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The growing complexity of the logistics chain, the need to take on an ever longer and distributed logistic chain, which in some cases even reaches the final stages of the production of the same goods, highlights the problem of managing the post-production phases of products and goods, but also of the personnel who work on these activities, charged to the companies that deal with logistics. We present in this paper the Integrated Logistic Platform (ILP 4.0), a software architectural model, whose purpose is to lead warehouse logistics to new levels of efficiency, not reachable as the sum of the operational functions within a single logistic company, but reachable only through a unique and cross-functional system, which surpasses the usual fragmented vision and consequent approaches, in favor a strategic coordination of all activities. In this paper it is described the general architecture of the realized platform, with its research contributions and innovations, in particular related to some typical problems of warehouse environments, solved with the help of machine learning and computer vision approaches, but also with the integration of augmented reality (AR) and virtual reality (VR) devices. The aim of the integration of these technologies in the project is the definition of a "smart" warehouse model environment, capable of mitigate problems of warehouse logistics such as: automation of the inventory process (through an UAV), control of warehouse movements, and management of logical / physical security of the premises. Based on the experiences made on the project, and focusing the attention on the safety and security aspects in logistics, that the platform covers, we discuss further a possible federated learning approach in order to mitigate the lack of data in order to solve some security and safety problems in the logistic area. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder is a neurological disorder that affects children at an early age and its symptoms appear in varying degrees. Early detection of ASD and follow-up of treatment by specialists help improve autistic people's lives and reduce the symptoms they suffer from. In the past few years, a group of studies conducted gait analysis to diagnose children with autism, and although the results of these studies showed weak motor skills of Autism Spectrum Disorder children (ASD) compared to their peers of Typically Developing children (TD), there are some challenges facing researchers in this field. The first is the ability to create A safe, effective, friendly environment that reduces tension in ASD children and helps classify children into ASD/TD effectively. And the second is the ability to create an automated ASD diagnosis system. In this work, a system based on machine learning and augmented reality technology has been proposed to create an effective and friendly interactive environment that helps diagnose ASD and classify children to ASD / TD. The movement of the lower limbs of 15 ASD children and 15 TD children are tracked via a marker-less technique through a Kinect v2 sensor when playing a specific game based on kicking the ball task, that is using augmented reality technology. Then the kinematics features are extracted through Linear Discriminant Analysis technology. To determine these features' ability in the classification process, three machine learning algorithms are used, namely Support Vector Machine, Linear Discriminant Analysis, and Decision Tree. The results showed that the obtained kinematics features can classify children into ASD/TD, where the Support Vector Machine classifier obtained a high accuracy of 96.7% with sensitivity equal to 93.3% and specificity equal to 100%. In addition, the Linear Discriminant Analysis classifier obtained less accuracy of 93.3% with sensitivity equal to 93.3% and specificity equal to 93.3% compared with the Support Vector Machine classifier. Where the Decision Tree classifier obtained the lowest accuracy of 80.0% with sensitivity equal to 73.3% and specificity equal to 86.7% compared with the Support Vector Machine and Linear Discriminant Analysis classifiers. In addition to that, when using Linear Discriminant Analysis to reduce dimensions and extract features, the accuracy, sensitivity, and specificity of the Support Vector Machine classifier were increased and achieved by 100%. Based on these results, it has been proven that the proposed method is effective in classifying children to ASD/TD. [ABSTRACT FROM AUTHOR]

As market demands are characterized by more customized products with shorter lifecycles, it is obligatory for modern operators to manage recurrent product or manufacturing system changes. In contrary to previous years, adaptation to such changes prerequires memorization of more information, and familiarization with more complex systems and resources in a shorter period of time. This manuscript presents a novel operator training framework based on Augmented Reality (AR) technology. More specifically, intuitive instructions enhanced with machine learning-based physical object detection are used for making steeper learning curves and providing hands-on experience to operators. The implemented application also supports a walkthrough mode where users can get familiarized with Information and Communication Technologies (ICT) data streams besides fenceless Human-Robot coexistence in collaborative schemes. An automotive case study is used for evaluating the performance of the training framework through a Human-Robot Collaboration (HRC) assembly scenario. [ABSTRACT FROM AUTHOR]

