This paper presents interfaces for reporting emotion in real-time during VR stimuli. Self-reported emotional responses are critical for developing emotion recognition systems. Such responses can vary throughout a stimulus such as 360° video, but most interfaces for reporting emotion are designed to be used after the experience. This reduces the entire experience to a single data point and raises concerns about validity when multiple emotions can be elicited across the stimulus. We introduce and compare user interfaces that allow for real-time emotion reporting throughout the length of the stimulus. Each interface varies on how emotion is physically input by the user and displayed back to them for confirmation. A preliminary study compared five such interfaces, gathering initial impressions, comparing control schemes, and rating intuitiveness. A primary study considered four refined interface designs and compared reporting precision and subjective opinions. Results suggest that a single interface face icon responding to arousal and valence reports and a gradiating color wheel are intuitive, precise, and unobtrusive. More broadly, results indicate the type of rating interface has a significant effect on the given ratings.

Emotion recognition models require datasets of physiological responses to stimuli designed to elicit targeted emotions, preferably stimuli similar to the experience during which the models will be used. Many libraries of such stimuli have been created to ease this data collection process, most of which involve passive media such as images or videos. Virtual Reality, however, offers an opportunity to investigate uniquely active emotion elicitation stimuli that directly center the user in the experience with an increased feeling of presence and potential to elicit stronger emotions. We leverage this to introduce a set of four active affective tasks in VR designed to quickly elicit targeted emotions without need for narrative understanding common to passive stimuli. We compare our tasks with selections from an existing affective library of passive 360° videos and validate our approach by comparing self-reported emotional responses to the stimuli. Results indicate that these types of active task stimuli can reliably elicit strong emotions comparable to other passive media and provide the basis for building a larger library of training- and education-relevant tasks.

We present a preliminary study on the design of visual interfaces for users to continuously rate their emotion while viewing VR content. Interfaces consist of two from the literature, a continuous adaptation of the popular SAM interface, and two novel interfaces. Designs were tested to discern what elements are intuitive or distracting. Study phases included initial impressions of interface visuals, tuning the interface control scheme, training by rating a list of emotion labels, and continuous rating of 360° video content. Results suggest that an interactive face icon (Smiley) is a promising design choice and suggest further evaluation of possible benefits.

We assess the effectiveness of attention guidance cues in an educational platform in local high schools with real students. Three eye-tracked visual cues, previously assessed for their ability to guide and restore attention, are compared against a baseline absence of cue in a VR field trip of a virtual solar energy field. Students experienced four presentations on solar energy production including in-world animations and teacher imagery, in three of which the visual cues guided attention to the relevant object or teacher in the scene. Attention guidance using visual cues is commonly studied using “search and selection” style tasks, but has not been studied in the context of maintaining attention in real-world environments.

We present and evaluate methods to redirect desktop inputs such as eye gaze and mouse pointing to a VR-embedded avatar. We use these methods to build a novel interface that allows a desktop user to give presentations in remote VR meetings such as conferences or classrooms. Recent work on such VR meetings suggests a substantial number of users continue to use desktop interfaces due to ergonomic or technical factors. Our approach enables desk-top and immersed users to better share virtual worlds, by allowing desktop-based users to have more engaging or present "cross-reality" avatars. The described redirection methods consider mouse pointing and drawing for a presentation, eye-tracked gaze towards audience members, hand tracking for gesturing, and associated avatar motions such as head and torso movement. A study compared different levels of desktop avatar control and headset-based control. Study results suggest that users consider the enhanced desktop avatar to be human-like and lively and draw more attention than a conventionally animated desktop avatar, implying that our interface and methods could be useful for future cross-reality remote learning tools.

We present a pilot study of four teacher avatars for an educational virtual field trip. The avatars consist of a depth-video-based mesh of a real person, a game-style human model, a robot model, and the robot with its head replaced by a video feed of the teacher's face. Multiple avatars were developed to consider alternatives to the mesh representation that required high-bandwidth networks and a non-immersive teacher interface. The pilot study presents a random avatar to the participant at each of 4 educational stations, and follows up with a subjective questionnaire. Most notably, we find positive affinity for the plain robot model to be similar to that of the video mesh, which was previously shown to provide high co-presence and good results for education. Results are guiding a larger study that will measure the educational efficacy of revised avatars.

