conversion

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Authors: Chitra Rajagopal, Indra Deo Kumar, Ila Chauhan, Ruchi Joshi, Binoy Bhargavan

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Reliability and safety are essential for the success of mission-critical and safety-critical defense systems. Humans are part of the entire life cycle of defense systems development and deployment. The majority of industrial accidents or disasters are attributed to human errors. Therefore, considerations of human performance and human reliability are critical in all complex systems, including defense systems. Defense systems are operating from the ground, naval and aerial platforms in diverse conditions impose unique physical and psychological challenges to the human operators. Some of the safety and mission-critical defense systems with human-machine interactions are fighter planes, submarines, warships, combat vehicles, aerial and naval platforms based missiles, etc. Human roles and responsibilities are also going through a transition due to the infusion of artificial intelligence and cyber technologies. Human operators, not accustomed to such challenges, are more likely to commit errors, which may lead to accidents or loss events. In such a scenario, it is imperative to understand the human factors in defense systems for better systems performance, safety, and cost-effectiveness. A case study using Task Analysis (TA) based methodology for assessment and reduction of human errors in the Air and Missile Defense System in the context of emerging technologies were presented. Action-oriented task analysis techniques such as Hierarchical Task Analysis (HTA) and Operator Action Event Tree (OAET) along with Critical Action and Decision Event Tree (CADET) for cognitive task analysis was used. Human factors assessment based on the task analysis helps in realizing safe and reliable defense systems. These techniques helped in the identification of human errors during different phases of Air and Missile Defence operations, leading to meet the requirement of a safe, reliable and cost-effective mission.

Keywords: defence systems, reliability, risk, safety

Procedia PDF Downloads 135

Authors: Davut Disci

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Objective: to measure the factors affecting expectations and intentions of using mobile phone in educational contexts by university students, using advanced equations and modeling techniques. Design and Methodology: According to the literature, Mobile Addiction, Parental Surveillance- Safety/Security, Social Relations, and Mobile Behavior are most used terms of defining mobile use of people. Therefore these variables are tried to be measured to find and estimate their effects on expectations and intentions of using mobile phone in educational context. 421 university students participated in this study and there are 229 Female and 192 Male students. For the purpose of examining the mobile behavior and educational expectations and intentions, a questionnaire is prepared and applied to the participants who had to answer all the questions online. Furthermore, responses to close-ended questions are analyzed by using The Statistical Package for Social Sciences(SPSS) software, reliabilities are measured by Cronbach’s Alpha analysis and hypothesis are examined via using Multiple Regression and Linear Regression analysis and the model is tested with Structural Equation Modeling(SEM) technique which is important for testing the model scientifically. Besides these responses, open-ended questions are taken into consideration. Results: When analyzing data gathered from close-ended questions, it is found that Mobile Addiction, Parental Surveillance, Social Relations and Frequency of Using Mobile Phone Applications are affecting the mobile behavior of the participants in different levels, helping them to use mobile phone in educational context. Moreover, as for open-ended questions, participants stated that they use many mobile applications in their learning environment in terms of contacting with friends, watching educational videos, finding course material via internet. They also agree in that mobile phone brings greater flexibility to their lives. According to the SEM results the model is not evaluated and it can be said that it may be improved to show in SEM besides in multiple regression. Conclusion: This study shows that the specified model can be used by educationalist, school authorities to improve their learning environment.

Keywords: education, mobile behavior, mobile learning, technology, Turkey

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Authors: F. Yildiz, S. Y. Oturanc

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Advances technology in the field of photogrammetry replaces analog cameras with reflection on aircraft GPS/IMU system with a digital aerial camera. In this system, when determining the position of the camera with the GPS, camera rotations are also determined by the IMU systems. All around the world, digital aerial cameras have been used for the photogrammetry applications in the last ten years. In this way, in terms of the work done in photogrammetry it is possible to use time effectively, costs to be reduced to a minimum level, the opportunity to make fast and accurate. Geo-referencing techniques that are the cornerstone of the GPS / INS systems, photogrammetric triangulation of images required for balancing (interior and exterior orientation) brings flexibility to the process. Also geo-referencing process; needed in the application of photogrammetry targets to help to reduce the number of ground control points. In this study, the use of direct and indirect geo-referencing techniques on the accuracy of the points was investigated in the production of photogrammetric mapping.