The representation of consistent mixed reality (XR) environments requires adequate real and virtual illumination composition in real-time. Estimating the lighting of a real scenario is still a challenge. Due to the ill-posed nature of the problem, classical inverse-rendering techniques tackle the problem for simple lighting setups. However, those assumptions do not satisfy the current state-of-art in computer graphics and XR applications. While many recent works solve the problem using machine learning techniques to estimate the environment light and scene's materials, most of them are limited to geometry or previous knowledge. This paper presents a CNN-based model to estimate complex lighting for mixed reality environments with no previous information about the scene. We model the environment illumination using a set of spherical harmonics (SH) environment lighting, capable of efficiently represent area lighting. We propose a new CNN architecture that inputs an RGB image and recognizes, in real-time, the environment lighting. Unlike previous CNN-based lighting estimation methods, we propose using a highly optimized deep neural network architecture, with a reduced number of parameters, that can learn high complex lighting scenarios from real-world high-dynamic-range (HDR) environment images. We show in the experiments that the CNN architecture can predict the environment lighting with an average mean squared error (MSE) of 7.85 × 10−4 when comparing SH lighting coefficients. We validate our model in a variety of mixed reality scenarios. Furthermore, we present qualitative results comparing relights of real-world scenes. [Display omitted] • Automatic end-to-end method to estimate the environment lighting in XR applications. • A CNN architecture that learns a latent-space of the environment lighting. • A methodology to generate egocentric mixed-reality-views from HDR panoramas. • Real-time lighting estimation that does not make assumptions about the XR scene. [ABSTRACT FROM AUTHOR]

• The proposed solution can reconstruct indoor scenes using only a single frame and an RGB camera. • An indoor scene composed by five objects can be reconstructed in just 21 seconds. • The proposed solution obtains a maximum error of 18% for the scaling factor, less than 9cm for the position and less than 18 ∘ for the rotation. • The innovative methodology can be employed for VR and AR applications, thus bridging the gap between the real and virtual words. [Display omitted] Indoor environment reconstruction is a challenging task in Computer Vision and Computer Graphics, especially when Extended Reality (XR) technologies are considered. Current solutions that employ dedicated depth sensors require scanning of the environment and tend to suffer from low resolution and noise, whereas solutions that rely on a single photo of a scene cannot predict the actual position and scale of objects due to scale ambiguity. The proposed system addresses these limitations by allowing the user to capture single views of objects using an Android smartphone equipped with a single RGB camera and supported by Google ARCore. The system includes 1) an Android app tracking the smartphone's position relative to the world, capturing a single RGB image for each object and estimating depth information of the scene, 2) a program running on a server that classifies the framed objects, retrieves the corresponding 3D models from a database and estimates their position, vertical rotation, and scale factor without deforming the shape. The system has been assessed measuring the translational, rotational and scaling errors of the considered objects with respect to the physical ones acting as a ground truth. The main outcomes show that the proposed solution obtains a maximum error of 18% for the scaling factor, less than nine centimeters for the position and less than 18 ∘ for the rotation. These results suggest that the proposed system can be employed for XR applications, thus bridging the gap between the real and virtual worlds. [ABSTRACT FROM AUTHOR]

Intelligent technologies are widely implemented in different areas of modern society but specific approaches should be applied in services. Basic relationships refer to supporting customers and people responsible for services offering for these customers. The aim of the paper is to analyze and evaluate the state-of-the art of artificial intelligence (AI) applications in the hospitality industry. Our findings show that the major deployments concern in-person customer services, chatbots and messaging tools, business intelligence tools powered by machine learning, and virtual reality & augmented reality. Moreover, we performed a survey (n = 178), asking respondents about their perceptions and attitudes toward AI, including its implementation within a hotel space. The paper attempts to discuss how the hotel industry can be motivated by potential customers to apply selected AI solutions. In our opinion, these results provide useful insights for understanding the phenomenon under investigation. Nevertheless, since the results are not conclusive, more research is still needed on this topic. Future studies may concern both qualitative and quantitative methods, devoted to developing models that: a) quantify the potential benefits and risks of AI implementations, b) determine and evaluate the factors affecting the AI adoption by the customers, and c) measure the user (guest) experience of the hotel services, fueled by AI-based technologies. [ABSTRACT FROM AUTHOR]

