<iframe src=https://www.youtube.com/embed/Lsn6bj3X8o8?feature=oembed width=1200 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nIn this paper a new method is proposed for learning computer programming. This method utilizes a set of human-readable graphemes and tokens that interactively replace the grammatical tokens of programming languages, using a concept similar to emoticons in social media. The theoretical framework of the proposed method is discussed in detail and two implementations are presented for the programming language ECMAScript (JavaScript). The results from user testing with undergraduate students show that the proposed technique improves the student’s learning outcomes in terms of syntax recall and logic comprehension, in comparison to traditional source code editors.

In this paper a novel computer-assisted culturally responsive teaching (CRT) framework is presented for teaching mathematics to 5th grade students. The curricular basis for this framework is Gloria JeanMerriex’s award winning curriculum program, which uses music and body gestures to help students build associations between mathematical concepts and culturally inspired metaphors. The proposed framework uses low-cost kinesthetic sensors along with a embodied virtual reality gamimg environment that extends such proven CRT methodologies from a traditional classroom into a digital form. A pilot study was performed to investigate the efficacy of this framework in 5th grade students. A group of 35 students participated in this study and the results are discussed in detail.

Although significant advances have been done with respect to vehicle technology and roadway construction, driver behavior remains the number one contributing factor of traffic crashes worldwide. Studies show that one of the major causes of crashes is driver inattention, which may occur when drivers are involved with secondary activities (e.g. texting, talking on the phone, or eating), and when they fail to follow the cues of the surrounding environment while driving. The objective of this study was to develop a method that monitors driver body posture and movements inside the cabin and test it among different drivers when performing merging and lane changing maneuvers, since these types of maneuvers require significant body movement and may also result in unsafe situations. The developed method was applied in a naturalistic setting where 35 drivers were invited to participate. Participants’ 3D body posture was recorded with the use of a low-cost infrared depth sensor (Microsoft Kinect). Participants’ eye gaze was also recorded with the help of an eye-tracking equipment. This paper presents analysis results of 3D body posture in conjunction with the eye tracking information during 236 merging and 287 lane changing maneuvers.

In this paper a novel augmented-reality environment is presented for enhancing locomotor training. The main goal of this environment is to excite kids for walking and hence facilitate their locomotor therapy and at the same time provide the therapist with a quantitative framework for monitoring and evaluating the progress of the therapy. This paper focuses on the quantitative part of our framework, which uses a depth camera to capture the patient's body motion. More specifically, we present a model-free graph-based segmentation algorithm that detects the regions of the arms and legs in the depth frames. Then, we analyze their motion patterns in real-time by extracting various features such as the pace, length of stride, symmetry of walking pattern, and arm-leg synchronization. Several experimental results are presented that demonstrate the efficacy and robustness of the proposed methods.

Real-time 3D reconstruction of the human body has many applications in anthropometry, telecommunications, gaming, fashion, and other areas of human-computer interaction. In this paper a novel framework is presented for reconstructing the 3D model of the human body from a sequence of RGBD frames. The reconstruction is performed in real time while the human subject moves arbitrarily in front of the camera. The method employs a novel parameterization of cylindrical-type objects using Cartesian tensor and b-spline bases along the radial and longitudinal dimension respectively. The proposed model, dubbed tensor body, is fitted to the input data using a multistep framework that involves segmentation of the different body regions, robust filtering of the data via a dynamic histogram, and energy-based optimization with positive-definite constraints. A Riemannian metric on the space of positive-definite tensor splines is analytically defined and employed in this framework. The efficacy of the presented methods is demonstrated in several real-data experiments using the Microsoft Kinect sensor.

<ul> \n <li>In this paper a prototype system is presented for home-based physical tele-therapy using a wearable device for haptic feedback. The haptic feedback is generated as a sequence of vibratory cues from 8 vibrator motors equally spaced along an elastic wearable band. The motors guide the patients’ movement as they perform a prescribed exercise routine in a way that replaces the physical therapists’ haptic guidance in an unsupervised or remotely supervised home-based therapy session. A pilot study of 25 human subjects was performed that focused on: a) testing the capability of the system to guide the users in arbitrary motion paths in the space and b) comparing the motion of the users during typical physical therapy exercises with and without haptic-based guidance. The results demonstrate the efficacy of the proposed system.</li> \n</ul>

The science of teaching/learning and the development of interactive technology are now at a stage where an effective interactive system can be developed for the teaching and learning of the basic vocabulary and grammar of early musical notation systems. Our interdisciplinary team is developing the first such system, in addition to the first assessment tool for evaluating the effectiveness of the system. We propose to offer a presentation session that will include the following: an explanation of the software and the learning research behind it; a demonstrate of the system; an explanation of the assessment process used to determine the effectiveness of the software; an audience participation segment in which audience members will see a short demonstration video regarding a particular segment of the notation work in self-correcting, interactive exercises take an online assessment. Participants in this presentation session will gain an understanding of the benefits of interactive learning; gain an understanding of an assessment process for an interactive learning that also provides a framework for ensuring an unbiased assessment; have an experience of a new interactive software, the principals behind which could be applied to various disciplines.

Research and development of real-time arts performance systems has been underway at the University of Florida Digital Worlds Institute since 2001. Significant attributes of this research include the successful facilitation of synchronous global-scale performing arts events, the evolution of process and practice for arts and engineering collaborations between multi-point performance sites across the high-speed network, and the development and utilization of a unique toolkit of techniques and technologies. Examples of our global-scale networked performances include: the synchronous musical union of ethnic performers located in seven cities across five continents for 'In Common: Time' at SIGGRAPH 2005; a quartet of modern dancers located in four remote cities across Asia and North American motion-captured and mapped into a single shared Cartesian coordinate space performing on virtual percussion instruments with 3D audio in 'Same Space Same Time' (2010); the integration of multiple remote audiences providing character choices and feedback on their mobile devices (aggregated, visualized and given to the performers in real-time) during a multi-continental performance featuring network-attached Kinect devices driving synchronous representations of the distributed performers in a gaming engine for 'Icons of Innovation' at IDMAA 2012. In addition to developing the methodologies necessary to integrate various traditional and emergent technologies into these multi-faceted real-time performance systems, a number of novel techniques and collaborative relationships have resulted from this work. Using several of our distributed performances as exemplars, we will outline and then detail the esthetic, procedural, technological, and logistic considerations inherent in working with artists, engineers, and media producers across multiple time zones, cultures, and sub-nets. We have learned a considerable number of lessons that can optimize the strategic planning and implementation of distributed performing arts events, and will offer not only background and recommendations for those interested in working in this space, but also examples of the specific tools, techniques and technologies we have developed and integrated into the design and production of this work.

Purpose/Hypothesis: \nFollowing lower extremity (LE) joint replacement, patients are increasingly prescribed virtual reality-based home exercise programs (HEP). One goal of virtual reality (VR) use is to promote HEP adherence. Exercise adherence, as well as exercise performance, is increased with human interaction and real-time therapist feedback, which is not commonly incorporated in commercially available VR systems. To address these limitations, a custom VR system was developed using an infrared camera for motion tracking, avatar streaming, and real-time remote therapist interactions. The primary aim of this study was to evaluate the use of this custom VR system on HEP performance in adults post LE joint replacement. We also examined patient and therapist opinions of VR system feedback features and ability to improve HEP adherence. \nNumber of Subjects: \n14 patients (11 female; 62.5±7.5 years) with unilateral hip (n=6) or knee (n=8) replacements (4.6±5.9 months post-surgery) and 11 therapists (6 PT, 4 OT, 1 COTA; female; &gt;2 yrs experience) participated. \nMaterials/Methods: \nSubjects completed two random-ordered LE exercise conditions using either the custom VR system or a conventional HEP with diagrams and written instructions while therapists observed remotely via video streaming. Four standing exercises were performed (hip flexion, abduction, extension, squats). Instructions and verbal feedback were standardized, and 3-D LE motions were recorded. Exercise performance was assessed by calculating peak joint angles and movement velocities. The effect of remote therapist interaction and verbal feedback on exercise performance during the VR condition was assessed by calculation of peak joint angles during aberrant, compensatory movements (i.e. trunk lean). Exercise performance during the two conditions was compared using paired t-tests. Patient and therapist preferences were assessed using standardized questionnaires with open-ended and Likert scale-based items. \nResults: \nPeak joint angles during the two conditions were not different (p&gt;.05), but movements were slower with VR use for 3 of 4 exercises (p&lt;.05) and compensations were reduced with remote therapist interactions and verbal feedback. 100% of patient and therapist participants reported preferences for remote interactions including verbal feedback and interactions with streaming avatars to display real-time movements. 79% of patients and 91% of therapists reported agreement that the VR system could improve HEP adherence. \nConclusion: \nA custom VR system that incorporates real-time remote therapist interactions improved HEP performance in individuals post LE joint replacement. Both patients and therapists reported high preferences for real-time interactions. \nClinical Relevance: \nVR systems should consider the role of real-time therapist interactions to promote engagement and adherence to HEPs, as well as provide opportunity for feedback to enhance exercise performance. Further, web-based systems can allow for multi-user group exercise sessions and engagement for those in rural locations.

