A museum hybrid space combines physical artifacts co-located with virtual and augmented reality displays. Although the technology exists to provide museums with hybrid space, there are no empirical studies on effectiveness of the museum hybrid space in terms of learning and enjoyment. This article takes an experimental approach and measures the enjoyment and learning (dependent variables) of participants in response to selected environments (independent variables) including a traditional environment (based on photos and labels), a video-enhanced environment (based on projected video clips), and a VR-enhanced environment (based on video game). The main outcome of this article is demonstrating that the use of VR technology and the resulting hybrid space (i.e., VR-enhanced environment) results in novel museum experiences that provide greater impacts on audience in terms of learning and enjoyment.

The number of video games that are developed based on real historical events and evidence is increasing. These history-based video games provide learning opportunities to players, but a certain type of such games-first- and third-person shooters-has not been carefully examined for their potentials. Knowing what players say about their game experience-even if the information and knowledge are inaccurate-helps researchers understand what type of learning could happen with such games. In this article, we propose a systematic approach to assessing games as learning environments, using the method of comparing the authenticity of popular history-based video games. Through a qualitative data analysis, we studied players' comments on the web-based communication services, such as game forums, digital distribution platforms, and discussion websites. Casual players' conversations on these websites showed that there exist several learning potentials in the games for players, including building their understanding about history and historical forces of the time, through personally relating to specific events, social artifacts, and places.

The use of sound feedback to assist in learning and correcting motor skills has some innate advantages. For example, sound leaves a person's eyes free for important tasks like avoiding obstacles, and ample psychological evidence shows a connection between the synthesis and perception of motion and sound. When motion is periodic, as it is with most forms of human locomotion, the sound feedback should also be periodic, i.e., rhythmic and synchronized to a person's motion. We present a method to track an athlete performing a known movement in real-time using an RGB-D camera. We use a Kalman filter in conjunction with iterative closest point to synchronize incoming data against a known movement pattern and produce a musical rhythm. The athlete and the rhythm move in phase with one another. We call this process rhythmic sonification. We demonstrate our method by tracking and rhythmically sonifying an advanced in-line speed skating movement called double-push. Rhythmically sonified feedback provides a new opportunity to teach and reinforce this and other athletic skills.

This paper discusses a collaborative project that inquires about the possibilities of the tactile sensation in art and its ability to reestablish human sensory relationships with consumer technology. It introduces the visitor to the interaction style practiced by the artists with their medium. The described interactive sculpture predisposes the visitor to explore tactile interaction as an aesthetic experience within multimodal multisensory system. Disturbed System takes the visitor's experience with the artwork into unfamiliar sensory territory. Touching the soft silicone surface of the sculpture provides electronic feedback of embedded vibration and directional spatialized sound in an installation format. The artwork presents this sensory information in a form of unexpected assemblage of pulsing organic sculptural surfaces and emitting sound. It also places visitors in a shared interactive space, an aura of traveling sound warped by their touch of the sculpture. This shared interaction investigates relationships between nature, artifice, technology, human body and human social group behavior.

Modern surveillance systems for practical applications with diverse and mobile sensors are large, complex, and expensive. It is known that unexpected behaviors can emerge from such systems, and when these behaviors correspond to weaknesses in a surveillance system, we call them emergent vulnerabilities. Given their cost and importance to security, it is essential to test these systems for such vulnerabilities prior to deployment. To that end, we automate the testing process with multiagent systems and machine learning. However, the conventional—and most intuitive–approach is to focus the machine learning on the subject system, which leads to a high‐dimensional problem that is intractable. Instead, we demonstrate in this paper that learning attacks on the system is tractable and provides a viable testing method. We demonstrate this with a series of studies in simulation and with a small‐scale model system featuring elements typically found in real physical surveillance systems. Our machine learning method finds successful attacks in simulation, which we can duplicate with the physical system. The method is scalable, with the implication that it could be used to test larger, real surveillance installations.

Large, complex systems can exhibit unforeseen behaviours. In the case of surveillance and security systems, these behaviours can be weaknesses that should be discovered by automated testing and ameliorated. Previous work has shown that such automated testing can be done using particle swarm optimization to learn behaviours that allow a set of attackers to defeat the system. However, for the optimization to succeed, it must have some knowledge about what constitutes a successful attack in order to guide the swarm. This knowledge is encapsulated in a goal ordering structure. In this paper, we examine the goal ordering structure and its role in the learning of system weakness. We specifically look at applications in harbour surveillance and security, and show how knowledge of the likely properties of a successful attack can be added to the goal ordering structure. Our experimental results show that adding knowledge to the goal ordering structure improves the search, when that knowledge is correctly inserted into the structure.

