Search results for: learning strategy

Authors: Wang Zhanquan, Yang Zeping, Gu Chunhua, Zhu Fazhi, Guo Weibin

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Recently, Massive Open Online Courses (MOOCs) are becoming the new trend of education. There are many problems under the environment of Database Principle curriculum teaching process in MOOCs, such as teaching ideas and theories which are out of touch with the reality, how to carry out the technical teaching and interactive practice in the MOOCs environment, thus the methods of database course under the environment of MOOCs are proposed. There are three processes to deal with problem solving in the research, which are problems proposed, problems solved, and inductive analysis. The present research includes the design of teaching contents, teaching methods in classroom, flipped classroom teaching mode under the environment of MOOCs, learning flow method and large practice homework. The database designing ability is systematically improved based on the researching methods.

Keywords: problem solving-driven, MOOCs, teaching art, learning flow;

Procedia PDF Downloads 356

Authors: Ehsan Atefi, Michael Grigware

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Respiratory failure secondary to surfactant deficiency resulting from respiratory distress syndrome is considered one major cause of morbidity in preterm infants. Surfactant replacement treatment (SRT) is considered an effective treatment for this disease. Here, we introduce an AI-mediated approach for estimating the distribution of surfactant in the lung airway of a newborn infant during SRT. Our approach implements machine learning to precisely estimate the splitting ratio of a liquid drop during bifurcation at different injection velocities and patient orientations. This technique can be used to calculate the surfactant residue remaining on the airway wall during the surfactant injection process. Our model works by minimizing the pressure drop difference between the two airway branches at each generation, subject to mass and momentum conservation. Our platform can be used to generate feedback for immediately adjusting the velocity of injection and patient orientation during SRT.

Keywords: respiratory failure, surfactant deficiency, surfactant replacement, machine learning

Procedia PDF Downloads 113

Authors: Kittivate Boonyopakorn

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The purposes of this study were to find the English competence of the public health volunteers and to develop the use of their English. The samples for the study were 41 public health village volunteers at Ban Prasukchai, in Thailand. The findings showed that the sum of all scores for the pre-test was 452, while the score for the post-test was 1,080. Therefore, the results of the experiment confirm that the post-test scores (1,080) significantly are higher than the pre-test (452). The mean score (N=41) for the pre-test was 11.02 while the mean score (N=41) for the post-test was 18.10. The standard deviation for the pre-test was 2.734; however, for the post-test it was 1.934. In addition to the experts-evaluated research tools, the results of the evaluation for the structured interviews (IOC) were 1 in value. The evaluation of congruence for the content with learning objectives (IOC) were 0.66 to 1.00 in value. The evaluation of congruence for the pre and post-test with learning objectives (IOC) are 1 in value.

Keywords: finding the English competency, developing public health, village volunteers

Procedia PDF Downloads 438

Authors: Shao-Di Feng, Von-Wun Soo

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To generate symbolic music with specific emotions is a challenging task due to symbolic music datasets that have emotion labels are scarce and incomplete. This research aims to generate more refined emotions based on the training datasets that are only labeled with four quadrants in Russel’s 2D emotion model. We focus on the theory of Music Fadernet and map arousal and valence to the low-level attributes, and build a symbolic music generation model by combining transformer and GM-VAE. We adopt an in-attention mechanism for the model and improve it by allowing modulation by conditional information. And we show the music generation model could control the generation of music according to the emotions specified by users in terms of high-level linguistic expression and by manipulating their corresponding low-level musical attributes. Finally, we evaluate the model performance using a pre-trained emotion classifier against a pop piano midi dataset called EMOPIA, and by subjective listening evaluation, we demonstrate that the model could generate music with more refined emotions correctly.

Keywords: music generation, music emotion controlling, deep learning, semi-supervised learning

Procedia PDF Downloads 73

Authors: Mathumalar Loganathan, Lina Syazana, Bryony Dean Franklin

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Background: The therapeutic benefit of self-administered medication for long-term use is limited by an average 50% non-adherence rate. Patient forgetfulness is a common factor in unintentional non-adherence. With a growing ageing population, strategies to improve self-administration of medication adherence are essential. Our aim was to review systematically the effects of interventions to optimise self-administration of medication. Method: Database searched were MEDLINE, EMBASE, PsynINFO, CINAHL from 1980 to 31 October 2013. Search terms included were ‘self-administration’, ‘self-care’, ‘medication adherence’, and ‘intervention’. Two independent reviewers undertook screening and methodological quality assessment, using the Downs and Black rating scale. Results: The search strategy retrieved 6 studies that met the inclusion and exclusion criteria. Three intervention strategies were identified: self-administration medication programme (SAMP), nursing education and medication packaging (pill calendar). A nursing education programme focused on improving patients’ behavioural self-management of drug prescribing. This was the most studied area and three studies highlighting an improvement in self-administration of medication. Conclusion: Results are mixed and there is no one interventional strategy that has proved to be effective. Nevertheless, self-administration of medication programme seems to show most promise. A multi-faceted approach and clearer policy guideline are likely to be required to improve prescribing for these vulnerable patients. Mixed results were found for SAMP. Medication packaging (pill calendar) was evaluated in one study showing a significant improvement in self-administration of medication. A meta-analysis could not be performed due to heterogeneity in the outcome measures.