<iframe src=https://www.youtube.com/embed/dt1CpBkZDNQ?feature=oembed width=1200 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nDigital Epigraphy Toolbox is an open-source cross-platform web-application designed to facilitate the digital preservation, study, and electronic dissemination of ancient inscriptions. It allows epigraphists to digitize in 3D their epigraphic squeezes using our novel cost-effective technique, which overcomes the limitations of the current methods for digitizing epigraphic data in 2-dimensions only. The proposed toolbox contains several options for 3D visualization of inscriptions as well as a set of scientific tools for analyzing the lettering techniques and performing quantitative analysis of the letterform variations. The users will have the option to share their data or search for other uploaded collections of 3D inscriptions in a semi-supervised dynamic library. This library will be organized thematically according to language, area of origin, and date and will contain a comprehensive record of the inscription in the form of plain text, 3D model, and 2D photographs.

This chapter presents examples of utilizing virtual environments for experiential learning and training purposes, with applications to several areas in the Science, Technology, Engineering &amp; Mathematics (STEM), and Health Sciences. The chapter starts with a general introduction to experiential learning, followed by a presentation of various technologies for enhancing the experience in virtual environments. The rest of the chapter is organized into two sections that discuss specific examples of virtual environments for experiential learning and therapeutic medical applications respectively. More specifically, the examples will demonstrate: the use of low-cost haptic devices in virtual environments for learning nanotechnology, experiential learning environments for forest education, the use of virtual reality theaters as an educational tool in the arts and the humanities, virtual environments for therapeutic solutions, interactive tools for treating motor disabilities using brain-computer interface for interaction with virtual environments, and experiential learning applications for microsurgical training using mixed reality and haptic feedback. The chapter concludes with a final section that discusses future research directions.

<iframe src=https://www.youtube.com/embed/0wY5gh8elq4?feature=oembed width=1200 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nThis paper presents a novel virtual reality system that offers immersive experiences for instrumental music conductor training. The system utilizes passive haptics that bring physical objects of interest, namely the baton and the music stand, within a virtual concert hall environment. Real-time object and finger tracking allow the users to behave naturally on a virtual stage without significant deviation from the typical performance routine of instrumental music conductors. The proposed system was tested in a pilot study (n=13) that assessed the role of passive haptics in virtual reality by comparing our proposed “smart baton” with a traditional virtual reality controller. Our findings indicate that the use of passive haptics increases the perceived level of realism and that their virtual appearance affects the perception of their physical characteristics. \n \nThe use of computer systems in instrumental music conductor education has been a well studied topic even outside the area of virtual reality [1]. Several systems have been proposed that offer targeted learning experiences [2,3] which may also combine gamified elements [6]. In the past decades, several visual interfaces have been designed using the available technologies at each given period of time [4,5,7], which most recently included eye tracking [8] and augmented and virtual reality platforms [3]. \n \nRecent advances in real-time object tracking and the availability of such systems as mainstream consumer products has opened new possibilities for virtual reality applications [13, 14,]. It has been shown that the use of passive haptics in VR contribute to a sensory-rich experience [15,16], as users have now the opportunity to hold and feel the main objects of interaction within a given immersive environment, such as tools, handles, and other instruments. For example, tracking the location of a real piano can help beginners learn how to play it using virtual reality [20]. However, the use of passive haptics in virtual environments for music education is an understudied area, because it requires precise real-time tracking of objects that are significantly smaller than a piano, such as hand held musical instruments, bows, batons, etc. \n \nIn this paper, we present a novel system for enhancing the training of novice instrumental music conductors through a tangible virtual environment. For the purposes of the proposed system a smart baton and a smart music stand have been designed using commercially available tracking sensors (VIVE trackers). The users wear a high-fidelity virtual reality headset (HTC VIVE), which renders the environment of a virtual concert hall from the conductor’s standpoint. Within this environment, the users can feel the key objects of interaction within their reach, namely the baton, the music stand, and the floor of the stage through passive haptics. A real-time hand and finger motion tracking system continuously tracks the left hand of the user in addition to the tracking of the baton, which is usually held in the right hand. This setup creates a natural user interface that allows the conductors to perform naturally on a virtual stage, thus creating a highly immersive training experience. \n \nThe main goals of the proposed system are the following: a) Enhance the traditional training of novice instrumental music conductors by increasing their practice time without requiring additional space allocation or time commitment from music players, which is also cost-effective. b) Provide an interface for natural user interaction that does not deviate from the traditional environment of conducting, including the environment, the tools, and the user behavior (hand gesture, head pose, and body posture), thus making the acquired skills highly transferable to the real-life scenario. c) Just-in-time feedback is essential in any educational setting, therefore one of the goals of the proposed system is to generate quantitative feedback on the timeliness of their body movement and the corresponding music signals. d) Last but not least, the proposed system recreates the conditions of a real stage performance, which may help the users reduce stage fright within a risk-free virtual environment [9,10,11,12]. \n \nA small scale pilot study (n=13) was performed in order to assess the proposed system and particularly the role of passive haptics in this virtual reality application. The main focus of the study was to test whether the use of passive haptics increases the perceived level of realism in comparison to a typical virtual reality controller, and whether the virtual appearance of a real physical object, such as the baton, affects the perception of its physical characteristics. These hypotheses were tested using A/B tests followed by short surveys. The statistical significance of the collected data was calculated, and the results are discussed in detail.  The reported findings support our hypotheses and set the basis for a larger-scale future study. \n \n<iframe src=https://www.youtube.com/embed/m8e_YHEgglo?feature=oembed width=1200 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe>

<iframe src=https://www.youtube.com/embed/QiO9ZzyffAY?feature=oembed width=1200 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nThis paper presents the results of a pilot study that assesses the effect of passive haptics on the user experience in virtual reality simulations of recreation and sports activities. A virtual reality kayaking environment with realistic physics simulation and water rendering was developed that allowed users to steer the kayak using natural motions. Within this environment the users experienced two different ways of paddling using: a) a pair of typical virtual reality controllers, and b) one custom-made “smart paddle” that provided the passive haptic feedback of a real paddle. The results of this pilot study indicate that the users learned faster how to steer the kayak using the paddle, which they found to be more intuitive to use and more appropriate for this application. The results also demonstrated an increase in the perceived level of enjoyment and realism of the virtual experience. \n \nKayaking is an outdoor activity that can be enjoyed with easy motions and with minimal skill, and can be performed on equal terms by both people who are physically able and those with disabilities [1]. For this reason, it is an ideal exercise for physical therapy and its efficacy as a rehabilitation tool has been demonstrated in several studies [1-6]. Kayaking simulations offer a minimal-risk environment, which, in addition to rehabilitation, can be used in training and recreational applications [5]. The mechanics of boat simulation in general have been well-studied and led to the design of high-fidelity simulation systems in the past decades [3,7]. These simulators immerse the users by rendering a virtual environment on a projector [1,4,6] or a computer screen that is mounted on the simulator system [2,8]. Furthermore, the users can control the simulation by imitating kayaking motions using remote controls equipped with accelerometers (such as Wii controllers) [5] or by performing the same motions in front of a kinesthetic sensor (such as Kinect sensors) [4,6]. \n \nThe recent advances in virtual reality technologies and in particular the availability of head mounted displays as self-contained low-cost consumer devices led to the development of highly immersive virtual experiences compared to the conventional virtual reality experiences with wall projectors and computer displays. Kayaking simulations have been published as commercial game titles in these virtual reality platforms [13]. However, the use of head mounted displays in intensive physical therapy exercises bears the risk of serious injuries due to the lack of user contact with the real environment. These risks could potentially be reduced if the users maintained continuous contact with the surrounding objects such as the simulator hardware, the paddle(s), and the floor of the room, with the use of passive haptics. Additionally, the overall user experience can be improved through sensory-rich interaction with the key components of the simulated environment. \n \nThis paper assesses the role of passive haptics in virtual kayaking applications. Passive haptics can be implemented in virtual reality systems by tracking objects of interest in real-time and aligning them with identically shaped virtual objects, which results in a sensory-rich experience [9,10]. This alignment between real and virtual objects allows users to hold and feel the main objects of interaction including hand-held objects, tables, walls, and various tools [11,12]. \n \nIn this paper we present a novel virtual reality kayaking application with passive haptic feedback on the key objects of interaction, namely the paddle and the kayak seat. These objects are being tracked in real-time with commercially available tracking sensors that are firmly attached to them. Although the users’ real-world view is occluded by the head-mounted display, the users can see the virtual representation of these objects and naturally feel, hold, and interact with them. Subsequently, the users can perform natural maneuvers during the virtual kayaking experience by interacting with our “smart” paddle using the same range of motions as in real kayaking. \n \nThe proposed system was assessed with a pilot user study (n=10) that tested the following hypotheses: a) The use of passive haptics helps users learn kayaking faster and operate the simulation better compared to the conventional controller-based interaction. b) The use of passive haptics improves the level of immersion while kayaking in virtual reality. \n \nThe study was undertaken at the Realities Lab of the Digital Worlds Institute at the University of Florida. The volunteers who participated in this experiment were randomly assigned to the study and the control group and experienced the proposed virtual kayaking system with and without the use of passive haptics respectively. The data collection was performed with pre- and post-test surveys. In addition, the progress of each individual user during kayaking was recorded and the collected timestamps were analyzed. \n \nThe results from this study are presented in detail and indicate that the use of passive haptics in this application has a statistically significant impact on the user experience and affects their enjoyment, learning progress, as well as the perceived level of realism of the virtual reality simulation.

