Search results for: hybrid learning

Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

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Authors: Lizmol Mathew, Divya Thomas, Shiney Rajan

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Educational paradigm all over the globe is undergoing significant reform today. Because of this, so-called flipped classroom model is becoming increasingly popular in higher education. Flipped classroom has proved to be more effective than traditional lecture based model as flipped classroom model promotes active learning by encouraging students to work on in collaborative tasks and peer-led learning during the class-time. However, success of flipped classrooms relies on students’ ability and their attitudes towards collaboration and group work. This paper examines: 1) Students’ attitudes towards collaborative learning; 2) Main challenges to successful collaboration from students’ experience and 3) Students’ perception of criteria for successful team work. 4) Recommendations for enhancing collaborative learning. This study’s methodology involves quantitative analysis of surveys collected from students enrolled in undergraduate Architecture program at Qatar University. Analysis indicates that in general students enrolled in the program do not have positive perceptions or experiences associated with group work. Positive and negative factors that influence collaborative learning in higher education have been identified. Recommendations for improving collaborative work experience have been proposed.

Keywords: architecture, collaborative learning, female, group work, higher education, Middle East, Qatar, student experience

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Authors: Kulachai Kultawanich, Prakob Koraneekij, Jaitip Na-Songkhla

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The way of learning has been changed into a new paradigm since the improvement of network and communication technology, so learners have to interact with massive amount of the information. Thus, information literacy has become a critical set of abilities required by every college and university in the world. Connectivism is considered to be an alternative way to design information literacy course in online learning environment, such as Virtual Classroom (VC). With the change of learning pedagogy, VC is employed to improve the social capability by integrating cloud-based technology. This paper aims to study the use of Cloud-based Virtual Classroom (CBVC) in Connectivism learning process to enhance information literacy and self-efficacy of twenty-one undergraduate students who registered in an e-publishing course at Chulalongkorn University. The data were gathered during 6 weeks of the study by using the following instruments: (1) Information literacy test (2) Information literacy rubrics (3) Information Literacy Self-Efficacy (ILSE) Scales and (4) Questionnaire. The result indicated that students have information literacy and self-efficacy posttest mean scores higher than pretest mean scores at .05 level of significant after using CBVC in Connectivism learning process. Additionally, the study identified that the Connectivism learning process proved useful for developing information rich environment and a sense of community, and the CBVC proved useful for developing social connection.

Keywords: cloud-based, virtual classroom, connectivism, information literacy

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Authors: Ben Nwokenkwo, Michael M. Eze, Felix Ike

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The need to study trends in land values around institutions of higher learning cannot be overemphasized. Numerous studies in Nigeria have investigated the economic, and social influence of the sitting of institutions of higher learning at the micro, meso and macro levels. However, very few studies have evaluated the temporal extent at which such institution influences local land values. Since institutions greatly influence both the physical and environmental aspects of their immediate vicinity, attention must be taken to understand the influence of such changes on land values. This study examines the trend in land values using the Mann-Kendall analysis in order to determine if, between its beginning and end, a monotonic increase, decrease or stability exist in the land values across six institutions of higher learning for the period between 2004 and 2014. Specifically, The analysis was applied to the time series of the price(or value) of the land .The results of this study revealed that land values has either been increasing or remained stabled across all the institution sampled. The study finally recommends measures that can be put in place as counter magnets for land values estimation across institutions of higher learning.