Keywords: self-administered medication, intervention, prescribing, older patients

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Authors: I Made Ginarsa, Agung Budi Muljono, I Made Ari Nrartha

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Transient voltage response appears in power system operation when an additional loading is forced to load bus of power systems. In this research, improvement of transient voltage response is done by using power system stabilizer-static var compensator (PSS-SVC) based on adaptive neuro-fuzzy inference system (ANFIS)-algorithm. The main function of the PSS is to add damping component to damp rotor oscillation through automatic voltage regulator (AVR) and excitation system. Learning process of the ANFIS is done by using off-line method where data learning that is used to train the ANFIS model are obtained by simulating the PSS-SVC conventional. The ANFIS model uses 7 Gaussian membership functions at two inputs and 49 rules at an output. Then, the ANFIS-PSS and ANFIS-SVC models are applied to power systems. Simulation result shows that the response of transient voltage is improved with settling time at the time of 4.25 s.

Keywords: improvement, transient voltage, PSS-SVC, ANFIS, settling time

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Authors: Tian Lan, Yixiang Wang, Wenxin Tai, Yilan Lyu, Zufeng Wu

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The monaural speech enhancement task in the time-frequencydomain has a myriad of approaches, with the stacked con-volutional neural network (CNN) demonstrating superiorability in feature extraction and selection. However, usingstacked single convolutions method limits feature represen-tation capability and generalization ability. In order to solvethe aforementioned problem, we propose an attention-basedadaptive convolutional network that integrates the multi-scale convolutional operations into a operation-specific blockvia input dependent attention to adapt to complex auditoryscenes. In addition, we introduce a two-stage progressivelearning method to enlarge the receptive field without a dra-matic increase in computation burden. We conduct a series ofexperiments based on the TIMIT corpus, and the experimen-tal results prove that our proposed model is better than thestate-of-art models on all metrics.

Keywords: speech enhancement, adaptive convolu-tion, progressive learning, time-frequency domain

Procedia PDF Downloads 108

Authors: Ahmed M. Debela, Mayreli Ortiz , Ciara K. O´Sullivan

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The completion of the human genome Project (HGP) has paved the way for mapping the diversity in the overall genome sequence which helps to understand the genetic causes of inherited diseases and susceptibility to drugs or environmental toxins. Arrayed primer extension (APEX) is a microarray based minisequencing strategy for screening disease causing mutations. It is derived from Sanger DNA sequencing and uses fluorescently dideoxynucleotides (ddNTPs) for termination of a growing DNA strand from a primer with its 3´- end designed immediately upstream of a site where single nucleotide polymorphism (SNP) occurs. The use of DNA polymerase offers a very high accuracy and specificity to APEX which in turn happens to be a method of choice for multiplex SNP detection. Coupling the high specificity of this method with the high sensitivity, low cost and compatibility for miniaturization of electrochemical techniques would offer an excellent platform for detection of mutation as well as sequencing of DNA templates. We are developing an electrochemical APEX for the analysis of SNPs found in the MYH7 gene for group of cardiomyopathy patients. ddNTPs were labeled with four different redox active compounds with four distinct potentials. Thiolated oligonucleotide probes were immobilised on gold and glassy carbon substrates which are followed by hybridisation with complementary target DNA just adjacent to the base to be extended by polymerase. Electrochemical interrogation was performed after the incorporation of the redox labelled dedioxynucleotide. The work involved the synthesis and characterisation of the redox labelled ddNTPs, optimisation and characterisation of surface functionalisation strategies and the nucleotide incorporation assays.