Head-mounted Augmented Reality (AR) displays overlay digital information on physical objects. Through eye tracking, they provide insights into user attention, intentions, and activities, and allow novel interaction methods based on this information. However, in physical environments, the implications of using gaze-enabled AR for human activity recognition have not been explored in detail. In an experimental study with the Microsoft HoloLens 2, we collected gaze data from 20 users while they performed three activities: Reading a text, Inspecting a device, and Searching for an object. We trained machine learning models (SVM, Random Forest, Extremely Randomized Trees) with extracted features and achieved up to 89.6% activity-recognition accuracy. Based on the recognized activity, our system—GEAR—then provides users with relevant AR feedback. Due to the sensitivity of the personal (gaze) data GEAR collects, the system further incorporates a novel solution based on the Solid specification for giving users fine-grained control over the sharing of their data. The provided code and anonymized datasets may be used to reproduce and extend our findings, and as teaching material. [ABSTRACT FROM AUTHOR]

The advancement of technology towards the realization of the evolving mobile computing paradigm brings a rapid paradigm shift in its usage, especially in the Internet, computation, and communications, that has a profound impact on businesses, services, and users. With the rise in resource-intensive or edge-based mobile applications such as autonomous driving, Amazon Go, virtual and augmented reality, and healthcare-related applications, countless challenges in computation and communication parameters like latency, bandwidth, and energy consumption are evolving. As a result, the multi-access edge computing (MEC) paradigm receives enormous attention where some portions of the user applications are offloaded to powerful machines for their efficient execution to optimize different evaluation metrics or to achieve performance goals. While a few survey works are available in this direction, none of them focuses explicitly on the emerging machine learning (ML) based computation offloading techniques and various associated sub-problems together. This paper aims to provide a detailed but precise overview of the research on using ML techniques for MEC environments. In this survey, we focus on how authors and researchers utilize the ML models in computation offloading problems on MEC architecture. We extend our study by considering several edge architectures, offloading parameters, ML approaches, and problem formulation strategies concerning computation offloading. Additionally, this paper discusses the potential challenges in the direction of computation offloading on MEC architecture. [ABSTRACT FROM AUTHOR]

Nowadays, in order to cover the needs of market for product mass customization, industries have started to move to hybrid production cells, involving both robots and human operators. Research has been done during previous years to promote and improve the collaboration between humans and robots, trying to address topics such as safety, awareness and cognitive support in form of Augmented Reality based instructions. Results of previous research show bottlenecks related to the way of interaction of the operators with such supportive systems though. Direct interaction approach with the use of push buttons or indirect-gesture based interaction, which are most often adopted by the researchers, require operators to constantly occupy their hands performing the relevant button presses or gestures. Moreover, previous approaches are hardware dependent and need a lot of customization to work with different hardware. This work tries to address these bottlenecks proposing the usage of wearable devices enhanced with AI in order to support the interaction of human operators with robots in human-robot collaborative environments in a seamless and non-intrusive way, wrapped around a framework called "Operator Support Module" (OSM). Among others, OSM supports a variety of hardware to easily fit in various industrial scenarios. Two case studies will be presented to demonstrate the approach. [ABSTRACT FROM AUTHOR]

In military, police, security companies, and shooting sports, precision shooting training is of the outmost importance. In order to achieve high accuracy, trainees need to do a lot of training. Consequently, they will consume a great number of rounds (cartridges) and a considerable amount of professional coaches' time - both could cost much. Our motivation is to reduce the costs and shorten the training time by implementing an augmented biofeedback system based on machine learning techniques. We designed a biofeedback system, which can detect and give feedback about three kinds of errors that regularly occur during precision shooting practice: excessive hand movement error, aiming error and triggering error. The system provides concurrent feedback about the first error and terminal feedback about the last two errors. Machine learning techniques are used for the identification of hand movement errors. A precision shooting accuracy prediction model based on random forest (RF) has been found as the most appropriate. The experimental results show that: (a) the proposed RF model achieves the prediction accuracy of 91.27%, higher than any of the existing reference models, and (b) the hand movement is strongly related to the accuracy of the precision shooting. An appropriate use of our system can lead to the reduced number of rounds used and to the disburdening of coaches as the trainee can learn about the most common mistakes from the system during the training. [ABSTRACT FROM AUTHOR]

