Search results for: teaching and learning mathematics

Authors: Nir Nissim, Erez Shalom, Tomer Lancewiki, Yuval Elovici, Yuval Shahar

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Background: A Medical Device (MD) is an instrument, machine, implant, or similar device that includes a component intended for the purpose of the diagnosis, cure, treatment, or prevention of disease in humans or animals. Medical devices play increasingly important roles in health services eco-systems, including: (1) Patient Diagnostics and Monitoring; Medical Treatment and Surgery; and Patient Life Support Devices and Stabilizers. MDs are part of the medical device eco-system and are connected to the network, sending vital information to the internal medical information systems of medical centers that manage this data. Wireless components (e.g. Wi-Fi) are often embedded within medical devices, enabling doctors and technicians to control and configure them remotely. All these functionalities, roles, and uses of MDs make them attractive targets of cyber-attacks launched for many malicious goals; this trend is likely to significantly increase over the next several years, with increased awareness regarding MD vulnerabilities, the enhancement of potential attackers’ skills, and expanded use of medical devices. Significance: We propose to develop and implement Cyber-Med, a unique collaborative project of Ben-Gurion University of the Negev and the Clalit Health Services Health Maintenance Organization. Cyber-Med focuses on the development of a comprehensive detection framework that relies on a critical attack repository that we aim to create. Cyber-Med will allow researchers and companies to better understand the vulnerabilities and attacks associated with medical devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The Cyber-Med detection framework will consist of two independent, but complementary detection approaches: one for known attacks, and the other for unknown attacks. These modules incorporate novel ideas and algorithms inspired by our team's domains of expertise, including cyber security, biomedical informatics, and advanced machine learning, and temporal data mining techniques. The establishment and maintenance of Cyber-Med’s up-to-date attack repository will strengthen the capabilities of Cyber-Med’s detection framework. Major Findings: Based on our initial survey, we have already found more than 15 types of vulnerabilities and possible attacks aimed at MDs and their eco-system. Many of these attacks target individual patients who use devices such pacemakers and insulin pumps. In addition, such attacks are also aimed at MDs that are widely used by medical centers such as MRIs, CTs, and dialysis engines; the information systems that store patient information; protocols such as DICOM; standards such as HL7; and medical information systems such as PACS. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched against MDs. Very little research has been conducted in order to protect these devices from cyber-attacks, since most of the development and engineering efforts are aimed at the devices’ core medical functionality, the contribution to patients’ healthcare, and the business aspects associated with the medical device.

Keywords: medical device, cyber security, attack, detection, machine learning

Procedia PDF Downloads 354

Authors: Eloise Thomson

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The COVID-19 global pandemic challenged our normative conceptualization of teaching across all age levels, requiring the transition to remote instruction, in some instances, literally overnight. Included in the rapidly changing education environment was the delivery of early childhood education. In Victoria, Australia, the capital city, Melbourne, became known as the most locked down city in the world. This presentation examines the ways educators used social media to collaborate with families before the COVID-19 pandemic and during the lockdown phase through the use of a Third Space conceptual framework and case study methodology. As a first step, the paper examines how social media may offer new opportunities for collaborative practice between educators and families. Second, the data is outlined and discussed with respect to collaborative practice and quality. Finally, a postscript then allows for insight into how educators’ practice of using social media to collaborate with families has been impacted by the COVID-19 global pandemic. Finally, the implications of the ways in which educators are using social media to collaborate with families are discussed. The use of social media in early-childhood education has the potential to provide a valuable platform for educators to connect with families and students. However, the use of social media by educators uncovered a dialogue of ‘quality’ and appeared to be dominated by evidence around compliance and attaining quality in a very specific, and perhaps narrow, way. The findings suggest a culture of compliance that is dominated by outcomes, standards and assessments and that this has changed the dynamics by which educators engage with families. Furthermore, findings highlighted the disparity between educators' and families' understanding of the intent of the collaborations themselves. This research was significant as it exposed the ways in which educators are engaging with social media, resulting in a discussion on the intent of collaborations, the questioning of imposed quality, and the notion that quality is measurable and exists in only one form.