Keywords: photogrammetry, GPS/IMU systems, geo-referecing, digital aerial camera

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Authors: S. Schröder, P. Ennen, T. Langer, S. Müller, M. Shehadeh, M. Haberstroh, F. Hees

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Since a bit over a year ago, Festo AG and Co. KG, Festo Didactic SE, robomotion GmbH, the researchers of the Cybernetics-Lab IMA/ZLW and IfU, as well as the Human-Computer Interaction Center at the RWTH Aachen University, have been working together in the focal point of assembly competences to realize different scenarios in the field of human-machine interaction (HMI). In the framework of project ARIZ, questions concerning the future of production within the fourth industrial revolution are dealt with. There are many perspectives of human-robot collaboration that consist Industry 4.0 on an individual, organization and enterprise level, and these will be addressed in ARIZ. The aim of the ARIZ projects is to link AI-Approaches to assembly problems and to implement them as prototypes in demonstrators. To do so, island and flow based production scenarios will be simulated and realized as prototypes. These prototypes will serve as applications of flexible robotics as well as AI-based planning and control of production process. Using the demonstrators, human interaction strategies will be examined with an information system on one hand, and a robotic system on the other. During the tests, prototypes of workspaces that illustrate prospective production work forms will be represented. The human being will remain a central element in future productions and will increasingly be in charge of managerial tasks. Questions thus arise within the overall perspective, primarily concerning the role of humans within these technological revolutions, as well as their ability to act and design respectively to the acceptance of such systems. Roles, such as the 'Trainer' of intelligent systems may become a possibility in such assembly scenarios.

Keywords: human-machine interaction, information technology, island based production, assembly competences

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Authors: Ozge Gurcanli

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Recent studies in active-learning focus on how student experience varies based on the content (e.g. STEM versus Humanities) and the medium (e.g. in-class exercises versus off-campus activities) of experiential learning. However, little is known whether the variation in classroom time and space within the same active learning context affects student experience. This study manipulated the use of classroom time for the active learning component of a developmental psychology course that is offered at a four-year university in the South-West Region of United States. The course uses a blended model: traditional and active learning. In the traditional learning component of the course, students do weekly readings, listen to lectures, and take midterms. In the active learning component, students make a documentary on a developmental topic as a final project. Students used the classroom time and space for the documentary in two ways: regular classroom time slots that were dedicated to the making of the documentary outside without the supervision of the professor (Classroom-time Outside) and lectures that offered basic instructions about how to make a documentary (Documentary Lectures). The study used the public teaching evaluations that are administered by the Office of Registrar’s. A total of two hundred and seven student evaluations were available across six semesters. Because the Office of Registrar’s presented the data separately without personal identifiers, One-Way ANOVA with four groups (Traditional, Experiential-Heavy: 19% Classroom-time Outside, 12% for Documentary Lectures, Experiential-Moderate: 5-7% for Classroom-time Outside, 16-19% for Documentary Lectures, Experiential Light: 4-7% for Classroom-time Outside, 7% for Documentary Lectures) was conducted on five key features (Organization, Quality, Assignments Contribution, Intellectual Curiosity, Teaching Effectiveness). Each measure used a five-point reverse-coded scale (1-Outstanding, 5-Poor). For all experiential conditions, the documentary counted towards 30% of the final grade. Organization (‘The instructors preparation for class was’), Quality (’Overall, I would rate the quality of this course as’) and Assignment Contribution (’The contribution of the graded work that made to the learning experience was’) did not yield any significant differences across four course types (F (3, 202)=1.72, p > .05, F(3, 200)=.32, p > .05, F(3, 203)=.43, p > .05, respectively). Intellectual Curiosity (’The instructor’s ability to stimulate intellectual curiosity was’) yielded a marginal effect (F (3, 201)=2.61, p = .053). Tukey’s HSD (p < .05) indicated that the Experiential-Heavy (M = 1.94, SD = .82) condition was significantly different than all other three conditions (M =1.57, 1.51, 1.58; SD = .68, .66, .77, respectively) showing that heavily active class-time did not elicit intellectual curiosity as much as others. Finally, Teaching Effectiveness (’Overall, I feel that the instructor’s effectiveness as a teacher was’) was significant (F (3, 198)=3.32, p <.05). Tukey’s HSD (p <.05) showed that students found the courses with moderate (M=1.49, SD=.62) to light (M=1.52, SD=.70) active class-time more effective than heavily active class-time (M=1.93, SD=.69). Overall, the findings of this study suggest that within the same active learning context, the time and the space dedicated to active learning results in different outcomes in intellectual curiosity and teaching effectiveness.