A smart city is one of a huge transformation in these millennia, where it combines latest and advanced technology to overcome the daily basis problem of humans. Artificial intelligence with augmented reality has been highlighted as the potential solution along with IOT and another system to support the smart city system. The smarter Jeddah is part of Saudi Vision 2030 where Saudi government tends aims to transform this second biggest city in the kingdom to be a smart city with a ton of applications that connected with IOT and they can monitor people inside the city in real time. The proposed system focused on providing the resident of the Jeddah city with smart apps that can help them on conducting their daily basis activities (tourism) or even in an emergency situation. The geo-location data and other information will be used as a feeder for deep learning to make the system learn and getting smarter to disseminate information to the user based on their needs, location, emergency factors, etc. Resident can access the municipality public facilities such as Jeddah waterfront location and see what the event happens inside. They also can find the location of other attractive location such as Al-Fakieh aquarium and observe the content of aquarium facilities with virtual reality technology. In term of emergency cases: accident, car stopping and another incident; the proposed apps will be able to inform the police patrol nearby or nearest workshop for tyre or car workshop. This app is capable for wide range utilization to support program of the Ministry of Municipal and Rural Affairs to apprehend the smart city integrated application. The simulation test has confidently proved that deep learning algorithm overcome other classifier Naïve Bayes and Random forest with 67.5% recognition rate. The future work can be integrated with IOT sensor based system to enhance the municipality service in smart city. [ABSTRACT FROM AUTHOR]

The authors have proposed the Active Textbook System (A-txt) as the next generation learning material system. A-txt is a system to enhance the textbook by adding digital content to the figures and formulas in a general book by using AR technology. Production of content and registration to the server can be done relatively easily using a general-purpose personal computer, and the content added to the book can be browed using the users' own smart terminal. Therefore, the introduction cost can be reduced without the need for a dedicated machine. Also, once content is downloaded, it can be used offline. This point is the other advantage that other systems do not have. So far, A-txt was limited to iOS devices that hardware are standardized, but this time the system for Android devices, that have more users, have been developed. Although challenges remain for AR and 3D display, an application that meets practical requirements has been completed. Also, since the Android OS is free, there are a wide variety of terminals adopted. Therefore, recommended hardware specifications have been formulated by benchmark evaluation. Moreover, in order to solve some problems that became apparent in the development process, it was decided to change to an OS-independent system by using Unity. [ABSTRACT FROM AUTHOR]

Many intelligent systems, such as assistive robots, augmented reality trainers or unmanned vehicles, need to know their physical location in the environment in order to fulfill their task. While relying exclusively on natural landmarks for that task is the preferred option, their use is somewhat limited because the proposed methods are complex, require high computational power, and are not reliable in all environments. On the other hand, artificial landmarks can be placed in order to alleviate these problems. In particular, square fiducial markers are one of the most popular tools for camera pose estimation due to their high performance and precision. However, the state-of-the-art methods still perform poorly under difficult image conditions, such as camera defocus, motion blur, small scale or non-uniform lighting. This paper proposes a method to robustly detect this type of landmarks under challenging image conditions present in realistic scenarios. To do so, we re-define the marker identification problem as a classification one based on state-of-the-art machine learning techniques. Second, we propose a procedure to create a training dataset of synthetically generated images affected by several challenging transformations. Third, we show that, in this problem, a classifier can be trained using exclusively synthetic data, performing well in real and challenging conditions. Different types of classifiers have been tested to prove the validity of our proposal (namely, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN) and Support Vector Machine (SVM)), and statistical analyses have been performed in order to determine the best approach for our problem. Finally, the obtained classifiers have been compared to the ArUco and AprilTags fiducial marker systems in challenging video sequences. The results obtained show that the proposed method performs significantly better than previous approaches, making the use of this technology more reliable in a wider range of realistic scenarios such as outdoor scenes or fast moving cameras. [ABSTRACT FROM AUTHOR]

Virtual human figures have been used in the conventional human figure sketch learning support system. A learner selects one of the virtual figures for a motif. However, the learner could not intuitively manipulate the virtual human figure in real time to change the orientation of the figure for learner’s favorite viewpoint. Therefore, in this research, we developed a system to change the orientation of the virtual model of human figure intuitively by using the drawing doll as a tangible interface. Specifically, it uses PCL which can acquire point cloud data by RGB-D camera of KINECT, then it acquires and tracks the three-dimensional coordinates of the real object and superimposes and displays the virtual human figure on the real drawing doll. In the verification experiment tracking accuracy was verified and the improvement of the system was discussed. [ABSTRACT FROM AUTHOR]

