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"abstract": "With the development of Augmented Reality (AR) technology, more and more research studies have explored the educational impact of AR technology applications. However, in the field of practical writing, few studies discussed the effectiveness of AR technology in Chinese idiom (inscription) learning. In this study we developed a Chinese idiom learning system, named Chinese Idioms Learning Card (CILC), which combined AR technology and game-based learning design. In the current state of the study, the proposed system has been developed and continues to address usability based on the advice of linguistic experts and students. Two classes of junior high school students in Taiwan will participate in continuous experiments. In the future, a mixed study will be conducted to explore effectiveness of students' self-learning in practical writing with the help of using this AR learning system.",
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"affiliation": "Beijing Institute of Technology",
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"affiliation": "Beijing Institute of Technology",
"fullName": "Yue Liu",
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{
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"affiliation": "Corporate Research & Development center, Toshiba Corporation, 1, Komukai-Toshiba-cho, Saiwai-ku, Kawasaki, 212-8582, Japan",
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"title": "2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
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"title": "3D-FRC: Depiction of the future road course in the Head-Up-Display",
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"abstract": "The introduction of Head-Up-Displays (HUDs) have opened up avenues for a whole range of novel AR applications. However, until these applications become available for the mass market a number of problems need to be tackled. For example, the field of view (FoV) of current HUDs is extremely limited, and real world tracking and 3D reconstruction are still not precise enough to show driving information embedded into wide areas of complex traffic environment. It is not possible to show true AR-visualizations in the display areas provided by the current FoVs. In this paper, we investigate how an AR-like visualization approach in current HUDs (with a limited FoV) can support drivers in foreseeing the future road course. This visualisation uses the already established concept of an electronic horizon. By complying with automotive standards, our application can be easily adapted for series production. With this visualisation we performed a user study, investigating the effect on drivers' gaze behaviour. For this reason the test subjects were equipped with an eye tracking system. The results showed a decrease in both, the number of gazes as well as total glance time on the head unit and the instrument cluster. We also investigated the test subjects' braking behaviour around sharp bends of the road which showed an overall improvement when the visualisation was enabled. Furthermore it showed an increase of the mean glance duration in the area of the HUD. Note that the eye tracking system is not capable of distinguishing between glances at the visualisation in the HUD and the users' glance at objects behind the visualisation - overlapping with the HUD. This would require tracking the test persons' depth of focus. The study showed that developers need to be concerned about not displaying excessively in the HUD, so as not to distract drivers. It furthermore showed that AR-like visualizations have the potential to decrease the time the driver is not looking at the road creating a safer driving experience.",
"abstracts": [
{
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"content": "The introduction of Head-Up-Displays (HUDs) have opened up avenues for a whole range of novel AR applications. However, until these applications become available for the mass market a number of problems need to be tackled. For example, the field of view (FoV) of current HUDs is extremely limited, and real world tracking and 3D reconstruction are still not precise enough to show driving information embedded into wide areas of complex traffic environment. It is not possible to show true AR-visualizations in the display areas provided by the current FoVs. In this paper, we investigate how an AR-like visualization approach in current HUDs (with a limited FoV) can support drivers in foreseeing the future road course. This visualisation uses the already established concept of an electronic horizon. By complying with automotive standards, our application can be easily adapted for series production. With this visualisation we performed a user study, investigating the effect on drivers' gaze behaviour. For this reason the test subjects were equipped with an eye tracking system. The results showed a decrease in both, the number of gazes as well as total glance time on the head unit and the instrument cluster. We also investigated the test subjects' braking behaviour around sharp bends of the road which showed an overall improvement when the visualisation was enabled. Furthermore it showed an increase of the mean glance duration in the area of the HUD. Note that the eye tracking system is not capable of distinguishing between glances at the visualisation in the HUD and the users' glance at objects behind the visualisation - overlapping with the HUD. This would require tracking the test persons' depth of focus. The study showed that developers need to be concerned about not displaying excessively in the HUD, so as not to distract drivers. It furthermore showed that AR-like visualizations have the potential to decrease the time the driver is not looking at the road creating a safer driving experience.",
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"Head Up Displays",
"Road Safety",
"Road Vehicles",
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"abstract": "Head-up displays (HUDs) have become common equipment in aircraft cockpits. One of the uses of HUDs is to provide a specific visual interface for pilots in the form of what is called a \"tunnel-in-the-sky\" (i.e. 3D geometry for the navigation path displayed on a flat screen). According to recent studies the \"tunnel-in-the-sky\" approach does not provide crucial advantages in comparison with more traditional methods of presenting navigation information to pilots. Our research considers a stereoscopic version of the 3D \"tunnel-in-the-sky\" realized as an augmented reality (AR) pocket-size system with see-through light-weight AR glasses. The system consists of low-cost items and does not suffer from the drawbacks tied with existing synthetic/enhanced vision systems for pilots. The results of the experiments with desktop simulators of different AR pilot's interfaces (2D, 3D and stereo 3D conditions) proved the effectiveness of the proposed stereo AR solution. A flight test of the prototype of the developed AR system was carried out on Cessna 172 aircraft and is showed in the accompanying video.",
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"abstract": "Visual vocabularies are standard tools in the object/image classification literature, and are emerging as a new tool for building point correspondences for pose estimation. This paper proposes several visual word based methods for point matching, with structure from motion and pose estimation applications in view. The three dimensional geometry of a scene is first extracted with bundle adjustment techniques based on the key point correspondences. These correspondences are obtained by grouping the set of all SIFT descriptors from the training images into visual words. We obtain a more accurate 3D geometry than with classical image-to-image point matching. In the second step, these visual words serve as 3D point descriptors robust to viewpoint change, and are then used for building 2D-3D correspondences for a test image, yielding the pose of the camera by solving the PnP problem. We compare several visual word formation techniques w.r.t robustness to viewpoint change between the learning and test images and discuss the required computational time.",