Keywords: collaboration, compliance, early childhood, third space, pedagogy of caring, social media

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Authors: L. Miranda

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The requirements of the engineering education are nowadays very broad and demand a set of skills which demands not only technical knowledge but also the ability to lead and innovate and personal and interpersonal skills. A framework for the assessment of a potential curricular change is necessary to guide the analysis of the program with respect to the stakeholders and the legislation of the country, in order to develop appropriate learning outcomes. A Conceive-Design-Implement-Operate (CDIO) approach was chosen for an evaluation conducted in a mechanical engineering degree in Brazil. The work consisted in the application of a survey with students and professors and a literature review of the legislation and studies that raised the required competences and skills for the modern engineer. The results show a great potential for a CDIO set of skills in engineering degrees in Brazil and reveal the frequent demands of stakeholders before a curricular change.

Keywords: curriculum change, conceive design implement operate, accreditation, personal and interpersonal skills

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Authors: Hrishi Rakshit, Pooneh Bagheri Zadeh

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This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods.

Keywords: drone classifications, deep convolutional neural network, hyperparameters, drone audio signal

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Authors: Kawai Nobuaki

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This paper describes the design and implementation of web system for continuable and viable collaboration. This study proposes the improvement of the system based on a result of a certain practice. As contemporary higher education information environments transform, this study highlights the significance of university identity and college identity that are formed continuously through independent activities of the students. Based on these discussions, the present study proposes a practical media environment design which facilitates the processes of organizational identity formation based on a continuous and cyclical model. Even if users change by this system, the communication system continues operation and cooperation. The activity becomes the archive and produces new activity. Based on the result, this study elaborates a plan with a re-design by a system from the viewpoint of second-order cybernetics. Systems theory is a theoretical foundation for our study.

Keywords: collaborative work, learning management system, second-order cybernetics, systems theory, user generated contents, viable system model

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Authors: Minhui Chiu

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According to relevant studies, clinical nurses have high work pressure and relatively high job adaptation needs. The nurses who work in oncology wards have more adaptation needs when they face repeating hospitalization patients. The aims of this study were to investigate the job stress adaptation and related factors of nurses in oncology wards and to understand the predictors of job stress adaptation needs. Convenience sampling was used in this study. The nurses in the oncology specialist ward of a regional teaching hospital in southern Taiwan were selected as the research objects. A cross-sectional survey was conducted using a structured questionnaire, random sampling, and the questionnaires were filled out by the participating nurses. A total of 68 people were tested, and 65 valid questionnaires (95.6%). One basic data questionnaire and nurses’ job stress adaptation needs questionnaire were used. The data was archived with Microsoft Excel, and statistical analysis was performed with JMP12.0. The results showed that the average age was 28.8 (±6.7) years old, most of them were women, 62 (95.38%), and the average clinical experience in the hospital was 5.7 years (±5.9), and 62 (95.38%) were university graduates. 39 people (60.0%) had no work experience. 39 people (60.0%) liked nursing work very much, and 23 people (35.3%) just “liked”. 47 (72.3%) people were supported to be oncology nurses by their families. The nurses' job stress adaptation needs were 119.75 points (±17.24). The t-test and variance analysis of the impact of nurses' job pressure adaptation needs were carried out. The results showed that the score of college graduates was 121.10 (±16.39), which was significantly higher than that of master graduates 96.67 (±22.81), and the degree of liking for nursing work also reached a Significant difference. These two variables are important predictors of job adaptation needs, and the R Square is 24.15%. Conclusion: Increasing the love of clinical nurses in nursing and encouraging university graduation to have positive effects on job pressure adaptation needs and can be used as a reference for the management of human resources hospitals for oncology nurses.

Keywords: oncology nurse, job stress, job stress adaptation needs, manpower

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Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

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The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: degree, initial cluster center, k-means, minimum spanning tree

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Authors: Ilario Gabriele Gerloni, Vincenza Carchiolo, Alessandro Longheu, Ugo Becciani, Eva Sciacca, Fabio Vitello

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ICT tools and platforms endorse more and more educational process. Many models and techniques for people to be educated and trained about specific topics and skills do exist, as classroom lectures with textbooks, computers, handheld devices and others. The choice to what extent ICT is applied within learning contexts is related to personal access to technologies as well as to the infrastructure surrounding environment. Among recent techniques, the adoption of Virtual Reality (VR) and Augmented Reality (AR) provides significant impulse in fully engaging users senses. In this paper, an application of AR/VR within Geographic Information Systems (GIS) context is presented. It aims to provide immersive environment experiences for educational and training purposes (e.g. for civil protection personnel), useful especially for situations where real scenarios are not easily accessible by humans. First acknowledgments are promising for building an effective tool that helps civil protection personnel training with risk reduction.