Over the last two decades, augmented reality (AR) has led to the rapid development of new interfaces in various fields of social and technological application domains. One such domain is medicine, and to a higher extent surgery, where these visualization techniques help to improve the effectiveness of preoperative and intraoperative procedures. Following this trend, this paper presents SLIMBRAIN, a real-time acquisition and processing AR system suitable to classify and display brain tumor tissue from hyperspectral (HS) information. This system captures and processes HS images at 14 frames per second (FPS) during the course of a tumor resection operation to detect and delimit cancer tissue at the same time the neurosurgeon operates. The result is represented in an AR visualization where the classification results are overlapped with the RGB point cloud captured by a LiDAR camera. This representation allows natural navigation of the scene at the same time it is captured and processed, improving the visualization and hence effectiveness of the HS technology to delimit tumors. The whole system has been verified in real brain tumor resection operations. [ABSTRACT FROM AUTHOR]

Objectives: Accurate reconstruction and visualisation of soft tissue deformation in real time is crucial in image-guided surgery, particularly in augmented reality (AR) applications. Current deformation models are characterised by a trade-off between accuracy and computational speed. We propose an approach to derive a patient-specific deformation model for brain pathologies by combining the results of pre-computed finite element method (FEM) simulations with machine learning algorithms. The models can be computed instantaneously and offer an accuracy comparable to FEM models.Method: A brain tumour is used as the subject of the deformation model. Load-driven FEM simulations are performed on a tetrahedral brain mesh afflicted by a tumour. Forces of varying magnitudes, positions, and inclination angles are applied onto the brain's surface. Two machine learning algorithms-artificial neural networks (ANNs) and support vector regression (SVR)-are employed to derive a model that can predict the resulting deformation for each node in the tumour's mesh.Results: The tumour deformation can be predicted in real time given relevant information about the geometry of the anatomy and the load, all of which can be measured instantly during a surgical operation. The models can predict the position of the nodes with errors below 0.3mm, beyond the general threshold of surgical accuracy and suitable for high fidelity AR systems. The SVR models perform better than the ANN's, with positional errors for SVR models reaching under 0.2mm.Conclusions: The results represent an improvement over existing deformation models for real time applications, providing smaller errors and high patient-specificity. The proposed approach addresses the current needs of image-guided surgical systems and has the potential to be employed to model the deformation of any type of soft tissue. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) is an augmented depiction of reality formed by overlaying digital information on an image of objects being seen through a device. Artificial Intelligence (AI) techniques have experienced unprecedented growth and are being applied in various industries. The combination of AR and AI is the next prominent direction in upcoming years with many industries and academia recognizing the importance of their adoption. With the advancements in the silicone industry that push the boundaries of Moore's law, processors will be less expensive, more efficient, and power-optimized in the forthcoming years. This is a tremendous support and necessity for an AR boom, and with the help of AI, there is an excellent potential for smart industries to increase the production speed and workforce training along with improved manufacturing, error handling, assembly, and packaging. In this work, we provide a systematic review of recent advances, tools, techniques, and platforms of AI-empowered AR along with the challenges of using AI in AR applications. This paper will serve as a guideline for future research in the domain of AI-assisted AR in industrial applications. • The trend of AR and AI applications in the industry is analyzed. • Types of AI techniques for mobile AR applications are provided. • Various platforms and frameworks for AR applications are illustrated. • Usage of AR/AI in various manufacturing sectors is reviewed. • Challenges for implementing AR & AI techniques in the industry are discussed. [ABSTRACT FROM AUTHOR]

Learning can occur at anytime and anywhere, even when you are travelling. In this article, we explain how augmented reality (AR) can be used to better educate tourists and visitors about the sites they choose to visit. Using the Temple of Debod in Madrid and its structures as an example, we focus on applying AR to the engravings on the walls of the temple. Through AR, these engravings take on a new life, giving visitors a more interesting and interactive experience that better educates them about the temple itself and about the different historical periods in which the ‘graffiti’ were engraved. Augmented reality, combined with audio commentary in different languages, will help visitors to locate the different graffiti more easily and appreciate the true wonder of the site and the motives behind the engravings. [ABSTRACT FROM AUTHOR]