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"abstract": "When we move through a real environment, egocentric location representations are effortlessly and automatically updated. While moving in synthetic environments, this effortless, continuous spatial updating is often disrupted or incomplete due to a lack of sensory, especially body-based, movement information. To prevent disorientation in virtual reality caused by missing body-based information, the support of spatial updating via other sensory movement cues is necessary. In the presented experiment, participants performed a spatial updating task in a sparse virtual scene presented inside a CAVE (Cave Automatic Virtual Environment). The task was to navigate back to a starting position after simulated movements with either no orientation cues, three visible distant landmarks or one continuous auditory cue present. The focus was not to compare visual and auditory cues but to explore the viability of auditory cueing with visual cues as a reference. Overall, the data showed improved task performance when an orientation cue was present, with auditory cues providing at least as much improvement as visual cues. Our results indicate that auditory cues in virtual environments can support spatial updating when body-based information is missing.",
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"content": "When we move through a real environment, egocentric location representations are effortlessly and automatically updated. While moving in synthetic environments, this effortless, continuous spatial updating is often disrupted or incomplete due to a lack of sensory, especially body-based, movement information. To prevent disorientation in virtual reality caused by missing body-based information, the support of spatial updating via other sensory movement cues is necessary. In the presented experiment, participants performed a spatial updating task in a sparse virtual scene presented inside a CAVE (Cave Automatic Virtual Environment). The task was to navigate back to a starting position after simulated movements with either no orientation cues, three visible distant landmarks or one continuous auditory cue present. The focus was not to compare visual and auditory cues but to explore the viability of auditory cueing with visual cues as a reference. Overall, the data showed improved task performance when an orientation cue was present, with auditory cues providing at least as much improvement as visual cues. Our results indicate that auditory cues in virtual environments can support spatial updating when body-based information is missing.",
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"Virtual Reality",
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"Virtual Environments",
"User Interfaces",
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"Virtual Reality",
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"affiliation": "Chemnitz University of Technology,Institute of Physics",
"fullName": "Christiane Breitkreutz",
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"affiliation": "Chemnitz University of Technology,Institute for Machine Tools and Production Processes",
"fullName": "Jennifer Brade",
"givenName": "Jennifer",
"surname": "Brade",
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"affiliation": "Chemnitz University of Technology,Institute for Machine Tools and Production Processes",
"fullName": "Sven Winkler",
"givenName": "Sven",
"surname": "Winkler",
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"affiliation": "Chemnitz University of Technology,Cognitive Systems Lab, Institute of Physics",
"fullName": "Alexandra Bendixen",
"givenName": "Alexandra",
"surname": "Bendixen",
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"affiliation": "Chemnitz University of Technology,Institute for Machine Tools and Production Processes",
"fullName": "Philipp Klimant",
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"affiliation": "Chemnitz University of Technology,Professorship of Applied Geropsychology and Cognition",
"fullName": "Georg Jahn",
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"abstract": "In this paper, we describe the process of designing, prototyping and testing spatial audio interfaces for supporting communication between people viewing <tex>Z_$360^{\\circ}$_Z</tex> images during a virtual tour. When several people are viewing the same <tex>Z_$360^{\\circ}$_Z</tex> image in Virtual Reality (VR) headsets and sharing live audio, it can be difficult to understand them when they are talking at the same time. However, spatial audio could be used to clearly disambiguate between the different speakers. We introduce the concept of Attention-Based Spatial Audio; playing spatial audio from the direction where the speaker is paying attention to. Results indicate statistically significant difference between Attention-Based Spatial Audio and standard spatial audio in terms of cognitive load, social presence, and vocal intelligibility.",
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"affiliation": "University of Auckland",
"fullName": "Alaeddin Nassani",
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"abstract": "This poster details an ongoing experiment aiming to test the prevalence of shooter bias in virtual reality. Further, we examine the interaction between shooter bias and perceived socioeconomic status. Even though shooter bias is a well-documented topic in psychology research, little experimentation has used virtual reality, instead opting for unrealistic two-dimensional simulations. This study will yield new insight into shooter bias, especially concerning virtual reality as a tool for use in future work, and will provide new understanding about the relationship between socioeconomic status and shooter bias.",
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"content": "This poster details an ongoing experiment aiming to test the prevalence of shooter bias in virtual reality. Further, we examine the interaction between shooter bias and perceived socioeconomic status. Even though shooter bias is a well-documented topic in psychology research, little experimentation has used virtual reality, instead opting for unrealistic two-dimensional simulations. This study will yield new insight into shooter bias, especially concerning virtual reality as a tool for use in future work, and will provide new understanding about the relationship between socioeconomic status and shooter bias.",
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"affiliation": "Davidson College",
"fullName": "Evan Blanpied",
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"affiliation": "Davidson College",
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"abstract": "Summary form only given. Our work explores the possibilities of using 3D projection on physical objects - a multi-projector set-up, which we aim to use for exhibition, teaching, design and early prototyping purposes. We also aim to create more immersive and engaging multimedia presentations, and change from conventional 2D media to 3D media by projecting the visual content on a physical object. This video demonstration presents two examples of tangible 3D projections. In our first demonstration we used a sphere shaped object and projected a visualization of rotating Earth on it with five projectors. We have recently done a study [1] that focused on audience's perceptions of 3D projection. Results were encouraging, and we will continue our work with the subject. In another demonstration we used a human shaped model and projected various datasets on it, including muscles, bones and digestive system. This setup contains two projectors, one in the back and the other one in the front of the body. 3D world contains 3D model of the human body and two camera views. In the future we will focus on enabling functional interactions to make projections more useful and interesting e.g. for teaching purposes. Interactions and automatic projector calibrations are the next steps to be implemented and examined in our project.",