Keywords: active learning, learning outcomes, student experience, learning context

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference

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Authors: Cristóbal Moënne-Loccoz, Rodrigo C. Vergara, Vladimir López, Domingo Mery, Diego Cosmelli

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Our daily interaction with computational interfaces is plagued of situations in which we go from inexperienced users to experts through self-motivated exploration of the same task. In many of these interactions, we must learn to find our way through a sequence of decisions and actions before obtaining the desired result. For instance, when drawing cash from an ATM machine, choices are presented in a step-by-step fashion so that a specific sequence of actions must be performed in order to produce the expected outcome. But, as they become experts in the use of such interfaces, do users adopt specific search and learning strategies? Moreover, if so, can we use this information to follow the process of expertise development and, eventually, predict future actions? This would be a critical step towards building truly adaptive interfaces that can facilitate interaction at different moments of the learning curve. Furthermore, it could provide a window into potential mechanisms underlying decision-making behavior in real world scenarios. Here we tackle this question using a simple game interface that instantiates a 4-level binary decision tree (BDT) sequential decision-making task. Participants have to explore the interface and discover an underlying concept-icon mapping in order to complete the game. We develop a Hidden Markov Model (HMM)-based approach whereby a set of stereotyped, hierarchically related search behaviors act as hidden states. Using this model, we are able to track the decision-making process as participants explore, learn and develop expertise in the use of the interface. Our results show that partitioning the problem space into such stereotyped strategies is sufficient to capture a host of exploratory and learning behaviors. Moreover, using the modular architecture of stereotyped strategies as a Mixture of Experts, we are able to simultaneously ask the experts about the user's most probable future actions. We show that for those participants that learn the task, it becomes possible to predict their next decision, above chance, approximately halfway through the game. Our long-term goal is, on the basis of a better understanding of real-world decision-making processes, to inform the construction of interfaces that can establish dynamic conversations with their users in order to facilitate the development of expertise.

Keywords: behavioral modeling, expertise acquisition, hidden markov models, sequential decision-making

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Authors: Mohammad Abu-Shaira, Weishi Shi

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Adaptive learning, a commonly employed solution to drift, involves updating predictive models online during their operation to react to concept drifts, thereby serving as a critical component and natural extension for online learning systems that learn incrementally from each example. This paper introduces LSTM-SCCM “Long Short-Term Memory Stream Cruise Control Method”, a drift adaptation-as-a-service framework for online learning. LSTM-SCCM automates drift adaptation through prompt detection, drift magnitude quantification, dynamic hyperparameter tuning, performing shortterm optimization and model recalibration for immediate adjustments, and, when necessary, conducting long-term model recalibration to ensure deeper enhancements in model performance. LSTM-SCCM is incorporated into a suite of cutting-edge online regression models, assessing their performance across various types of concept drift using diverse datasets with varying characteristics. The findings demonstrate that LSTM-SCCM represents a notable advancement in both model performance and efficacy in handling concept drift occurrences. LSTM-SCCM stands out as the sole framework adept at effectively tackling concept drifts within regression scenarios. Its proactive approach to drift adaptation distinguishes it from conventional reactive methods, which typically rely on retraining after significant degradation to model performance caused by drifts. Additionally, LSTM-SCCM employs an in-memory approach combined with the Self-Adjusting Memory (SAM) architecture to enhance real-time processing and adaptability. The framework incorporates variable thresholding techniques and does not assume any particular data distribution, making it an ideal choice for managing high-dimensional datasets and efficiently handling large-scale data. Our experiments, which include abrupt, incremental, and gradual drifts across both low- and high-dimensional datasets with varying noise levels, and applied to four state-of-the-art online regression models, demonstrate that LSTM-SCCM is versatile and effective, rendering it a valuable solution for online regression models to address concept drift.

