Search results for: online games.

Authors: Essam Almuhsin, Ben Soh, Alice Li, Azmat Ullah

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It is widely accepted that university students must acquire new knowledge, skills, awareness, and understanding to increase opportunities for professional and personal growth. The study reveals a significant increase in users engaging in e-coaching activities and a growing need for it during the COVID-19 pandemic. The paper proposes an e-coaching methodology for higher education in Saudi Arabia to address the need for effective coaching in the current online learning environment.

Keywords: Role of e-coaching, e-coaching in higher education, Saudi higher education environment, e-coaching methodology, importance of e-coaching.

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Authors: Ahmed Abbas Helmy, M. R. M. Rizk, Mohamed El-Sayed

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A model predictive controller based on recursive learning is proposed. In this SISO adaptive controller, a model is automatically updated using simple recursive equations. The identified models are then stored in the memory to be re-used in the future. The decision for model update is taken based on a new control performance index. The new controller allows the use of simple linear model predictive controllers in the control of nonlinear time varying processes.

Keywords: Adaptive control, model predictive control, dynamic matrix control, online model identification

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: Virtual Reality, effective computing, effective VR, emotion-based effective physiological database.

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Authors: Leon Fu, Xiangyang Xue

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In this paper, we propose a new approach to query-by-humming, focusing on MP3 songs database. Since MP3 songs are much more difficult in melody representation than symbolic performance data, we adopt to extract feature descriptors from the vocal sounds part of the songs. Our approach is based on signal filtering, sub-band spectral processing, MDCT coefficients analysis and peak energy detection by ignorance of the background music as much as possible. Finally, we apply dual dynamic programming algorithm for feature similarity matching. Experiments will show us its online performance in precision and efficiency.

Keywords: DP, MDCT, MP3, QBH.

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Authors: E. Happ, A. Albert Tóth

Abstract:

The aim of the research is to investigate the forms of the demands of leisure tourism in a West-Hungarian industrial city, Győr. Today, Győr is still a traditional industrial city, its industry is mainly based on vehicle industry, but the role of tourism is increasing in the life of the city as well. Because of the industrial nature and the strong economy of the city, the ratio of business tourists is high. It can be stated that MICE tourism is dominating in Győr. Developments of the last decade can help the city with new tourism products to increase the leisure tourism. The new types of tourism – besides business tourism – can help the providers to increase the occupancy rates and the demand at the weekends. The research demonstrates the theoretical background of the topic, and it shows the present situation of the tourism in Győr with secondary data. The secondary research contains statistical data from the Hungarian Statistical Office and the city council, and it is based on the providers’ data. The next part of the paper shows the potential types of leisure tourism with the help of primary research. The primary research contains the results of an online questionnaire with a sample of 1000 potential customers. It is completed with 10 in-depth interviews with tourism experts, who explained their opinions about the opportunities of leisure tourism in Győr from the providers’ side. The online questionnaire was filled out in spring 2017 by customers, who have already stayed in Győr or plan to visit the city. At the same time in-depth interviews were made with hotel managers, head of touristic institutions and employees at the council. Based on the research it can be stated that the touristic supply of Győr allows the increase of the leisure tourism ratio in the city. Primarily, the cultural and health tourism show potential development, but the supply side of touristic services can be developed in order to increase the number of guest nights. The tourism marketing needs to be strengthened in the city, and a distinctive marketing activity - from other cities - is needed as well. To conclude, although Győr is an industrial city, it has a transforming industrial part, and tourism is also strongly present in its economy. Besides the leading role of business tourism, different types of leisure tourism have the opportunity to take place in the city.

Keywords: Business Tourism, Győr, industrial city, leisure tourism, touristic demand.

