Search results for: structure learning

Authors: Inkeri Jaaskelainen

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The aim of this study is to shed light on what is it like to study in apprenticeship training using intermediate (or even lower level) Finnish. The aim is to find out and describe these students' experiences and feelings while acquiring a profession in Finnish as it is important to understand how immigrant background adult learners learn and how their needs could be better taken into account. Many students choose apprenticeships and start vocational training while their language skills in Finnish are still very weak. At work, students should be able to simultaneously learn Finnish and do vocational studies in a noisy, demanding, and stressful environment. Learning and understanding new things is very challenging under these circumstances, and sometimes students get exhausted and experience a lot of stress - which makes learning even more difficult. Students are different from each other, and so are their ways to learn. Both duties at work and school assignments require reasonably good general language skills, and, especially at work, language skills are also a safety issue. The empirical target of this study is a group of students with an immigrant background who studied in various fields with intensive L2 support in 2016–2018 and who by now have completed a vocational upper secondary qualification. The interview material for this narrative study was collected from those who completed apprenticeship training in 2019–2020. The data collection methods used are a structured thematic interview, a questionnaire, and observational data. Interviewees with an immigrant background have an inconsistent cultural and educational background - some have completed an academic degree in their country of origin while others have learned to read and write only in Finland. The analysis of the material utilizes thematic analysis, which is used to examine learning and related experiences. Learning a language at work is very different from traditional classroom teaching. With evolving language skills, at an intermediate level at best, rushing and stressing makes it even more difficult to understand and increases the fear of failure. Constant noise, rapidly changing situations, and uncertainty undermine the learning and well-being of apprentices. According to preliminary results, apprenticeship training is well suited to the needs of an adult immigrant student. In apprenticeship training, students need a lot of support for learning and understanding a new communication and working culture. Stress can result in, e.g., fatigue, frustration, and difficulties in remembering and understanding. Apprenticeship training can be seen as a good path to working life. However, L2 support is a very important part of apprenticeship training, and it indeed helps students to believe that one day they will graduate and even get employed in their new country.

Keywords: apprenticeship training, vocational basic degree, Finnish learning, wee-being

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Authors: Debdut Roy, Vidyasagar Guggilla

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In this research, we highlight our exploratory work on modified zeolite based catalysts for catalytic cracking of hydrocarbons for production of light olefin i.e. ethylene and propylene. The work is focused on understanding the catalyst structure and activity correlation. Catalysts are characterized by surface area and pore size distribution analysis, inductively coupled plasma optical emission spectrometry (ICP-OES), Temperature Programmed Desorption (TPD) of ammonia, pyridine Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Thermo-gravimetric Analysis (TGA) and correlated with the catalytic activity. It is observed that the yield of lighter olefins increases with increase of Bronsted acid strength.

Keywords: catalytic cracking, zeolite, propylene, structure-activity correlation

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Authors: Rohan Roberts

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The purpose of this project was to collaborate with a group of high-functioning, more-able students (aged 15-18) to promote STEM Education and a love for science by bringing a cosmic perspective into the classroom and high school environment. This was done using 21st century science of learning, a focus on the latest research on Neuroeducation, and modern pedagogical methods based on Howard Gardner's theory of Multiple Intelligences, Bill Lucas’ theory of New Smarts, and Sir Ken Robinson’s recommendations on encouraging creativity. The result was an increased sense of passion, excitement, and wonder about science in general, and about the marvels of space and the universe in particular. In addition to numerous unique and innovative science-based initiatives, clubs, workshops, and science trips, this project also saw a marked rise in student-teacher collaboration in science learning and in student engagement with the general public through the press, social media, and community-based initiatives. This paper also outlines the practical impact that bringing a cosmic perspective into the classroom has had on the lives, interests, and future career prospects of the students involved in this endeavour.