Keywords: influence, land, trend, value

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Authors: Mohammad Saleem, Neeta Kumar, Anita Sharma, Sazina Muzammil

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It is observed that some of the dental students in our setting take less interest in medical subjects. Various teaching methods are focus of research interest currently and being tried to generate interest among students. An approach of interactive integrated tutorial was used to assess its feasibility in teaching medical subjects to dental undergraduates. The aim was to generate interest and promote active self-learning among students. The objectives were to (1) introduce the integrated interactive learning method through two departments, (2) get feedback from the students and faculty on feasibility and effectiveness of this method. Second-year students in Bachelor of Dental Surgery course were divided into two groups. Each group was asked to study physiology and pathology of a common and important condition (anemia and hypertension) in a week’s time. During the tutorial, students asked questions on physiology and pathology of that condition from each other in the presence of teachers of both physiology and pathology departments. The teachers acted only as facilitators. After the session, the feedback from students and faculty on this alternative learning method was obtained. Results: Majority of the students felt that this method of learning is enjoyable, helped to develop reasoning skills and ability to correlate and integrate the knowledge from two related fields. Majority of the students felt that this kind of learning led to better understanding of the topic and motivated them towards deep learning. Teachers observed that the study promoted interdepartmental cross-discipline collaboration and better students’ linkages. Conclusion: Interactive integrated tutorial is effective in motivating dental students for better and deep learning of medical subjects.

Keywords: active learning, education, integrated, interactive, self-learning, tutorials

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Maria Luisa Boninelli

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This study wants to explore the effects of a mediated intervention program in a primary school. The participants where 120 students aged 8-9, half of them Italian and half immigrants of first or second generation. The activities consisted on the cognitive enhancement of the participants through Feuerstein’s Instrumental Enrichment, (IE) and on an activity centred on body awareness and mediated learning experience. Given that there are limited studied on learners in remedial schools, the current study intented to hypothesized that participants exposed to mediation would yiel a significant improvement in cognitive functioning. Hypothesis One proposed that, following the intervention, improved Q1vata scores of the participants would occur in each of the groups. Hypothesis two postulated that participants within the Mediated Learning Experience would perform significantly better than those group of control. For the intervention a group of 60 participants constituted a group of Mediation sample and were exposed to Mediated Learning Experience through Enrichment Programm. Similiary the other 60 were control group. Both the groups have students with special needs and were exposed to the same learning goals. A pre-experimental research design, in particular a one-group pretest-posttest approach was adopted. All the participants in this study underwent pretest and post test phases whereby they completed measures according to the standard instructions. During the pretest phase, all the participants were simultaneously exposed to Q1vata test for logical and linguistic evaluation skill. During the mediation intervention, significant improvement was demonstrated with the group of mediation. This supports Feuerstein's Theory that initial poor performance was a result of a lack of mediated learning experience rather than inherent difference or deficiencies. Furthermore the use of an appropriate mediated learning enabled the participants to function adequately.

Keywords: cognitive structural modifiability, learning to learn, mediated learning experience, Reuven Feuerstein, special needs

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Authors: William Albeiro Alvarez, Gloria Patricia Jaramillo, Ivan Reinaldo Sarmiento

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Understanding the behavior of individuals and the different human factors that influence the choice when faced with a complex system such as transportation is one of the most complicated aspects of measuring in the components that constitute the modeling of route choice due to that various behaviors and driving mode directly or indirectly affect the choice. During the last two decades, with the development of information and communications technologies, new data collection techniques have emerged such as GPS, geolocation with mobile phones, apps for choosing the route between origin and destination, individual service transport applications among others, where an interest has been generated to improve discrete choice models when considering the incorporation of these developments as well as psychological factors that affect decision making. This paper implements a discrete choice model that proposes and estimates a hybrid model that integrates route choice models and latent variables based on the observation on the route of a sample of public taxi drivers from the city of Medellín, Colombia in relation to its behavior, personality, socioeconomic characteristics, and driving mode. The set of choice options includes the routes generated by the individual service transport applications versus the driver's choice. The hybrid model consists of measurement equations that relate latent variables with measurement indicators and utilities with choice indicators along with structural equations that link the observable characteristics of drivers with latent variables and explanatory variables with utilities.