Keywords: education, virtual reality, augmented reality, GIS, civil protection

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Authors: Darren Zou

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Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: BART, Bayesian, predict, stock

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Authors: Nayak Amar, Turner Naomi, Gobina Edward

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The Scottish Higher Education Institutes must report their scope 1 & 2 emissions, whereas reporting scope 3 is optional. Scope 3 is indirect emissions which embodies a significant component of total carbon footprint and therefore it is important to record, measure and report it accurately. Robert Gordon University (RGU) reported only business travel, grid transmission and distribution, water supply and transport, and recycling scope 3 emissions. This study estimates the RGUs total scope 3 emissions by comparing it with a similar HEI in scale. The scope 3 emission reporting of sixteen Scottish HEI was studied. Glasgow Caledonian University was identified as the nearest neighbour by comparing its students' full time equivalent, staff full time equivalent, research-teaching split, budget, and foundation year. Apart from the peer, data was also collected from the Higher Education Statistics Agency database. RGU reported emissions from business travel, grid transmission and distribution, water supply, and transport and recycling. This study estimated RGUs scope 3 emissions from procurement, student-staff commute, and international student trip. The result showed that RGU covered only 11% of the scope 3 emissions. The major contributor to scope 3 emissions were procurement (48%), student commute (21%), international student trip (16%), and staff commute (4%). The estimated scope 3 emission was more than 14 times the reported emissions. This study has shown the relative importance of each scope 3 emissions source, which gives a guideline for the HEIs, on where to focus their attention to capture maximum scope 3 emissions. Moreover, it has demonstrated that it is possible to estimate the scope 3 emissions with limited data.

Keywords: HEI, university, emission calculations, scope 3 emissions, emissions reporting

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Authors: M. Lesage, G. Raîche, M. Riopel, F. Fortin, D. Sebkhi

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This paper describes a Web server implementation of the hierarchical aggregate assessment process in the field of education. This process describes itself as a field of teamwork assessment where teams can have multiple levels of hierarchy and supervision. This process is applied everywhere and is part of the management, education, assessment and computer science fields. The E-Assessment website named “Cluster” records in its database the students, the course material, the teams and the hierarchical relationships between the students. For the present research, the hierarchical relationships are team member, team leader and group administrator appointments. The group administrators have the responsibility to supervise team leaders. The experimentation of the application has been performed by high school students in geology courses and Canadian army cadets for navigation patrols in teams. This research extends the work of Nance that uses a hierarchical aggregation process similar as the one implemented in the “Cluster” application.

Keywords: e-learning, e-assessment, teamwork assessment, hierarchical aggregate assessment

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Authors: Naglaa Baskhroun Thabet Wasef

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Specific education services for students with Autism remains in its early developmental stages in Egypt. In spite of many more children with autism are attending schools since The Egyptian government introduced the Education Provision for Students with Disabilities Act in 2010, the services students with autism and their families receive are generally not enough. This pointed study used Attitude and Reaction to Teach Students with Autism Scale to investigate 50 primary school teachers’ attitude and reaction to teach students with autism in the general education classroom. Statistical analysis of the data found that student behavior was the most noticeable factor in building teachers’ wrong attitudes students with autism. The minority of teachers also indicated that their service education did not prepare them to meet the learning needs of children with autism in special, those who are non-vocal. The study is descriptive and provides direction for increasing teacher awareness for inclusivity in Egypt.