Keywords: array based primer extension, labelled ddNTPs, electrochemical, mutations

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Authors: Tebogo Sethibe, Jabulile Mabuza

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The COVID-19 pandemic has been commonly referred to as a ‘black swan event’; it has changed the world, from how people live, learn, work and socialise. It is believed that the pandemic has fast-tracked the adoption of technology in many organisations to ensure business continuity and business sustainability; broadly said, the pandemic has fast-tracked digital transformation (DT) in different organisations. This paper aims to study the impact of the COVID-19 pandemic on DT in organisations in South Africa by focusing on the changes in IT capabilities in the DT framework. The research design is qualitative. The data collection was through semi-structured interviews with information communication technology (ICT) leaders representing different organisations in South Africa. The data were analysed using the thematic analysis process. The results from the study show that, in terms of ICT in the organisation, the pandemic had a direct and positive impact on ICT strategy and ICT operations. In terms of IT capability transformation, the pandemic resulted in the optimisation and expansion of existing IT capabilities in the organisation and the building of new IT capabilities to meet emerging business needs. In terms of the focus of activities during the pandemic, there seems to be a split in organisations between the primary focus being on ‘digital IT’ or ‘traditional IT’. Overall, the findings of the study show that the pandemic had a positive and significant impact on DT in organisations. However, a definitive conclusion on this would require expanding the scope of the research to all the components of a comprehensive DT framework. This study is significant because it is one of the first studies to investigate the impact of the COVID-19 pandemic on organisations, on ICT in the organisation, on IT capability transformation and, to a greater extent, DT. The findings from the study show that in response to the pandemic, there is a need for: (i) agility in organisations; (ii) organisations to execute on their existing strategy; (iii) the future-proofing of IT capabilities; (iv) the adoption of a hybrid working model; and for (v) organisations to take risks and embrace new ideas.

Keywords: digital transformation, COVID-19, bimodal-IT, digital transformation framework

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Authors: R. R. Govind

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In the digital age, artificial intelligence (AI) is having a significant impact on the rapid changes that cities are experiencing. This study explores the profound impact of AI on urban morphology, especially with regard to promoting friendly design choices. It addresses a significant research gap by examining the real-world effects of integrating AI into urban design and management. The main objective is to outline a framework for integrating AI to transform urban settings. The study employs an urban design framework to effectively navigate complicated urban environments, emphasize the need for urban management, and provide efficient planning and design strategies. Taking Gangtok's informal settlements as a focal point, the study employs AI methodologies such as machine learning, predictive analytics, and generative AI to tackle issues of 'urban informality'. The insights garnered not only offer valuable perspectives but also unveil AI's transformative potential in addressing contemporary urban challenges.

Keywords: urban design, artificial intelligence, urban challenges, machine learning, urban informality

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Authors: Luiz G. Véras, Felipe L. Medeiros, Lamartine F. Guimarães

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This work approaches the automatic planning of paths for Unmanned Aerial Vehicles (UAVs) through the application of the Rapidly Exploring Random Tree Star-Smart (RRT*-Smart) algorithm. RRT*-Smart is a sampling process of positions of a navigation environment through a tree-type graph. The algorithm consists of randomly expanding a tree from an initial position (root node) until one of its branches reaches the final position of the path to be planned. The algorithm ensures the planning of the shortest path, considering the number of iterations tending to infinity. When a new node is inserted into the tree, each neighbor node of the new node is connected to it, if and only if the extension of the path between the root node and that neighbor node, with this new connection, is less than the current extension of the path between those two nodes. RRT*-smart uses an intelligent sampling strategy to plan less extensive routes by spending a smaller number of iterations. This strategy is based on the creation of samples/nodes near to the convex vertices of the navigation environment obstacles. The planned paths are smoothed through the application of the method called quintic pythagorean hodograph curves. The smoothing process converts a route into a dynamically-viable one based on the kinematic constraints of the vehicle. This smoothing method models the hodograph components of a curve with polynomials that obey the Pythagorean Theorem. Its advantage is that the obtained structure allows computation of the curve length in an exact way, without the need for quadratural techniques for the resolution of integrals.

Keywords: path planning, path smoothing, Pythagorean hodograph curve, RRT*-Smart

Procedia PDF Downloads 159

Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

Procedia PDF Downloads 87

Authors: Rie Adachi

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In Japan, it is required to improve students’ English communicative proficiency and the Education Ministry will start English education for the third grade and upper from year 2020 on. Considering the problems with the educational system, Content and Language Integrated Learning (CLIL) is more appropriate to be employed in elementary schools rather than just introducing English lessons. Effective CLIL takes place in the 4Cs Framework, and different strategies are used in various activities, such as arts and crafts, bodily expression, singing, playing roles, etc. After a CLIL workshop for local teachers focused on the 4Cs, the writer conducted a survey of the 36 participants using a questionnaire and found that they did not know the word CLIL, but seemed to have an interest after attending the workshop. The writer concluded that researchers and practitioners need to spread awareness of the 4Cs framework, to apply CLIL into Japanese educational context, to provide CLIL teacher training program and so on, in order to practice CLIL in Japanese elementary schools and nurture students with a global mindset.