Keywords: cosmic perspective, gifted and talented, neuro-education, STEM education

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Authors: J. Ramos, V. Gil, A. F. Torres

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The nodular iron is a material that has shown great advantages respect to other materials (steel and gray iron) in the production of machine elements. The engineering industry, especially automobile, are potential users of this material. As it is known, the alloying elements modify the properties of steels and castings. Copper has been investigated as a structural modifier of nodular iron, but studies of its mechanical and tribological implications still need to be addressed for industrial use. With the aim of improving the mechanical properties of nodular iron, alloying elements (Mn, Si, and Cu) are added in order to increase their pearlite (or ferrite) structure according to the percentage of the alloying element. In this research (using induction furnace process) nodular iron with three different percentages of copper (residual, 0,5% and 1,2%) was obtained. Chemical analysis was performed by optical emission spectrometry and microstructures were characterized by Optical Microscopy (ASTM E3) and Scanning Electron Microscopy (SEM). The study of mechanical behavior was carried out in a mechanical test machine (ASTM E8) and a Pin on disk tribometer (ASTM G99) was used to assess wear resistance. It is observed that copper increases the pearlite structure improving the wear behavior; tension behavior. This improvement is observed in higher proportion with 0,5% due to the fact that too much increase of pearlite leads to ductility loss.

Keywords: copper, mechanical properties, nodular iron, pearlite structure, wear

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Authors: Thanh Phuong Doan, Narueporn Sutanthavibul

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The influence of freezing cycle on influenza haemagglutinin (HA) conformational stability was investigated in terms of freezing rates and formulation compositions. The results showed that appropriate HA conformation could be evaluated using circular dichroism (CD) spectroscopy with HA concentration of greater than 0.09 mg/ml. The intermediate freezing rate of approximately 1.0oC/min preserved the original HA conformation better than at slow freezing rate (0.5oC/min) and rapid freezing rate (2.6oC/min). The changes in CD spectra of the secondary HA structure were more pronounced than those of the tertiary HA structure during the evaluation. Additionally, the formulations, which resulted in the highest conformational stability were found to have sucrose present in the composition. As opposed to when only glycine was used, the stability of HA conformation was poor.

Keywords: freezing, haemagglutinin, influenza, circular dichroism

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Authors: Abdul Hakim Chikho

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Calculating the ultimate load of a column in a sway framed structure involves, in the currently used design method, the calculation of the column effective length and utilizing the interaction formulas or tables. Therefore, no allowance is usually made for the effects of the presence of semi rigid connections or the presence of infill panels. In this paper, a new and simple design procedure is recommend to calculate the ultimate load of a framed Column allowing for the presence of rotational end restraints, semi rigid connections, the column end moments resulted from the applied vertical and horizontal loading and infill panels in real steel structure. In order to verify the accuracy of the recommended method to predict good and safe estimations of framed column ultimate loads, several examples have been solved utilizing the recommended procedure, and the results were compared to those obtained using a second order computer program, and good correlation had been obtained. Therefore, the accuracy of the proposed method to predict the Behaviour of practical steel columns in framed structures has been verified.

Keywords: column ultimate load, semi rigid connections, steel column, infill panel, steel structure

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Mauludi Ariesto Pamungkas, Wafa Maftuhin

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Gallium with three outer electrons commonly are used as dopants of silicon to make it P type and N type semiconductor respectively. Silicene, one-atom-thick silicon layer is one of emerging two dimension materials after the success of graphene. The effects of Gallium doping on electronic structure of silicine are investigated by using first principle calculation based on Density Functional Theory (DFT) calculation and norm conserving pseudopotential method implemented in ABINIT code. Bandstructure of Pristine silicene is similar to that of graphene. Effect of Ga doping on bandstructure of silicene depend on the position of Ga adatom on silicene

Keywords: silicene, effects of Gallium doping, Density Functional Theory (DFT), graphene

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Authors: Ava Dodd, Jonathan Adams

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The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools, and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represents another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

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Authors: Chuen-arom Janthimachai-amorn, Chirawadee Harnrittha

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This study aims to identify the factors facilitating the knowledge management to develop the graduate study programs to achieve success and to identify the approaches in developing the graduate study programs in the Rajbhat Suansunantha University. The 10 respondents were the administrators, the faculty, and the personnel of its Graduate School. The research methodology was based on Pla-too Model of the Knowledge Management Institute (KMI) by allocating the knowledge indicators, the knowledge creation and search, knowledge systematization, knowledge processing and filtering, knowledge access, knowledge sharing and exchanges and learning. The results revealed that major success factors were knowledge indicators, evident knowledge management planning, knowledge exchange and strong solidarity of the team and systematic and tenacious access of knowledge. The approaches allowing the researchers to critically develop the graduate study programs were the environmental data analyses, the local needs and general situations, data analyses of the previous programs, cost analyses of the resources, and the identification of the structure and the purposes to develop the new programs.