Discover DaVinci is a novel augmented reality system that incorporates blockchain technology with experiential learning to engage participants in an interactive discovery of Leonardo da Vinci’s ouvre. In the true spirit of this “Renaissance man”, Discover DaVinci explores new ideas and technologies “ahead of their time”. \n \nIn order to illustrate the emerging potential at the intersection of art and blockchain, we present a case study of a new interactive system produced at the University of Florida Digital Worlds Institute. \n \n<iframe src=https://www.youtube.com/embed/0uKWQFqtIuA?feature=oembed width=800 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nThe technologies of mobile computing, augmented reality (AR), and blockchain are starting to merge, creating new opportunities and scenarios to interact with our environment. In AR we can look at virtual objects superimposed within a real environment and resize them, rotate them, explore and interact with them on multiple levels. With the combination of AR and blockchain, we can create a system capable of keeping track of digital assets located virtually in 3D space (i.e., spatial computing). The global scale of blockchain and related technologies heightens the potential for trade and digital distribution with a fully automated and trusted way to keep track of their creations without a “middle-man”. \n \nDiscover DaVinci is a novel educational tool that teaches concepts of blockchain technology through an augmented reality experiential learning game. \n \nThis project was developed in collaboration with several units from the University of Florida and industry partners: \n• Digital Arts & Sciences Faculty (Computer Science and Digital Worlds Institute) \n• Digital Worlds Studios’ Artists and Programmers \n• Gator Blockchain Club (gatorblockchainclub.com) – Student-run blockchain club at the University of Florida \n• Center for Innovation and Entrepreneurship (College of Business) \n• Creative Campus Committee at the University of Florida \nIndustry Partners: \n• DLUX, decentralized content network (dlux.io) \n• Steem (steem.com), and Steemit (steemit.com) \n• A-Frame, web VR platform (aframe.io) \n \nDiscover DaVinci utilizes the format of a digital, collectible trading & drafting card game with AR elements on the STEEM blockchain. Although each player “owns” their cards, all transactions are public. Every collectible card is a unique token, owned by the player - a digital asset registered to the player’s account. The aim is to draw new question cards daily, answer the questions about Leonardo DaVinci, collect the special AR invention cards, and ultimately submit the accumulated card collection into a drawing for prizes. The app was developed to honor the 500th anniversary of Leonardo Davinci by promoting new and innovative technologies.

This artifact depicts the depth map of the Rosetta stone, which was algorithmically generated in 2018 as part of the Digital Rosetta Stone project. The Digital Rosetta Stone is a project developed at Leipzig University by the Chair of Digital Humanities and the Egyptological Institute/Egyptian Museum Georg Steindorff in collaboration with the British Museum and the Digital Epigraphy and Archaeology Project of the University of Florida. The aims of the project are to produce a collaborative digital edition of the Rosetta Stone, address standardization and customization issues for the scholarly community, create data that can be used by students to understand the document in terms of language and content, and produce a high-resolution 3D model of the inscription. The three versions of the text were transcribed and outputted in XML, according to the EpiDoc guidelines. Next, the versions were aligned with the Ugarit iAligner tool that supports the alignment of ancient texts with modern languages, such as English and German. All three texts were then parsed syntactically and morphologically through Treebank annotation. Finally, the project explored new 3D-digitization methodologies of the Rosetta Stone in the British Museum that enhances traditional archaeological methods and facilitates the study of the artifact. The results of this work were used in different courses in Digital Humanities, Digital Philology, and Egyptology.

Human movement classification and analysis are important in the research of health sciences and the arts. Laban movement analysis is an effective method to annotate human movement in dance that describes communication and expression. Technology-supported human movement analysis employs motion sensors, infrared cameras, and other wearable devices to capture critical joints of the human skeleton and facial key points. However, the aforementioned technologies are not mainstream, and the most popular form of motion capture is conventional video recording, usually from a single stationary camera. Such video recordings can be used to evaluate human movement or dance performance. Any methods that can systematically analyze and annotate these raw video footage would be of great importance to this field. Therefore, this research offers an analysis and comparison of AI-based computer vision methods that can annotate the human movement automatically. This study trained and compared four different machine learning algorithms (random forest, K neighbors, neural network, and decision tree) through supervised learning on existing video datasets of dance performances. The developed system was able to automatically produce annotation in the four dimensions (effort, space, shape, body) of Laban movement analysis. The results demonstrate accurately produced annotations in comparison to manually entered ground truth Laban annotation.

Prostate cancer is the most common internal malignancy among males. Micro-Ultrasound is a promising imaging modality for cancer identification and computer-assisted visualization. Identifying the prostate capsule area is essential in active surveillance monitoring and treatment planning. In this paper, we present a pilot study that assesses prostate capsule segmentation using the U-Net deep neural network framework. To the best of our knowledge, this is the first study on prostate capsule segmentation in Micro-Ultrasound images. For our study, we collected multi-frame volumes of Micro-Ultrasound images, and then expert prostate cancer surgeons annotated the capsule border manually. The lack of clear boundaries and variation of shapes between patients make the task challenging, especially for novice Micro-Ultrasound operators. In total 2099 images were collected from 8 subjects, 1296 of which were manually annotated and were split into a training set (1008), a validation set (112), and a test set from a different subject (176). The performance of the model was evaluated by calculating the Intersection over Union (IoU) between the manually annotated area of the capsule and the segmentation mask computed from the trained deep neural network. The results demonstrate high IoU values for the training set (95.05%), the validation set (93.18%) and the test set from a separate subject (85.14%). In 10-fold cross-validation, IoU was 94.25%, and accuracy was 99%, validating the robustness of the model. Our pilot study demonstrates that deep neural networks can produce reliable segmentation of the prostate capsule in Micro-Ultrasound images and pave the road for the segmentation of other anatomical structures within the capsule, which will be the subject of our future studies.

Digital Arts and Sciences curricula have been known for combining topics of emerging technologies and artistic creativity for the professional preparation of future technical artists and other creative media professionals. One of the key challenges in such an interdisciplinary curriculum is the instruction of complex technical concepts to an audience that lacks prior computer science background. This paper discusses how developing small custom virtual and augmented reality game engines can become an effective and engaging method for teaching various fundamental technical topics from Digital Arts and Sciences curricula. Based on empirical evidence, we demonstrate examples that integrate concepts from geometry, linear algebra, and computer programming to 3D modeling, animation, and procedural art. The paper also introduces an open-source framework for implementing such a curriculum in Quest VR headsets, and we provide examples of small-scale focused exercises and learning activities.

<h3>Background</h3> \n<div class=section-paragraph>Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diffusion-weighted MRI can distinguish between parkinsonian syndromes using an automated imaging approach.</div> \n<h3>Methods</h3> \n<div class=section-paragraph>We did an international study at 17 MRI centres in Austria, Germany, and the USA. We used diffusion-weighted MRI from 1002 patients and the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) to develop and validate disease-specific machine learning comparisons using 60 template regions and tracts of interest in Montreal Neurological Institute space between Parkinson's disease and atypical parkinsonism (multiple system atrophy and progressive supranuclear palsy) and between multiple system atrophy and progressive supranuclear palsy. For each comparison, models were developed on a training and validation cohort and evaluated in an independent test cohort by quantifying the area under the curve (AUC) of receiving operating characteristic curves. The primary outcomes were free water and free-water-corrected fractional anisotropy across 60 different template regions.</div> \n<h3>Findings</h3> \n<div class=section-paragraph>In the test cohort for disease-specific comparisons, the diffusion-weighted MRI plus MDS-UPDRS III model (Parkinson's disease <em>vs</em> atypical parkinsonism had an AUC 0·962; multiple system atrophy <em>vs</em> progressive supranuclear palsy AUC 0·897) and diffusion-weighted MRI only model had high AUCs (Parkinson's disease <em>vs</em> atypical parkinsonism AUC 0·955; multiple system atrophy <em>vs</em> progressive supranuclear palsy AUC 0·926), whereas the MDS-UPDRS III only models had significantly lower AUCs (Parkinson's disease <em>vs</em> atypical parkinsonism 0·775; multiple system atrophy <em>vs</em> progressive supranuclear palsy 0·582). These results indicate that a non-invasive imaging approach is capable of differentiating forms of parkinsonism comparable to current gold standard methods.</div> \n<h3>Interpretations</h3> \n<div class=section-paragraph>This study provides an objective, validated, and generalisable imaging approach to distinguish different forms of parkinsonian syndromes using multisite diffusion-weighted MRI cohorts. The diffusion-weighted MRI method does not involve radioactive tracers, is completely automated, and can be collected in less than 12 min across 3T scanners worldwide. The use of this test could positively affect the clinical care of patients with Parkinson's disease and parkinsonism and reduce the number of misdiagnosed cases in clinical trials.</div> \n<h3>Funding</h3> \n<div class=section-paragraph>National Institutes of Health and Parkinson's Foundation.</div>

<iframe src=https://www.youtube.com/embed/DOQcUWMz8xU?feature=oembed width=1200 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nThe basic knowledge of computer programming is generally considered a valuable skill for educated citizens outside computer science and engineering professions. However, learning programming can be a challenging task for beginners of all ages especially outside of formal CS education. This paper presents a novel source code editing method that assists novice users understand the logic and syntax of the computer code they type. The method is based on the concept of text replacements that interactively provide the learners with declarative knowledge and help them transform it to procedural knowledge, which has been shown to be more robust against decay. An active tokenization algorithm splits the typed code into tokens as they are typed and replaces them with a pre-aligned translation in a human natural language. The feasibility of the proposed method is demonstrated in seven structurally different natural languages (English, Chinese, German, Greek, Italian, Spanish, and Turkish) using examples of computer code in ECMAScript (JavaScript). \n \n

<iframe src=https://www.youtube.com/embed/Lsn6bj3X8o8?feature=oembed width=1200 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nThis paper presents a case study for assessing the effect of emoticoding during the students’ first encounter with text-based coding interfaces, in which period a student could have a deeply disappointing experience that may lead to “blank page trauma” as well as negative attitude towards the subject. A prototype metaphor-based source code editor was developed using novel human-computer interaction mechanics based on the concept of emoticon-like scripting. Similarly to the use of shortcuts for typing emoticons in social media, visual or textual replacements appear in the proposed text editor when the user types complete valid tokens from a given programming language. Appropriate metaphors can be used in the design of the token replacements so that they are appealing to a particular age, gender, or cultural groups of users. Quantitative analysis of data from 5^th-grade students (n = 40) shows that metaphor-based emoticoding improves significantly the students’ performance in terms of syntax recall when they transition from block- to text-based programming in comparison to transitioning without emoticoding.