Keywords: CLIL, 4Cs, homeroom teachers, intercultural understanding

Procedia PDF Downloads 158

Authors: Hannah S. Waldron

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Background: The government has proposed sugar reduction targets in an effort to tackle childhood obesity, focussing on those of low socioeconomic status (SES). Supermarkets are a key location for reducing the amount of sugar purchased, but success so far in this environment has been limited. Building on previous research, this study will assess the impact of restricting the availability of higher sugar biscuits as a strategy to encourage lower sugar biscuit purchasing, and whether the effects vary by customer SES. Method: 14 supermarket convenience stores were divided between control (n=7) and intervention (n=7) groups. In the intervention stores, biscuits with sugar above the government’s target (26.2g/100g) were removed from sale and replaced with lower sugar ( < 26.2g sugar/100g) alternatives. Sales and customer demographic information were collected using loyalty card data and point-of-sale transaction data for 8-weeks pre and post the intervention for lower sugar biscuits, total biscuits, alternative higher sugar products, and all products. Results were analysed using three-way and two-way mixed ANOVAs. Results: The intervention resulted in a significant increase in lower sugar biscuit purchasing (p < 0.001) and a significant decline in overall biscuit sales (p < 0.001) between the time periods compared to control stores. Sales of higher sugar products and all products increased significantly between the two time periods in both the intervention and control stores (p < 0.05). SES showed no significant effect on any of the reported outcomes (p > 0.05). Conclusion: Restricting the availability of higher sugar products may be a successful strategy for encouraging lower sugar purchasing across all SES groups. However, larger-scale interventions are required in additional categories to assess the long term implications for both consumers and retailers.

Keywords: biscuits, nudging, sugar, supermarket

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Authors: Joy Aworookoroh

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Writing is one of the active skills in language learning. Students of English as a foreign language are expected to write efficiently and proficiently in the language; however, there are usually challenges to optimal performance and competence in writing. Errors, on the other hand, in a foreign language learning situation are more positive than negative as they provide the basis for solving the limitations of the students. This paper investigates the situation in the Department of English, Taraba State University Jalingo. Students are administered a descriptive writing test across different levels of study. The target students are multilingual with an L1 of either Kuteb, Hausa or Junkun languages. The essays are accessed to identify the different kinds of errors in them alongside the classification of the order. Errors of correctness, clarity, engagement, and delivery were identified. However, the study identified that the degree of errors reduces alongside the experience and exposure of the students to an EFL classroom.

Keywords: errors, writings, descriptive essay, multilingual

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Anoff Anim, Lila Mahmound, Maria Katsikogianni, Sanjit Nayak

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Sustained and controlled delivery of antimicrobial drugs have been largely studied recently using metal organic frameworks (MOFs)and different polymers. However, much attention has not been given to combining both MOFs and biodegradable polymers which would be a good strategy in providing a sustained gradual release of the drugs. Herein, we report a comparative study of the sustained and controlled release of widely used antibacterial drugs, cephalexin and metronidazole, from zinc-based MOF-5 incorporated in biodegradable polycaprolactone (PCL) and poly-lactic glycolic acid (PLGA) membranes. Cephalexin and metronidazole were separately incorporated in MOF-5 post-synthetically, followed by their integration into biodegradable PLGA and PCL membranes. The pristine MOF-5 and the loaded MOFs were thoroughly characterized by FT-IR, SEM, TGA and PXRD. Drug release studies were carried out to assess the release rate of the drugs in PBS and distilled water for up to 48 hours using UV-Vis Spectroscopy. Four bacterial strains from both the Gram-positive and Gram-negative types, Staphylococus aureus, Staphylococuss epidermidis, Escherichia coli, Acinetobacter baumanii, were tested against the pristine MOF, pure drugs, loaded MOFs and the drug-loaded MOF-polymer composites. Metronidazole-loaded MOF-5 composite of PLGA (PLGA-Met@MOF-5) was found to show highest efficiency to inhibit the growth of S. epidermidis compared to the other bacteria strains while maintaining a sustained minimum inhibitory concentration (MIC). This study demonstrates that the combination of biodegradable MOF-polymer composites can provide an efficient platform for sustained and controlled release of antimicrobial drugs, and can be a potential strategy to integrate them in biomedical devices.