Keywords: program development, knowledge management, graduate study programs, Rajbhat Suansunantha University

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Authors: Colleen Cleveland, W. Adam Baldowski

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In the dynamic realm of online education, the integration of games and entertainment has emerged as a powerful strategy to captivate learners, drive active participation, and cultivate meaningful learning experiences. This abstract presents an overview of the upcoming conference, "Game On," dedicated to exploring the transformative impact of gamification, interactive simulations, and multimedia content in the digital learning landscape. Introduction: The conference aims to blur the traditional boundaries between education and entertainment, inspiring learners of diverse ages and backgrounds to actively engage in their online learning journeys. By leveraging the captivating elements of games and entertainment, educators can enhance motivation, retention, and deep understanding among virtual classroom participants. Conference Highlights: Commencing with an exploration of theoretical foundations drawing from educational psychology, instructional design, and the latest pedagogical research, participants will gain valuable insights into the ways gamified elements elevate the quality of online education. Attendees can expect interactive sessions, workshops, and case studies showcasing best practices and innovative strategies, including game-based assessments and virtual reality simulations. Inclusivity and Diversity: The conference places a strong emphasis on inclusivity, accessibility, and diversity in the integration of games and entertainment for educational purposes. Discussions will revolve around accommodating diverse learning styles, overcoming potential challenges, and ensuring equitable access to engaging educational content for all learners. Educational Transformation: Educators, instructional designers, and e-learning professionals attending "Game On" will acquire practical techniques to elevate the quality of their online courses. The conference promises a stimulating and informative exploration of blending education with entertainment, unlocking the untapped potential of games and entertainment in online education. Conclusion: "Game On" invites participants to embark on a journey that transforms online education by harnessing the power of entertainment. This event promises to be a cornerstone in the evolution of virtual learning, offering valuable insights for those seeking to create a more engaging and effective online educational experience. Join us as we explore new horizons, pushing the boundaries of online education through the fusion of games and entertainment.

Keywords: online education, games, entertainment, psychology, therapy, pop culture

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Authors: Inezia Aurelia, Soebowo Musa

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Many firms still fail to develop organizational agility. There are more than 40% of organizations think that they are low/not agile in facing market change. Organizational culture plays an important role in developing the organizations to be adaptive in order to manage the VUCA effectively. This study examines the relationships of organizational culture towards participative leadership, employee satisfaction, employee work motivation, organizational learning, and absorptive capacity in developing organizational agility in managing the VUCA environment. 263 employees located from international chemical-based company offices across the globe who have worked for more than three years were the respondents in this study. This study showed that organizational clan culture promotes the development of participative leadership, which it has an empowering effect on people in the organization resulting in employee satisfaction. The study also confirms the role of organizational culture in creating organizational behavior within the organization that fosters organizational learning, absorptive capacity, and organizational agility, while the study also found that the relationship between participative leadership and employee work motivation is not significant.

Keywords: absorptive capacity, employee satisfaction, employee work motivation, organizational agility, organizational culture, organizational learning, participative leadership

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Authors: Shaan Khosla, Jon Krohn

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In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

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Authors: Hirokatsu Kawashima

