Electrical muscle stimulation (EMS) provides direct, felt feedback by actuating muscles, offering a novel haptic channel for extended reality (XR). We present EMSTouch, a system that augments XR scenes through controlled muscle activation for virtual object manipulation and handshake tasks. EMS was evaluated against vibrotactile and visual baselines, focusing on the perception of replayed socially meaningful gestures. In a user study (N=12), EMS feedback yielded higher realism scores than visual only interaction and produced greater naturalness and immersion than vibrotactile feedback. Findings highlight the realism–agency trade-off inherent in EMS-based interaction, showing how more realistic stimulation can influence autonomy. We further explore EMS in creative practice through a case study in XR art, where electromyography (EMG) captures the biodynamics of strokes during virtual sculpture creation and EMS replays the movements. Altogether, EMS emerges as a socially accepted feedback modality that elevates realism and immersion in both virtual interaction and creative expression.

Large language models (LLMs) are reshaping interactive digital narratives (IDNs). However, creating complex interactive narratives while preserving narrative consistency remains challenging. We present Orchid‑Creator (Orchid), an LLM-based authoring tool that represents IDNs as story graphs with a card‑based interface for scene definition and conditional transitions. We evaluated Orchid in two studies: a usability study with eight authors, and a comparative study with 20 participants (authors, developers, and players) that compared Orchid to Twine and AI Dungeon. Authors reported that Orchid’s features met their needs (card‑based interface: 4.0/5; story graph: 4.38/5; variable setup: 4.5/5). Structuring narratives with Orchid was easier (M = 6.0/7, p <.01) and produced better‑structured stories (M = 5.3/7, p <.05) than the alternatives, balancing author control (M = 5.5/7) with outcome diversity (M = 5/7, p <.01) and maintaining comparable usability. Finally, a case study with an artist demonstrates Orchid’s utility for interactive art.

Asynchronous design ideation is increasingly vital in virtual reality (VR) environments. Annotations, referring to notes, marks, or comments overlaid on virtual designs, are essential for providing context, feedback, and clarity, facilitating communication among designers using Creativity Support Tools (CSTs) in the design sector. However, the effectiveness of various annotation methods in enhancing creativity during VR design ideation remains underexplored. This study aims to identify effective annotation methods that promote creativity in VR-based design ideation, addressing the shift from traditional paper-and-pen practices to immersive environments. We adopted a three-phase approach: (1) semi-structured interviews with design experts, (2) an empirical mixed-design study with design professionals, leading to the development of AsyncCreativity, our VR CST system, and (3) a deployment study evaluating annotations via AsyncCreativity in VR design ideation. Our findings reveal that multimodal annotations, especially audio annotations, significantly enhance user engagement and creativity compared to unimodal annotations. Participants reported improved ideation experiences when utilizing diverse annotation types during ideation when searching, sketching, and presenting. Our study provides valuable insights for developing effective CSTs in VR and design field, advancing the understanding of how optimized annotation strategies can foster creativity in immersive design environments.

Integrating Large Language Models (LLMs) into Interactive Digital Narratives (IDNs) enables dynamic storytelling where user interactions shape the narrative in real time, challenging traditional authoring methods.This paper presents the design study of Orchid, a creative approach for authoring LLM-driven IDNs.Orchid allows users to structure the hierarchy of narrative stages and define the rules governing LLM narrative generation and transitions between stages.The development of Orchid consists of three phases.1) Formulating Orchid through desk research on existing IDN practices.2) Implementation of a technology probe based on Orchid.3) Evaluating how IDN authors use Orchid to design IDNs, verify Orchid's hypotheses, and explore user needs for future authoring tools.This study confirms that authors are open to LLM-driven IDNs but desire strong authorial agency in narrative structures, highlighted in accuracy in branching transitions and story details.Future design implications for Orchid include introducing deterministic variable handling, support for trans-media applications, and narrative consistency across branches.

We leverage 3D Gaussian Splatting (3DGS) as a scene representation and propose a novel test-time camera pose refinement (CPR) framework, GS-CPR. This framework enhances the localization accuracy of state-of-the-art absolute pose regression and scene coordinate regression methods. The 3DGS model renders high-quality synthetic images and depth maps to facilitate the establishment of 2D-3D correspondences. GS-CPR obviates the need for training feature extractors or descriptors by operating directly on RGB images, utilizing the 3D foundation model, MASt3R, for precise 2D matching. To improve the robustness of our model in challenging outdoor environments, we incorporate an exposure-adaptive module within the 3DGS framework. Consequently, GS-CPR enables efficient one-shot pose refinement given a single RGB query and a coarse initial pose estimation. Our proposed approach surpasses leading NeRF-based optimization methods in both accuracy and runtime across indoor and outdoor visual localization benchmarks, achieving new state-of-the-art accuracy on two indoor datasets.