We create interactive art that can be enjoyed by groups such as audiences at public events with the intent to encourage communication with those around us as we play with the art. Video systems are an attractive mechanism to provide interaction with artwork. However, public spaces are complex environments for video analysis systems. Interaction becomes even more difficult when the art is viewed by large groups of people. We describe a video system for interaction with art in public spaces and with large audiences using a model-free, appearance-based approach. Our system extracts parameters that describe the field of motion seen by a camera, and then imposes structure on the scene by introducing a swarm of particles that moves in reaction to the motion field. Constraints placed on the particle movement impose further structure on the motion field. The artistic display reacts to the particles in a manner that is interesting and predictable for participants. We demonstrate our video interaction system with a series of interactive art installations tested with the assistance of a volunteer audience.

This paper describes SwarmArt, a collaborative project between computer science and art, which resulted in two installations of interactive art that incorporates swarm intelligence. We describe the scientific context of the artwork, technical details of the video system, through which user interaction is controlled, and the realization of the swarm-based simulations, which were projected onto a large screen. Finally, we explain and illustrate our two recent SwarmArt installations in a Calgary gallery.

In the perception of gaits, timing is everything; specifi-cally, the relative timing of the individual motions in a gait, and when events occur periodically, as they do in a gait,then relative timing is equivalent to phase. The importance of phase in gaits appears in the medical, psychology, andcomputer vision literature. The video phase locked loop (vPLL) [3] is a novel sys-tem that perceives gaits, is sensitive to the phases of the component motions of the gait, and is model-free. vPLLsprovide a mechanism to perform two critical tasks in gait perception: frequency entrainment and phase locking [1].A vPLL can lock on oscillations in pixels that arise because of oscillatory motion. In doing so, the vPLL matches its in-ternal oscillators to the oscillations in pixel intensities, thus performing frequency entrainment. Phase locking occursas individual phased-locked loops at each pixel site lock simultaneously. The abundance of data extracted by the vPLLmakes gait recognition possible.

One method to estimate image values in the presence of noise and discontinuities is to try all possible configurations of discontinuities in a neighborhood and then select the one that yields the best estimate according to some criterion. We call these methods multiple oriented filters since each configuration is usually a rotated and shifted version of another. This paper describes a method that optimizes the implementation of multiple oriented filters, reduces the time complexity, and makes the algorithm practical, even when noise levels are high. We do this by exploiting the fact that each neighborhood configuration is nearly the same as several others. We apply the optimization to smoothing estimators with discontinuities and binary restoration of thin sheets, and speculate that the optimization may also be applied well to estimation of optical flow with discontinuities.

Applications that interpret video data need to track objects as they move in a scene. Tracking methods that estimate the state trajectories of objects as they change over time (e.g. Kalman filter) have difficulty as the number of objects and clutter increase. We present an alternative, called statistical tracking, that is based on the concept of network tomography. A scene is modeled as a network of interconnected regions. Statistical tracking estimates the number of trips made from one region to another based on inter-region boundary traffic counts accumulated over time. It is not necessary to track an object through a scene, just to determine when an object crosses a boundary, something that is generally easier than estimating a continuous trajectory. In achieving this simplicity, statistical tracking gives up the ability to determine an object's state as the motion occurs. Instead, it determines mean traffic intensities based on statistics accumulated over a period of time. In spite of this limitation, there are several applications for which statistical tracking is useful. We demonstrate the application of the method to a large sample of video traffic surveillance data. The method does not require any data association which has some important implications concerning personal privacy.

We present a method for restoration of noisy tomographic images for detecting thin objects, such as explosives. Use of a weighted mean-square estimate optimizes the solution to place emphasis on the infrequent, but significant local structure associated with thin objects. Experimental results show successful restoration at very high noise levels.

Medical imaging is shifting from film to electronic images. The STARE (structured analysis of the retina) system is a sophisticated image management system that will automatically diagnose images, compare images, measure key features in images, annotate image contents, and search for images similar in content. We concentrate on automated diagnosis. The images are annotated by segmentation of objects of interest, classification of the extracted objects, and reasoning about the image contents. The inferencing is accomplished with Bayesian networks that learn from image examples of each disease. This effort at image understanding in fundus images anticipates the future use of medical images. As these capabilities mature, we expect that ophthalmologists and physicians in other fields that rely in images will use a system like STARE to reduce repetitive work, to provide assistance to physicians in difficult diagnoses or with unfamiliar diseases, and to manage images in large image databases. 1.

Ambiguity in the solution of inverse problems arises when data are insufficient to define a unique solution (i.e., the problem is ill-posed). Data fusion has the potential to reduce this ambiguity by using other sensory data that complement the original data. This paper examines the application of data fusion to limited-angle computed tomography (CT) to resolve ambiguity. While CT in its conventional form is ill-posed with a small null space, limited-angle CT has a much larger null space. Structures that lie primarily in the null space of the limited-angle Radon transform are particularly prone to ambiguity. We describe a novel constraint-based data fusion system that fuses spatial support and ultrasound measurements with x-ray data. The ensuing problem is less ambiguous, has a reduced null space, and permits accurate reconstruction of a sandwich structure where otherwise impossible.>