Augmented Reality works for learning (growing tendency) in aeronautical sector demand an evaluation that requires experimental sampling, and this is often an expensive process. Usually, sample size is determined by resources availability constraint, which does not guarantee the level of significance (α) and the margin of error (E) required. This paper introduces a congruent sequence of statistical procedures to determine the estimated work sample size for traineeship in assembly operations. Sample size estimations for an aeronautical case are presented considering various scenarios. A minimum necessary sample size is calculated with statistical rigor via formulation and Operating Characteristic Curves. [ABSTRACT FROM AUTHOR]

In this text, we intend to answer to the following question: is Augmented Reality an enhancer for Higher Education students in math's learning? For this purpose, we define augmented reality and present a state of the art mapped mainly by studies that focus AR in educational contexts. We also describe our research, including methodological aspects in data collection and the creation of 3D contents in AR. Then, we synthesize the analysis of some preliminary data, briefly presenting perceptions and practices of students in math's learning with AR contents. Finally, we conclude that the challenges that are nowadays put to teaching methods, acquisition and subsequent knowledge consolidation may be met, to some extent, by the application of available technologies. These, in turn, should enhance a more complete understanding of contents, leading to knowledge endogenization and also to the internalization of more sustained competencies. Among those technologies, we highlight augmented reality since it can encourage motivation, comprehension and a higher involvement with the contents to be learned. Thus, it may increase the use of information and the access to knowledge, improving digital and info-inclusion. [ABSTRACT FROM AUTHOR]

Abstract: A proper operation and maintenance of marine systems require give specific instructions and descriptions focused on the parts of any device. This is usually taught by the use of texts and figure descriptions, but the learning process is not as immersive as reality itself. Augmented reality over real objects with mobile devices can change this learning process into a more immersive and engaging experience for the students. This technology permits the use of instructional information like texts, videos and 3D virtual objects even with animations over real elements. This powerful tool lets the student recognize any drawings and real objects in one step, and also any specific operating and/or maintenance instructions can be given. For creating these augmented reality experiences we pretend to use metaio Creator combined with metaio Toolbox. Metaio GmbH firstly released this free app on October 2012 in the Apple App Store. It lets capturing a real object (mapping) in order to easily create augmented realty experiences using real objects as references. On a first instance, we pretend to map objects like valves, pumps and other piping elements, so small groups of students will be able to observe and study this objects through their own mobile devices using a QR code for object recognition. The understanding of every object by every group of students becomes their challenge. First every group will have to discuss every relevant aspect they have discovered: working principles, operating and maintenance. Secondly they should discuss their results with other team members. Finally they will explain what they have learned to their instructor using the same AR technology. [Copyright &y& Elsevier]

Abstract: This article introduces a new Serious Game with Augmented Reality for children and handicapped people. A platform based on a zoo has been virtually created. With this application, the users can increase their knowledge about a specific topic, in this case, animals. We have designed several 3D elements according to the theme, including a main scene that leads to the different scenes contained in the game. The user can interact with those elements and scenes through the mobile phone, thanks to the touch screen. [Copyright &y& Elsevier]

Abstract: Objective of this study is to improve educational processes in abstract or technical courses, by providing a mobile Augmented Reality (AR) tool. This tool is a mobile software system aiming to provide a supportive, e-learning material for students. By using the tool, students can view 3D animations, and special-made videos to have more idea about a course subject, or have a chance to improve their knowledge on the related course content. In order to achieve this, students are enabled to use mobile device camera interface on special signs placed in course books or any other supportive, physical materials that are given by course lecturers. Additionally, it is also possible to watch course materials after focusing on some physical objects in the real-life. Consequently, the software tool has aimed to ensure an effective learning experience by employing advantages of mobile devices and forming interactive sessions between virtual and real environment. [Copyright &y& Elsevier]