360-degree cameras streamline data collection for radiance field 3D reconstruction by capturing comprehensive scene data. However, traditional radiance field methods do not address the specific challenges inherent to 360-degree images. We present SC-OmniGS, a novel self-calibrating omnidirectional Gaussian splatting system for fast and accurate omnidirectional radiance field reconstruction using 360-degree images. Rather than converting 360-degree images to cube maps and performing perspective image calibration, we treat 360-degree images as a whole sphere and derive a mathematical framework that enables direct omnidirectional camera pose calibration accompanied by 3D Gaussians optimization. Furthermore, we introduce a differentiable omnidirectional camera model in order to rectify the distortion of real-world data for performance enhancement. Overall, the omnidirectional camera intrinsic model, extrinsic poses, and 3D Gaussians are jointly optimized by minimizing weighted spherical photometric loss. Extensive experiments have demonstrated that our proposed SC-OmniGS is able to recover a high-quality radiance field from noisy camera poses or even no pose prior in challenging scenarios characterized by wide baselines and non-object-centric configurations. The noticeable performance gain in the real-world dataset captured by consumer-grade omnidirectional cameras verifies the effectiveness of our general omnidirectional camera model in reducing the distortion of 360-degree images.

This paper presents Lite VLoc, a hierarchical vi-sual localization framework that uses a lightweight topo-metric map to represent the environment. The method consists of three sequential modules that estimate camera poses in a coarse-to-fine manner. Unlike dense 3D mapping methods, LiteVLoc reduces storage by avoiding geometric reconstruction. It uses a learning-based feature matcher to establish dense correspondences between sparse keyframes and observations, and then refines poses with a geometric solver, enabling robustness to viewpoint changes. The system assumes depth sensors or stereo camera for deployment. A novel dataset for the map-free relocalization task is also introduced. Extensive experiments including localization and navigation in both simulated and real-world scenarios have validate the system's performance and demonstrated its precision and efficiency for large-scale deployment. Code and data will be made publicly available at the webpage:https://rpl-cs-ucl.github.io/LiteVLoc.

State-of-the-art hierarchical localisation pipelines (HLoc) employ image retrieval (IR) to establish 2D-3D correspondences by selecting the top-k most similar images from a reference database. While increasing$k$improves localisation robustness, it also linearly increases computational cost and runtime, creating a significant bottleneck. This paper investigates the relationship between global and local descriptors, showing that greater similarity between the global descriptors of query and database images increases the proportion of feature matches. Low similarity queries significantly benefit from increasing k, while high similarity queries rapidly experience diminishing returns. Building on these observations, we propose an adaptive strategy that adjusts$k$based on the similarity between the query's global descriptor and those in the database, effectively mitigating the feature-matching bottleneck. Our approach reduces computational costs and processing time without sacrificing accuracy. Experiments on three indoor and outdoor datasets show that AIR-HLoc reduces feature matching time by up to 30% while preserving state-of-the-art accuracy. The results demonstrate that AIR-HLoc facilitates a latency-sensitive localisation system.

Virtual reality (VR) cloud gaming is increasingly developing in the gaming industry. Yet, the performance of the congestion control algorithms on top of which these systems build remains under-explored. In this study, we implement two industry-standard network congestion control algorithms, Google Congestion Control (GCC) and Network-Assisted Dynamic Adaptation (NADA), according to their Requests for Comments (RFCs), and integrate them into an open-source VR gaming system (ALVR). Including ALVR's congestion control (ALVR-ABR), we conduct extensive experiments on real-world networks to evaluate each algorithm's frame latency, target-to-receiving bitrate gap, dropped frames, image quality, and fairness among heterogeneous competing flows. GCC decreases frame latency by 352ABR present significant gaps between the selected and received bitrate, causing substantial congestion-induced frame drops, while GCC has a minimal gap, resulting in minor frame drops, suggesting its suitability for game-player interaction. GCC exhibits a 2.7ABR, respectively, indicating slight immersion degradation. However, only NADA ensures a fair bandwidth share against loss-based flows due to its bitrate response to loss-induced congestion signals and lower sensitivity to delay gradients compared to GCC.

This paper addresses metric 3D reconstruction of indoor scenes by exploiting their inherent geometric regularities with compact representations. Using planar 3D primitives -- a well-suited representation for man-made environments -- we introduce PLANA3R, a pose-free framework for metric $\underline{Plana}$r $\underline{3}$D $\underline{R}$econstruction from unposed two-view images. Our approach employs Vision Transformers to extract a set of sparse planar primitives, estimate relative camera poses, and supervise geometry learning via planar splatting, where gradients are propagated through high-resolution rendered depth and normal maps of primitives. Unlike prior feedforward methods that require 3D plane annotations during training, PLANA3R learns planar 3D structures without explicit plane supervision, enabling scalable training on large-scale stereo datasets using only depth and normal annotations. We validate PLANA3R on multiple indoor-scene datasets with metric supervision and demonstrate strong generalization to out-of-domain indoor environments across diverse tasks under metric evaluation protocols, including 3D surface reconstruction, depth estimation, and relative pose estimation. Furthermore, by formulating with planar 3D representation, our method emerges with the ability for accurate plane segmentation. The project page is available at: \url{https://lck666666.github.io/plana3r/}.