Keywords: behavior choice model, human factors, hybrid model, real time data

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Authors: Vaibhav Singh Rajput, Ravi Kumar Jatoth, Nagu Bhookya, Bhasker Boda

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In this paper proportional, integral, derivative (PID) controller is used to control the pitch angle of the aircraft when the elevation angle is changed or modified. The pitch angle is dependent on elevation angle; a change in one corresponds to a change in the other. The PID controller helps in restricted change of pitch rate in response to the elevation angle. The PID controller is dependent on different parameters like Kp, Ki, Kd which change the pitch rate as they change. Various methodologies are used for changing those parameters for getting a perfect time response pitch angle, as desired or wished by a concerned person. While reckoning the values of those parameters, trial and guessing may prove to be futile in order to provide comfort to passengers. So, using some metaheuristic techniques can be useful in handling these errors. Hybrid GA-PSO is one such powerful algorithm which can improve transient and steady state response and can give us more reliable results for PID gain scheduling problem.

Keywords: pitch rate, elevation angle, PID controller, genetic algorithm, particle swarm optimization, phugoid

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Authors: Belkhadir Radouane

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In this study, author tried to explore the beliefs of some Moroccan teachers working in the delegations of Safi and Youcefia about the usefulness of first and second languages in learning the third language. More specifically, author attempted to see the extent to which these teachers believe that a first and second language can serve students in learning a third one. The first language in this context is Arabic, the second is French, and the third is English. The teachers’ beliefs were gathered through a questionnaire that was addressed via Google Forms. Then, the results were analyzed using the same application. It was found that teachers are positive about the usefulness of the first and second language in learning the third one, but most of them rarely use in a conscious way activities that serve this purpose.

Keywords: Bilinguilism, teachers beliefs, English as ESL, Morocco

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Authors: Kamal Haider

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Numerical simulations over landing gear of simplified and partially-dressed configurations with closed cavity have been performed to compute aerodynamically and aeroacoustics parameters using commercial engineering software. The objective of numerical computations is two folds. Firstly, to validate experimental data of newly built nose landing gear and secondly perform high-fidelity calculations using CFD/FW-H hybrid approach, as future engineering challenges need more advanced aircraft configurations such as performance noise and efficiency. Both geometries are used for multi-block structured, and unstructured/hybrid meshed to develop some understanding of physics in terms of aerodynamics and aeroacoustics. Detached Eddy Simulation (DES) approach is employed to compute surface pressure. Also far-field noise calculations have been generated by Ffowcs-William and Hawking solver. Both results of aerodynamics and aeroacoustics are compared with experimental data.

Keywords: landing gear, computational aeroacoustics, computational aerodynamics, detached eddy simulation

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Authors: Gayathri Sadanala, Shibam Pokhrel, Owen Murphy

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In the ever-evolving landscape of education, Virtual Reality (VR) environments offer a promising avenue for enhancing student engagement and learning experiences. However, understanding and predicting student behavior within these immersive settings remain challenging tasks. This paper presents a comprehensive study on the predictive modeling of student behavior in VR using machine learning techniques. We introduce a rich data set capturing student interactions, movements, and progress within a VR orientation program. The dataset is divided into training and testing sets, allowing us to develop and evaluate predictive models for various aspects of student behavior, including engagement levels, task completion, and performance. Our machine learning approach leverages a combination of feature engineering and model selection to reveal hidden patterns in the data. We employ regression and classification models to predict student outcomes, and the results showcase promising accuracy in forecasting behavior within VR environments. Furthermore, we demonstrate the practical implications of our predictive models for personalized VR-based learning experiences and early intervention strategies. By uncovering the intricate relationship between student behavior and VR interactions, we provide valuable insights for educators, designers, and developers seeking to optimize virtual learning environments.