Abstract: This paper describes the design and evaluation of an AR alphabet book, an Augmented Reality based book for teaching the alphabet to preschool children. Used together with camera and computer, children could view the superimposed virtual alphabet in a fun and interactive manner using the pattern markers as an interaction tool. Generally, teaching young children could be difficult this is due to the focus of young children is different from elderly since they could only focus on something for short period of time. Introducing the fun and interactive learning could grab the attention therefore enhance teaching and learning for young learner. Fun-learning emphasized interactive learning through play, songs; dance, drama and the use of information and communication technology (ICT). Researches showed that fun learning also increase the ability to memorize and understanding of the user. Flashcard is one of the learning approaches to teach children the alphabet. The tangible manner of flashcard introduce the interaction of learning therefore it could create the joyful learning. However, the potential of flash-card could be further enhance through the use of AR technology. Therefore, introducing the use of AR could encompass fun learning since AR offer rich media learning. Besides displaying each alphabet upon presentation of its corresponding pattern marker, children have the options to see 3D models of objects that begin with each alphabet character. Additional book features includes pattern markers for children to view animation of how each letter is drawn and jigsaw puzzle game for each letter to test children understanding. An informal study was conducted among 15 preschool children aged between 5-6 years old to examine user perception of the book. The preliminary results indicate the children reacted positively towards the books; most reported they like and enjoy using the AR book. Observation of the children behaviors during study corroborates this finding. Most students requested to use the AR book repeatedly. These results suggest the potential of AR book as a tool to create fun learning environment especially for preschool children. Furthermore, the brief interview with the teacher of the preschool also suggested that the system seem to grab the attention of the children. [Copyright &y& Elsevier]

Abstract: This article shows the experience of applying technologies based on web 2.0 in an environment of learning at university level. Students are more and more familiar with such tools and learning at the University should be sensitive to this evolution. This tutorial 2.0 is based on two virtual environments that support and improve communication in the teaching-learning process: Moodle as an “institutional adapted” platform, and Site Google as an open platform. The temporary planning has been done with the design of a timeline. The implementation of the tutorial 2.0 has been highly accepted by the students. With respect to the teaching staff, they need permanent updates and the adaptation of web 2.0 tools in the development of the subject's guide. [Copyright &y& Elsevier]

Abstract: Mathematics is one of the main courses in education, learning the basics of this area is essential for any student. But along time Teachers have found several factors that takes the pupil attention away. Here is where Kinesthetic Learning have proven to be a determinant factor on learning this courses, with the inclusion of mechanical, and virtual devices is possible to extend the pupil knowledge, minimize distraction and gain focus on difficult topics and practices. In the past years, technology have become an important part of the educational system in many countries, in most cases the school or university requires the student to bring a laptop or mobile device to school, but in most cases this devices becomes the main distraction. The purpose of this research is to demonstrate that kinesthetic learning offer a new experience in education, allowing better understanding of mathematical concepts, graphs and formulas and allow the student to take action in the learning process. Trying to find a viable way to implement the kinesthetic learning, several test where held containing elements of augmented reality, the results of this demonstrate that AR provides a huge boost on the learning curve but is limited by the “marker” and the amount of movement, here is where Kinect comes in. With this capacity the Kinect makes a perfect hardware piece for Kinesthetic learning, now the main objective is to develop a set of tools involving augmented reality and virtual reality for the understanding and learning of mathematics for College students. [Copyright &y& Elsevier]

Abstract: This paper presents the development of an interactive mobile augmented reality magical playbook for preschool children in learning numbers using an old folklore literature, The Thirsty Crow, via mobile augmented reality application and interactive physical book interface design. Augmented Reality (AR) refers to a technology that gives the ability to the user the sense of the real world while interacting with the virtual and physical object. Mobile refers to portability usefulness of the application itself, thus mobile AR application can be referred as a portable AR application. The real world can be enhanced by AR through augmented virtual object into real environment and providing some additional information for users. By applying this concept to AR storybook, the physical book (the real world) will be enhanced by augmenting the virtual object (3D models, animations, and sounds) viewed over a mobile device. This paper specifically highlights an innovative development of the interfaces for providing an AR storybook that enhanced story reading and learning experience for preschool children via mobile AR application and highly interactive physical book interface design of the AR book. Findings from the initial observational study based on developed prototype are also presented. For future work, there will be a series of interactive mobile AR magical playbook using other old folklore story. [Copyright &y& Elsevier]