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Authors: June Tay, Ivy Chia

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There are numerous changes, both mental and physical, taking place when people age. We need to understand the different aspects required for healthy living, including meeting nutritional needs, regular physical activities to keep agility, sufficient rest and sleep to have physical and mental well-being, social engagement to avoid the risk of social isolation and depression, and access to healthcare to detect and manage chronic conditions. Promoting physical activities for an ageing population is necessary as many may have enjoyed sedentary lifestyles for some time. In our study, we evaluate the considerations when designing a computer vision video game for the elderly. We need to design some low-impact activities, such as stretching and gentle movements, because some elderly individuals may have joint pains or mobility issues. The exercise game should consist of simple movements that are easy to follow and remember. It should be fun and enjoyable so that they can be motivated to do some exercise. Social engagement can keep the elderly motivated and competitive, and they are more willing to engage in game exercises. Elderly citizens can compare their game scores and try to improve them. We propose a computer vision-based video game for the elderly that will capture and track the movement of the elderly hand pushing a ball on the screen into a circle. It can be easily set up using a PC laptop with a webcam. Our video game adhered to the design framework we employed, and it encompassed ease of use, a simple graphical interface, easy-to-play game exercise, and fun gameplay.

Keywords: Computer vision, video games, gerontology technology, caregiving.

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Authors: A. Ionita, A. Zafiu, C. Ghita

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In this article, we present a web server based solution for implementing a system for intelligent navigation. In this solution we use real time collected data and traffic history to establish the best route for navigation. This is a low cost solution that is easily to implement and extend. There is no need any infrastructure at road network level except only a device that collect data about traffic in key road crossing. The presented solution creates a strong base for traffic pursuit and offers an infrastructure for navigation applications.

Keywords: navigation, real time, route, traffic pursuit, webservice.

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Authors: Lee Bih Ni

Abstract:

This paper discusses that good learning involves all academic groups in the school. Blended learning is learning outside the classroom. Google Classroom is a free service learning app for schools, non-profit organizations and anyone with a personal Google account. Facilities accessed through computers and mobile phones are very useful for school teachers and students. Blended learning classrooms using both traditional and technology-based methods for teaching have become the norm for many educators. Using Google Classroom gives students access to online learning. Even if the teacher is not in the classroom, the teacher can provide learning. This is the supervision of the form of the teacher when the student is outside the school.

Keywords: Blended learning, learning app, Google classroom, schools.

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Authors: Karen Armstrong

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This paper explores the importance of privacy in a contemporary online world. Crucial to the discussion is the idea of the Lacanian postmodern fragmented self and the problem of how to ensure that we have room to fully explore various aspects of our personalities in an environment which is–or at least feels--safe and free from observation by others. The paper begins with an exploration of the idea of the self with particular regard to the ways in which contemporary life and technology seems to have multiplied the various faces or masks which we present in different contexts. A brief history of privacy and surveillance follows. Finally, the paper ends with an affirmation of the importance of private space as an essential component of our spiritual and emotional well-being in today-s wired world.

Keywords: Lacan, panopticon, postmodern, privacy, surveillance.

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Authors: B. O. Diyaolu

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Viability and competency during competition is the dream of every team sports so as to have a good result. But it seems factors abound which deter the performance of even a good sports team. Different individuals with different state of mind all come together to perform in team sports with different degree of satisfaction. This study investigated the role of satisfaction on performance among Afe Babalola University team sports. Descriptive survey research design was used and the population consists of all male and female athletes in the team sports that participated in the last 2019 Ekiti State Higher Institution games (ESHIGA). Total enumeration technique was used for the three team sports; football (44), basketball (24) and volleyball (24). A total of 92 participants were involved in the research. The instrument used for the study was a modified Athlete Satisfaction Scale (ASS). The questionnaire was divided into two sections. The Cronbach’s Alpha reliability coefficient of 0.71 was obtained. The hypotheses were tested at 0.05 significant levels. The completed questionnaire was collated, coded, and analyzed using descriptive statistics of frequency counts and percentage and inferential statistics of chi-square (X²). Findings of this study revealed that satisfaction significantly influences team sports performance among Athletes of Afe Babalola University. The responsibility of satisfying athlete lies on the coaches, fans, sports administrators as well as organizers of such event, as it is not only financial reward that gives satisfaction. The performance of a team sports is quiet important and its being determined by the degree of satisfaction of each individual that make up the team. All effort must be made to satisfy athlete in order to guarantee optimum performance.