Keywords: interaction, machine learning, predictive modeling, virtual reality

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: Eirini Busack

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Since the outbreak of the Covid-19 pandemic and the sudden transfer to online teaching, teachers have struggled to reconstruct their teaching and learning materials to adapt them to the new reality of online teaching and learning. Consequently, the pupils’ learning was disrupted during this orientation phase. Due to the above situation, teachers from all fields concluded that it is vital that their pupils should be able to continue their learning even without the teacher being physically present. Various websites and applications have been in use since then in hope that pupils will still enjoy a qualitative education; unfortunately, this was often not the case. To address this issue, it was therefore decided to focus the research on the development of digital learning paths. The fundamentals of these learning paths include the implementation of scenario-based learning (digital storytelling), the integration of media-didactic theory to make it pedagogically appropriate for learners, alongside instructional design knowledge and the drive to promote autonomous learners. This particular research is being conducted within the frame of the research project “Sustainable integration of subject didactic digital teaching-learning concepts” (InDiKo, 2020-2023), which is currently conducted at the University of Education Karlsruhe and investigates how pre-service teachers can acquire the necessary interdisciplinary and subject-specific media-didactic competencies to provide their future learners with digitally enhanced learning opportunities, and how these competencies can be developed continuously and sustainably. As English is one of the subjects involved in this project, the English Department prepared a seminar for the pre-service secondary teachers: “Media-didactic competence development: Developing learning paths & Digital Storytelling for English grammar teaching.” During this seminar, the pre-service teachers plan and design a Moodle-based differentiated lesson sequence on an English grammar topic that is to be tested by secondary school pupils. The focus of the present research is to assess the secondary school pupils’ expectations from an English grammar-focused digital learning path created by pre-service English teachers. The nine digital learning paths that are to be distributed to 25 pupils were produced over the winter and the current summer semester as the artifact of the seminar. Finally, the data to be quantitatively analysed and interpreted derive from the online questionnaires that the secondary school pupils fill in so as to reveal their expectations on what they perceive as a stimulating and thus effective grammar-focused digital learning path.

Keywords: digital storytelling, learning paths, media-didactics, autonomous learning

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Authors: Kevin Worrall, Euan McGookin

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The use of optimization algorithms is common across a large number of diverse fields. This work presents the use of a hybrid optimization algorithm applied to a mobile robot tasked with carrying out a search of an unknown environment. The algorithm is then applied to the multiple robots case, which results in a reduction in the time taken to carry out the search. The hybrid algorithm is a Random Search Algorithm fused with a Tabu mechanism. The work shows that the algorithm locates the desired points in a quicker time than a brute force search. The Tabu Random algorithm is shown to work within a simulated environment using a validated mathematical model. The simulation was run using three different environments with varying numbers of targets. As an algorithm, the Tabu Random is small, clear and can be implemented with minimal resources. The power of the algorithm is the speed at which it locates points of interest and the robustness to the number of robots involved. The number of robots can vary with no changes to the algorithm resulting in a flexible algorithm.

Keywords: algorithms, control, multi-agent, search and rescue

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Authors: Vaida Jurgile

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Intercultural learning is one of the approaches taken to understand the cultural diversity of the modern world and to accept changes in cultural identity and otherness and the expression of tolerance. During intercultural learning, students develop their abilities to interact and communicate with their group members. These abilities help to understand social and cultural differences, to form one’s identity, and to give meaning to intercultural learning. Intercultural education recognizes that a true understanding of differences and similarities of another culture is necessary in order to lay the foundations for working together with others, which contributes to the promotion of intercultural dialogue, appreciation of diversity, and cultural exchange. Therefore, it is important to examine the concept of intercultural learning, revealed through students’ learning experiences and understanding of how this learning takes place and what significance this phenomenon has in higher education. At a scientific level, intercultural learning should be explored in order to uncover the influence of cultural identity, i.e., intercultural learning should be seen in a local context. This experience would provide an opportunity to learn from various everyday intercultural learning situations. Intercultural learning can be not only a form of learning but also a tool for building understanding between people of different cultures. The research object of the study is the process of intercultural learning. The aim of the dissertation is to develop a grounded theory of the process of learning in an intercultural study environment, revealing students’ learning experiences. The research strategy chosen in this study is a constructivist grounded theory (GT). GT is an inductive method that seeks to form a theory by applying the systematic collection, synthesis, analysis, and conceptualization of data. The targeted data collection was based on the analysis of data provided by previous research participants, which revealed the need for further research participants. During the research, only students with at least half a year of study experience, i.e., who have completed at least one semester of intercultural studies, were purposefully selected for the research. To select students, snowballing sampling was used. 18 interviews were conducted with students representing 3 different fields of sciences (social sciences, humanities, and technology sciences). In the process of intercultural learning, language expresses and embodies cultural reality and a person’s cultural identity. It is through language that individual experiences are expressed, and the world in which Others exist is perceived. The increased emphasis is placed on the fact that language conveys certain “signs’ of communication and perception with cultural value, enabling the students to identify the Self and the Other. Language becomes an important tool in the process of intercultural communication because it is only through language that learners can communicate, exchange information, and understand each other. Thus, in the process of intercultural learning, language either promotes interpersonal relationships with foreign students or leads to mutual rejection.