Keywords: automated drift detection and adaptation, concept drift, hyperparameters optimization, online and adaptive learning, regression

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Authors: Elham Fariborzi, Hoda Anvari Kazemabad

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Today, ICT plays an important role in all matters and it affects the development of creative cities. According to virtual space in this technology, it use especially for expand terms like smart schools, Virtual University, web-based training and virtual classrooms that is in parallel with the traditional teaching. Nowadays, the educational systems in different countries such as Iran are changing and start increasing creativity in the learning environment. It will contribute to the development of innovative ideas and thinking of the people in this environment; such opportunities might be cause scientific discovery and development issues. The creativity means the ability to generate ideas and numerous, new and suitable solutions for solving the problems of real and virtual individuals and society, which can play a significant role in the development of creative current physical cities or virtual borders ones in the future. The purpose of this paper is to study strategies to increase creativity in a virtual learning to develop a creative city. In this paper, citation/ library study was used. The full description given in the text, including how to create and enhance learning creativity in a virtual classroom by reflecting on performance and progress; attention to self-directed learning guidelines, efficient use of social networks, systematic discussion groups and non-intuitive targeted controls them by involved factors and it may be effective in the teaching process regarding to creativity. Meanwhile, creating a virtual classroom the style of class recognizes formally the creativity. Also the use of a common model of creative thinking between student/teacher is effective to solve problems of virtual classroom. It is recommended to virtual education’ authorities in Iran to have a special review to the virtual curriculum for increasing creativity in educational content and such classes to be witnesses more creative in Iran's cities.

Keywords: virtual learning, creativity, e-learning, bioinformatics, biomedicine

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

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Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: Kevin Smith

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Publicly traded company in the US are legally obligated to host earnings calls that discuss their most recent financial reports. During these calls, investors are able to ask these companies questions about these financial reports and on the future direction of the company. This paper examines whether common institutional owners use these calls as a way to indirectly signal to companies in their portfolio to not take actions that could hurt the common owner's interests. This paper uses transcripts taken from the earnings calls of the six largest health insurance companies in the US from 2014 to 2019. This data is analyzed using text analysis and sentiment analysis to look for patterns in the statements made by common owners. The analysis found that common owners where more likely to recommend against direct price competition and instead redirect the insurance companies towards more passive actions, like investing in new technologies. This result indicates a mechanism that common owners use to reduce competition in the health insurance market.

Keywords: common ownership, text analysis, sentiment analysis, machine learning

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Authors: Shan Jiang, Xinyu Tang

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Game-based learning is an emerging pedagogy to make the learning experience more effective, enjoyable, and fun. However, most games used in classroom settings have been overly simplistic. This paper presents a case study on a Python-based online game designed to improve the effectiveness in both teaching and research in higher education. The proposed game system not only creates a fun and enjoyable experience for students to learn various topics in programming but also improves the effectiveness of teaching in several aspects, including material presentation, helping students to recognize the importance of the subjects, and linking theoretical concepts to practice. The proposed game system also serves as an information cyber-infrastructure that automatically collects and stores data from players. The data could be useful in research areas including human-computer interaction, decision making, opinion mining, and artificial intelligence. They further provide other possibilities beyond these areas due to the customizable nature of the game.