Summary Cloud computing has become a potential resource for businesses and individuals to outsource their data to remote but highly accessible servers. However, the potential of cloud services has not been fully realized due to users concerns about data privacy and security. User‐side encryption techniques can be employed to mitigate the security concerns, but once the data is encrypted, no processing (eg, searching) can be performed on the outsourced data. Searchable Encryption (SE) techniques have been widely studied to enable searching on the data while they are encrypted. These techniques enable various types of search on the encrypted data and offer different levels of security. While these techniques enable different search types and vary in details, they share similarities in their components and architectures. In this paper, we provide a comprehensive survey on different secure search techniques, a high‐level architecture for these systems, and an analysis of their performance and security level.

Summary Cloud storage is a widely utilized service for both personal and enterprise demands. However, despite its advantages, many potential users with enormous amounts of sensitive data (big data) refrain from fully utilizing the cloud storage service due to valid concerns about data privacy. An established solution to the cloud data privacy problem is to perform encryption on the client‐end. This approach, however, restricts data processing capabilities (eg, searching over the data). Accordingly, the research problem we investigate is how to enable real‐time searching over the encrypted big data in the cloud. In particular, semantic search is of interest to clients dealing with big data. To address this problem, in this research, we develop a system (termed S3BD) for searching big data using cloud services without exposing any data to cloud providers. To keep real‐time response on big data, S3BD proactively prunes the search space to a subset of the whole dataset. For that purpose, we propose a method to cluster the encrypted data. An abstract of each cluster is maintained on the client‐end to navigate the search operation to appropriate clusters at the search time. Results of experiments, carried out on real‐world big datasets, demonstrate that the search operation can be achieved in real‐time and is significantly more efficient than other counterparts. In addition, a fully functional prototype of S3BD is made publicly available.

We present a VR field trip framework, Kvasir-VR, and assess its two approaches to teacher-guided content. In one approach, networked student groups are guided by a live teacher captured as live-streamed depth camera imagery. The second approach is a standalone (non-networked) version allowing students to individually experience the field trip based on depth camera recordings of the same teacher. Both approaches were tested at two high schools using a VR environment that teaches students about solar energy production via tours of a solar plant. We show that our live networked approach can produce promising test score gains and very high ratings of co-presence, affective attraction, overall opinion, etc. Results show a benefit of live networked VR, as the standalone approach had lower performance in terms of gains and most ratings, although its ratings were still positive. We further consider possible differences of school environment (dedicated vs. integrated classroom), and we conclude with tradeoffs and implications to benefit future design of educational VR.

This demo presents an approach to networked educational virtual reality for virtual field trips and guided exploration. It shows an asymmetric collaborative interface in which a remote teacher stands in front of a large display and depth camera (Kinect) while students are immersed with HMDs. The teacher's front-facing mesh is streamed into the environment to assist students and deliver instruction. Our project uses commodity virtual reality hardware and high-performance networks to allow students who are unable to visit a real facility with an alternative that provides similar educational benefits. Virtual facilities can further be augmented with educational content through interactables or small games. We discuss motivation, features, interface challenges, and ongoing testing.

The recent rapid growth in Internet speeds and file storage requirements has made cloud storage an appealing option on both a personal and enterprise level. Despite the many benefits offered by cloud storage, many potential users with sensitive data refrain from fully utilizing this service due to valid concerns about information privacy. An established solution to this concern is to perform encryption on the user side with the key stored on a local machine, meaning the cloud will never see the user's plaintext data. However, by encrypting data on the user side data processing capabilities (e.g., searching) are lost. In particular, the ability to semantically search is of the user's interest in large datasets. In this paper, we present S3C, a system that provides a semantic search functionality over encrypted data in the cloud. S3C combines approaches from traditional keyword-based searchable encryption and semantic web searching. It offers a user transparent experience that accepts a simple multi-phrase query and returns a list of documents ranked by semantic relevance to the query. Our proposed approach is space-efficient, which makes it suitable for large scale datasets. Our minimal processing also allows the system to be run on thin clients such as smart-phones or tablets. We evaluate the performance of our system against various real-world datasets, and our results show that it produces accurate search results while maintaining minimal storage overhead (~0.3% of the dataset size).