Keywords: intercultural learning, grounded theory, students, other

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Authors: Youngmin Kim, Jae-Hwan Ahn, Seog-Ku Kim, Hye-Cheol Oh, Bokjin Lee, Hee-Jun Kang

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The high energy consuming processes such as advanced oxidation and reverse osmosis are used as a reuse process. This study aims at developing an energy efficient reuse process by combination of nanofiltration (NF) and capacitive deionization processes (CDI) processes. Lab scale experiments were conducted by using effluents from a wastewater treatment plant located at Koyang city in Korea. Commercial NF membrane (NE4040-70, Toray Ltd.) and CDI module (E40, Siontech INC.) were tested in series. The pollutant removal efficiencies were evaluated on the basis of Korean water quality criteria for water reuse. In addition, the energy consumptions were also calculated. As a result, the hybrid process showed lower energy consumption than conventional reverse osmosis process even though its effluent did meet the Korean standard. Consequently, this study suggests that the hybrid process is feasible for the energy efficient water reuse.

Keywords: capacitive deionization, energy efficient process, nanofiltration, water reuse

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Authors: Eronini Iheanyi Umez-Eronini

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Most studies and existing implementations of compressed air energy storage (CAES) coupled with a wind farm to overcome intermittency and variability of wind power are based on bulk or centralized CAES plants. A dynamic model of a hybrid wind farm with wind turbines and distributed CAES, consisting of air storage tanks and compressor and expander trains at each wind turbine station, is developed and simulated in MATLAB. An ad hoc supervisory controller, in which the wind turbines are simply operated under classical power optimizing region control while scheduling power production by the expanders and air storage by the compressors, including modulation of the compressor power levels within a control range, is used to regulate overall farm power production to track minute-scale (3-minutes sampling period) TSO absolute power reference signal, over an eight-hour period. Simulation results for real wind data input with a simple wake field model applied to a hybrid plant composed of ten 5-MW wind turbines in a row and ten compatibly sized and configured Diabatic CAES stations show the plant controller is able to track the power demand signal within an error band size on the order of the electrical power rating of a single expander. This performance suggests that much improved results should be anticipated when the global D-CAES control is combined with power regulation for the individual wind turbines using available approaches for wind farm active power control. For standalone power plant fuel electrical efficiency estimate of up to 60%, the round trip electrical storage efficiency computed for the distributed CAES wherein heat generated by running compressors is utilized in the preheat stage of running high pressure expanders while fuel is introduced and combusted before the low pressure expanders, was comparable to reported round trip storage electrical efficiencies for bulk Adiabatic CAES.