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"abstract": "Flying Head is a telepresence system that remotely connects humans and unmanned aerial vehicles (UAVs). UAVs are teleoperated robots used in various situations, including disaster area inspection and movie content creation. This study aimed to integrate humans and machines with different abilities (i.e., flying) to virtually augment human abilities. Precise manipulation of UAVs normally involves simultaneous control of motion parameters and requires the skill of a trained operator. This paper proposes a new method that directly connects the user's body and head motion to that of the UAV. The user's natural movement can be synchronized with UAV motions such as rotation and horizontal and vertical movements. Users can control the UAV more intuitively since such manipulations are more in accordance with their kinesthetic imagery; in other words, a user can feel as if he or she became a flying machine.",
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"title": "Tactile Telepresence for Isolated Patients",
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"abstract": "For isolated patients, such as COVID-19 patients in an intensive care unit, conventional video tools can provide a degree of visual telepresence. However, video alone offers, at best, an approximation of a \"through a window\" metaphor—remote visitors, such as loved ones, cannot touch the patient to provide reassurance. Here, we present preliminary work aimed at providing an isolated patient and remote visitors with audiovisual interactions that are augmented by mediated social touch—the perception of being touched for the isolated patient, and the perception of touching for the remote visitor. We developed a tactile telepresence system prototype that provides a remote visitor with a tablet-based, touch-video interface for conveying touch patterns on the forehead of an isolated patient. The isolated patient can see the remote visitor, see themselves with the touch patterns indicated on their forehead, and feel the touch patterns through a vibrotactile headband interface. We motivate the work, describe the system prototype, and present results from pilot studies investigating the technical feasibility of the system, along with the social and emotional affects of using the prototype system.",
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"content": "For isolated patients, such as COVID-19 patients in an intensive care unit, conventional video tools can provide a degree of visual telepresence. However, video alone offers, at best, an approximation of a \"through a window\" metaphor—remote visitors, such as loved ones, cannot touch the patient to provide reassurance. Here, we present preliminary work aimed at providing an isolated patient and remote visitors with audiovisual interactions that are augmented by mediated social touch—the perception of being touched for the isolated patient, and the perception of touching for the remote visitor. We developed a tactile telepresence system prototype that provides a remote visitor with a tablet-based, touch-video interface for conveying touch patterns on the forehead of an isolated patient. The isolated patient can see the remote visitor, see themselves with the touch patterns indicated on their forehead, and feel the touch patterns through a vibrotactile headband interface. We motivate the work, describe the system prototype, and present results from pilot studies investigating the technical feasibility of the system, along with the social and emotional affects of using the prototype system.",
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"title": "2017 IEEE Virtual Reality (VR)",
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"title": "Experiencing guidance in 3D spaces with a vibrotactile head-mounted display",
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"abstract": "Vibrotactile feedback is broadly used to support different tasks in virtual and augmented reality applications, such as navigation, communication, attentional redirection, or to enhance the sense of presence in virtual environments. Thus, we aim to include the haptic component to the most popular wearable used in VR applications: the VR headset. After studying the acuity around the head for vibrating stimuli, and trying different parameters, actuators, and configurations, we developed a haptic guidance technique to be used in a vibrotactile Head-mounted Display (HMD). Our vi-brotactile HMD was made to render the position of objects in a 3D space around the subject by varying both stimulus loci and vibration frequency. In this demonstration, the participants will interact with different scenarios where the mission is to select a number of predefined objects. However, instead of displaying occlusive graphical information to point to these objects, vibrotactile cues will provide guidance in the VR setup. Participants will see that our haptic guidance technique can be both easy to use and entertaining. (See Video: https://youtu.be/_H0MQy6QD7M).",
"abstracts": [
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"content": "Vibrotactile feedback is broadly used to support different tasks in virtual and augmented reality applications, such as navigation, communication, attentional redirection, or to enhance the sense of presence in virtual environments. Thus, we aim to include the haptic component to the most popular wearable used in VR applications: the VR headset. After studying the acuity around the head for vibrating stimuli, and trying different parameters, actuators, and configurations, we developed a haptic guidance technique to be used in a vibrotactile Head-mounted Display (HMD). Our vi-brotactile HMD was made to render the position of objects in a 3D space around the subject by varying both stimulus loci and vibration frequency. In this demonstration, the participants will interact with different scenarios where the mission is to select a number of predefined objects. However, instead of displaying occlusive graphical information to point to these objects, vibrotactile cues will provide guidance in the VR setup. Participants will see that our haptic guidance technique can be both easy to use and entertaining. (See Video: https://youtu.be/_H0MQy6QD7M).",
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"authors": [
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"affiliation": "Universidade Federal do Rio Grande do Sul - UFRGS, Instituto de Informática. Porto Alegre, Brazil",
"fullName": "Victor Adriel de Jesus Oliveira",
"givenName": "Victor Adriel",
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"affiliation": "Fondazione Istituto Italiano di Tecnologia - IIT, RBCS. Genoa, Italy",
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"affiliation": "Universidade Federal do Rio Grande do Sul - UFRGS, Instituto de Informática. Porto Alegre, Brazil",
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"affiliation": "Universidade Federal do Rio Grande do Sul - UFRGS, Instituto de Informática. Porto Alegre, Brazil",
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"title": "2022 IEEE on Conference Virtual Reality and 3D User Interfaces (VR)",
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"doi": "10.1109/VR51125.2022.00064",