Keywords: Athlete satisfaction, Optimum achievement, Optimum performance, Sports performance, Team sports.

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Authors: Kaiwan S. Ismael, Ergun Ercelebi, Majeed Nader

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This paper describes the design and implementation of a hardware setup for online monitoring of 24 refrigerators inside blood bank center using the microcontroller and CAN bus for communications between each node. Due to the security of locations in the blood bank hall and difficulty of monitoring of each refrigerator separately, this work proposes a solution to monitor all the blood bank refrigerators in one location. CAN-bus system is used because it has many applications and advantages, especially for this system due to easy in use, low cost, providing a reduction in wiring, fast to repair and easily expanding the project without a problem.

Keywords: Control Area Network (CAN), monitoring blood bank center, PIC microcontroller, MPLAB IDE.

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Authors: Ming-Hui Lee, Shou-Jen Hsu

Abstract:

This study presents an active vibration control technique to reduce the earthquake responses of a retained structural system. The proposed technique is a synthesis of the adaptive input estimation method (AIEM) and linear quadratic Gaussian (LQG) controller. The AIEM can estimate an unknown system input online. The LQG controller offers optimal control forces to suppress wall-structural system vibration. The numerical results show robust performance in the active vibration control technique.

Keywords: Active vibration control, AIEM, LQG, Optimal control

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Authors: Sungju Yun, Anjin Park, Keechul Jung

Abstract:

Motion capture devices have been utilized in producing several contents, such as movies and video games. However, since motion capture devices are expensive and inconvenient to use, motions segmented from captured data was recycled and synthesized to utilize it in another contents, but the motions were generally segmented by contents producers in manual. Therefore, automatic motion segmentation is recently getting a lot of attentions. Previous approaches are divided into on-line and off-line, where on-line approaches segment motions based on similarities between neighboring frames and off-line approaches segment motions by capturing the global characteristics in feature space. In this paper, we propose a graph-based high-level motion segmentation method. Since high-level motions consist of several repeated frames within temporal distances, we consider all similarities among all frames within the temporal distance. This is achieved by constructing a graph, where each vertex represents a frame and the edges between the frames are weighted by their similarity. Then, normalized cuts algorithm is used to partition the constructed graph into several sub-graphs by globally finding minimum cuts. In the experiments, the results using the proposed method showed better performance than PCA-based method in on-line and GMM-based method in off-line, as the proposed method globally segment motions from the graph constructed based similarities between neighboring frames as well as similarities among all frames within temporal distances.

Keywords: Capture Devices, High-Level Motion, Motion Segmentation, Normalized Cuts

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Authors: Ashwaq Alsulami, Jianhua Shao

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Various organisations often need to understand discussions on social media, such as what trending topics are and characteristics of the people engaged in the discussion. A number of approaches have been proposed to extract attributes that would characterise a discussion group. However, these approaches are largely based on supervised learning, and as such they require a large amount of labelled data. We propose an approach in this paper that does not require labelled data, but rely on lexical sources to detect meaningful attributes for online discussion groups. Our findings show an acceptable level of accuracy in detecting attributes for Twitter discussion groups.

Keywords: Attributed community, attribute detection, community, social network.

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Authors: Fatimah Almah Saaid, Darfiana Nur, Robert King

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This paper presents a procedure for estimating VAR using Sequential Discounting VAR (SDVAR) algorithm for online model learning to detect fraudulent acts using the telecommunications call detailed records (CDR). The volatility of the VAR is observed allowing for non-linearity, outliers and change points based on the works of [1]. This paper extends their procedure from univariate to multivariate time series. A simulation and a case study for detecting telecommunications fraud using CDR illustrate the use of the algorithm in the bivariate setting.