Keywords: hybrid wind farm, distributed CAES, diabatic CAES, active power control, dynamic modeling and simulation

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Authors: Jesus Ruano, Asensi Oliva

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The hydrodynamic field generated by a jet expansion is computed via three dimensional compressible Large Eddy Simulation (LES). Finite Volume Method (FVM) will be the discretization used during this simulation as well as hybrid schemes based on Kinetic Energy Preserving (KEP) schemes and up-winding Godunov based schemes with instabilities detectors. Velocity and pressure fields will be stored at different surfaces near the jet, but far enough to enclose all the fluctuations, in order to use them as input for the acoustic solver. The acoustic field is obtained in the far-field region at several locations by means of a hybrid method based on Ffowcs-Williams and Hawkings (FWH) equation. This equation will be formulated in the spectral domain, via Fourier Transform of the acoustic sources, which are modeled from the results of the initial simulation. The obtained results will allow the study of the broadband noise generated as well as sound directivities.

Keywords: far-field noise, Ffowcs-Williams and Hawkings, finite volume method, large eddy simulation, jet noise

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Authors: David S. Rosenstein

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The author believes that modern spoken languages should be equally difficult (or easy) to learn, since all normal children learning their native languages do so at approximately the same rate and with the same competence, progressing from easy to more complex grammar and syntax in the same way. Why then, do some languages seem more difficult than others? Perhaps people are referring to the written language, where it may be true that mastering Chinese requires more time than French, which in turn requires more time than Spanish. But this may be marginal, since Chinese and French children quickly catch up to their Spanish peers in reading comprehension. Rather, the real differences in difficulty derive from two sources: hardened L1 language habits trying to cope with contrasting L2 habits; and unfamiliarity with unique L2 characteristics causing faulty expectations. It would seem that effective L2 teaching and learning must take these two sources of difficulty into consideration. The author feels that the latter (faulty expectations) causes the greatest difficulty, making effective teaching and learning somewhat different for each given foreign language. Examples from Chinese and other languages are presented.

Keywords: learning different languages, language learning difficulties, faulty language expectations

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Authors: Cristina Busuioc, Georgeta Voicu, Sorin-Ion Jinga

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The human bone presents in its dry form piezoelectric properties, which means that a mechanical stress results in electric polarization and an applied electric field causes strain. The immediate consequence was the revealing of piezoelectricity role in bone remodelling, as well as the integration of ceramic materials with piezoelectric behaviour in the composition of unitary or composite biomaterials. Thus, we prepared hydroxyapatite - collagen hybrid materials with barium titanate addition in order to achieve a better osseointegration. Barium titanate powder synthesized by a combined sol-gel-hydrothermal method, commercial hydroxyapatite and laboratory extracted collagen gel were employed as starting materials. Before the composites, fabrication, the powder with piezoelectric features was characterized in detail from the compositional, structural, morphological and electrical point of view. The next step was to elucidate the influence of barium titanate presence especially on the biological properties of the final materials. The biocompatibility of the hybrid supports without or with piezoelectric addition was investigated on mouse osteoblast cells through LDH cytotoxicity assay, LIVE/DEAD cell viability assay, and MTT cell proliferation assay. All results indicated that the analysed materials do not exert cytotoxic effects and present the ability to sustain cell survival and to promote their proliferation. In conclusion, barium titanate nanoparticles exhibit a good biocompatibility and osteoinductive properties, while the derived composite materials based on hydroxyapatite as oxide phase and collagen as polymeric phase can be successfully used for tissue engineering applications.

Keywords: barium titanate, hybrid composites, piezoelectricity, tissue engineering

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Authors: Eugene Stepanov, Arcady Ponosov

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To model gene regulatory networks one uses ordinary differential equations with switching nonlinearities, where the initial value problem is known to be well-posed if the trajectories cross the discontinuities transversally. Otherwise, the initial value problem is usually ill-posed, which lead to theoretical and numerical complications. In the presentation, it is proposed to apply the theory of hybrid dynamical systems, rather than switched ones, to regularize the problem. 'Hybridization' of the switched system means that one attaches a dynamic discrete component ('automaton'), which follows the trajectories of the original system and governs its dynamics at the points of ill-posedness of the initial value problem making it well-posed. The construction of the automaton is based on the classification of the attractors of the specially designed adjoint dynamical system. Several examples are provided in the presentation, which support the suggested analysis. The method can also be of interest in other applied fields, where differential equations contain switchings, e.g. in neural field models.