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In an attempt to elucidate relationships between listening to music and pronunciation learning, a classroom-based investigation was conducted with Japanese EFL learners (n=45). The subjects were instructed to listen to English songs they liked on YouTube, especially paying attention to phonologically similar vowel and consonant minimal pair words (e.g., live and leave). This kind of activity, which included taking notes, was regularly carried out in the classroom, and the same kind of task was given to the subjects as homework in order to reinforce the in-class activity. The duration of these activities was eight weeks, after which the program was evaluated on a 9-point scale (1: the lowest and 9: the highest) by learners’ self-evaluation. The main questions for this evaluation included 1) how good the learners had been at pronouncing vowel and consonant minimal pair words originally, 2) how often they had listened to songs good for pronouncing vowel and consonant minimal pair words, 3) how frequently they had moved their mouths to vowel and consonant minimal pair words of English songs, and 4) how much they thought the program would support and enhance their pronunciation learning of phonologically similar vowel and consonant minimal pair words. It has been found, for example, A) that the evaluation of this program is by no means low (Mean: 6.51 and SD: 1.23), suggesting that listening to music may support and enhance pronunciation learning, and B) that listening to consonant minimal pair words in English songs and moving the mouth to them are more related to the program’s evaluation (r =.69, p=.00 and r =.55, p=.00, respectively) than listening to vowel minimal pair words in English songs and moving the mouth to them (r =.45, p=.00 and r =.39, p=.01, respectively).

Keywords: minimal pair, music, pronunciation, song

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Authors: Saifunnahar

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As a former English colony, the relationship of Bangladesh with the English language goes a long way back. English is taught as a compulsory subject in Bangladesh from an early age starting from grade 1 and continuing through the 12th, yet, students are not competent enough to communicate in English proficiently. To improve students’ English language competency, the Bangladesh Ministry of Education introduced communicative language teaching (CLT) methods in English classrooms in the 1990s. It has been decades since this effort was taken, but the students’ level of proficiency is still not satisfactory. The main reason behind this failure is that CLT-based teaching-learning methods have not been effectively implemented. Very little research has been conducted to address the issues English as a foreign language (EFL) classrooms are facing to carry out CLT methodologies in secondary schools (grades 6 to 10) in Bangladesh. Though the secondary level is crucial for students’ language learning and retention, EFL classrooms are marked with various issues that make teaching-learning harder for teachers and students. This study provides an overview of the status of CLT in EFL classrooms and the reasons behind failing to implement CLT in secondary schools in Bangladesh through an analysis of the qualitative data collected from different literature. Based on the findings, effective approaches have been recommended to employ CLT in EFL classrooms.

Keywords: Bangladesh, communicative language teaching, English as a foreign language, secondary schools, pedagogy

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Aminath Waseela, Vinesh Chandra, Shaun Nykvist,

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The success of technology integration initiatives hinges on the knowledge and skills of teachers to effectively integrate technology in classroom teaching. Consequently, gaining an understanding of teachers' technology knowledge and its integration can provide useful insights on strategies that can be adopted to enhance teaching and learning, especially in developing country contexts where research is scant. This paper extends existing knowledge on teachers' use of technology by developing a conceptual framework that recognises how three key types of knowledge; content, pedagogy, technology, and their integration are at the crux of teachers' technology use while at the same time is amenable to empirical studies. Although the aforementioned knowledge is important for effective use of technology that can result in enhanced student engagement, literature on how this knowledge leads to effective technology use and enhanced student engagement is limited. Thus, this theoretical paper proposes a framework to explore teachers' knowledge through the lens of the Technological Pedagogical and Content Knowledge (TPACK); the integration of technology in classroom teaching through the Substitution Augmentation Modification and Redefinition (SAMR) model and how this affects students' learning through the Bloom's Digital Taxonomy (BDT) lens. Studies using this framework could inform the design of professional development to support teachers to develop skills for effective use of available technology that can enhance student learning engagement.

Keywords: information and communication technology, ICT, in-service training, small island developing states, SIDS, student engagement, technology integration, technology professional development training, technological pedagogical and content knowledge, TPACK

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Authors: Jiang Niu, Yue Jiang

