skip to main content

Title: Woes, Workarounds, and Wishes of Users Living in a Multinetwork Reality
Despite efforts towards pervasive, high-speed broadband connectivity, users worldwide continue to experience a persistent multinetwork reality–a reality of intermittent Internet access over multiple networks of varying capacities across space and time. In this late-breaking work, we investigate the challenges users face while using different Internet-based services and the mitigating strategies they adopt to overcome those challenges in a multinetwork reality. In addition, we also investigate how users envision software-based interventions that might augment their existing strategies and help them better manage their activities in a multinetwork reality. Finally, based on our findings from a qualitative analysis of semi-structured interviews, we explore a two-dimensional design space defined by cognitive and resource costs and discuss directions for future work.  more » « less
Award ID(s):
Author(s) / Creator(s):
Date Published:
Journal Name:
CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Many have predicted the future of the Web to be the integration of Web content with the real-world through technologies such as Augmented Reality (AR). This has led to the rise of Extended Reality (XR) Web Browsers used to shorten the long AR application development and deployment cycle of native applications especially across different platforms. As XR Browsers mature, we face new challenges related to collaborative and multi-user applications that span users, devices, and machines. These collaborative XR applications require: (1) networking support for scaling to many users, (2) mechanisms for content access control and application isolation, and (3) the ability to host application logic near clients or data sources to reduce application latency. In this paper, we present the design and evaluation of the AR Edge Networking Architecture (ARENA) which is a platform that simplifies building and hosting collaborative XR applications on WebXR capable browsers. ARENA provides a number of critical components including: a hierarchical geospatial directory service that connects users to nearby servers and content, a token-based authentication system for controlling user access to content, and an application/service runtime supervisor that can dispatch programs across any network connected device. All of the content within ARENA exists as endpoints in a PubSub scene graph model that is synchronized across all users. We evaluate ARENA in terms of client performance as well as benchmark end-to-end response-time as load on the system scales. We show the ability to horizontally scale the system to Internet-scale with scenes containing hundreds of users and latencies on the order of tens of milliseconds. Finally, we highlight projects built using ARENA and showcase how our approach dramatically simplifies collaborative multi-user XR development compared to monolithic approaches. 
    more » « less
  2. Extensive HCI research has investigated how to prevent and mitigate harassment in virtual spaces, particularly by leveraging human-based and Artificial Intelligence (AI)-based moderation. However, social Virtual Reality (VR) constitutes a novel social space that faces both intensified harassment challenges and a lack of consensus on how moderation should be approached to address such harassment. Drawing on 39 interviews with social VR users with diverse backgrounds, we investigate the perceived opportunities and limitations for leveraging AI-based moderation to address emergent harassment in social VR, and how future AI moderators can be designed to enhance such opportunities and address limitations. We provide the first empirical investigation into re-envisioning AI’s new roles in innovating content moderation approaches to better combat harassment in social VR. We also highlight important principles for designing future AI-based moderation incorporating user-human-AI collaboration to achieve safer and more nuanced online spaces. 
    more » « less
  3. Augmented Reality (AR) applications can enable geographically distant users to collaborate using shared video feeds or interactive 3D holograms, and may be particularly useful in the socially distant context of the Covid-19 pandemic. However, a good user experience is key for their success and could be negatively impacted by network impairments, which are an inevitable occurrence in today's best-effort Internet. In this paper, we present the findings of an empirical user study, aimed at understanding the effects of network outages, on user experience and behavior, in a collaborative AR task. We highlight how network outages affected users in different ways depending on their role in the collaborative task, and how giving users explicit information about poor network conditions helped them deal with some of these negative effects. Furthermore, we report the strategies that users themselves adopted, to deal with outages, such as batching instructions, or shifting to a different spatial referencing style when communicating with their partners. Lastly, based on our findings, we present some design implications for future remote-collaborative AR applications. 
    more » « less
  4. Given the strategic importance of the semiconductor manufacturing sector and the CHIPS Act impact on microelectronics, it is more imperative than ever to train the next generation of scientists and engineers in the field. However, this is a challenging feat since nanofabrication education uses hands-on cleanroom facilities. Since cleanrooms are expensive, have access constraints due to safety concerns, and offer limited instructional space, class sizes and outreach events are limited. To complement instruction in nanotechnology education, there is some open- or educational-access software, which is computer-based and focuses only on training for individual equipment, not on the typical workflow for device fabrication. The objective of this work was to develop an accessible virtual reality ecosystem that provides an immersive education and outreach on device nanofabrication that is user-friendly for a broad range of audiences. At the George Washington University (GWU), a virtual reality cleanroom prototype has been developed. It consists of a 45-minute gameplay module that covers the process flow for the fabrication of micro-scale resistors, from sample preparation to electrical characterization. We also performed a mixed methods study to investigate how 5 students in a nanoelectronics course utilized this virtual reality cleanroom prototype and what changes they recommend to improve its user interface and learner experience. The study population for this work-in-progress consisted of students enrolled in a nanoelectronics course at GWU during the 2022-2023 school year. Students taking this course can be undergraduate (junior or senior) or graduate (masters or PhD). The research questions for this study were 1) what is the user experience with the virtual reality cleanroom prototype, 2) what challenges, if any, did students experience, and 3) what changes did students recommend to improve the virtual reality cleanroom prototype learner experience? Preliminary results indicate that the students found the virtual reality cleanroom simulator helpful in repeatedly exploring the cleanroom space and the nanofabrication process flow in a safe way, thus developing more confidence in utilizing the actual cleanroom facility. The results of this study will provide insight on the design of future modules with more complicated levels and device process flows. Moreover, the study could inform the development of other virtual reality simulators for other lab activities. The improved usability of the proposed software could provide students in large classes or attending online programs in electrical and computer engineering, as well as K-12 students participating in nanotechnology-related outreach events, the opportunity to conduct realistic process workflows, learn first-hand about nanofabrication, and practice using a nanofabrication lab via trial and error in a safe virtual environment. 
    more » « less
  5. The ever increasing size of deep neural network (DNN) models once implied that they were only limited to cloud data centers for runtime inference. Nonetheless, the recent plethora of DNN model compression techniques have successfully overcome this limit, turning into a reality that DNN-based inference can be run on numerous resource-constrained edge devices including mobile phones, drones, robots, medical devices, wearables, Internet of Things devices, among many others. Naturally, edge devices are highly heterogeneous in terms of hardware specification and usage scenarios. On the other hand, compressed DNN models are so diverse that they exhibit different tradeoffs in a multi-dimension space, and not a single model can achieve optimality in terms of all important metrics such as accuracy, latency and energy consumption. Consequently, how to automatically select a compressed DNN model for an edge device to run inference with optimal quality of experience (QoE) arises as a new challenge. The state-of-the-art approaches either choose a common model for all/most devices, which is optimal for a small fraction of edge devices at best, or apply device-specific DNN model compression, which is not scalable. In this paper, by leveraging the predictive power of machine learning and keeping end users in the loop, we envision an automated device-level DNN model selection engine for QoE-optimal edge inference. To concretize our vision, we formulate the DNN model selection problem into a contextual multi-armed bandit framework, where features of edge devices and DNN models are contexts and pre-trained DNN models are arms selected online based on the history of actions and users' QoE feedback. We develop an efficient online learning algorithm to balance exploration and exploitation. Our preliminary simulation results validate our algorithm and highlight the potential of machine learning for automating DNN model selection to achieve QoE-optimal edge inference. 
    more » « less