Keywords: hybrid dynamical systems, ill-posed problems, singular perturbation analysis, switching nonlinearities

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Authors: Hakim S. Sultan Aljibori

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An experimental procedure is developed to study the performance of composite thin wall cylinders subjected to internal pressure loading for investigations of stress distribution through the composite cylinders wall. Three types of fibers were used in this study are; woven roving glass fiber/epoxy, hybrid fiber/epoxy, and Kevlar fiber/epoxy composite specimens were fabricated and tested. All of these specimens subjected to uniformed pressure load using the hydraulic pump. Axial stress is identified, and values were found after collecting all the results. Comparison between the deferent types of specimens was done. Thus, the present investigation concludes the efficient and effective composite cylinder experimentally and provides a considerable advantage for using woven roving fibers in pressure vessels applications.

Keywords: stress distribution, composite material, internal pressure, glass fiber, hybrid fiber

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Authors: Leidy M. Aldana, Jorge E. Camargo

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Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense.

Keywords: Malware, adversarial, machine learning, defense, attack

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Authors: Gihan Thilakarathna, Fernando Ishara, Rathnayake Yasith, Bandara A. M. R. Y.

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E-learning is a crucial component of intelligent education. Even in the midst of a pandemic, E-learning is becoming increasingly important in the educational system. Several e-learning programs are accessible for students. Here, we decided to create an e-learning framework for children. We've found a few issues that teachers are having with their online classes. When there are numerous students in an online classroom, how does a teacher recognize a student's focus on academics and below-the-surface behaviors? Some kids are not paying attention in class, and others are napping. The teacher is unable to keep track of each and every student. Key challenge in e-learning is online exams. Because students can cheat easily during online exams. Hence there is need of exam proctoring is occurred. In here we propose an automated online exam cheating detection method using a web camera. The purpose of this project is to present an E-learning platform for math education and include games for kids as an alternative teaching method for math students. The game will be accessible via a web browser. The imagery in the game is drawn in a cartoonish style. This will help students learn math through games. Everything in this day and age is moving towards automation. However, automatic answer evaluation is only available for MCQ-based questions. As a result, the checker has a difficult time evaluating the theory solution. The current system requires more manpower and takes a long time to evaluate responses. It's also possible to mark two identical responses differently and receive two different grades. As a result, this application employs machine learning techniques to provide an automatic evaluation of subjective responses based on the keyword provided to the computer as student input, resulting in a fair distribution of marks. In addition, it will save time and manpower. We used deep learning, machine learning, image processing and natural language technologies to develop these research components.

Keywords: math, education games, e-learning platform, artificial intelligence

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Authors: Matthias Grünke

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In this single-case study, we evaluated the effects of a self-graphing intervention to help students improve their reading fluency. Our participant was a 10-year-old girl with a suspected learning disability in reading. We applied an ABAB reversal design to test the efficacy of our approach. The dependent measure was the number of correctly read words from a children’s book within five minutes. Our participant recorded her daily performance using a simple line diagram. Results indicate that her reading rate improved simultaneously with the intervention and dropped as soon as the treatment was suspended. The findings give reasons for optimism that our simple strategy can be a very effective tool in supporting students with learning disabilities to boost their reading fluency.