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With the ultimate goal to automatically generate translated texts with high quality, machine translation has made tremendous improvements. However, its translations of literary works are still plagued with problems in coherence, esp. the translation between distant language pairs. One of the causes of the problems is probably the lack of linguistic knowledge to be incorporated into the training of machine translation systems. In order to enable readers to better understand the problems of machine translation in coherence, to seek out the potential knowledge to be incorporated, and thus to improve the quality of machine translation products, this study applies Theme-Rheme structure to examine how a machine-translated literary text is organized and developed in terms of coherence. Theme-Rheme structure in Systemic Functional Linguistics is a useful tool for analysis of textual coherence. Theme is the departure point of a clause and Rheme is the rest of the clause. In a text, as Themes and Rhemes may be connected with each other in meaning, they form thematic and rhematic progressions throughout the text. Based on this structure, we can look into how a text is organized and developed in terms of coherence. Methodologically, we chose Chinese and English as the language pair to be studied. Specifically, we built a comparable corpus with two modes of English translations, viz. machine translation (MT) and human translation (HT) of one Chinese literary source text. The translated texts were annotated with Themes, Rhemes and their progressions throughout the texts. The annotated texts were analyzed from two respects, the different types of Themes functioning differently in achieving coherence, and the different types of thematic and rhematic progressions functioning differently in constructing texts. By analyzing and contrasting the two modes of translations, it is found that compared with the HT, 1) the MT features “pseudo-coherence”, with lots of ill-connected fragments of information using “and”; 2) the MT system produces a static and less interconnected text that reads like a list; these two points, in turn, lead to the less coherent organization and development of the MT than that of the HT; 3) novel to traditional and previous studies, Rhemes do contribute to textual connection and coherence though less than Themes do and thus are worthy of notice in further studies. Hence, the findings suggest that Theme-Rheme structure be applied to measuring and assessing the coherence of machine translation, to being incorporated into the training of the machine translation system, and Rheme be taken into account when studying the textual coherence of both MT and HT.

Keywords: coherence, corpus-based, literary translation, machine translation, Theme-Rheme structure

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Authors: Ifeanyi Adigwe, Thomas Van der Walt

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Digital media has been integrated into the social and entertainment life of young children, and as such, the impact of digital media appears to affect young people of all ages and it is believed that this will continue to shape the world of young children. Since, technological advancement of digital media presents adolescents with diverse contexts, platforms and avenues to engage with digital media outside the home environment and from parents' supervision, a wide range of new challenges has further complicated the already difficult tasks for parents and altered the landscape of parenting. Despite the fact that adolescents now have access to a wide range of digital media technologies both at home and in the learning environment, parenting practices such as active, restrictive, co-use, participatory and technical mediations are important in mitigating of online risks adolescents may encounter as a result of digital media use. However, these mediation practices only focus on the home environment including digital media present in the home and may not necessarily transcend outside the home and other learning environments where adolescents use digital media for school work and other activities. This poses the question of who mediates adolescent's digital media use outside the home environment. The learning environment could be a ''loose platform'' where an adolescent can maximise digital media use considering the fact that there is no restriction in terms of content and time allotted to using digital media during school hours. That is to say that an adolescent can play the ''bad boy'' online in school because there is little or no restriction of digital media use and be exposed to online risks and play the ''good boy'' at home because of ''heavy'' parental mediation. This is the reason why parent mediation practices have been ineffective because a parent may not be able to track adolescents digital media use considering the diversity of contexts, platforms and avenues adolescents use digital media. This study argues that due to the diverse nature of digital media technology, parents may not be able to monitor the 'whereabouts' of their children in the digital space. This is because adolescent digital media usage may not only be confined to the home environment but other learning environments like schools. This calls for urgent attention on the part of teachers to understand the intricacies of how digital media continue to shape the world in which young children are developing and learning. It is, therefore, imperative for parents to liaise with the schools of their children to mediate digital media use during school hours. The implication of parents- teachers mediation practices are discussed. The article concludes by suggesting that third party mediation by teachers in schools and other learning environments should be encouraged and future research needs to consider the emergent strategy of teacher-children mediation approach and the implication for policy for both the home and learning environments.