Keywords: attitude, autism, teachers, sports activates, movement skills, motor skills, autism attitude

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Authors: Khiari Nor El Houda, Hiouani Amira Sarra

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Many EFL students with high capacities are hidden because they suffer from speaking anxiety (SA). Most of them find public speaking much demanding. They feel unable to communicate, they fear to make mistakes and they fear negative evaluation or being called on. With the growing number of the learners who suffer from foreign language speaking anxiety (FLSA), it is becoming increasingly difficult to ignore its harmful outcomes on their performance and success, especially during their first contact with the pupils, as they will be teaching in the near future. Different researchers suggested different ways to minimize the negative effects of FLSA. The present study sheds light on emotional intelligence skills training as an effective strategy not only to influence public speaking success but also to help pre-service EFL teachers lessen their speaking anxiety and eventually to prepare them for their professional career. A quasi-experiment was used in order to examine the research hypothesis. We worked with two groups of third-year EFL students at Oum El Bouaghi University. The Foreign Language Classroom Anxiety Scale (FLCAS) and the Emotional Quotient Inventory (EQ-i) were used to collect data about the participants’ FLSA and EI levels. The analysis of the data has yielded that the assumption that there is a negative correlation between EI and FLSA was statistically validated by the Pearson Correlation Test, concluding that, the more emotionally intelligent the individual is the less anxious s/he will be. In addition, the lack of amelioration in the results of the control group and the noteworthy improvement in the experimental group results led us to conclude that EI skills training was an effective strategy in minimizing the FLSA level and therefore, we confirmed our research hypothesis.

Keywords: emotional intelligence, emotional intelligence skills training, EQ-I, FLCAS, foreign language speaking anxiety, pre-service EFL teachers

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Authors: Sunanta Klibthong

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Inclusive education services for students with Autism remains in its early developmental stages in Thailand. Despite many more children with autism are attending schools since the Thai government introduced the Education Provision for People with Disabilities Act in 2008, the services students with autism and their families receive are generally lacking. This quantitative study used Attitude and Preparedness to Teach Students with Autism Scale (APTSAS) to investigate 110 primary school teachers’ attitude and preparedness to teach students with autism in the general education classroom. Descriptive statistical analysis of the data found that student behaviour was the most significant factor in building teachers’ negative attitudes students with autism. The majority of teachers also indicated that their pre-service education did not prepare them to meet the learning needs of children with autism in particular, those who are non-verbal. The study is significant and provides direction for enhancing teacher education for inclusivity in Thailand.

Keywords: attitude, autism, teachers, Thailand

Procedia PDF Downloads 270

Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

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Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: cross-language analysis, machine learning, machine translation, sentiment analysis

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Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

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Authors: Georgiana Ciobotaru

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The vocabulary that foreign students assimilate once they start studying the Romanian language must allow them to develop the linguistic competence of oral and written expression, but also the intercultural one, necessary for their integration into the new socio-cultural environment. Therefore, the familiarization courses with Romanian as a foreign language aim at fundamental language acquisitions in order to obtain the expected level of Romanian language. They also relate differently to the new culture and the new language they come in contact with, having a distinct way of expressing themselves. Foreign students want to continue their university and postgraduate studies at specialized faculties in the country; therefore, they need both a general language for their integration into society and for interaction with others, Romanians or students from countries other than their own, but also from a specialized language that facilitates didactic communication and professional development. The complexity of the vocabulary must thus cover the daily communication needs, but also the subsequent evolution of each one. This paper aims to illustrate the most important semantic fields that students must assimilate in order to crystallize a linguistic identity in the new context of their personal and professional development and to help them cope with the culture shock.

Keywords: integration, intercultural, language, linguistic, vocabulary

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Authors: Sultan Noman Qasem

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This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature.

Keywords: radial basis function network, hybrid learning, multi-objective optimization, genetic algorithm