Keywords: antimicrobial resistance, biodegradable polymers, cephalexin, drug release metronidazole, MOF-5, PCL, PLGA

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Authors: Waleed Maqableh, Ahmad Al-Hamad, Manjit Sidhu

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Augmented reality (AR) technology has the capability to provide many benefits in the field of education as a modern technology which aided learning and improved the learning experience. This paper evaluates AR based application with multiple markers interaction technique (touch-to-print) which is designed for analyzing the kinematics of 4BL mechanism in mechanical engineering. The application is termed as AR-4BL-MAST and it allows the users to touch the symbols on a paper in natural way of interaction. The evaluation of this application was performed with mechanical engineering students and human–computer interaction (HCI) experts to test its effectiveness as a tangible user interface application where the statistical results show its ability as an interaction technique, and it gives the users more freedom in interaction with the virtual mechanical objects.

Keywords: augmented reality, multimedia, user interface, engineering, education technology

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Authors: Lucy Tsado

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As cybercrime and cyberattacks continue to increase, the need to respond will follow suit. When cybercrimes occur, the duty to respond sometimes falls on law enforcement. However, criminal justice students are not taught concepts in cybersecurity and digital forensics. There is, therefore, an urgent need for many more institutions to begin teaching cybersecurity and related courses to social science students especially criminal justice students. However, many faculty in universities, colleges, and high schools are not equipped to teach these courses or do not have the knowledge and resources to teach important concepts in cybersecurity or digital forensics to criminal justice students. This research intends to develop curricula and training programs to equip faculty with the skills to meet this need. There is a current call to involve non-technical fields to fill the cybersecurity skills gap, according to experts. There is a general belief among non-technical fields that cybersecurity education is only attainable within computer science and technologically oriented fields. As seen from current calls, this is not entirely the case. Transitioning into the field is possible through curriculum development, training, certifications, internships and apprenticeships, and competitions. There is a need to identify how a cybersecurity eco-system can be created at a university to encourage/start programs that will lead to an interest in cybersecurity education as well as attract potential students. A short-term strategy can address this problem through curricula development, while a long-term strategy will address developing training faculty to teach cybersecurity and digital forensics. Therefore this research project addresses this overall problem in two parts, through curricula development for the criminal justice discipline; and training of faculty in criminal justice to teaching the important concepts of cybersecurity and digital forensics.

Keywords: cybersecurity education, criminal justice, curricula development, nontechnical cybersecurity, cybersecurity, digital forensics

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Authors: Lily Chimuanya, Ehioghae Esther

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Language, a versatile and sophisticated tool, is fundamentally sacrosanct to mankind especially within the realm of politics. In this dynamic world, political leaders adroitly use language to engage in a strategic show aimed at manipulating or mechanising the opinion of discerning people. This nuanced synergy is marked by different rhetorical strategies, meticulously synced with contextual factors ranging from cultural, ideological, and political to achieve multifaceted persuasive objectives. This study investigates the (de)legitimation strategies inherent in African presidential inaugural speeches, as African leaders not only state their policy agenda through inaugural speeches but also subtly indulge in a dance of legitimation and delegitimation, performing a twofold objective of strengthening the credibility of their administration and, at times, undermining the performance of the past administration. Drawing insights from two different legitimation models and a dataset of 4 African presidential inaugural speeches obtained from authentic websites, the study describes the roles of authorisation, rationalisation, moral evaluation, altruism, and mythopoesis in unmasking the structure of political discourse. The analysis takes a mixed-method approach to unpack the (de)legitimation strategy embedded in the carefully chosen speeches. The focus extends beyond a superficial exploration and delves into the linguistic elements that form the basis of presidential discourse. In conclusion, this examination goes beyond the nuanced landscape of language as a potent tool in politics, with each strategy contributing to the overall rhetorical impact and shaping the narrative. From this perspective, the study argues that presidential inaugural speeches are not only linguistic exercises but also viable weapons that influence perceptions and legitimise authority.