Keywords: Telecommunications Fraud, SDVAR Algorithm, Multivariate time series, Vector Autoregressive, Change points.

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Authors: Jonghyun Park, Soonyoung Park, Sanggyun Kim, Wanhyun Cho, Sunworl Kim

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In general, image-based 3D scenes can now be found in many popular vision systems, computer games and virtual reality tours. So, It is important to segment ROI (region of interest) from input scenes as a preprocessing step for geometric stricture detection in 3D scene. In this paper, we propose a method for segmenting ROI based on tensor voting and Dirichlet process mixture model. In particular, to estimate geometric structure information for 3D scene from a single outdoor image, we apply the tensor voting and Dirichlet process mixture model to a image segmentation. The tensor voting is used based on the fact that homogeneous region in an image are usually close together on a smooth region and therefore the tokens corresponding to centers of these regions have high saliency values. The proposed approach is a novel nonparametric Bayesian segmentation method using Gaussian Dirichlet process mixture model to automatically segment various natural scenes. Finally, our method can label regions of the input image into coarse categories: “ground", “sky", and “vertical" for 3D application. The experimental results show that our method successfully segments coarse regions in many complex natural scene images for 3D.

Keywords: Region segmentation, tensor voting, image-based 3D, geometric structure, Gaussian Dirichlet process mixture model

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Authors: Elizabeth Downs, Judith Repman, Kenneth Clark

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This paper describes the evolution of strategies to evaluate ePortfolios in an online Master-s of Education (M.Ed.) degree in Instructional Technology. The ePortfolios are required as a culminating activity for students in the program. By using Web 2.0 tools to develop the ePortfolios, students are able to showcase their technical skills, integrate national standards, demonstrate their professional understandings, and reflect on their individual learning. Faculty have created assessment strategies to evaluate student achievement of these skills. To further develop ePortfolios as a tool promoting authentic learning, faculty are moving toward integrating transparency as part of the evaluation process.

Keywords: e-learning evaluation, ePortfolios, transparency, Web 2.0

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Authors: Dinna N. M. N., Leau Y. B., Habeeb S. A. H., Yanti A. S.

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Phishing scheme is a new emerged security issue of E-Commerce Crime in globalization. In this paper, the legal scaffold of Malaysia, United States and United Kingdom are analyzed and followed by discussion on critical issues that rose due to phishing activities. The result revealed that inadequacy of current legal framework is the main challenge to govern this epidemic. However, lack of awareness among consumers, crisis on merchant-s responsibility and lack of intrusion reports and incentive arrangement contributes to phishing proliferating. Prevention is always better than curb. By the end of this paper, some best practices for consumers and corporations are suggested.

Keywords: Phishing, Online Fraud, Business risks, Consumers privacy, Legal Issue, Cyber law.

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Authors: Sergei Yendiyarov, Sergei Petrushenko

Abstract:

In this article we present a change point detection algorithm based on the continuous wavelet transform. At the beginning of the article we describe a necessary transformation of a signal which has to be made for the purpose of change detection. Then case study related to iron ore sinter production which can be solved using our proposed technique is discussed. After that we describe a probabilistic algorithm which can be used to find changes using our transformed signal. It is shown that our algorithm works well with the presence of some noise and abnormal random bursts.

Keywords: Change detection, sinter production, wavelet transform.