Abstract: This work applies Augmented Reality technology in the educational process through a didactic prototype that promotes visualization skills related to the learning of mathematical content. An initial prototype has been designed and built with the purpose of arriving at 3 dimensional objects performing specific actions, in space and time, executed with 2 dimensional objects. The AR production of mathematical objects with which student may interact offers the opportunity to mentally record the process through which they are generated, favoring visualization skills. In the initial academic phase, an analysis of the first three college calculus courses was carried out. The objective was the identification of a transversal content suitable to be developed in AR environment. Once this content was established and discussed, the conceptualization of the prototype was carried out, identifying first the platform of technological and human resources available for the project. The technical phase was focused on developing the AR technology prototype around the didactic design concept. The adjustment decisions in this process were based around the academic-technical integration meetings. A pilot experience for exploratory purposes was developed with Mathematics I for engineering students during May 2013. The aim was to describe the actions the prototype encourages from the students and to capitalize these results to determine limitations and reaches of this first prototype, from a didactically and technically point of view. The pilot experience confirms that AR technology in education increases the current motivation to learn by students. The work aims to study about the development of didactic resources that serve students in the learning of a visual and tangible mathematics. [Copyright &y& Elsevier]

Abstract: This paper presents a virtual try-on system based on augmented reality for design personalization of facial accessory products. The system offers several novel functions that support real-time evaluation and modification of eyeglasses frame. 3D glasses model is embedded within video stream of the person who is wearing the glasses. Machine learning algorithms are developed for instantaneous tracking of facial features without use of markers. The tracking result enables continuously positioning of the glasses model on the user’s face while it is moving during the try-on process. In addition to color and texture, the user can instantly modify the glasses shape through simple semantic parameters. These functions not only facilitate evaluating products highly interactive with human users, but also engage them in the design process. This work has thus implemented the concept of human-centric design personalization. [Copyright &y& Elsevier]

Abstract: Welcome to the 2013 International conference on Virtual and Augmented Reality in Education, which is being held on 7th-9th November 2013 in Puerto de la Cruz, Tenerife, Spain. This year's conference will take place in the beautiful city of Puerto de la Cruz at the Beatriz Atlantis Hotel. VARE 2013 is organized by the University of La Laguna and Sociotechnical Systems Engineering Institute of Vidzeme University of Applied Sciences. The International Conference on Virtual and Augmented Reality in Education is an biannual conference that brings together teachers in all areas of knowledge and educational levels as well as researchers and scientists from computing disciplines linked with virtual technologies. In general, teachers and researchers with research lines for improvement of teaching through virtual technologies will discuss any problems and solutions in the area while identifying new issues and giving shape to directions for future research. In this second edition of VARE, the number of participants as well as all their works has been exceptional as a consequence of the virtual technologies, which are reaching every educational level. This year we expect over 125 participants. The aim of these proceedings is becoming a major intellectual resource for researchers of virtual technologies while also intending to define and advance the state of the art in this field. The theme for VARE 2013 is “Introducing Virtual Technologies in the Classroom” to mark the ever- increasing need of teaching with technologies that students are used to, for motivating them and also showing the didactic potential of the developed tools in both virtual and augmented reality. The conference will be an event focused on recent evolution of augmented and virtual reality for developing systems and complex applications across different educational fields: compulsory studies, business education as well as learning of any kind. [Copyright &y& Elsevier]

• A novel autonomous learning framework is presented to integrate the benefits of both depth vision and EMG signals. • Combination of depth information and sEMG with HSOM and MNN adopted to achieve better accuracy for the designed VR application. • A hand gesture recognition demonstration is implemented to verify the effectiveness of the proposed framework. Hand gesture recognition using surface electromyography (sEMG) has been one of the most efficient motion analysis techniques in human–computer interaction in the last few decades. In particular, multichannel sEMG techniques have achieved stable performance in hand gesture recognition. However, the general solution of collecting and labeling large data manually leads to time-consuming implementation. A novel learning method is therefore needed to facilitate efficient data collection and preprocessing. In this paper, a novel autonomous learning framework is proposed to integrate the benefits of both depth vision and EMG signals, which automatically label the class of collected EMG data using depth information. It then utilizes a multiple layer neural network (MNN) classifier to achieve real-time recognition of the hand gestures using only the sEMG. The overall framework is demonstrated in an augmented reality application by the recognition of 10 hand gestures using the Myo armband and an HTC VIVE PRO. The results show prominent performance by introducing depth information for real-time data labeling. [ABSTRACT FROM AUTHOR]