Keywords: game-based learning, programming, research-teaching integration, Hearthstone

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Authors: M. S. Sukumar

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Our objective is to develop a full-fledged system for exploring and studying nature of fossils and to extend this to underwater archaeology and mineral mapping. This includes aerial surveying, imaging techniques, artefact extraction and spectrum analysing techniques. These techniques help in regular monitoring of fossils and also the sensing system. The ROV was designed to complete several tasks which simulate collecting data and samples. Given the time constraints, the ROV was engineered for efficiency and speed in performing tasks. Its other major design consideration was modularity, allowing the team to distribute the building process, to easily test systems as they were completed and troubleshoot and replace systems as necessary. Our design itself had several challenges of on-board waterproofed sensor mounting, waterproofing of motors, ROV stability criteria, camera mounting and hydrophone sound acquisition.

Keywords: remotely operated vehicle (ROV) dragonair, underwater archaeology, full-fledged system, aerial imaging and detection

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Authors: Anantdeep Kaur, Sharanjeet Singh

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Early childhood education and assessment of children is a very essential tool that helps them in their growth and development. Techniques should be developed, and tools should be created in this field as it is a very important learning phase of life. Some information and sources are included for student assessment to provide a record of growth in all developmental areas cognitive, physical, Language, social-emotional, and approaches to learning. As an early childhood educator, it is very important to identify children who need special support and counseling to improve them because they are not mentally mature to discuss with the teacher their problems and needs. It is the duty and responsibility of the educator to assess children from their body language, behavior, and their routine actions about their skills that can be improved and which can take them forward in their future life. And also, children should be assessed with their weaker points because this is the right time to correct them, and they be improved with certain methods and tools by working on them constantly. Observing children regularly with all their facets of development, including intellectual, linguistic, social-emotional, and physical development. Every day, a physical education class should be regulated to check their physical growth activities, which can help to assess their physical activeness and motor abilities. When they are outside on the playgrounds, it is very important to instill environmental understanding among them so that they should know that they are very part of this nature, and it will help them to be one with the universe rather than feeling themselves individually. This technique assists them in living their childhood full of energy all the time. All types of assessments have unique purposes. It is important first to determine what should be measured, then find the program that best assesses those.

Keywords: special needs, motor ability, environmental understanding, physical development

Procedia PDF Downloads 94

Authors: K. Y. You, Y. L. Then

Abstract:

In the recent years, the hair building fiber has become popular, in other words, it is an effective method which helps people who suffer hair loss or sparse hair since the hair building fiber is capable to create a natural look of simulated hair rapidly. In the markets, there are a lot of hair fiber brands that have been designed to formulate an intense bond with hair strands and make the hair appear more voluminous instantly. However, those products have their own set of properties. Thus, in this report, some measurement techniques are proposed to identify those products. Up to five different brands of hair fiber are tested. The electrostatic and dielectric properties of the hair fibers are macroscopically tested using design DC and high-frequency microwave techniques. Besides, the hair fibers are microscopically analysis by magnifying the structures of the fiber using scanning electron microscope (SEM). From the SEM photos, the comparison of the uniformly shaped and broken rate of the hair fibers in the different bulk samples can be observed respectively.

Keywords: hair fiber, electrostatic, dielectric properties, broken rate, microwave techniques

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Authors: Hossein Ghadimi, Alireza Alizadeh, Oveis Abedinia, Noradin Ghadimi

Abstract:

This paper presents an enhanced Honey Bee Mating Optimization (HBMO) to solve the optimal design of multi machine power system stabilizer (PSSs) parameters, which is called the Interactive Honey Bee Mating Optimization (IHBMO). Power System Stabilizers (PSSs) are now routinely used in the industry to damp out power system oscillations. The design problem of the proposed controller is formulated as an optimization problem and IHBMO algorithm is employed to search for optimal controller parameters. The proposed method is applied to multi-machine power system (MPS). The method suggested in this paper can be used for designing robust power system stabilizers for guaranteeing the required closed loop performance over a prespecified range of operating and system conditions. The simplicity in design and implementation of the proposed stabilizers makes them better suited for practical applications in real plants. The non-linear simulation results are presented under wide range of operating conditions in comparison with the PSO and CPSS base tuned stabilizer one through FD and ITAE performance indices. The results evaluation shows that the proposed control strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other controllers.