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Authors: Rick Evans

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Despite over 40 years of research into why women choose not to enroll or leave undergraduate engineering programs, along with the subsequent and serious efforts to attract more women, women receiving bachelor's degrees in engineering in the US have remained disappointingly low. We know that even despite their struggles to become more welcoming and inclusive, engineering programs remain gendered, raced and classed. However, our research team has found that women who participate and indeed thrive in undergraduate engineering project teams do so in numbers that far exceed their participation in undergraduate programs. We believe part of the answer lies in the ways that project teams facilitate experiential learning, specifically providing opportunities for members to play, practice and perform. We employ a multi-case study method and assume a feminist, activist and interpretive perspective. We seek to generate concrete and context-dependent knowledge in order to explore potentially new variables and hypotheses. Our focus is to learn from those select women who are thriving. For this oral or e-poster presentation, we will focus on the results of the second of our semi-structured interviews – the learning journey interview. During this interview, we ask participants to tell us the story/ies of their participation in project teams. Our results suggest these women find joy in their experience of developing and applying engineering expertise. They experience this joy and develop their expertise in the highly patterned progression of play, practice and performance. Play is a purposeful activity in which someone enters an imaginary world, a world not yet real to them. However, this imaginary world is still very much connected to the real world, in this case, a particular kind of engineering, in that the ways of engaging are already established, codified and rule-governed. As such, these women are novices motivated to join a community of actors. Practice, better understood as practices, a count noun, is an embodied, materially interconnected collection of actions organized around the shared understandings of that community of actors. Those shared understandings reveal a social order – a particular field of engineering. No longer novices, these women begin to develop and display their emergent identities as engineers. Perform is activity meant either to demonstrate competence and/or to enable, even teach play and practice to others. As performers, these women participants become models for others. They direct play and practice, contextualizing both within a field of engineering and the specific aims of the project team community. By playing, practicing and performing engineering, women claim their identities as engineers and, equally important, have those identities acknowledged by team members. If we hope to transform our gendered, raced, classed institutions, we need to learn more about women who thrive within those institutions. We need to learn more about their processes of becoming and belonging as engineers. Our research presentation begins with a description of project teams and our multi-case study method. We then offer detailed descriptions of play, practice, and performance using the voices of women in project teams.

Keywords: engineering education, gender, identity, project teams

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Sol Girouard, Zona Kostic

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A home is often the largest and most expensive purchase a person makes. Whether the decision leads to a successful outcome will be determined by a combination of critical factors. In this paper, we propose a method that efficiently handles all the factors in residential real estate and performs predictions given a feature space with high dimensionality while controlling for overfitting. The proposed method was built on gradient descent and boosting algorithms and uses a mixed optimizing technique to improve the prediction power. Usually, a single model cannot handle all the cases thus our approach builds multiple models based on different subsets of the predictors. The algorithm was tested on 3 million homes across the U.S., and the experimental results demonstrate the efficiency of this approach by outperforming techniques currently used in forecasting prices. With everyday changes on the real estate market, our proposed algorithm capitalizes from new events allowing more efficient predictions.

Keywords: real estate prediction, gradient descent, boosting, ensemble methods, active learning, training

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Authors: Yi Yang, Jiasong Sun

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Almost every aspect of our daily lives is now intertwined with some degree of human-computer interaction (HCI). HCI courses draw on knowledge from disciplines as diverse as computer science, psychology, design principles, anthropology, and more. Our HCI courses, named the Media and Cognition course, are constantly updated to reflect state-of-the-art technological advancements such as virtual reality, augmented reality, and artificial intelligence-based interactions. For more than a decade, our course has used an interest-based approach to teaching, in which students proactively propose some research-based questions and collaborate with teachers, using course knowledge to explore potential solutions. Semantic communication plays a key role in facilitating understanding and interaction between users and computer systems, ultimately enhancing system usability and user experience. The advancements in AI-generated technology, which have gained significant attention from both academia and industry in recent years, are exemplified by language models like GPT-3 that generate human-like dialogues from given prompts. Our latest version of the Human-Computer Interaction course practices a semantic communication platform based on AI-generated techniques. The purpose of this semantic communication is twofold: to extract and transmit task-specific information while ensuring efficient end-to-end communication with minimal latency. An AI-generated semantic communication platform evaluates the retention of signal sources and converts low-retain ability visual signals into textual prompts. These data are transmitted through AI-generated techniques and reconstructed at the receiving end; on the other hand, visual signals with a high retain ability rate are compressed and transmitted according to their respective regions. The platform and associated research are a testament to our students' growing ability to independently investigate state-of-the-art technologies.