Keywords: CDA, legitimation, inaugural speeches, delegitmation

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

Procedia PDF Downloads 123

Authors: B. Bricklin Zeff

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This research explores the development and application of a specialized chatbot tailored for a nursing English course, with a primary objective of augmenting student engagement through situational content and responsiveness to key expressions and vocabulary. Introducing the chatbot, elucidating its purpose, and outlining its functionality are crucial initial steps in the research study, as they provide a comprehensive foundation for understanding the design and objectives of the specialized chatbot developed for the nursing English course. These elements establish the context for subsequent evaluations and analyses, enabling a nuanced exploration of the chatbot's impact on student engagement and language learning within the nursing education domain. The subsequent exploration of the intricate language model development process underscores the fusion of scientific methodologies and artistic considerations in this application of artificial intelligence (AI). Tailored for educators and curriculum developers in nursing, practical principles extending beyond AI and education are considered. Some insights into leveraging technology for enhanced language learning in specialized fields are addressed, with potential applications of similar chatbots in other professional English courses. The overarching vision is to illuminate how AI can transform language learning, rendering it more interactive and contextually relevant. The presented chatbot is a tangible example, equipping educators with a practical tool to enhance their teaching practices. Methodologies employed in this research encompass surveys and discussions to gather feedback on the chatbot's usability, effectiveness, and potential improvements. The chatbot system was integrated into a nursing English course, facilitating the collection of valuable feedback from participants. Significant findings from the study underscore the chatbot's effectiveness in encouraging more verbal practice of target expressions and vocabulary necessary for performance in role-play assessment strategies. This outcome emphasizes the practical implications of integrating AI into language education in specialized fields. This research holds significance for educators and curriculum developers in the nursing field, offering insights into integrating technology for enhanced English language learning. The study's major findings contribute valuable perspectives on the practical impact of the chatbot on student interaction and verbal practice. Ultimately, the research sheds light on the transformative potential of AI in making language learning more interactive and contextually relevant, particularly within specialized domains like nursing.

Keywords: chatbot, nursing, pragmatics, role-play, AI

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Authors: Jiao Li

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Language teachers have been expected to play diversified new roles in times of educational changes. Considering the critical role that materials play in teaching and learning, language teachers have been increasingly involved in developing materials. Using identity as an analytic lens, this study aims to explore language teachers’ experiences as materials developers in China, focusing on the challenges they face and responses to them. It will adopt a multimethod approach. At the first stage, about 12 language teachers who have developed or are developing materials will be interviewed to have a broad view of their experiences. At the second stage, three language teachers who are developing materials will be studied by collecting interview data, policy documents, and data obtained from online observation of their group meetings so as to gain a deeper understanding of their experiences in materials development. It is expected that this study would have implications for teacher development, materials development, and curriculum development as well.

Keywords: educational changes, teacher development, teacher identity, teacher learning, materials development

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Authors: Kuniyoshi Abe

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Bi-conjugate gradient (Bi-CG) is a well-known method for solving linear equations Ax = b, for x, where A is a given n-by-n matrix, and b is a given n-vector. Typically, the dimension of the linear equation is high and the matrix is sparse. A number of hybrid Bi-CG methods such as conjugate gradient squared (CGS), Bi-CG stabilized (Bi-CGSTAB), BiCGStab2, and BiCGstab(l) have been developed to improve the convergence of Bi-CG. Bi-CGSTAB has been most often used for efficiently solving the linear equation, but we have seen the convergence behavior with a long stagnation phase. In such cases, it is important to have Bi-CG coefficients that are as accurate as possible, and the stabilization strategy, which stabilizes the computation of the Bi-CG coefficients, has been proposed. It may avoid stagnation and lead to faster computation. Motivated by a large number of processors in present petascale high-performance computing hardware, the scalability of Krylov subspace methods on parallel computers has recently become increasingly prominent. The main bottleneck for efficient parallelization is the inner products which require a global reduction. The resulting global synchronization phases cause communication overhead on parallel computers. The parallel variants of Krylov subspace methods reducing the number of global communication phases and hiding the communication latency have been proposed. However, the numerical stability, specifically, the convergence speed of the parallel variants of Bi-CGSTAB may become worse than that of the standard Bi-CGSTAB. In this paper, therefore, we compare the convergence speed between the standard Bi-CGSTAB and the parallel variants by numerical experiments and show that the convergence speed of the standard Bi-CGSTAB is faster than the parallel variants. Moreover, we propose the stabilization strategy for the parallel variants.

Keywords: bi-conjugate gradient stabilized method, convergence speed, Krylov subspace methods, linear equations, parallel variant

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Authors: Michele Gattullo, Vito M. Manghisi, Alessandro Evangelista, Enricoandrea Laviola

Abstract:

In this work, we explored the use of Augmented Reality (AR) to support students in laboratory lectures in Distance Learning (DL), designing an application that proved to be ready for use next semester. AR could help students in the understanding of complex concepts as well as increase their motivation in the learning process. However, despite many prototypes in the literature, it is still less used in schools and universities. This is mainly due to the perceived limited advantages to the investment costs, especially regarding changes needed in the teaching modalities. However, with the spread of epidemiological emergency due to SARS-CoV-2, schools and universities were forced to a very rapid redefinition of consolidated processes towards forms of Distance Learning. Despite its many advantages, it suffers from the impossibility to carry out practical activities that are of crucial importance in STEM ("Science, Technology, Engineering e Math") didactics. In this context, AR perceived advantages increased a lot since teachers are more prepared for new teaching modalities, exploiting AR that allows students to carry on practical activities on their own instead of being physically present in laboratories. In this work, we designed an AR application for the support of engineering students in the understanding of assembly drawings of complex machines. Traditionally, this skill is acquired in the first years of the bachelor's degree in industrial engineering, through laboratory activities where the teacher shows the corresponding components (e.g., bearings, screws, shafts) in a real machine and their representation in the assembly drawing. This research aims to explore the effectiveness of AR to allow students to acquire this skill on their own without physically being in the laboratory. In a preliminary phase, we interviewed students to understand the main issues in the learning of this subject. This survey revealed that students had difficulty identifying machine components in an assembly drawing, matching between the 2D representation of a component and its real shape, and understanding the functionality of a component within the machine. We developed a mobile application using Unity3D, aiming to solve the mentioned issues. We designed the application in collaboration with the course professors. Natural feature tracking was used to associate the 2D printed assembly drawing with the corresponding 3D virtual model. The application can be displayed on students’ tablets or smartphones. Users could interact with selecting a component from a part list on the device. Then, 3D representations of components appear on the printed drawing, coupled with 3D virtual labels for their location and identification. Users could also interact with watching a 3D animation to learn how components are assembled. Students evaluated the application through a questionnaire based on the System Usability Scale (SUS). The survey was provided to 15 students selected among those we participated in the preliminary interview. The mean SUS score was 83 (SD 12.9) over a maximum of 100, allowing teachers to use the AR application in their courses. Another important finding is that almost all the students revealed that this application would provide significant power for comprehension on their own.

Keywords: augmented reality, distance learning, STEM didactics, technology in education

Procedia PDF Downloads 115

Authors: Kunyu Yan

Abstract:

Early childhood music education plays a significant role in an individual’s growth. Music can help children understand themselves and relate to others, and make connections between family, school, and society. In recent years, with the development of early childhood education in China, an increasing number of kindergartens have been established, and many of them pay more attention to music education. This research has two main aims. One is to discover how and why music is used in both public and private kindergartens. The second aim is to make recommendations for widening the use of music in kindergartens. In order to achieve these aims, the research uses two main methods. Firstly, it considers the historical background and cultural context of early childhood education in China; and secondly, it uses an approach that compares public and private kindergartens. In this research, six kindergartens were chosen from Qingdao city in Shandong Province as case studies, including 3 public kindergartens and 3 private kindergartens. This research was based on using three types of data collection methods: observation, semi-structured interviews with teachers, and questionnaires with parents. Participant and non-participant observational methods were used and included in daily routines at the kindergartens in order to experience the situation of music education first-hand. Interviews were associated with teachers’ views of teaching and learning music, the perceptions of the music context, and their strategies of using music. Lastly, the questionnaire was designed to obtain the views of current music education from the children’s parents in the respective kindergartens. The results are shown with three main themes: (1) distinct characteristics of public kindergartens (e.g., similar equipment, low tuition fee, qualified teachers, etc); (2) distinct characteristics of private kindergartens (e.g., various tuition fees, own teaching system, trained teachers, etc); and (3) differences between public and private kindergartens (e.g., funding, requirements for teachers, parents’ demands, etc). According to the results, we can see that the main purpose of using music in China is to develop the musical ability of children, and teachers focus on musical learning, such as singing in tune and playing instruments. However, as revealed in this research, there are many other uses and functions of music in these educational settings, including music used for non-musical learning (e.g., counting, learning language, etc.) or in supporting social routines.

Keywords: differences between private and public school, early childhood education, music education, uses and functions of music

Procedia PDF Downloads 208

Authors: Nguyen Quynh Trang, Nguyễn Thị Hông Nhung

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The study aimed to identify the effectiveness of using the mind map technique to enhance students’ medical vocabulary retention among a group of students at a higher education institution - Thai Nguyen University of Medicine and Pharmacy during the first semester of the school year 2022-2023. The research employed a quasi-experimental method, exploring primary sources such as questionnaires and the analyzed results of pre-and-post tests. Almost teachers and students showed high preferences for the implementation of the mind map technique in language teaching and learning. Furthermore, results from the pre-and-post tests between the experimental group and control one pointed out that this technique brought back positive academic performance in teaching and learning English. The research findings revealed that there should be more supportive policies to evoke the use of the mind map technique in a pedagogical context. Aim of the Study: The purpose of this research was to investigate whether using mind mapping can help students to enhance nursing students’ medical vocabulary retention and to assess the students’ attitudes toward using mind mapping as a tool to improve their vocabulary. The methodology of the study: The research employed a quasi-experimental method, exploring primary sources such as questionnaires and the analyzed results of pre-and-post tests. The contribution of the study: The research contributed to the innovation of teaching vocabulary methods for English teachers at a higher education institution. Moreover, the research helped the English teachers and the administrators at a university evoke and maintain the motivation of students not only in English classes but also in other subjects. The findings of this research were beneficial to teachers, students, and researchers interested in using mind mapping to teach and learn English vocabulary. The research explored and proved the effectiveness of applying mind mapping in teaching and learning English vocabulary. Therefore, teaching and learning activities were conducted more and more effectively and helped students overcome challenges in remembering vocabulary and creating motivation to learn English vocabulary.