Keywords: single-case study, learning disabilities, elementary education, reading problems, reading fluency

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Authors: Robin Ruowei Yang

Abstract:

The Khitan-Liao dynasty (907-1125) was founded by the nomadic Khitan people and showed its peculiarities. This regime not only effectively ruled their own nomads, but also ruled the farming ethnic groups of the Han (漢) and others. It also controlled vast territories that included the northern border domains of China, East Asia, and even Central Asia. Different from the practice of other nomadic people, Khitan-Liao built settlement towns and cities on the Mongolia steppe from beginning of the dynasty, which made the politics, economy and culture of the steppe area develop significantly. In the process of cities’ construction, Khitan-Liao set up five capitals respectively. Except three of them were rebuilt or expanded based on existing cities in agricultural areas, two new capitals were built, namely up-capital (Shangjing) and middle-capital (Zhongjing) in the steppe area. This article takes the up-capital (Shangjing) as an example to investigate a special zone in the capital for settling movable tents which adapted Khitan nomads, especially its nomadic government officials to live in. By using primary Chinese historical records and newly archaeological excavations, this article examines how the special tent zone used by the Khitan rulers, discusses how the Khitan-Liao manifested its hybrid cultural characteristics in urban planning, and its implications in studying the history of Khitan-Liao. The information, discussion and argument presented in this article are also worth considering the influence of urbanization of Khitan-Liao’s urbaization on subsequent dynasties in pre-modern China and Inner Asia.

Keywords: cities on Steppe, special tent zone in cities, Khitan-Liao, hybrid culture

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Authors: Enrique Barra, Aldo Gordillo, Juan Quemada

Abstract:

This paper presents the Virtual Science Hub platform. It is an open source platform that combines a social network, an e-learning authoring tool, a video conference service and a learning object repository for science teaching enrichment. These four main functionalities fit very well together. The platform was released in April 2012 and since then it has not stopped growing. Finally we present the results of the surveys conducted and the statistics gathered to validate this approach.

Keywords: e-learning, platform, authoring tool, science teaching, educational sciences

Procedia PDF Downloads 382

Authors: Suriyani Ariffin, Nor Aziah Alias, Khairil Iskandar Othman, Haslinda Yusoff

Abstract:

An Integrated Cumulative Grade Point Average (iCGPA) is a mechanism and strategy to ensure the curriculum of an academic programme is constructively aligned to the expected learning outcomes and student performance based on the attainment of those learning outcomes that is reported objectively in a spider web. Much effort and time has been spent to develop a viable mechanism and trains academics to utilize the platform for reporting. The question is: How well do learners conceive the idea of their achievement via iCGPA and whether quality learner attributes have been nurtured through the iCGPA mechanism? This paper presents the architecture of an integrated CGPA mechanism purported to address a holistic evaluation from the evaluation of courses learning outcomes to aligned programme learning outcomes attainment. The paper then discusses the students’ understanding of the mechanism and evaluation of their achievement from the generated spider web. A set of questionnaires were distributed to a group of students with iCGPA reporting and frequency analysis was used to compare the perspectives of students on their performance. In addition, the questionnaire also explored how they conceive the idea of an integrated, holistic reporting and how it generates their motivation to improve. The iCGPA group was found to be receptive to what they have achieved throughout their study period. They agreed that the achievement level generated from their spider web allows them to develop intervention and enhance the programme learning outcomes before they graduate.

Keywords: learning outcomes attainment, iCGPA, programme learning outcomes, spider web, iCGPA reporting skills

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Authors: Guanghua Zhang, Fubao Wang, Weijun Duan

Abstract:

Convolution neural network (CNN) has attracted more and more attention on recent years. Especially in the field of computer vision and image classification. However, unsupervised learning with CNN has received less attention than supervised learning. In this work, we use a new powerful tool which is deep convolutional generative adversarial networks (DCGANs) to generate images from Sloan Digital Sky Survey. Training by various star and galaxy images, it shows that both the generator and the discriminator are good for unsupervised learning. In this paper, we also took several experiments to choose the best value for hyper-parameters and which could help to stabilize the training process and promise a good quality of the output.

Keywords: convolution neural network, discriminator, generator, unsupervised learning

Procedia PDF Downloads 255