Keywords: human-computer interaction, media and cognition course, semantic communication, retainability, prompts

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Authors: Tuiana A. Aiusheeva, Sergey S. Tamozhnikov, Alexander E. Saprygin, Arina A. Antonenko, Valentina V. Stepanova, Natalia N. Tolstykh, Alexander N. Savostyanov

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Situation of choice is an important condition for the formation of essential character qualities of a child, such as being initiative, responsible, hard-working. We have studied the behavioral and EEG reactions in Russian schoolchildren during recognition of syntactic errors in emotionally colored sentences that describe the choice situation. Twenty healthy children (mean age 9,0±0,3 years, 12 boys, 8 girls) were examined. Forty sentences were selected for the experiment; the half of them contained a syntactic error. The experiment additionally had the hidden condition: 50% of the sentences described the children's own choice and were emotionally colored (positive or negative). The other 50% of the sentences described the forced-choice situation, also with positive or negative coloring. EEG were recorded during execution of error-recognition task. Reaction time and quality of syntactic error detection were chosen as behavioral measures. Event-related spectral perturbation (ERSP) was applied to characterize the oscillatory brain activity of children. There were two time-frequency intervals in EEG reactions: (1) 500-800 ms in the 3-7 Hz frequency range (theta synchronization) and (2) 500-1000 ms in the 8-12 Hz range (alpha desynchronization). We found out that behavioral and brain reactions in child brain during recognition of positive and negative sentences describing forced-choice situation did not have significant differences. Theta synchronization and alpha desynchronization were stronger during recognition of sentences with children's own choice, especially with negative coloring. Also, the quality and execution time of the task were higher for this types of sentences. The results of our study will be useful for improvement of teaching methods and diagnostics of children affective disorders.

Keywords: choice situation, electroencephalogram (EEG), emotionally colored sentences, schoolchildren

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Authors: Shah Muhammad Butt, Ahmed Masaud Ansair

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Present financial crisis in Higher Educational Institutes (HEIs) is causing lots of problems such as considerable budget cuts, which makes it difficult to meet the ever growing IT based research and learning needs. Institutions are rapidly planning and promoting cloud based approaches for their academic and research needs. A cost-effective hybrid cloud framework for HEIs will provide educational services for campus or intercampus communication. Hybrid cloud framework comprises private and public cloud approaches. This paper will propose the framework based on the Open Source Cloud (OpenNebula for Virtualization, Eucalyptus for Infrastructure and Aneka for programming development environment) combined with CSPs services which are delivered to the end-user via the internet from public clouds such as Google, Microsoft, Zoho, and Salesforce.

Keywords: educational services, hybrid campus cloud, open source, higher educational institutes

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Authors: Peter Okpamen

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The issue of environmental degradation in Nigeria is of serious concern. The colonial period brought a major change in environmental awareness and relationship with the environment. This period introduced a model of development, the major thrust of which was the exploration and transformation of natural and human resources for the benefit of the colonial masters. There is abundant evidence in the literature that there are various manifestations of environmental degradation in Nigeria, which have resulted in the various problems found throughout the Nigeria national space. The idea of the environment acting as a constraint to the growth of human activity has given way to the contrary. Environmental education, going by the literature, exists at the primary, secondary and tertiary institutions. In short, the 1st National conference on environmental education gave several suggestions on how it could be realised. Thus, to realise sustainable environmental development we need to accelerate the process of providing basic education for both the old and young. Environmental education should cover the whole federation, and resources should be made available for the training of environmental education teachers and research into environmental education for the development of appropriate learning resources.

Keywords: degradation, development, education, environment, sustainable

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Authors: Michela Quadrini

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Chord diagrams occur in mathematics, from the study of RNA to knot theory. They are widely used in theory of knots and links for studying the finite type invariants, whereas in molecular biology one important motivation to study chord diagrams is to deal with the problem of RNA structure prediction. An RNA molecule is a linear polymer, referred to as the backbone, that consists of four types of nucleotides. Each nucleotide is represented by a point, whereas each chord of the diagram stands for one interaction for Watson-Crick base pairs between two nonconsecutive nucleotides. A chord diagram is an oriented circle with a set of n pairs of distinct points, considered up to orientation preserving diffeomorphisms of the circle. A linear chord diagram (LCD) is a special kind of graph obtained cutting the oriented circle of a chord diagram. It consists of a line segment, called its backbone, to which are attached a number of chords with distinct endpoints. There is a natural fattening on any linear chord diagram; the backbone lies on the real axis, while all the chords are in the upper half-plane. Each linear chord diagram has a natural genus of its associated surface. To each chord diagram and linear chord diagram, it is possible to associate the intersection graph. It consists of a graph whose vertices correspond to the chords of the diagram, whereas the chord intersections are represented by a connection between the vertices. Such intersection graph carries a lot of information about the diagram. Our goal is to define an LCD equivalence class in terms of identity of intersection graphs, from which many chord diagram invariants depend. For studying these invariants, we introduce a new representation of Linear Chord Diagrams based on a set of appropriate topological operators that permits to model LCD in terms of the relations among chords. Such set is composed of: crossing, nesting, and concatenations. The crossing operator is able to generate the whole space of linear chord diagrams, and a multiple context free grammar able to uniquely generate each LDC starting from a linear chord diagram adding a chord for each production of the grammar is defined. In other words, it allows to associate a unique algebraic term to each linear chord diagram, while the remaining operators allow to rewrite the term throughout a set of appropriate rewriting rules. Such rules define an LCD equivalence class in terms of the identity of intersection graphs. Starting from a modelled RNA molecule and the linear chord, some authors proposed a topological classification and folding. Our LCD equivalence class could contribute to the RNA folding problem leading to the definition of an algorithm that calculates the free energy of the molecule more accurately respect to the existing ones. Such LCD equivalence class could be useful to obtain a more accurate estimate of link between the crossing number and the topological genus and to study the relation among other invariants.