"title": "HoloLogger: Keystroke Inference on Mixed Reality Head Mounted Displays",
"normalizedTitle": "HoloLogger: Keystroke Inference on Mixed Reality Head Mounted Displays",
"abstract": "When using personal computing services in mixed reality (MR) such as online payment and social media, sensitive information and account passwords must be typed in MR. To design secure MR systems and build up user trust, it is imperative to first understand the security threat to the sensitive MR input. Although keystroke inference attacks by analyzing human-computer interaction in videos or via wireless signals have been successful, they require placing extra hardware near the user which is easily noticeable in practice. In this paper, we expose a more dangerous malware-based attack through the vulnerability that no permission is required for accessing MR motion data. We aim to monitor MR headset motion and infer the user input through a benign App. Realizing the attack system requires addressing unique challenges in MR such as six-degree-of-freedom (6DoF) device motion and no explicit motion signal for keystroke identification. To this end, we present HoloLogger, the first malware-based keystroke inference attack system on HoloLens. HoloLogger is empowered by a 6DoF-head-motion-driven key tracking scheme and an air-tap-pattern-based keystroke inference framework. Extensive evaluations with 25 users and 750 inference trials of passwords consisting of 4–8 lowercase English letters demonstrate that HoloLogger successfully achieves a top-5 accuracy of 93%. HoloLogger is also robust in various environments such as different user positions and input categories.",
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"content": "When using personal computing services in mixed reality (MR) such as online payment and social media, sensitive information and account passwords must be typed in MR. To design secure MR systems and build up user trust, it is imperative to first understand the security threat to the sensitive MR input. Although keystroke inference attacks by analyzing human-computer interaction in videos or via wireless signals have been successful, they require placing extra hardware near the user which is easily noticeable in practice. In this paper, we expose a more dangerous malware-based attack through the vulnerability that no permission is required for accessing MR motion data. We aim to monitor MR headset motion and infer the user input through a benign App. Realizing the attack system requires addressing unique challenges in MR such as six-degree-of-freedom (6DoF) device motion and no explicit motion signal for keystroke identification. To this end, we present HoloLogger, the first malware-based keystroke inference attack system on HoloLens. HoloLogger is empowered by a 6DoF-head-motion-driven key tracking scheme and an air-tap-pattern-based keystroke inference framework. Extensive evaluations with 25 users and 750 inference trials of passwords consisting of 4–8 lowercase English letters demonstrate that HoloLogger successfully achieves a top-5 accuracy of 93%. HoloLogger is also robust in various environments such as different user positions and input categories.",
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"normalizedAbstract": "When using personal computing services in mixed reality (MR) such as online payment and social media, sensitive information and account passwords must be typed in MR. To design secure MR systems and build up user trust, it is imperative to first understand the security threat to the sensitive MR input. Although keystroke inference attacks by analyzing human-computer interaction in videos or via wireless signals have been successful, they require placing extra hardware near the user which is easily noticeable in practice. In this paper, we expose a more dangerous malware-based attack through the vulnerability that no permission is required for accessing MR motion data. We aim to monitor MR headset motion and infer the user input through a benign App. Realizing the attack system requires addressing unique challenges in MR such as six-degree-of-freedom (6DoF) device motion and no explicit motion signal for keystroke identification. To this end, we present HoloLogger, the first malware-based keystroke inference attack system on HoloLens. HoloLogger is empowered by a 6DoF-head-motion-driven key tracking scheme and an air-tap-pattern-based keystroke inference framework. Extensive evaluations with 25 users and 750 inference trials of passwords consisting of 4–8 lowercase English letters demonstrate that HoloLogger successfully achieves a top-5 accuracy of 93%. HoloLogger is also robust in various environments such as different user positions and input categories.",
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"keywords": [
"Augmented Reality",
"Helmet Mounted Displays",
"Invasive Software",
"Mobile Computing",
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"Holo Logger",
"6 Do F Head Motion Driven Key Tracking Scheme",
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"Input Categories",
"Mixed Reality Head Mounted Displays",
"Personal Computing Services",
"Online Payment",
"Social Media",
"Sensitive Information",
"Account Passwords",
"Secure MR Systems",
"User Trust",
"Security Threat",
"Sensitive MR Input",
"Keystroke Inference Attacks",
"Human Computer Interaction",
"Wireless Signals",
"Extra Hardware",
"Dangerous Malware Based Attack",
"Accessing MR Motion Data",
"MR Headset Motion",
"Six Degree Of Freedom Device Motion",
"Explicit Motion Signal",
"Keystroke Identification",
"Malware Based Keystroke Inference Attack System",
"Wireless Communication",
"Wireless Sensor Networks",
"Solid Modeling",
"Three Dimensional Displays",
"Tracking",
"Social Networking Online",
"Mixed Reality",
"Human Centered Computing",
"Human Computer Interaction",
"Mixed Augmented Reality",
"Security And Privacy",
"Human And Societal Aspects Of Security And Privacy",
"Privacy Protection"
],
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"affiliation": "George Mason University",
"fullName": "Shiqing Luo",
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"affiliation": "Automatic Data Processing, Inc.",
"fullName": "Xinyu Hu",
"givenName": "Xinyu",
"surname": "Hu",
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{
"affiliation": "George Mason University",
"fullName": "Zhisheng Yan",
"givenName": "Zhisheng",
"surname": "Yan",
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}
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"year": "2022",
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"title": "2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
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"abstract": "Wearable VR/AR devices provide users with fully immersive experience in a virtual environment, enabling possibilities to reshape the forms of entertainment and telepresence. While the body language is a crucial element in effective communication, wearing a head-mounted display (HMD) could severely hinder the eye contact and block facial expressions. We present a novel headset removal technique that enables high-quality occlusion-free communication in virtual environment. In particular, our solution synthesizes photoreal faces in the occluded region with faithful reconstruction of facial expressions and eye movements. Towards this goal, we develop a novel capture setup that consists of two near-infrared (NIR) cameras inside the HMD for eye capturing and one external RGB camera for recording visible face regions. To enable realistic face synthesis with consistent illuminations, we propose a data-driven approach to fuse the narrow-field-of-view NIR images with the RGB image captured from the external camera. In addition, to generate pho-torealistic eyes, a dedicated algorithm is proposed to colorize the NIR eye images and further rectify the color distortion caused by the non-linear mapping of IR light sensitivity. Experimental results demonstrate that our framework is capable to synthesize high-fidelity unoccluded facial images with accurate tracking of head motion, facial expression and eye movement.",