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision making has not been far-fetched. Proper classification of these textual information in a given context has also been very difficult. As a result, a systematic review was conducted from previous literature on sentiment classification and AI-based techniques. The study was done in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that could correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy using the knowledge gain from the evaluation of different artificial intelligence techniques reviewed. The study evaluated over 250 articles from digital sources like ACM digital library, Google Scholar, and IEEE Xplore; and whittled down the number of research to 52 articles. Findings revealed that deep learning approaches such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Encoder Representations from Transformer (BERT), and Long Short-Term Memory (LSTM) outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also required to develop a robust sentiment classifier. Results also revealed that data can be obtained from places like Twitter, movie reviews, Kaggle, Stanford Sentiment Treebank (SST), and SemEval Task4 based on the required domain. The hybrid deep learning techniques like CNN+LSTM, CNN+ Gated Recurrent Unit (GRU), CNN+BERT outperformed single deep learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of development simplicity and AI-based library functionalities. Finally, the study recommended the findings obtained for building robust sentiment classifier in the future.

Keywords: Artificial Intelligence, Natural Language Processing, Sentiment Analysis, Social Network, Text.

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Authors: Song Meng, Dianfu Ma, Yongwang Zhao, Jianxin Li

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Cooperative visual modeling is more and more necessary in our complicated world. A collaborative environment which supports interactive operation and communication is required to increase work efficiency. We present a collaborative visual modeling framework which collaborative platform could be built on. On this platform, cooperation and communication is available for designers from different regions. This framework, which is different from other collaborative frameworks, contains a uniform message format, a message handling mechanism and other functions such as message pretreatment and Role-Communication-Token Access Control (RCTAC). We also show our implementation of this framework called Orchestra Designer, which support BPLE workflow modeling cooperatively online.

Keywords: colllaborative framework; visual modeling; message handling mechanism

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Authors: Alan C. Lin, Ricky N. Joevan

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This paper focuses on developing a system to transfer mold base plates and standard parts faster during the stage of injection mold design. This system not only provides a way to compare the file version, but also it utilizes Siemens NX 10 to isolate the updated information into a single executable file (.dll), and then, the file can be transferred without the need of transferring the whole file. By this way, the system can help the user to download only necessary mold base plates and standard parts, and those parts downloaded are only the updated portions.

Keywords: CAD, injection molding, mold base, data retrieval.

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Authors: Hongkui Li, Tongli Lu , Jianwu Zhang

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This paper focuses on developing an estimation method of clutch drag torque in wet DCT. The modelling of clutch drag torque is investigated. As the main factor affecting the clutch drag torque, dynamic viscosity of oil is discussed. The paper proposes an estimation method of clutch drag torque based on recursive least squares by utilizing the dynamic equations of gear shifting synchronization process. The results demonstrate that the estimation method has good accuracy and efficiency.

Keywords: Clutch drag torque, wet DCT, dynamic viscosity, recursive least squares.

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Authors: Proud Arunrangsiwed

Abstract:

This research is a collaborative narrative research, which is mixed with issues of selected papers and researcher's experience as an anonymous user on social networking sites. The objective of this research is to understand the reasons of the regular users who reject to contact with anonymous users, and to study the communication traditions used in the selected studies. Anonymous users are rejected by regular users, because of the fear of cyber bully, the fear of unpleasant behaviors, and unwillingness of changing communication norm. The suggestion for future research design is to use longitudinal design or quantitative design; and the theory in rhetorical tradition should be able to help develop a strong trust message.

Keywords: Anonymous, anonymity, online identity, trust message, reliability.

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Authors: Jie Liu, Xudong Luo, Pingping Lin, Yifan Fan

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Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially the fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, ma-chine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.

Keywords: sentiment analysis, fine-grained, machine learning, deep learning

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Authors: A. Schuchter, M. Promegger

Abstract:

The outbreak of the COVID-19 pandemic has shown us that online education is so much more than just a cool feature for teachers – it is an essential part of modern teaching. In online math teaching, it is common to use tools to share screens, compute and calculate mathematical examples, while the students can watch the process. On the other hand, flipped classroom models are on the rise, with their focus on how students can gather knowledge by watching videos and on the teacher’s use of technological tools for information transfer. This paper proposes a co-educational teaching approach for coding and engineering subjects with the help of drone-building to spark interest in technology and create a platform for knowledge transfer. The project combines aspects from mathematics (matrices, vectors, shaders, trigonometry), physics (force, pressure and rotation) and coding (computational thinking, block-based programming, JavaScript and Python) and makes use of collaborative-shared 3D Modeling with clara.io, where students create mathematics knowhow. The instructor follows a problem-based learning approach and encourages their students to find solutions in their own time and in their own way, which will help them develop new skills intuitively and boost logically structured thinking. The collaborative aspect of working in groups will help the students develop communication skills as well as structural and computational thinking. Students are not just listeners as in traditional classroom settings, but play an active part in creating content together by compiling a Handbook of Knowledge (called “open book”) with examples and solutions. Before students start calculating, they have to write down all their ideas and working steps in full sentences so other students can easily follow their train of thought. Therefore, students will learn to formulate goals, solve problems, and create a ready-to use product with the help of “reverse engineering”, cross-referencing and creative thinking. The work on drones gives the students the opportunity to create a real-life application with a practical purpose, while going through all stages of product development.

Keywords: Flipped classroom, co-creational education, coding, making, drones, co-education, ARCS-model, problem-based learning.

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Authors: Sheetal R. Jadhwani, Daisy Sang, Chang-Shyh Peng

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Web applications are an integral part of modem life. They are mostly based upon the HyperText Markup Language (HTML). While HTML meets the basic needs, there are some shortcomings. For example, applications can cease to work once user goes offline, real-time updates may be lagging, and user interface can freeze on computationally intensive tasks. The latest language specification HTML5 attempts to rectify the situation with new tools and protocols. This paper studies the new Web Storage, Geolocation, Web Worker, Canvas, and Web Socket APIs, and presents applications to test their features and efficiencies.

Keywords: HTML5, Web Worker, Canvas, Web Socket.

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Authors: M. Olmedilla, M. R. Martinez-Torres, S. L. Toral

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Electronic Word-Of-Mouth (eWOM) communities represent today an important source of information in which more and more customers base their purchasing decisions. They include thousands of reviews concerning very different products and services posted by many individuals geographically distributed all over the world. Due to their massive audience, eWOM communities can help users to find the product they are looking for even if they are less popular or rare. This is known as the long tail effect, which leads to a larger number of lower-selling niche products. This paper analyzes the long tail effect in a well-known eWOM community and defines a tool for finding niche products unavailable through conventional channels.

Keywords: eWOM, Online user reviews, Long tail theory, Product categorization, Social Network Analysis.

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Authors: Yolanda Ramírez, Ángel Tejada, Agustín Baidez

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The purpose of this paper is to examine the quality of voluntary intellectual capital disclosure by public Spanish universities on their websites. To this end, a content analysis was used to analyze the websites of 50 public Spanish universities i 2016. The results of this study show that human capital was the most disclosed category with relational capital being the least frequently disclosed in Spain. However, the quality of structural capital disclosures was higher than relational and human capital. Finally, most IC disclosures were narrative in nature.

Keywords: Intellectual capital, quality disclosure, websites, universities, Spain.

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Authors: Trung-Kien Pham, Yoonsu Nam, Hyungun Kim, Jaehoon Son

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This paper addresses linear quadratic regulation (LQR) for variable speed variable pitch wind turbines. Because of the inherent nonlinearity of wind turbine, a set of operating conditions is identified and then a LQR controller is designed for each operating point. The feedback controller gains are then interpolated linearly to get control law for the entire operating region. Besides, the aerodynamic torque and effective wind speed are estimated online to get the gain-scheduling variable for implementing the controller. The potential of the method is verified through simulation with the help of MATLAB/Simulink and GH Bladed. The performance and mechanical load when using LQR are also compared with that when using PI controller.

Keywords: variable speed variable pitch wind turbine, multi-MW size wind turbine, wind energy conversion system, LQR control.

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