Keywords: chord diagrams, linear chord diagram, equivalence class, topological language

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Authors: Manal Ahmed, Amal Shehata, Shereen Deeb

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The study's purpose was to determine the effect of the perioperative protocol of care on clinical outcomes among patients undergoing coronary artery bypass graft. Subjects: A sample of 100 adult patients who were planned for coronary artery bypass graft, were selected and divided alternatively and randomly into two equal groups (50 study -50 control).The study was carried out at National heart Institute in Cairo and open heart surgical intensive care unit in Shebin El-Kom Teaching Hospital. Instruments: Four instruments were used for data collection: Interviewing questionnaire, dyspnea analogue scale, Biophysiological measurement instrument, and Compliance assessment sheet. Results: There were statistically significant differences between both groups regarding most respiratory system assessment findings at discharge. More than two-thirds of the study group of the current study had a continuous and regular commitment to diet regimen, which ranked first followed by the compliance of daily living activities then quitting smoking. Conclusions: The perioperative protocol of care has a significant improving effect on respiratory findings, dyspnea degree, duration of mechanical ventilation, length of hospital stay, compliance to diet, therapeutic regimen, daily living activities, and quit smoking among study group undergoing CABG. Recommendations: Perioperative protocol of care should be carried out for CABG patients at open-heart surgical units as well as an illustrative colored booklet about CAD, CABG and perioperative care should be available and distributed to all CABG patients.

Keywords: perioperative, effect, clinical outcomes, coronary artery, bypass graft, protocol of care

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Authors: Yizhi Zhang, Xiaozan Wang, Mingming Guo, Pengpeng Li

Abstract:

Introduction: The purpose of this study is to examine the effectiveness of after-school football training in improving the physical fitness of primary school students, so as to provide corresponding suggestions for carrying out after-school football training in primary schools. Methods: A total of 72 students from the experimental primary school of Mouping district, Yantai city, Shandong province, participated in this experiment. The experiment was conducted for two semesters. During the experiment period, the experimental group conducted one-hour football training after school from Monday to Thursday afternoon every week, and two hours of football training on Saturday morning every week. The control group conducted sports teaching and extracurricular activities as usual without other intervention. Before and after the experiment, both the experimental group and the control group underwent physical fitness tests according to the physical fitness test standards of Chinese students, including lung capacity, 50-meter run, one-minute skipping rope, sitting forward flexor, and one-minute sit-ups. The test results were all converted to the 100-point system according to the scoring standards. Results: (1) Before the experiment, there was no significant difference between the experimental group and the control group in various physical fitness indicators (p > 0.05). (2) After the experiment, the lung capacity score (T = 3.108, p < 0.05), the 50-meter run score (T = 6.593, p < 0.05), the skipping score (T = 9.227, p < 0.05), the sitting forward flexor score (T = 3.742, p < 0.05), and the sit-up score (T = 5.210, p < 0.05) of the experimental group were significantly higher than that of the control group. Conclusion: This study shows that the physical fitness of primary school students can be improved by football training in their spare time. It is suggested to carry out after-school football training activities in primary schools so as to effectively improve the physical fitness of pupils.