Keywords: digital media, digital age, parent mediation, third party mediation

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Authors: Nor Mazlin Zahari, Lian Gan, Xuerui Mao

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The power production in wind farms and the mechanical loads on the turbines are strongly impacted by the wake of the wind turbine. Thus, there is a need for understanding and modelling the turbine wake dynamic in the wind farm and the layout optimization. Having a good wake model is important in predicting plant performance and understanding fatigue loads. In this paper, the Dynamic Mode Decomposition (DMD) was applied to the simulation data generated by a Direct Numerical Simulation (DNS) of flow around a turbine, perturbed by upstream inflow noise. This technique is useful in analyzing the wake flow, to predict its future states and to reflect flow dynamics associated with the coherent structures behind wind turbine wake flow. DMD was employed to describe the dynamic of the flow around turbine from the DNS data. Since the DNS data comes with the unstructured meshes and non-uniform grid, the interpolation of each occurring within each element in the data to obtain an evenly spaced mesh was performed before the DMD was applied. DMD analyses were able to tell us characteristics of the travelling waves behind the turbine, e.g. the dominant helical flow structures and the corresponding frequencies. As the result, the dominant frequency will be detected, and the associated spatial structure will be identified. The dynamic mode which represented the coherent structure will be presented.

Keywords: coherent structure, Direct Numerical Simulation (DNS), dominant frequency, Dynamic Mode Decomposition (DMD)

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Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

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With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: machine learning, imbalanced data, data mining, big data

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Authors: Sherelle Lou Sumera Icutan, Sheila P. Cayabyab, Mary Jane Laruan, Paulo V. Cenas, Agustina R. Tactay

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This study critically analyzed the recent theses and dissertations of the Basic Education Curriculum (BEC) teachers who finished their graduate programs in selected higher educational institutions in Region I and CAR to be able to come up with a unified result from the varied results of the analyzed research works. All theses and dissertations completed by the educators/teachers/school personnel in the secondary and elementary public and private schools in Region 1 and CAR from AY 2003–2004 to AY 2007–2008 were classified first–as to work or non-work related; second–as to the different aspects of the curriculum: implementation, content, instructional materials, assessment instruments, learning, teaching, and others; third–as to being eligible for meta-analysis or not. Only studies found eligible for meta-analysis were subjected to the procedure. Aside from documentary analysis, the statistical treatments used in meta-analysis include the standardized effect size, Pearson’s correlation (r), the chi-square test of homogeneity and the inverse of the Fisher transformation. This study found out that the BEC-teachers usually probe on work-related researchers with topics that are focused on the learning performances of the students and on factors related to teaching. The development of instructional materials and assessment of implemented programs are also equally explored. However, there are only few researches on content and assessment instrument. Research findings on the areas of learning and teaching are the only aspects that are meta-analyzable. The research findings across studies in Region I and CAR of BEC teachers that focused on similar variables correlated to teaching do not vary significantly. On the contrary, research findings across studies in Region I and CAR that focused on variables correlated to learning performance significantly vary. Within each region, variations on the findings of research works related to learning performance that considered similar variables still exist. The combined finding on the effect size or relationship of the variables that are correlated to learning performance are low which means that effect is small but definite while the combined findings on the relationship of the variables correlated to teaching are slight or almost negligible.

Keywords: meta-analysis, BEC teachers, work-related research,

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Authors: Gichun Cha, Byoungjoon Yu, Jihwan Park, Minsoo Park, Junghyun Im, Sehwan Park, Sujung Sin, Seunghee Park

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The goal of this research is to estimate a structural shape change using terrestrial laser scanning. This study proceeds with development of data reduction and shape change estimation algorithm for large-capacity scan data. The point cloud of scan data was converted to voxel and sampled. Technique of shape estimation is studied to detect changes in structure patterns, such as skyscrapers, bridges, and tunnels based on large point cloud data. The point cloud analysis applies the octree data structure to speed up the post-processing process for change detection. The point cloud data is the relative representative value of shape information, and it used as a model for detecting point cloud changes in a data structure. Shape estimation model is to develop a technology that can detect not only normal but also immediate structural changes in the event of disasters such as earthquakes, typhoons, and fires, thereby preventing major accidents caused by aging and disasters. The study will be expected to improve the efficiency of structural health monitoring and maintenance.

Keywords: terrestrial laser scanning, point cloud, shape information model, displacement measurement

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Authors: Wei Zhang

Abstract:

With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator.