"abstracts": [
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"content": "Wearable VR/AR devices provide users with fully immersive experience in a virtual environment, enabling possibilities to reshape the forms of entertainment and telepresence. While the body language is a crucial element in effective communication, wearing a head-mounted display (HMD) could severely hinder the eye contact and block facial expressions. We present a novel headset removal technique that enables high-quality occlusion-free communication in virtual environment. In particular, our solution synthesizes photoreal faces in the occluded region with faithful reconstruction of facial expressions and eye movements. Towards this goal, we develop a novel capture setup that consists of two near-infrared (NIR) cameras inside the HMD for eye capturing and one external RGB camera for recording visible face regions. To enable realistic face synthesis with consistent illuminations, we propose a data-driven approach to fuse the narrow-field-of-view NIR images with the RGB image captured from the external camera. In addition, to generate pho-torealistic eyes, a dedicated algorithm is proposed to colorize the NIR eye images and further rectify the color distortion caused by the non-linear mapping of IR light sensitivity. Experimental results demonstrate that our framework is capable to synthesize high-fidelity unoccluded facial images with accurate tracking of head motion, facial expression and eye movement.",
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"normalizedAbstract": "Wearable VR/AR devices provide users with fully immersive experience in a virtual environment, enabling possibilities to reshape the forms of entertainment and telepresence. While the body language is a crucial element in effective communication, wearing a head-mounted display (HMD) could severely hinder the eye contact and block facial expressions. We present a novel headset removal technique that enables high-quality occlusion-free communication in virtual environment. In particular, our solution synthesizes photoreal faces in the occluded region with faithful reconstruction of facial expressions and eye movements. Towards this goal, we develop a novel capture setup that consists of two near-infrared (NIR) cameras inside the HMD for eye capturing and one external RGB camera for recording visible face regions. To enable realistic face synthesis with consistent illuminations, we propose a data-driven approach to fuse the narrow-field-of-view NIR images with the RGB image captured from the external camera. In addition, to generate pho-torealistic eyes, a dedicated algorithm is proposed to colorize the NIR eye images and further rectify the color distortion caused by the non-linear mapping of IR light sensitivity. Experimental results demonstrate that our framework is capable to synthesize high-fidelity unoccluded facial images with accurate tracking of head motion, facial expression and eye movement.",
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"authors": [
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"affiliation": "University of Kentucky",
"fullName": "Yajie Zhao",
"givenName": "Yajie",
"surname": "Zhao",
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"affiliation": "USC Institute for Creative Technologies",
"fullName": "Qingguo Xu",
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"fullName": "Weikai Chen",
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"affiliation": "University of Kentucky",
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"affiliation": "University of Kentucky",
"fullName": "Jun Xing",
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"title": "Comparing Techniques for Visualizing Moving Out-of-View Objects in Head-mounted Virtual Reality",
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"abstract": "Current head-mounted displays (HMDs) have a limited field-of-view (FOV). A limited FOV further decreases the already restricted human visual range and amplifies the problem of objects receding from view (e.g., opponents in computer games). However, there is no previous work that investigates how to best perceive moving out-of-view objects on head-mounted displays. In this paper, we compare two visualization approaches: (1) Overview+detail, with 3D Radar, and (2) Focus+context, with EyeSee360, in a user study to evaluate their performances for visualizing moving out-of-view objects. We found that using 3D Radar resulted in a significantly lower movement estimation error and higher usability, measured by the system usability scale. 3D Radar was also preferred by 13 out of 15 participants for visualization of moving out-of-view objects.",
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"affiliation": "University of Oldenburg",
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"abstract": "Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations. Despite the fact that this poses a security concern for the self-driving industry, there has been very little exploration in terms of 3D perception, as most adversarial attacks have only been applied to 2D flat images. In this paper, we address this issue and present a method to generate universal 3D adversarial objects to fool LiDAR detectors. In particular, we demonstrate that placing an adversarial object on the rooftop of any target vehicle to hide the vehicle entirely from LiDAR detectors with a success rate of 80%. We report attack results on a suite of detectors using various input representation of point clouds. We also conduct a pilot study on adversarial defense using data augmentation. This is one step closer towards safer self-driving under unseen conditions from limited training data.",
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"abstract": "Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order to apply a physical patch. Furthermore, to hide multiple objects, an adversarial patch must be applied to each object. In this paper, we propose a contactless translucent physical patch containing a carefully constructed pattern, which is placed on the camera’s lens, to fool state-of-the-art object detectors. The primary goal of our patch is to hide all instances of a selected target class. In addition, the optimization method used to construct the patch aims to ensure that the detection of other (untargeted) classes remains unharmed. Therefore, in our experiments, which are conducted on state-of-the-art object detection models used in autonomous driving, we study the effect of the patch on the detection of both the selected target class and the other classes. We show that our patch was able to prevent the detection of 42.27% of all stop sign instances while maintaining high (nearly 80%) detection of the other classes.",
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"abstract": "In recent years, more and more visualization methods for explanations of artificial intelligence have been proposed that focus on untangling black box models for single instances of the data set. While the focus often lies on supervised learning algorithms, the study of uncertainty estimations in the unsupervised domain for high-dimensional data sets in the explainability domain has been neglected so far. As a result, existing visualization methods struggle to visualize global uncertainty patterns over whole datasets. We propose Unsupervised DeepView, the first global uncertainty visualization method for high dimensional data based on a novel unsupervised proxy for local uncertainties. In this paper, we exploit the mathematical notion of local intrinsic dimensionality as a measure of local data complexity. As a label-agnostic measure of model uncertainty in unsupervised machine learning, it shows two highly desirable features: It can be used for global structure visualization as well as for the detection of local adversarials. In our empirical evaluation, we demonstrate its ability both in visualizations and quantitative analysis for unsupervised models on multiple datasets.",