Keywords: after-school football training, physical fitness, primary school students, school sports

Procedia PDF Downloads 134

Authors: Gamzegul Koca, Evrim Eveyik-Aydin

Abstract:

Genre and discipline-specific knowledge of academic discourse in writing has long been acknowledged as being a core skill to achieve formidable tasks that are expected of graduate students in academic settings. Genre analysis approaches can be adopted to unveil the challenges encountered in these tasks to be able to take instructional actions addressing the aspects of graduate writing that need improvement. In an attempt to find genre-specific academic writing needs of Turkish students enrolled in a graduate program in ELT, this study examines the rhetorical structure of discussion sections of research articles written during the course load stage of their graduate studies. The 35.437-word specialized corpus of graduate papers compiled for the purpose of the study includes discussions of 58 unpublished reports of empirical studies, 31 written in MA courses and 27 in Ph.D. courses by a total of 44 graduate students. The study does sentence-based move structure analysis using the framework developed by Eveyik-Aydın, Karabacak and Akyel in a corpus-based study that analyzed the discussion moves of expert writers in published articles in ELT journals indexed by Social Sciences Citation. The coding of 1577 sentences by three graders using this framework revealed that while the graduate papers included the same moves used in published articles, the rhetorical structure of MA and Ph.D. papers showed considerable differences in terms of the frequency of occurrence of main discussion moves, including interpretation of the results and drawing implications. The implications of these findings will be discussed with respect to the needs of graduate writers and the expectations of discourse community.

Keywords: discussion moves, genre-specific rhetorical structure, move analysis, research articles, the specialized corpus of graduate papers

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Authors: Jiaying Zhang, Xin Mu, Chang Ni, Jeff L. Zhang

Abstract:

Magnetic resonance elastography (MRE) non-invasively assesses tissue elastic properties, such as shear modulus, by measuring tissue’s displacement in response to mechanical waves. The estimated metrics on tissue elasticity or stiffness have been shown to be valuable for monitoring physiologic or pathophysiologic status of tissue, such as a tumor or fatty liver. To quantify tissue shear modulus from MRE-acquired displacements (essentially an inverse problem), multiple approaches have been proposed, including Local Frequency Estimation (LFE) and Direct Inversion (DI). However, one common problem with these methods is that the estimates are severely noise-sensitive due to either the inverse-problem nature or noise propagation in the pixel-by-pixel process. With the advent of deep learning (DL) and its promise in solving inverse problems, a few groups in the field of MRE have explored the feasibility of using DL methods for quantifying shear modulus from MRE data. Most of the groups chose to use real MRE data for DL model training and to cut training images into smaller patches, which enriches feature characteristics of training data but inevitably increases computation time and results in outcomes with patched patterns. In this study, simulated wave images generated by Finite Differential Time Domain (FDTD) simulation are used for network training, and U-Net is used to extract features from each training image without cutting it into patches. The use of simulated data for model training has the flexibility of customizing training datasets to match specific applications. The proposed method aimed to estimate tissue shear modulus from MRE data with high robustness to noise and high model-training efficiency. Specifically, a set of 3000 maps of shear modulus (with a range of 1 kPa to 15 kPa) containing randomly positioned objects were simulated, and their corresponding wave images were generated. The two types of data were fed into the training of a U-Net model as its output and input, respectively. For an independently simulated set of 1000 images, the performance of the proposed method against DI and LFE was compared by the relative errors (root mean square error or RMSE divided by averaged shear modulus) between the true shear modulus map and the estimated ones. The results showed that the estimated shear modulus by the proposed method achieved a relative error of 4.91%±0.66%, substantially lower than 78.20%±1.11% by LFE. Using simulated data, the proposed method significantly outperformed LFE and DI in resilience to increasing noise levels and in resolving fine changes of shear modulus. The feasibility of the proposed method was also tested on MRE data acquired from phantoms and from human calf muscles, resulting in maps of shear modulus with low noise. In future work, the method’s performance on phantom and its repeatability on human data will be tested in a more quantitative manner. In conclusion, the proposed method showed much promise in quantifying tissue shear modulus from MRE with high robustness and efficiency.

Keywords: deep learning, magnetic resonance elastography, magnetic resonance imaging, shear modulus estimation

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