<iframe src=https://www.youtube.com/embed/Lsn6bj3X8o8?feature=oembed width=1200 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nEmoticoding is a technique for learning computer programming that has been shown to improve student learning outcomes and reduce blank page trauma during the students' first encounter with text editing interfaces. In this paper, a generalized method is presented for integrating computer education with other learning topics, such as algebra, geometry, music, and 3D art, using emoticoding. The proposed method is based on the theoretical framework of brain-activating text replacements, which assists students to make connections between the tokens of a typed language (such as computer code) and a set of replacing graphemes (such as interpretative visual or textual replacements). When the computer code is instantly being replaced with graphemes from another learning topic, for example geometric shapes or music notation, the students can build associations between the underlying concepts, which in turn reinforces learning of the associated topics. A work-in-progress user interface with four sets of visual replacements is presented in this paper for substituting the discrete tokens of a computer program (JavaScript) with symbols from algebra, geometry, music notation, and solid shapes. The proposed replacements are demonstrated with computer scripts through the emoticoding framework using learning objectives from K-12 common core standards.

This paper presents the results of an interaction design study that focuses on the use of natural user interfaces for professionals in the fields of epigraphy and archaeology. This study proposes solutions for utilizing the sensors that can be found in popular handheld devices, such as tablets and smart phones, in order to naturally perform common tasks from the typical work-flow of epigraphists. The developed interface allows the users to naturally hold digitized inscriptions, interact with them in order to relight or manipulate them as if they were real physical objects, and interact with metadata or other multi-modal data, such as text and images.

Started in October 2012, the Trophy Catch program run by the Florida Fish and Wildlife Conservation Commission (FWC) has been used to encourage catch-and-release of the largest, oldest, and most valuable bass in Florida’s waters. A trophy bass is defined by the program as one that weighs greater than eight pounds. The Trophy Catch program provides incentives for anglers to serve as citizen scientists by properly recording data on their catch and submitting it online so that FWC can better enhance, conserve, and promote trophy bass fishing. Trophy Catch participants who successfully submit a “Hall of Fame” bass, which is a bass weighing greater than thirteen pounds receive a replica mount of their fish. The current process to create a replica mount requires taxidermists to have a collection of fish molds of similar length and girth to the fish the angler caught. The trophy fish used to make these molds are rare and must be sacrificed to make the mold. Most taxidermists’ collections consist of only a few molds. Our goals are to explore the suitability of three-dimensional (3D) modeling to capture the structure of a trophy Largemouth Bass and to assess the current limitations of the technology to produce models suitable for 3D printing. \n

The Digital Epigraphy and Archaeology Project (DEA) is a unique initiative in the field of digital epigraphy as it provides the methods to digitize in 3D squeezes (ektypa, Abklatsch, estampages), plaster casts of coins and seals, lace, and paper embossments, using cost-effective 3D reconstruction technique that utilizes images taken by regular office scanners. The Digital Epigraphy Toolbox is a web application that focuses on the digitisation, 3D visualisation, data mining, and electronic dissemination of such artefacts. Also, it offers several visualisation modules, thus facilitating the electronic dissemination and study of historically important artefacts. A current phase of the project involves the digitisation of Latin inscriptions from Rome, Spain, North Africa, and Eastern Mediterranean from the CIL collection. Latin inscriptions are of inestimable value as sources for the study of Roman life and history in all its aspects. As a direct legacy of the ancient world, they are speaking evidence of a past culture whose enduring influence has shaped Europe. As such they provide a reliable guide through the rubble of archaeological remains and are often our first means of placing ancient monuments in their everyday context. From early Rome – although firstly in significant numbers from the time of Augustus – right through the imperial period to the sixth century AD, Latin inscriptions are a constant feature of the thousand-year history of the city of Rome, its provinces and people; as a widely used medium they reflect communication within that society in all its facets. Most of the existing databases only provide access to textual information assembled from previous printed editions or still photographs – a fact that limits the potential for original research, as the scholar is reduced to studying the information from someone else’s perspective. The DEA is not only meant to be another database that also includes alternative visual representations; its goal is to use those state-of-the-art technologies so that the researchers may study the text and lettering technique of the inscriptions anew, having access to the 3D model of squeeze and being able to visually manipulate it. Finally, it is not feasible to conduct manual study and examine the lettering techniques of hundreds of squeezes in a reasonable amount of time without automatic computer-assisted techniques. The Digital Epigraphy team develops methods for the automatic segmentation of letters of each inscription and automatic grouping. This module is meant to present the affinities between all the letters in the form of dendrograms, effectuating automatic comparison of hundreds of inscriptions in a manner of seconds. The purpose of this process will be to give the researcher the opportunity to date, identify pieces of broken inscriptions, and perform numerous additional analyses that up to now require incalculable work hours, if they are at all possible in some cases. When contested fragments are housed in different institutions, if their digital copies exist online, it will be a unique opportunity to be identified and paired.

A significant amount of research has been involved with the development of advanced driver-assistance systems. Such systems typically include radars, laser or video sensors that detect the vehicle trajectory and warn for an imminent lane departure, or sense the front vehicle's speed and apply the brakes of the following vehicle to maintain safe distance headways (i.e., collision avoidance system). However, most of these systems rely on the subject vehicle and surrounding vehicles' position and do not explicitly consider the driver's actions during the driving task. In addition, safety research has focused on eye tracking as a means of capturing driver's attention, fatigue, or drowsiness; however, the body posture has not been investigated in depth. This paper presents a novel approach for studying the actual movements of drivers inside the vehicle, when performing specific maneuver types such as lane changing and merging. A pilot study was conducted along a freeway and arterial segment, where the 3D shapes of selected participants were constructed with the use of Microsoft Kinect range camera while merging and changing lanes. A 7-point human skeletal model was fit to the captured range data (depth frame sequences) using the proposed framework. The analysis of the captured 3D data showed that there are important differences between participants when performing similar driving maneuvers. The preliminary results of this pilot research set the basis for implementing the proposed methodological framework for conducting full-scale experiments with a variety of participants, and exploring differences due to driver behavior attributes, such as age, gender and driving experience.

In this paper a novel framework is presented for interactive feature-based retrieval and visualization of human statues, using depth sensors for mobile devices. A skeletal model is fitted to the depth image of a statue or human body in general and is used as a feature vector that captures the pose variations in a given collection of skeleton data. A scale- and twist- invariant distance function is defined in the feature space and is employed in a topology-preserving low-dimensional lattice mapping framework. The user can interact with this self-organizing map by submitting queries in the form of a skeleton from a statue or a human body. The proposed methods are demonstrated in a real dataset of 3D digitized Graeco-Roman statues from Palazzo Altemps.

This article explores one application of a digital imaging technique to historic objects and online delivery of the virtual object. Shape-from-shading is a computer algorithm used to reconstruct three-dimensional shapes from two-dimensional images. The Digital Epigraphy and Archaeology (DEA) project used shape-from-shading to develop a digital website and toolbox to enhance computer-assisted examination of inscriptions preserved as ektypa. This article reports the results of a project to see if the DEA website and toolbox could be used with other materials and objects with information-rich textured surfaces. Seventeen samples were tested, including lace-like textiles placed on different backing materials; wax seal impressions made from a range of materials; and excavated metals. It was found that image capture with a flatbed scanner achieved better results than a multi-functional printer, and that the unprocessed flatbed scan images were sometimes more informative than digital photographs. It was possible to modify the DEA algorithm for both the lace-like samples and the seal impressions to obtain good three-dimensional (3D) reconstructed images. However, this was not possible with the metal samples due to the nature of the material. The 3D reconstructed images are useful to online users in providing an interactive resource that can be manipulated by the user, rather than a two-dimensional (2D) image.

I contributi qui presentati sono il risultato dei lavori del panel «Technology &amp; \nTradition: A Synergic Approach to Deciphering, Analyzing and Annotating \nEpigraphic Writings», organizzato da M. Lamé e F. Boschetti, tenutosi il 30 \nsettembre 2014 presso l’École Normale Supérieure di Parigi nell’ambito della \nInternational Conference on Information Technologies for Epigraphy and Digital \nCultural Heritage in the Ancient World. L’edizione dei lavori è a cura di M. Lamé e \nG. Sarullo. I limiti di spazio e di espressività della carta hanno portato ad una \ndialettica tra la sinteticità e la staticità del discorso scritto, presentato in queste \npagine, e la dinamicità delle riflessioni preliminari degli autori, consultabili per \nesteso, commentate e ulteriormente aggiornate, su Épigraphie en Réseau1, dove \nproseguono il dialogo e l’interattività. I due livelli si sono articolati in modo \ncomplementare, per un maggior profitto del lettore.