Keywords: power system stabilizer, IHBMO, multimachine, nonlinearities

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Authors: Arezou Javadi

Abstract:

The use of algorithmic models dependent on software calculations and simulation of new government management assays with the help of specialized software had increased the productivity and efficiency of the government management system recently. This has caused the management approach to change from the old bitch & fix model, which has low efficiency and less usefulness, to the capable management model with higher efficiency called the partnership with resident model. By using Govpilot TM software, the relationship between people in a system and the government was examined. The method of two tailed interaction was the outsourcing of a goal in a system, which is formed in the order of goals, qualified executive people, optimal executive model, and finally, summarizing additional activities at the different statistical levels. The results showed that the participation of people in a financial implementation system with a statistical potential of P≥5% caused a significant increase in investment and initial capital in the government system with maximum implement project in a smart government.

Keywords: machine learning, financial income, statistical potential, govpilot

Procedia PDF Downloads 88

Authors: Arezou Javadi

Abstract:

The use of algorithmic models dependent on software calculations and simulation of new government management assays with the help of specialized software had increased the productivity and efficiency of the government management system recently. This has caused the management approach to change from the old bitch & fix model, which has low efficiency and less usefulness, to the capable management model with higher efficiency called the partnership with resident model. By using Govpilot TM software, the relationship between people in a system and the government was examined. The method of two tailed interaction was the outsourcing of a goal in a system, which is formed in the order of goals, qualified executive people, optimal executive model, and finally, summarizing additional activities at the different statistical levels. The results showed that the participation of people in a financial implementation system with a statistical potential of P≥5% caused a significant increase in investment and initial capital in the government system with maximum implement project in a smart government.

Keywords: machine learning, financial income, statistical potential, govpilot

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Authors: João Rato, Nuno Costa

Abstract:

The human verbal communication is a two-way process which requires a mutual understanding that will result in some considerations. This kind of communication, also called dialogue, besides the supposed human agents it can also be performed between human agents and machines. The interaction between Men and Machines, by means of a natural language, has an important role concerning the improvement of the communication between each other. Aiming at knowing the performance of some speech recognition systems, this document shows the results of the accomplished tests according to the Word Error Rate evaluation method. Besides that, it is also given a set of information linked to the systems of Man-Machine communication. After this work has been made, conclusions were drawn regarding the Speech Recognition Systems, among which it can be mentioned their poor performance concerning the voice interpretation in noisy environments.

Keywords: automatic speech recognition, man-machine conversation, speech recognition, spoken dialogue systems, word error rate

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Authors: Noran Adel Emara, Elham Ghazi Mohammad

Abstract:

Qatar University is engaged in a systemic education reform that includes integrating the latest and most effective technologies for teaching and learning. ICube is high-immersive virtual reality technology is used to teach educational scenarios that are difficult to teach in real situations. The trends toward delivering science education via virtual reality applications have accelerated in recent years. However, research on students perceptions of integrating virtual reality especially ICube technology is somehow limited. Students often have difficulties focusing attention on learning science topics that require imagination and easily lose attention and interest during the lesson. The aim of this study was to examine students’ perception of integrating ICube technology in the solar system lesson. Moreover, to explore how ICube could engage students in learning scientific concept of the solar system. The research framework included the following quantitative research design with data collection and analysis from questionnaire results. The solar system lesson was conducted by teacher candidates (Diploma students) who taught in the ICube virtual lab in Qatar University. A group of 30 students from eighth grade were randomly selected to participate in the study. Results showed that the students were extremely engaged in learning the solar system and responded positively to integrating ICube in teaching. Moreover, the students showed interest in learning more lessons through ICube as it provided them with valuable learning experience about complex situations.

Keywords: ICube, integrating technology, science education, virtual reality

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Authors: Pitigalage Chamath Chandira Peiris

Abstract:

A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

Procedia PDF Downloads 105