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"abstract": "In the mobile phone glass industry, curved glass is widely used. Whether the curved glass is measured with high efficiency and precision and whether the curved edge profile error can be evaluated directly affects the quality and service cycle of glass products. In view of the difficulty of accurate measurement and evaluation of 3D mobile phones curved glass profile, a visual measurement method of 3D mobile phones curved glass profile error based on laser scanning and shape template matching is proposed in this paper. Accurate measurement of mobile phone curved glass greatly improves the fit between curved glass and mobile phone and the efficiency of production. Therefore, this paper proposes a method for evaluating the contour degree of curved glass on mobile phones based on Open Cascade and OpenCV. This method uses the lasers and camera equipment to extract the profile outline of the curved glass, then extract the profile outline of the mobile phone curved glass model that has been drawn in 3D software. The extracted contour lines are matched by methods such as translation and rotation, and finally the distance between the corresponding points is calculated as the contour error. Experimental results show that the method is able to accurately measure the profile error and greatly reduce the measurement instability and have good robustness.",
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"content": "In the mobile phone glass industry, curved glass is widely used. Whether the curved glass is measured with high efficiency and precision and whether the curved edge profile error can be evaluated directly affects the quality and service cycle of glass products. In view of the difficulty of accurate measurement and evaluation of 3D mobile phones curved glass profile, a visual measurement method of 3D mobile phones curved glass profile error based on laser scanning and shape template matching is proposed in this paper. Accurate measurement of mobile phone curved glass greatly improves the fit between curved glass and mobile phone and the efficiency of production. Therefore, this paper proposes a method for evaluating the contour degree of curved glass on mobile phones based on Open Cascade and OpenCV. This method uses the lasers and camera equipment to extract the profile outline of the curved glass, then extract the profile outline of the mobile phone curved glass model that has been drawn in 3D software. The extracted contour lines are matched by methods such as translation and rotation, and finally the distance between the corresponding points is calculated as the contour error. Experimental results show that the method is able to accurately measure the profile error and greatly reduce the measurement instability and have good robustness.",
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"abstract": "In this paper, a novel visual secret sharing (VSS) scheme with using QR codes is investigated. The proposed visual secret sharing scheme based on QR codes(VSSQR) can visually reveal secret image by stacking k or more shares (shadow images) from all the n QR codes as well as scan the QR code by a QR code reader. Our VSSQR exploits the error correction mechanism in the QR code structure, to embed the bits corresponding to shares generated by VSS from a secret bit into the same locations of QR codes in the processing of encoding QR. Each output share is a valid QR code, which may reduce the likelihood of attracting the attention of potential attackers, that can be scanned and decoded utilizing a QR code reader. The secret image can be recovered by stacking for case (k, n) based on the human visual system without any computation. In addition, it can assist alignment for VSS recovery. The experiment results show the effectiveness of our scheme.",
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"content": "In this paper, a novel visual secret sharing (VSS) scheme with using QR codes is investigated. The proposed visual secret sharing scheme based on QR codes(VSSQR) can visually reveal secret image by stacking k or more shares (shadow images) from all the n QR codes as well as scan the QR code by a QR code reader. Our VSSQR exploits the error correction mechanism in the QR code structure, to embed the bits corresponding to shares generated by VSS from a secret bit into the same locations of QR codes in the processing of encoding QR. Each output share is a valid QR code, which may reduce the likelihood of attracting the attention of potential attackers, that can be scanned and decoded utilizing a QR code reader. The secret image can be recovered by stacking for case (k, n) based on the human visual system without any computation. In addition, it can assist alignment for VSS recovery. The experiment results show the effectiveness of our scheme.",
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"proceeding": {
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"title": "2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)",
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"abstract": "Barcodes have been widely popular. Their popularity has encouraged an ongoing invention of decoding methods. Barcodes can be categorized into 2 main groups, namely one-dimension (1D) barcodes at which information is stored horizontally and two-dimension (2D) barcodes which contain information in both vertical and horizontal direction, promising a higher storage capacity compared to 1D barcodes. Despite high data density, an amount of information obtained in 2D barcodes still limited to some extent. This study selected QR Code (Quick Response Code) is a type of 2D barcode because firstly, it can handle a variety of information. Secondly, decoding is reasonably straightforward. Finally, the structure of QR code is specified clearly by its developer. This research aimed to increase QR Code capacity by proposing a color Quick Response Code (color QR code) encoding concept which can hold a larger amount of information than that of the traditional black and white QR Code regarding their physical size. A two-color (black and white) QR Code can store 1 bit in each module only, whereas a module of a color QR code with sixteen different colors can contain 4-bit data. In order to decode a color QR code, this study used a code reader equipped with at least an 8-megapixel camera and a decoding application was developed on Android (Android application on mobile phone) and Java (Java application on PC) platform.",
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"content": "Barcodes have been widely popular. Their popularity has encouraged an ongoing invention of decoding methods. Barcodes can be categorized into 2 main groups, namely one-dimension (1D) barcodes at which information is stored horizontally and two-dimension (2D) barcodes which contain information in both vertical and horizontal direction, promising a higher storage capacity compared to 1D barcodes. Despite high data density, an amount of information obtained in 2D barcodes still limited to some extent. This study selected QR Code (Quick Response Code) is a type of 2D barcode because firstly, it can handle a variety of information. Secondly, decoding is reasonably straightforward. Finally, the structure of QR code is specified clearly by its developer. This research aimed to increase QR Code capacity by proposing a color Quick Response Code (color QR code) encoding concept which can hold a larger amount of information than that of the traditional black and white QR Code regarding their physical size. A two-color (black and white) QR Code can store 1 bit in each module only, whereas a module of a color QR code with sixteen different colors can contain 4-bit data. In order to decode a color QR code, this study used a code reader equipped with at least an 8-megapixel camera and a decoding application was developed on Android (Android application on mobile phone) and Java (Java application on PC) platform.",