Keywords: medical vocabulary retention, mind map technique, nursing students, medical vocabulary

Procedia PDF Downloads 60

Authors: Altilio Rosa, Chirico Francesco, Foglia Goffredo

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In recent years, the number of Unmanned Aerial Vehicles (UAVs) has significantly increased. The rapid development of commercial and recreational drones makes them an important part of our society. Despite the growing list of their applications, these vehicles pose a huge threat to civil and military installations: detection, classification and neutralization of such flying objects become an urgent need. Radar is an effective remote sensing tool for detecting and tracking flying objects, but scenarios characterized by the presence of a high number of tracks related to flying birds make especially challenging the drone detection task: operator PPI is cluttered with a huge number of potential threats and his reaction time can be severely affected. Flying birds compared to UAVs show similar velocity, RADAR cross-section and, in general, similar characteristics. Building from the absence of a single feature that is able to distinguish UAVs and birds, this paper uses a multiple features approach where an original feature selection technique is developed to feed binary classifiers trained to distinguish birds and UAVs. RADAR tracks acquired on the field and related to different UAVs and birds performing various trajectories were used to extract specifically designed target movement-related features based on velocity, trajectory and signal strength. An optimization strategy based on a genetic algorithm is also introduced to select the optimal subset of features and to estimate the performance of several classification algorithms (Neural network, SVM, Logistic regression…) both in terms of the number of selected features and misclassification error. Results show that the proposed methods are able to reduce the dimension of the data space and to remove almost all non-drone false targets with a suitable classification accuracy (higher than 95%).

Keywords: birds, classification, machine learning, UAVs

Procedia PDF Downloads 205

Authors: Naci Büyükkaracığan, Yıldırım Akyol

Abstract:

Information technology is called the technology which provides transmission of information elsewhere regardless of time, location, distance. Today, information technology is providing the occurrence of ground breaking changes in all areas of our daily lives. Information can be reached quickly to millions of people with help of information technology. In this Study, effects of information technology on students for educations and their learning preferences were demonstrated with using data obtained from questionnaires administered to students of 2015-2016 academic year at Selcuk University Kadınhanı Faik İçil Vocational School Construction Department. The data was obtained by questionnaire consisting of 30 questions that was prepared by the researchers. SPSS 21.00 package programme was used for statistical analysis of data. Chi-square tests, Mann-Whitney U test, Kruskal-Wallis and Kolmogorov-Smirnov tests were used in the data analysis for Descriptiving statistics. In a study conducted with the participation of 61 students, 93.4% of students' reputation of their own information communication device (computer, smart phone, etc.) That have been shown to be at the same rate and to the internet. These are just a computer of itself, then 45.90% of the students. The main reasons for the students' use of the Internet, social networking sites are 85.24%, 13.11% following the news of the site, as seen. All student assignments in information technology, have stated that they use in the preparation of the project. When students acquire scientific knowledge in the profession regarding their preferred sources evaluated were seen exactly when their preferred internet. Male students showed that daily use of information technology while compared to female students was statistically significantly less. Construction Package program where students are eager to learn about the reputation of 72.13% and 91.80% identified in the well which they agreed that an indispensable element in the professional advancement of information technology.

Keywords: information technologies, computer, construction, internet, learning systems

Procedia PDF Downloads 286

Authors: Mekamı Hayet, Bounoua Nacer, Benabderrahmane Sidahmed, Taleb Ahmed

Abstract:

Pose estimation is an important task in computer vision. Though the majority of the existing solutions provide good accuracy results, they are often overly complex and computationally expensive. In this perspective, we propose the use of dimensionality reduction techniques to address the problem of facial pose estimation. Firstly, a face image is converted into one-dimensional time series using Hilbert space filling curve, then the approach converts these time series data to a symbolic representation. Furthermore, a distance matrix is calculated between symbolic series of an input learning dataset of images, to generate classifiers of frontal vs. profile face pose. The proposed method is evaluated with three public datasets. Experimental results have shown that our approach is able to achieve a correct classification rate exceeding 97% with K-NN algorithm.

Keywords: machine learning, pattern recognition, facial pose classification, time series

Procedia PDF Downloads 339