Research has shown that driver inattention is the most prevalent cause of traffic collisions accounting for an estimated 25 to 56% of crashes in the US. Driver inattention may result from drivers engagement in secondary activities (such as texting or cell-phone use), or lack of awareness of the surrounding environment. The main objective of this research is to investigate the relationship between potentially unsafe driving events and the actual driver body posture and movements when performing a driving maneuver under different traffic configurations. The paper presents results from a pilot study that captured the 3-D posture and activity of three drivers while performing both mandatory (merging) and discretionary (lane changing) maneuvers on freeway and arterial segments in Gainesville, Florida. The body posture of the drivers was captured through the use of a low-cost infrared depth sensor. A 7-point human skeletal model was fit to the captured depth frame sequences using our proposed framework. The comparative analysis of the participants' body movements while performing the maneuvers revealed differences between the participants' body activity when performing the same maneuvers. The findings of this research provide significant insights regarding which body movements may hide unsafe situations while performing a driving maneuver that requires the attention of the surrounding environment.

The learning objectives of this chapter are the following: The reader will understand the strengths and limitations of various types of artificial neural networks. Several representative neural network algorithms will be presented and discussed in detail. Practical applications of neural networks will be explored by the reader through a set of exercises at the end of the chapter.

A significant amount of research has been involved with the development of advanced driver-assistance systems. Such systems typically include radars, laser or video sensors that detect the vehicle trajectory and warn for an imminent lane departure, or sense the front vehicle's speed and apply the brakes of the following vehicle to maintain safe distance headways (i.e., collision avoidance system). However, most of these systems rely on the subject vehicle and surrounding vehicles' position and do not explicitly consider the driver's actions during the driving task. In addition safety research has focused on eye tracking as a means of capturing driver's attention, fatigue, or drowsiness; however, the body posture has not been investigated in depth. This paper presents a novel approach for studying the actual movements of drivers inside the vehicle, when performing specific maneuver types such as lane changing and merging. This information can be useful for identifying specific body movements that may hide potentially unsafe situations. A pilot study was conducted along a freeway and arterial segment, where the 3D shapes of selected participants were constructed with the use of a low-cost infrared depth sensor (Microsoft Kinect) while merging and changing lanes. The analysis of the 3D shapes shows that there are important differences between participants when performing similar driving maneuvers. The preliminary results of this pilot research set the basis for implementing the proposed methodological framework for conducting full-scale experiments with a variety of participants, and exploring differences due to driver behavior attributes, such as age, gender and driving experience.

In this paper a framework is presented for monitoring shape changes on the human body with applications to obesity control. This framework uses a lowcost infrared depth camera in order to capture the 3D shape of the human body and approximate it as a set of spherical functions.

Information on the directionality and structure of axonal fibers in neural tissue can be obtained by analyzing Diffusion-Weighted MRI datasets. Several fiber tracking algorithms have been presented in literature that trace the underlying field of principal orientations of water diffusion, which correspond to the local primary eigenvectors of the diffusion tensor field. However, the majority of the existing techniques ignore the secondary and tertiary orientations of diffusion, which contain significant information on the local patterns of diffusion. In this paper we introduce the idea of perpendicular fiber tracking and we present a novel dynamic programming method that traces surfaces, which are locally perpendicular to the axonal fibers. This is achieved by using a cost function, with geometric and fiber orientation constraints, that is evaluated dynamically for every voxel in the image domain starting from a given seed point. The proposed method is tested using synthetic and real DW-MRI datasets. The results conclusively demonstrate the accuracy and effectiveness of our method.

Preservation and dissemination of archaeological material has always been an issue of concern for the academic research community. On the one hand, the fragility of the material limits their study. On the other hand, such material is housed in museums, libraries, and institutions worldwide, something that significantly thwarts their accessibility. Technology, high resolution 2D pictures, and electronic databases have attempted to overcome the aforementioned limitations. However, lack of contact with the physical object as a tridimensional structure still significantly obstructs research. In this paper we present the latest advances of the Digital Epigraphy Toolbox, a novel project that focuses on the digitization in 3D of ancient inscriptions from ektypa, the multi-modal visualization of their 3D models, the facilitation of interlinked 3D digitized records, and the easy and effective electronic dissemination of archaeological material. This project offers options for cost-effective shape-from-shading 3D digitization of ektypa, using a flatbed scanner, and various visualization modes, such as photorealistic 3D views and informative fingerprint map and depth map that assist scholars understand the structural characteristics of the artifacts. Finally, the project facilitates the dissemination of the 3D digitized objects by providing the users with an embeddable 3D viewer which can be easily imported in third-party databases, collections, and personal websites.

During the last decade the area of Diffusion Weighted MRI analysis has gained significant scientific attention and many techniques have been introduced for analyzing the diffusivity patterns in neural tissues and visualizing the neural fiber tracts. However, the majority of these methods have not been adopted for clinical use due to various reasons, including the computational demands as well as the large complexity of the analyzed neural fiber tracking information. In this paper we present an interactive algorithm for fiber bundle analysis that computes in real time statistics on the variation of the structure, size, and curvature along a fiber bundle. The proposed fiber metrics can be used as markers for diagnosing and monitoring atrophy for epilepsy, schizophrenia, depression, hypoxia-ischemia, trauma, Alzheimers disease, and other dementias.

Tensors of various orders can be used for modeling physical quantities such as strain and diffusion as well as curvature and other quantities of geometric origin. Depending on the physical properties of the modeled quantity, the estimated tensors are often required to satisfy the positivity constraint, which can be satisfied only with tensors of even order. Although the space P^2m_0 of 2m^th- order symmetric positive semi-definite tensors is known to be a convex cone, enforcing positivity constraint directly on P^2m_0 is usually not straightforward computationally because there is no known analytic description of P^2m_0 for m &gt; 1. In this paper, we propose a novel approach for enforcing the positivity constraint on even-order tensors by approximating the cone P^2m_0 for the cases 0 &lt; m &lt; 3, and presenting an explicit characterization of the approximation Sigma_2m &lt; Omega_2m for m &gt;= 1, using the subset Omega_2m &lt; P^2m_0 of semi-definite tensors that can be written as a sum of squares of tensors of order m. Furthermore, we show that this approximation leads to a non-negative linear leastsquares (NNLS) optimization problem with the complexity that equals the number of generators in Sigma_2m. Finally, we experimentally validate the proposed approach and we present an application for computing 2mth-order diffusion tensors from Diffusion Weighted Magnetic Resonance Images.

A novel method for estimating a field of fiber orientation distribution (FOD) based on signal de-convolution from a given set of diffusion weighted magnetic resonance (DW-MR) images is presented. We model the FOD by higher order Cartesian tensor basis using a parametrization that explicitly enforces the positive semi-definite property to the computed FOD. The computed Cartesian tensors, dubbed Cartesian Tensor-FOD (CT-FOD), are symmetric positive semi-definite tensors whose coefficients can be efficiently estimated by solving a linear system with non-negative constraints. Next, we show how to use our method for converting higher-order diffusion tensors to CT-FODs, which is an essential task since the maxima of higher-order tensors do not correspond to the underlying fiber orientations. Finally, we propose a diffusion anisotropy index computed directly from CT-FODs using higher order tensor distance measures thus consolidating the whole analysis pipeline of diffusion imaging solely using CT-FODs. We evaluate our method qualitatively and quantitatively using simulated DW-MR images, phantom images, and human brain real dataset. The results conclusively demonstrate the superiority of the proposed technique over several existing multifiber reconstruction methods.

In our presentation we intend to introduce the Digital Epigraphy Toolbox which is a crossplatform web-application designed to facilitate the digital preservation, study, and electronic dissemination of ancient inscriptions. It allows epigraphists to digitize in 3D their epigraphic squeezes using our novel cost-effective technique, which overcomes the limitations of the current methods for digitizing epigraphic data in 2-dimensions only. Our toolbox contains several options for 3D visualization of inscriptions, as well as a set of scientific tools for analyzing the lettering techniques and performing quantitative analysis of the letterform variations. Furthermore, the users have the option to share their data, as well as search other uploaded collections of 3D inscriptions in a semi-supervised dynamic library. This dynamic library is organized thematically according to language, area of origin, and date and contains a comprehensive record of the inscription in the form of plain text, 3D model, 2D photographs, and other epigraphic information. \n \n<iframe src=https://www.youtube.com/embed/WUd333VdgCk?feature=oembed width=800 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n

Several tensor-based models have been presented in literature for parameterizing the water diffusion in Diffusion-Weighted MRI datasets, namely Diffusion Tensor Imaging (DTI), Generalized Tensor Imaging (GTI), and Diffusion Kurtosis Imaging (DKI). In this paper we use homogeneous trivariate polynomials to show that GTI is a special case of DKI for single angular shell acquisitions, and then we employ the theory for imposing positive semi-definite (PSD) constraints to GTIs in order to performrobust estimation of the DKI parameters. We propose a novel framework for DKI estimation that simultaneously imposes constraints to the diffusivity function, diffusion tensor and diffusion kurtosis. These three constraints are parameterized explicitly as a set of linear systems that can be efficiently solved using the non-negative least squares technique. The robustness of our framework is demonstrated using synthetic and real data from a human brain.