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"proceeding": {
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"title": "2018 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)",
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"title": "Deep Convolutional Encoder-Decoder Architecture for Neuronal Structure Segmentation",
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"abstract": "Electro microscopic connectomics is a practical application of research direction. It determines whether a nerve is damaged by judging the connectivity of nerves. However, the formidable size of Electro Microscopic (EM) image data generated by serial-section Transmitted Electron Microscopy (ssTEM) severely depends on human annotation, which is impractical. One of the main challenges in connectomics research is to take minimal user intervention into account during neuronal structures automatic segmentation. To address this problem, a network is constructed to segment neuronal structures automatically, which expands receptive field of feature maps. Besides, we also introduce data augmentation method to use the available training data more efficiently. Our model is proposed based on a context network, and its architecture consists of an encoding path that enables feature extraction. The novel introduction of summation-based skip connection is aimed to connect decoding path with encoding path. Finally, real experiments with ISBI EM dataset validate the approach.",
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"content": "Electro microscopic connectomics is a practical application of research direction. It determines whether a nerve is damaged by judging the connectivity of nerves. However, the formidable size of Electro Microscopic (EM) image data generated by serial-section Transmitted Electron Microscopy (ssTEM) severely depends on human annotation, which is impractical. One of the main challenges in connectomics research is to take minimal user intervention into account during neuronal structures automatic segmentation. To address this problem, a network is constructed to segment neuronal structures automatically, which expands receptive field of feature maps. Besides, we also introduce data augmentation method to use the available training data more efficiently. Our model is proposed based on a context network, and its architecture consists of an encoding path that enables feature extraction. The novel introduction of summation-based skip connection is aimed to connect decoding path with encoding path. Finally, real experiments with ISBI EM dataset validate the approach.",
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"abstract": "Machine Learning (ML) models are often determined by relying on knowledge and experience. In recent years, many automatic building methods are proposed, but there are some problems related to accuracy, computation cost, and explainability. We propose the Stochastic Schemata Exploiter-Based AutoML. The Stochastic Schemata Exploiter (SSE) is one of the Evolutionary Algorithms. In each generation, SSE calculates fitness of the individuals, defines the best subsets according to their average fitness, generates individuals based on the subsets, and applies a mutation operator. Since the original SSE uses a binary string representation, we have to modify the SSE algorithm in the following points for parameter optimization: the initialization method, the schema extraction method, the new individual generation method, the mutation method, and the generation update method. In this paper, we propose a genetic representation of the stacking model and optimize the stacking model using SSE. Compared with the Genetic Algorithm, the Tree-structured Parzen Estimator, the Covariance Matrix Adaptation - Evolution Strategy, and Random Search, SSE shows an interesting feature: a better accuracy for combinatorial optimization problems with categorical, discrete, and continuous variables, such as hyper-parameter optimization. In addition, we propose the visualization of the process of SSE. The visualization helps us to understand the process, which is another advantage of the SSE-based optimization (SSEopt).",
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"abstract": "Machine learning has made slow inroads into quantitative social science due to both a mismatch of machine learning's strengths to the causal and inferential tasks domain researchers pursue [1] and also a lack of algorithmic training among many domain experts [2]. However, conflict research- the empirical examination of political unrest, violence and civil war-has seen a growing emphasis on prediction and forecasting models. We describe automated machine learning (AutoML) to identify models, and human-guided machine learning (HGML), and show how these can incorporate domain knowledge and research requirements into model selection and assessment, and provide high quality machine learning pipelines to domain experts comparable to state-of-the-literature solutions. We examine three peer-reviewed papers with predictive models of conflict [3, 4, 5] and run their data through our HGML system using multiple AutoML engines and find this system produces slightly elevated performance on each paper's model, without any ML expertise required of the user. Our research has three takeaways for computational social science. First, predictive models of conflict would benefit from even minimal applications of AutoML; Secondly, human-guided machine learning offers the attractive option of constraining AutoML systems to address the kinds of questions conflict researchers assess with predictive models; Finally, current existing AutoML implementations produce divergent solutions and so can be productively harnessed in parallel.",
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"content": "Machine learning has made slow inroads into quantitative social science due to both a mismatch of machine learning's strengths to the causal and inferential tasks domain researchers pursue [1] and also a lack of algorithmic training among many domain experts [2]. However, conflict research- the empirical examination of political unrest, violence and civil war-has seen a growing emphasis on prediction and forecasting models. We describe automated machine learning (AutoML) to identify models, and human-guided machine learning (HGML), and show how these can incorporate domain knowledge and research requirements into model selection and assessment, and provide high quality machine learning pipelines to domain experts comparable to state-of-the-literature solutions. We examine three peer-reviewed papers with predictive models of conflict [3, 4, 5] and run their data through our HGML system using multiple AutoML engines and find this system produces slightly elevated performance on each paper's model, without any ML expertise required of the user. Our research has three takeaways for computational social science. First, predictive models of conflict would benefit from even minimal applications of AutoML; Secondly, human-guided machine learning offers the attractive option of constraining AutoML systems to address the kinds of questions conflict researchers assess with predictive models; Finally, current existing AutoML implementations produce divergent solutions and so can be productively harnessed in parallel.",