Keywords: deep learning, field programmable gate array, FPGA, hardware accelerator, convolutional neural networks, CNN

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Authors: Xu Jie

Abstract:

As a key network security method, cryptographic services must fully cope with problems such as the wide variety of cryptographic algorithms, high concurrency requirements, random job crossovers, and instantaneous surges in workloads. Its complexity and dynamics also make it difficult for traditional static security policies to cope with the ever-changing situation. Cyber Threats and Environment. Traditional resource scheduling algorithms are inadequate when facing complex decisionmaking problems in dynamic environments. A network cryptographic resource allocation algorithm based on reinforcement learning is proposed, aiming to optimize task energy consumption, migration cost, and fitness of differentiated services (including user, data, and task security). By modeling the multi-job collaborative cryptographic service scheduling problem as a multiobjective optimized job flow scheduling problem, and using a multi-agent reinforcement learning method, efficient scheduling and optimal configuration of cryptographic service resources are achieved. By introducing reinforcement learning, resource allocation strategies can be adjusted in real time in a dynamic environment, improving resource utilization and achieving load balancing. Experimental results show that this algorithm has significant advantages in path planning length, system delay and network load balancing, and effectively solves the problem of complex resource scheduling in cryptographic services.

Keywords: multi-agent reinforcement learning, non-stationary dynamics, multi-agent systems, cooperative and competitive agents

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Authors: Saleit Ron

Abstract:

The Mitar program was established to drive a shift in middle school mathematics education, emphasizing the connection of math to real-life situations, exploring mathematical modeling and literacy, and integrating social and emotional learning (SEL) components for enhanced excellence. The program envisions math coordinators as catalysts for change, equipping them to create educational materials, strengthen leadership skills, and develop SEL competencies within coordinator communities. These skills are then employed to lead transformative efforts within their respective schools. The program engaged 90 participants across six math coordinator communities during 2022-2023, involving 30-60 hours of annual learning. The process includes formative and summative evaluations through questionnaires and interviews, revealing participants' high contentment and successful integration of acquired skills into their schools. Reflections from participants highlighted the need for enhanced change leadership processes, often seeking more personalized mentoring to navigate challenges effectively.

Keywords: math coordinators, mathematical literacy, mathematical modeling, SEL competencies

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Authors: Soressa Tolcha Jarra

Abstract:

Looking into firm-level internationalization strategies and their effects on firms' productivity is needed in order to understand the role of firms’ participation in trading activities on the one hand and the effects of firms’ internalization strategies on firm-level productivity on the other. Thus, this study aims to investigate firms' imports of intermediates and export strategies and their impact on firm productivity using an establishment-level panel dataset from Ethiopian manufacturing firms over the period 2011–2020. Methodologically, the joint firm’s decision to import intermediates and estimate exports is undertaken by system GMM using Wooldridge's approach. The translog-production function is used to estimate firm-level productivity by considering a general Markov process. The size of the firm is used in a mediating role. The result indicates evidence of the self-selection of more productive firms into exporting and importing intermediates, which is indicative of sizable export and import market entry costs. Furthermore, there is evidence in favor of learning by exporting (LBE) and learning by importing (LBI) hypotheses for smaller and medium Ethiopian manufacturing firms. However, for large firms, there is only evidence in support of the learning by exporting (LBE) hypothesis.

Keywords: Ethiopia, export, firm productivity, intermediate imports

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Authors: Sokkhey Phauk, Takeo Okazaki

Abstract:

The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

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Authors: Michael Lousis

Abstract:

The kind of the most important and conspicuous errors the students made during the three-years of testing of their progress in Algebra are presented in this article. The way these students’ errors changed over three-years of school Algebra learning also is shown. The sample is comprised of two hundred (200) English students and one hundred and fifty (150) Greek students, who were purposefully culled according to their participation in each occasion of testing in the development of the three-year Kassel Project in England and Greece, in both domains at once of Arithmetic and Algebra. Hence, for each of these English and Greek students, six test-scripts were available and corresponded to the three occasions of testing in both Arithmetic and Algebra respectively.

Keywords: algebra, errors, Kassel Project, progress of learning

Procedia PDF Downloads 296