Cartesian tensors of various orders have been employed for either modeling the diffusivity or the orientation distribution function in Diffusion-Weighted MRI datasets. In both cases, the estimated tensors have to be positive-definite since they model positive-valued functions. In this paper we present a novel unified framework for estimating positive-definite tensors of any order, in contrast to the existing methods in literature, which are either order-specific or fail to handle the positive-definite property. The proposed framework employs a homogeneous polynomial parametrization that covers the full space of any order positive-definite tensors and explicitly imposes the positive-definite constraint on the estimated tensors. We show that this parametrization leads to a linear system that is solved using the non-negative least squares technique. The framework is demonstrated using synthetic and real data from an excised rat hippocampus.

In this paper we present a novel method for estimating a field of orientation distribution functions (ODF) from a given set of Diffusion- Weighted MR images. In our technique the ODF is modeled by Cartesian tensor basis using a parametrization that explicitly enforces the positive definite property to the computed ODFs. The computed Cartesian tensors, dubbed Cartesian Tensor-ODFs (CT-ODFs), are symmetric positive definite tensors whose coefficients can be efficiently estimated by solving a linear system with non-negative constraints. Furthermore, we show how to use our method for converting higher-order diffusion tensors to CT-ODFs, which is an essential task since the maxima of higher-order tensors do not correspond to the underlying fiber orientations. We quantitatively evaluate our method using simulated DW-MR images as well as a real brain dataset from a post-mortem porcine brain. The results conclusively demonstrate the superiority of the proposed technique over several existing multi-fiber reconstruction methods.

In Diffusion Weighted Magnetic Resonance Image (DW-MRI) processing, a 2nd order tensor has been commonly used to approximate the diffusivity function at each lattice point of the DW-MRI data. From this tensor approximation, one can compute useful scalar quantities (e.g. anisotropy, mean diffusivity) which have been clinically used for monitoring encephalopathy, sclerosis, ischemia and other brain disorders. It is now well known that this 2nd-order tensor approximation fails to capture complex local tissue structures, e.g. crossing fibers, and as a result the scalar quantities derived from these tensors are grossly inaccurate at such locations. In this paper we employ a 4th order symmetric positive-definite (SPD) tensor approximation to represent the diffusivity function and present a novel technique to estimate these tensors from the DW-MRI data guaranteeing the SPD property. Several articles have been reported in literature on higher order tensor approximations of the diffusivity function but none of them guarantee the positivity of the estimates, which is a fundamental constraint since negative values of the diffusivity are not meaningful. In this paper we represent the 4th-order tensors as ternary quartics and then apply Hilbert's theorem on ternary quartics along with the Iwasawa parametrization to guarantee an SPD 4th-order tensor approximation from the DW-MRI data. The performance of this model is depicted on synthetic data as well as real DW-MRIs from a set of excised control and injured rat spinal cords, showing accurate estimation of scalar quantities such as generalized anisotropy and trace as well as fiber orientations.

Modeling illumination effects and pose variations of a face is of fundamental importance in the field of facial image analysis. Most of the conventional techniques that simultaneously address both of these problems work with the Lambertian assumption and thus, fall short of accurately capturing the complex intensity variation that the facial images exhibit or recovering their 3D shape in presence of specularities and cast shadows. In this paper we present a novel anti-symmetric tensor spline based framework for facial image analysis. We show that using this framework, facial apparent BRDF field can be accurately estimated while seamlessly accounting for cast shadows and specularities. Further, using local neighborhood information, the same framework can be exploited to recover the 3D shape of the face (to handle pose variation). We quantitatively validate the accuracy of the anti-symmetric tensor spline model using a more general continuous mixture of single lobed spherical functions. We demonstrate the effectiveness of our technique by presenting extensive experimental results for face relighting, 3D shape recovery and face recognition using the Extended Yale B and CMU PIE benchmark datasets.

Registration of Diffusion-Weighted MR Images (DW-MRI) can be achieved by registering the corresponding 2nd-order Diffusion Tensor Images (DTI). However, it has been shown that higher-order diffusion tensors (e.g. order-4) outperform the traditional DTI in approximating complex fiber structures such as fiber crossings. In this paper we present a novel method for unbiased group-wise non-rigid registration and atlas construction of 4th-order diffusion tensor fields. To the best of our knowledge there is no other existing method to achieve this task. First we define a metric on the space of positive-valued functions based on the Riemannian metric of real positive numbers (denoted by R+). Then, we use this metric in a novel functional minimization method for non-rigid 4th-order tensor field registration. We define a cost function that accounts for the 4th-order tensor re-orientation during the registration process and has analytic derivatives with respect to the transformation parameters. Finally, the tensor field atlas is computed as the minimizer of the variance defined using the Riemannian metric. We quantitatively compare the proposed method with other techniques that register scalar-valued or diffusion tensor (rank-2) representations of the DWMRI. This comparison is achieved using synthetic data and atlas construction results are depicted for real human hippocampal data sets.

In this paper we present a novel method for multi-fiber reconstruction given a diffusion-weighted MRI dataset. There are several existing methods that employ various spherical deconvolution kernels for achieving this task. However the kernels in all of the existing methods rely on certain assumptions regarding the properties of the underlying fibers, which introduce inaccuracies and unnatural limitations in them. Our model is a non trivial generalization of the spherical deconvolution model, which unlike the existing methods does not make use of a fix-shaped kernel. Instead, the shape of the kernel is estimated simultaneously with the rest of the unknown parameters by employing a general adaptive model that can theoretically approximate any spherical deconvolution kernel. The performance of our model is demonstrated using simulated and real diffusion-weighed MR datasets and compared quantitatively with several existing techniques in literature. The results obtained indicate that our model has superior performance that is close to the theoretic limit of the best possible achievable result.

Recently various mathematical models have been proposed to model the signal attenuation obtained from Diffusion Weighted Magnetic Resonance Imaging (DW-MRI). Though effective to various extents, almost all of the existing methods involve model parameters which are abstract mathematical quantities without any tangible connection to physical quantities (e.g. the b-value, gradient pulse duration, pulse separation etc.) involved in the DW-MRI acquisition process. To address this disconnect, in this paper, we present a multi-compartmental model which uses a physical model for restricted diffusion in the cylindrical geometry as the constituent basis function for multi-fiber reconstruction. Through extensive experiments on synthetic data we establish the superiority of the proposed method over the state-of-the-art techniques in terms of fiber orientation detection accuracy. We also present detailed results using human and rat brain data and demonstrate that our method leads to meaningful multi-fiber reconstruction even in the case of real data.

Concepts from Information Theory have been used quite widely in Image Processing, Computer Vision and Medical Image Analysis for several decades now. Most widely used concepts are that of KL-divergence, minimum description length (MDL), etc. These concepts have been popularly employed for image registration, segmentation, classification etc. In this chapter we review several methods, mostly developed by our group at the Center for Vision, Graphics & Medical Imaging in the University of Florida, that glean concepts from Information Theory and apply them to achieve analysis of Diffusion-Weighted Magnetic Resonance (DW-MRI) data. This relatively new MRI modality allows one to non-invasively infer axonal connectivity patterns in the central nervous system. The focus of this chapter is to review automated image analysis techniques that allow us to automatically segment the region of interest in the DWMRI image wherein one might want to track the axonal pathways and also methods to reconstruct complex local tissue geometries containing axonal fiber crossings. Implementation results illustrating the algorithm application to real DW-MRI data sets are depicted to demonstrate the effectiveness of the methods reviewed. \n

Studying ancient inscriptions is based up to date mostly on observation and manual analysis by means of which epigraphists attempt to establish a geographical and chronological classification as well as to analyze the lettering techniques. In this paper we propose a novel framework for efficient 3D reconstruction of inscriptions and for statistical analysis of their reconstructed surfaces. \n \n<iframe src=https://www.youtube.com/embed/dt1CpBkZDNQ?feature=oembed width=800 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nThe proposed framework employs a shape-from-shading technique to reconstruct in 3D the shape of the inscribed surfaces. The obtained surfaces are segmented into smaller box-shaped regions containing single letters. These letters are classified into groups of same characters or symbols and then an atlas (average) letter shape is created for each character. For the construction of those atlases we employ a functional minimization method that registers the surfaces of same letters to the unknown average surface, which is also estimated simultaneously. Using the estimated atlases an automated analysis of the inscribed letters is performed. This framework can be effectively used for the study of the variations of the lettering techniques within an inscription or a set of inscriptions. We applied our framework to five ancient Greek inscriptions. Our results are reported in detail and the variations found in lettering techniques are commented on by archaeologists who also validate the accuracy of our proposed method.

In this paper we present a novel method for estimating a field of asymmetric spherical functions, dubbed tractosemas, given the intra-voxel displacement probability information. The peaks of tractosemas correspond to directions of distinct fibers, which can have either symmetric or asymmetric local fiber structure. This is in contrast to the existing methods that estimate fiber orientation distributions which are naturally symmetric and therefore cannot model asymmetries such as splaying fibers. We propose a method for extracting tractosemas from a given field of displacement probability iso-surfaces via a diffusion process. The diffusion is performed by minimizing a kernel convolution integral, which leads to an update formula expressed in the convenient form of a discrete kernel convolution. The kernel expresses the probability of diffusion between two neighboring spherical functions and we model it by the product of Gaussian and von-Mises distributions. The robustness of our model in estimating accurate fiber orientations is validated via experiments on synthetic and real diffusion-weighted magnetic resonance (DW-MRI) datasets from an isolated rat hippocampus and a cats spinal cord.