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"affiliation": "Harvard John A. Paulson School of Engineering and Applied Sciences,Center for Research on Computation and Society,Cambridge,MA",
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"abstract": "Web users display their preferences implicitly by a sequence of pages they navigated. Web recommendation systems use methods to extract useful knowledge about user interests from such data. We propose a Bayesian nonparametric approach to the problem of modeling user interests in recommender systems using implicit feedback like user navigations and clicks on items. Our approach is based on the discovery of a set of latent interests that are shared among users in the system and make a key assumption that each user activity is motivated only by several interests amongst user interest profile which is quite different from most of the existing recommendation algorithms. By using a beta process and a Dirichlet prior, the number of hidden interests and the relationships between interests and items are both inferred from the data. In order to model the sequential information on user's visits, we make a Markovian assumption on each user's navigated item sequence. We develop a Markov chain Monte Carlo inference method based on the Indian buffet process representation of the beta process. We validate our sampling algorithm using synthetic data and real world datasets to demonstrate promising results on recovering the hidden user interests.",
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"content": "Web users display their preferences implicitly by a sequence of pages they navigated. Web recommendation systems use methods to extract useful knowledge about user interests from such data. We propose a Bayesian nonparametric approach to the problem of modeling user interests in recommender systems using implicit feedback like user navigations and clicks on items. Our approach is based on the discovery of a set of latent interests that are shared among users in the system and make a key assumption that each user activity is motivated only by several interests amongst user interest profile which is quite different from most of the existing recommendation algorithms. By using a beta process and a Dirichlet prior, the number of hidden interests and the relationships between interests and items are both inferred from the data. In order to model the sequential information on user's visits, we make a Markovian assumption on each user's navigated item sequence. We develop a Markov chain Monte Carlo inference method based on the Indian buffet process representation of the beta process. We validate our sampling algorithm using synthetic data and real world datasets to demonstrate promising results on recovering the hidden user interests.",
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"Hidden Markov Models",
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"Collaboration",
"Filtering",
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"fullName": "Qinjiao Mao",
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"fullName": "Boqin Feng",
"givenName": "Boqin",
"surname": "Feng",
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"fullName": "Shanliang Pan",
"givenName": "Shanliang",
"surname": "Pan",
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"title": "2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)",
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"title": "Inferring Relationships in Microbiomes from Signed Bayesian Networks",
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"abstract": "Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. In this paper, we report a surprising association between directed edges in BNs and known colonization order. Furthermore, when combined with the sign of the correlations from CoNs, BNs allow for many useful conclusions.In this paper, we ask the pertinent question: what can BNs reveal about the relationships between microbial taxa in a microbiome? In particular, we argue that BNs are able to capture temporal order (colonization order) when combined with the sign of correlation. The main contribution of this paper is to show how to carefully use BNs in conjunction with CoNs to make potential inferences about colonization order.We carried out our experiments on two datasets. The first is oral microbiome data from the Human Microbiome Project (HMP) [1], which included eight different sites all from within the oral cavity from 242 healthy adults (129 males, 113 females). The second is data from preterm infant gut microbiome samples as described in the paper by La Rosa et al. [2]. A total of 922 stool samples from 58 premature babies (each weighing ≤ 1500 g at birth) collected on different days were used for our analysis.",
"abstracts": [
{
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"content": "Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. In this paper, we report a surprising association between directed edges in BNs and known colonization order. Furthermore, when combined with the sign of the correlations from CoNs, BNs allow for many useful conclusions.In this paper, we ask the pertinent question: what can BNs reveal about the relationships between microbial taxa in a microbiome? In particular, we argue that BNs are able to capture temporal order (colonization order) when combined with the sign of correlation. The main contribution of this paper is to show how to carefully use BNs in conjunction with CoNs to make potential inferences about colonization order.We carried out our experiments on two datasets. The first is oral microbiome data from the Human Microbiome Project (HMP) [1], which included eight different sites all from within the oral cavity from 242 healthy adults (129 males, 113 females). The second is data from preterm infant gut microbiome samples as described in the paper by La Rosa et al. [2]. A total of 922 stool samples from 58 premature babies (each weighing ≤ 1500 g at birth) collected on different days were used for our analysis.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. In this paper, we report a surprising association between directed edges in BNs and known colonization order. Furthermore, when combined with the sign of the correlations from CoNs, BNs allow for many useful conclusions.In this paper, we ask the pertinent question: what can BNs reveal about the relationships between microbial taxa in a microbiome? In particular, we argue that BNs are able to capture temporal order (colonization order) when combined with the sign of correlation. The main contribution of this paper is to show how to carefully use BNs in conjunction with CoNs to make potential inferences about colonization order.We carried out our experiments on two datasets. The first is oral microbiome data from the Human Microbiome Project (HMP) [1], which included eight different sites all from within the oral cavity from 242 healthy adults (129 males, 113 females). The second is data from preterm infant gut microbiome samples as described in the paper by La Rosa et al. [2]. A total of 922 stool samples from 58 premature babies (each weighing ≤ 1500 g at birth) collected on different days were used for our analysis.",
"fno": "08542086",
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"Bayes Methods",
"Correlation",
"Pediatrics",
"Diseases",
"Cavity Resonators",
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"Bayesian Networks",
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"Colonization",
"PC Stable"
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"affiliation": "Bioinformatics Research Group (BioRG), Florida International University, Miami, FL, 33199, USA",
"fullName": "Musfiqur Rahman Sazal",
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