Human faces are neither exactly Lambertian nor entirely convex and hence most models in literature which make the Lambertian assumption, fall short when dealing with specularities and cast shadows. In this paper, we present a novel anti-symmetric tensor spline (a spline for tensor-valued functions) based method for the estimation of the Apparent BRDF (ABRDF) field for human faces that seamlessly accounts for specularities and cast shadows. Furthermore, unlike other methods, it does not require any 3D information to build the model and can work with as few as 9 images. In order to validate the accuracy of our anti-symmetric tensor spline model, we present a novel approximation of the ABRDF using a continuous mixture of single-lobed spherical functions. We demonstrate the effectiveness of our anti-symmetric tensor-spline model in comparison to other popular models in the literature, by presenting extensive results for face relighting and face recognition using the Extended Yale B database.

In this paper we propose a method for reconstructing the Diffusion Weighted Magnetic Resonance (DW-MR) signal at each lattice point using a novel continuousmixture of von Mises-Fisher distribution functions. Unlike most existing methods, neither does this model assume a fixed functional form for the MR signal attenuation (e.g. 2nd or 4th order tensor) nor does it arbitrarily fix important mixture parameters like the number of components. We show that this continuous mixture has a closed form expression and leads to a linear system which can be easily solved. Through extensive experimentation with synthetic data we show that this technique outperforms various other state-of-the-art techniques in resolving fiber crossings. Finally, we demonstrate the effectiveness of this method using real DW-MRI data from rat brain and optic chiasm.

Cartesian tensor basis have been widely used to approximate spherical functions. In Medical Imaging, tensors of various orders have been used to model the diffusivity function in Diffusion-weighted MRI data sets. However, it is known that the peaks of the diffusivity do not correspond to orientations of the underlying fibers and hence the displacement probability profiles should be employed instead. In this paper, we present a novel representation of the probability profile by a 4th order tensor, which is a smooth spherical function that can approximate single-fibers as well as multiple-fiber structures. We also present a method for efficiently estimating the unknown tensor coefficients of the probability profile directly from a given high-angular resolution diffusion-weighted (HARDI) data set. The accuracy of our model is validated by experiments on synthetic and real HARDI datasets from a fixed rat spinal cord.

In this paper, we present novel algorithms for statistically robust interpolation and approximation of diffusion tensors - which are symmetric positive definite (SPD) matrices - and use them in developing a significant extension to an existing probabilistic algorithm for scalar field segmentation, in order to segment DT-MRI data sets. Using the Riemannian metric on the space of SPD matrices, we present a novel and robust higher order (cubic) continuous tensor product of B-splines algorithm to approximate the SPD diffusion tensor fields. The resulting approximations are appropriately dubbed tensor splines. Next, we segment the diffusion tensor field by jointly estimating the label (assigned to each voxel) field, which is modeled by a Gauss Markov Measure Field (GMMF) and the parameters of each smooth tensor spline model representing the labeled regions. Results of interpolation, approximation and segmentation are presented for synthetic data and real diffusion tensor fields from an isolated rat hippocampus, along with validation. We also present comparisons of our algorithms with existing methods and show significantly improved results in the presence of noise as well as outliers.

Registration of Diffusion Weighted (DW)-MRI datasets has been commonly achieved to date in literature by using either scalar or 2nd-order tensorial information. However, scalar or 2nd-order tensors fail to capture complex local tissue structures, such as fiber crossings, and therefore, datasets containing fiber-crossings cannot be registered accurately by using these techniques. In this paper we present a novel method for non-rigidly registering DW-MRI datasets that are represented by a field of 4th-order tensors. We use the Hellinger distance between the normalized 4th-order tensors represented as distributions, in order to achieve this registration. Hellinger distance is easy to compute, is scale and rotation invariant and hence allows for comparison of the true shape of distributions. Furthermore, we propose a novel 4th-order tensor re- transformation operator, which plays an essential role in the registration procedure and shows significantly better performance compared to the re-orientation operator used in literature for DTI registration. We val- idate and compare our technique with other existing scalar image and DTI registration methods using simulated diffusion MR data and real HARDI datasets. \n

In Diffusion Tensor Magnetic Resonance Image (DT-MRI) processing a 2nd order tensor has been commonly used to approximate the diffusivity function at each lattice point of the 3D volume image. These tensors are symmetric positive definite matrices and the appropriate constraints required in algorithms for processing them makes these algorithms complex and significantly increases their computational complexity. In this paper we present a novel parameterization of the diffusivity function using which the positive definite property of the function is guaranteed without any increase in computation. This parameterization can be used for any order tensor approximations; we present Cartesian tensor approximations of order 2, 4, 6 and 8 respectively, of the diffusivity function all of which retain the positivity property in this parameterization without the need for any explicit enforcement. Furthermore, we present an efficient framework for computing distances and geodesics in the space of the coefficients of our proposed diffusivity function. Distances & geodesics are useful for performing interpolation, computation of statistics etc. on high rank positive definite tensors. We validate our model using real diffusion weighted MR data from excised, perfusion-fixed rat optic chiasm.

In Diffusion Weighted Magnetic Resonance Image (DW-MRI) processing a 2nd order tensor has been commonly used to approximate the diffusivity function at each lattice point of the DW-MRI data. It is now well known that this 2nd-order approximation fails to approximate complex local tissue structures, such as fibers crossings. In this paper we employ a 4th order symmetric positive semi-definite (PSD) tensor approximation to represent the diffusivity function and present a novel technique to estimate these tensors from the DW-MRI data guaranteeing the PSD property. There have been several published articles in literature on higher order tensor approximations of the diffusivity function but none of them guarantee the positive semi-definite constraint, which is a fundamental constraint since negative values of the diffusivity coefficients are not meaningful. In our methods, we parameterize the 4th order tensors as a sum of squares of quadratic forms by using the so called Gram matrix method from linear algebra and its relation to the Hilbert's theorem on ternary quartics. This parametric representation is then used in a nonlinear-least squares formulation to estimate the PSD tensors of order 4 from the data. We define a metric for the higher-order tensors and employ it for regularization across the lattice. Finally, performance of this model is depicted on synthetic data as well as real DW-MRI from an isolated rat hippocampus.

Lattice based neural networks are capable of resolving some difficult non-linear problems and have been successfully employed to solve real-world problems. In this paper a novel model of a lattice neural network (LNN) is presented. This new model generalizes the standard basis lattice neural network (SB-LNN) based on dendritic computing. In particular, we show how each neural dendrite can work on a different orthonormal basis than the other dendrites. We present experimental results that demonstrate superior learning performance of the new Orthonormal Basis Lattice Neural Network (OB-LNN) over SB-LNNs.

In this paper, we present a novel and robust spline approximation algorithm given a noisy symmetric positive definite (SPD) tensor field. Such tensor fields commonly arise in the field of Medical Imaging in the form of Diffusion Tensor (DT) MRI data sets. We develop a statistically robust algorithm for constructing a tensor product of B-splines - for approximating and interpolating these data - using the Riemannian metric of the manifold of SPD tensors. Our method involves a two step procedure wherein the first step uses Riemannian distances in order to evaluate a tensor spline by computing a weighted intrinsic average of diffusion tensors and the second step involves minimization of the Riemannian distance between the evaluated spline curve and the given data. These two steps are alternated to achieve the desired tensor spline approximation to the given tensor field. We present comparisons of our algorithm with four existing methods of tensor interpolation applied to DT-MRI data from fixed heart slices of a rabbit, and show significantly improved results in the presence of noise and outliers. We also present validation results for our algorithm using synthetically generated noisy tensor field data with outliers. This interpolation work has many applications e.g., in DT-MRI registration, in DT-MRI Atlas construction etc. This research was in part funded by the NIH ROI NS42075 and the Department of Radiology, University of Florida. \n

Lattice based neural networks are capable of resolving some difficult non-linear problems and have been successfully employed to solve real-world problems. In this paper a novel model of a lattice neural network (LNN) is presented. This new model generalizes the standard basis lattice neural network (SB-LNN) based on dendritic computing. In particular, we show how each neural dendrite can work on a different orthonormal basis than the other dendrites. We present experimental results that demonstrate superior learning performance of the new Orthonormal Basis Lattice Neural Network (OB-LNN) over SB-LNNs.

Smart dust is envisaged as swarms of miniature communication/sensor devices useful for remote monitoring in space exploration. With diameters and densities comparable to sand particles the behaviour of passive dust will be identical to the movement of airborne sand. Here we examine algorithms for the adaptive shape change of smart dust modes that permits a change in drag coefficient depending on whether or not the random motion is in a favourable direction. Monte Carlo simulations are reported for swarms of smart dust devices transporting in the wind-dominated environment of the Martian landscape. It is concluded that relatively simple shape changing algorithms, activated through an electro-active polymer sheath, will permit self-organised transport over large distances.

Smart dust has been conceived as millimeter scale autonomous systems that form the basis for massively distributed wireless sensor networks according to B. A. Warneke and K. S. J. Pister (2002) and B. A. Warneke and K. S. J. Pister (2004). Smart dust motes have been demonstrated that pack sensors, interfaces, power sources, digital control communications and processing circuitry into a few cubic millimeters volume. The authors address the problem of how to subsequently move dust motes around in their application environment. Solutions involving robot insect motes have been advocated where distances and times are small; but this introduces additional mechanical and electronic complexity plus severe constraints on power sources. Instead, the authors focus on the possibility of extracting power from the natural fluctuating forces that act on the motes.

In this paper an algorithm that processes a video sequence for humean face 3D pose estimation, is presented. The procedure that is followed is described briefly below. In the beginning, facial features are extracted for each frame. Afterwards, these are used in order to make an initial estimation of their position in 3D space. The results produced are optimized, either by taking into consideration anthropometric features, or by using a 3D model of the human face. In that way, the initial prediction is greately improved and the resulting accuracy is more than satisfactory. Such rechniques could process videos displaying news, journalists, actors or even people in general, or even be used in object-based techniques for video coding (eg. MPEG-4), in machine vision applications and in human computer interaction enviroments.

Systems and Techniques for real-time 3D reconstruction of the human body are described. Avatars (the rendered 3D reconstruction of the human body) can be generated from real-time captured RGB-D images of a person. Avatars can be synthesized from the RGB-D data received from a single RGB-D camera by performing body segmentation (into cylindrical-type objects) and dynamic robust data filtering on sequential frames of the captured data. Cylindrical-type objects of the body, including arms, legs, and torso are parameterized using tensor splines; and positive-definite constrains are imposed to the estimated tensor splines using a Riemannian metric defined on the space of positive-definite tensor splines. These generated avatars have an articulated body with separately translatable and rotatable arms, legs, and other limbs or cylindrical features.

Although significant advances have been done with respect to vehicle technology and roadway construction, driver behavior remains the number one contributing factor of traffic crashes worldwide. Studies show that one of the major causes of crashes is driver inattention. Driver inattention may occur when drivers are involved with secondary activities (e.g., texting, talking on the phone, or eating), and when they fail to follow the cues of the surrounding environment while driving. The latter is particularly important when drivers are negotiating maneuvers and are required to interact with other vehicles as in the case of changing lanes or merging onto the freeway. The main objective of this research is to investigate the relationship between driver behavior and safety, by looking at the actual body movements and posture, as well as the eye fixation of the drivers when they are performing lane changing and merging maneuvers under different traffic conditions. To accomplish this objective, a total of 35 drivers were recruited to participate in an instrumented vehicle field study, where each participant drove for approximately two hours along a pre-selected route. Participants’ 3D body posture was recorded with the use of a low-cost infrared depth sensor (Microsoft Kinect). In addition, participants’ eye gaze throughout the entire data collection effort was recorded with the help of an eye-tracking equipment. Lastly, the vehicle was equipped with two cameras that faced the front and the rear, and therefore, information about the traffic conditions during the data collection period was obtained. A rich dataset of driver behavior was developed and analyzed as part of this research. The analysis findings relate the 3D sequence of driver motion and posture with the actual eye and head movement of drivers. The paper presents the research approach, summarizes findings, and provides recommendations for enhancing traffic safety. The findings are expected to assist in establishing monitoring guidelines for advanced driver assistance systems that take into account the driver’s body position and movements, rather than considering solely the vehicle position relative to other vehicles on the road. The results can also assist in developing appropriate alert mechanisms for increasing driver alertness.

The year 2017 is a turning point for many disciplines as within the past year new technologies, such as virtual and augmented reality, have been established as low-cost and easy-to-use consumer devices. The potential advantages were eloquently predicted by Bodel et al. in the paper titled “Epigraphy in 2017”: What we hope will change and improve by 2017 are the tools available for gathering information and analyzing it…” \n \nIn this work we present a method for a novel interface for studying inscriptions, using augmented reality head-mounted displays, such as Microsoft’s Hololens glasses. This interface allows the users to browse through 3D databases of inscriptions and visualize the inscription within their actual physical space, such as office or classroom. Once the user positions the hologram of the inscription in a particular location, as the top of the desk, it remains there, allowing him/her to move around the inscription and study the artifact as a whole, up close, and from different perspectives. Similarly multiple inscriptions can be positioned next to one another, providing the opportunity for comparative readings, profound study of lettering techniques, and potential identification and pairing of fragments. \n \nTo the best of our knowledge this is the first project that utilizes this kind of holographic technology, opening for the first time the possibilities to epigraphists to conveniently study the inscriptions in a more natural way as physical objects instead of viewing the artifacts simply as 3D files on a desktop computer.

Locomotor training is a rehabilitation approach that optimizes task-specific sensory input during intense stepping practice. This approach is beneficial for adults with spinal cord injuries and is a promising intervention for children with neurological injuries. During locomotor training stepping and standing often are practiced for over an hour causing many children to lose motivation and become bored. As attention and focus wane, critical task-specific movements, such as upright trunk posture and reciprocal arm swing, become nearly impossible to evoke. Most importantly, these less intense and effective training sessions may compromise a child's recovery. Incorporation of interactive and engaging video games is an innovative approach to enhance rehabilitation. Although commercial games have demonstrated therapeutic effects when applied to children with neurological injuries, most games do not consider the specific impairments that are common in children with neurological injuries and are not designed for use during locomotor training. We are therefore, developing an interactive video game, designed to enhance locomotor training in children with neurological injuries. We have assembled a multidisciplinary team with experts in neuroscience, rehabilitation, computer science, and video game design. We are using existing, affordable technologies to develop a game that is not only interactive and motivating to children, but that also is based on key determinants of walking control. Specifically, the video game will be controlled by critical features of locomotor training, which are based on the neural control of walking. These features, such as trunk posture, reciprocal arm swing, and stepping can be recorded during the use of the game to optimize the parameters of the game and to collect valuable data about the patient's rehabilitation session.

In this paper we propose a novel method for 3D reconstruction of inscriptions using a shape-from-shading technique and for statistical analysis of their reconstructed surfaces using images of scanned squeezes. The squeeze is scanned twice from two different directions. Using the shading information a 3D model of the original surface is reconstructed and parametrized as a height map. This map is automatically segmented into smaller regions containing single letters, which are divided into groups of same characters and are further employed for a statistical analysis of the inscribed letters. The results can be used for studying the variations of the lettering techniques within a set of inscriptions. \n \n<iframe src=https://www.youtube.com/embed/WUd333VdgCk?feature=oembed width=800 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nWe have applied our framework to five inscribed fragments from the archaeological site of Epidauros, containing religious hymns for Asclepius and other deities (IG IV I 2, 129-135; SEG 30, 390 = Robert Wagman, Inni di Epidauro, 1995). Although found in different locations, these inscriptions show close affinities in content (sacred poetry), material (red local limestone) and writing (uncial lettering of the third century CE). The 3D reconstruction of the letterforms provides us with definite proof that all of them came from the same monument, possibly an inscribed wall from one of the buildings in the sanctuary's main area. A closer observation of the results reveals even some minor groups of letters with similar construction, which validates the accuracy of the proposed method. The advantages of 3D reconstruction in epigraphical research are especially obvious in the case of badly weathered or damaged inscribed surfaces, where the lettering is difficult to discern with the naked eye.

<iframe src=https://www.youtube.com/embed/DOQcUWMz8xU?feature=oembed width=1200 height=600 frameborder=0 allowfullscreen=allowfullscreen></iframe> \n \nIn many ways, learning to program can be challenging for humanists although computer programming is an essential skill for digital humanities. According to published studies this is often attributed to poor self-efficacy, limited prior experience with computers, or inability to relate personal experiences to abstract programming concepts. In this session, a new educational framework will be demonstrated that overcomes the problems of the existing teaching/learning approaches by adding a human-readable layer on the top of existing programming languages. \n \nThe proposed method is based on the use of emoticon-like typing that has become popular with social networks. Emoticons are visual representations that have one to one relationship with a corresponding combination of characters such as :). These can be perceived as visual interpretations of the corresponding characters that provide instant feedback to the user regarding the meaning associated with the typed code. The proposed framework utilizes a set of meaningful visual replacements of each grammatical token in a given programming language that appear instantly when complete valid tokens are typed. The proposed method, is based on the following three hypotheses: a) the immediate feedback given to the programmer can result in improved learning outcomes as it stimulates the brain to build one-to-one connections, b) the unique correspondence of each visual replacement, with a valid programming token re-enforces the learning of the syntax in an intuitive trial-and-error framework, c) the use of visual replacements remove visually the grammatical and syntactical details of a programming language and reveal to the users the logic of the program in the form of a pseudo code. The smallest units in any writing system are known as graphemes. Graphemes are not only the characters in a given alphabet but also the accents, punctuation marks, and other symbols that may be used in the corresponding writing system. Similarly, in any programming language a set of graphemes is used, which usually includes the graphemes of the Latin alphabet as well as other logical, mathematical, and structural symbols required for the needs of a particular programming language. Let us consider the following written sample: not:(or:|!be:) and its equivalent in another written language with different graphemes (emoticons): notor!be. Obviously, the latter is easier to read, but the former is easier to write in the form of a typed text in a computer device. This example shows that there exist written languages that are primarily meant to be written (possibly to serve as an input to a computer system), and others that are primarily meant to be read. The proposed educational framework is based on a rigid theoretical foundation regarding grammatical construction of languages and employs a set of visual or textual metaphors to teach computer programming to humanists. The technique has been preliminary tested using 35 adult subjects and it has improved significantly their learning outcome in terms of syntax recall and logic comprehension, compared to the performance achieved using traditional text editors for source code editing. The audience will be invited to bring their own tablet/laptop computers during this workshop.