In Shenzhen’s Sea World in China’s Greater Bay Area, on reclaimed land, ocean-side promenades are set in front of impressive glass towers. Next to it lie container port, construction sites, and densely populated older areas. A rapidly built vision of Shenzhen’s future co-exists with traces from its various pasts. “High Sections / Low Leaps” is an audiovisual artwork that reconstructs actual and imagined development phases of China’s Greater Bay Area, one of the fastest developing areas on the planet. This artwork element, with the subtitle “Shenzhen’s Sea World Ocean Mall,” is an interactive scene rendered in a game engine, presented to the audience on two screens, showing otherworldly office towers that grow and shrink. 3D modeling, AI-generated facade elements, and spatial audio are employed to create dynamic audiovisual cityscape speculations. A camera tracks the number of spectators in proximity and switches the scene from past to future phases, allowing for subtle control over the work’s temporality. The artwork investigates how rapid urban development, such as in the Greater Bay Area, can be rendered perceptible for a public, fostering reflection on the relationship between new technologies (including GenAI) and imagined (and built) urban futures. At the intersection of computer graphics, data visualization, urbanism, creative technologies, spatial audio, and generative AI, this approach argues for a reflection on the consequences of human-technological collaboration in view of desired humane rather than technological outcomes. The pilot city Shenzhen holds significance for cities globally.

Portable 360° cameras are becoming a cheap and efficient tool to establish large visual databases. By capturing omnidirectional views of a scene, these cameras could expedite building environment models that are essential for visual localization. However, such an advantage is often overlooked due to the lack of valuable datasets. This paper introduces a new benchmark dataset, 360Loc, composed of 360° images with ground truth poses for visual localization. We present a practical implementation of 360° mapping combining 360° images with lidar data to generate the ground truth 6DoF poses. 360Loc is the first dataset and benchmark that explores the challenge of cross-device visual positioning, involving 360° reference frames, and query frames from pinhole, ultra-wide FoV fisheye, and 360° cameras. We propose a virtual camera approach to generate lower-FoV query frames from 360° images, which ensures a fair comparison of performance among different query types in visual localization tasks. We also extend this virtual camera approach to feature matching-based and pose regression-based methods to alleviate the performance loss caused by the cross-device domain gap, and evaluate its effectiveness against state-of-the-art base-lines. We demonstrate that omnidirectional visual localization is more robust in challenging large-scale scenes with symmetries and repetitive structures. These results provide new insights into 360-camera mapping and omnidirectional visual localization with cross-device queries. Project Page and dataset: https://huajianup.github.io/research/360Loc/

Absolute Pose Regressors (APRs) directly estimate camera poses from monocular images, but their accuracy is unstable for different queries. Uncertainty-aware APRs provide uncertainty information on the estimated pose, alleviating the impact of these unreliable predictions. However, existing uncertainty modelling techniques are often coupled with a specific APR architecture, resulting in suboptimal performance compared to state-of-the-art (SOTA) APR methods. This work introduces a novel APR-agnostic framework, HR-APR, that formulates uncertainty estimation as cosine similarity estimation between the query and database features. It does not rely on or affect APR network architecture, which is flexible and computationally efficient. In addition, we take advantage of the uncertainty for pose refinement to enhance the performance of APR. The extensive experiments demonstrate the effectiveness of our framework, reducing 27.4% and 15.2% of computational overhead on the 7Scenes and Cambridge Landmarks datasets while maintaining the SOTA accuracy in single-image APRs.

Pervasive Augmented Reality (AR) requires accurate pose registration of the device in real-time at a neighbourhood-to-city scale. At such a scale, most pose registration techniques suffer from exponential computational and storage costs and a significant data collection burden. This paper introduces AnchorLoc, a framework that relies on visual anchors (stable and highly recognisable visual elements in a scene) to perform fast and accurate pose registration. Anchorloc automatically identifies these anchors from large image sequences to optimise the search space in later image retrieval and pose registration. As such, it significantly improves the computational efficiency of existing hierarchical localisation pipelines without compromising accuracy. We collect a large-scale localisation dataset consisting of image sequences and 3D reconstruction of a university campus. AnchorLoc reduces localisation runtime by 83% on our campus dataset and 69% on the Cambridge Landmarks dataset without significantly increasing mean pose estimation errors. It is also more accurate and faster than SLD, a localisation algorithm that takes a comparable approach at the keypoint level. This work informs the development of more efficient pervasive AR applications that rely on both absolute and relative camera pose tracking on image sequences.

WallStreetBets (WSB), a Reddit community, impacted stock markets during the 2021 GameStop Short Squeeze. We examine the content and user properties that influence engagement in WSB. Despite WSB's association with emojis and informal terms, engagement among community members depends on more than surface-level factors. Although emojis are commonly used, they are not as effective at fostering interactions among users. Community members engage more with posts that have longer and topic-specific text. Simply producing a high volume of posts is not enough to attract an audience. Consistent topical focus, reciprocal interactions, and previous authorship of catalyst posts influence engagement. WSB posts, regardless of length, generally remain relevant to the community's theme of stock trading. Our findings provide insights into WSB engagement patterns and can be useful for downstream research, such as financial predictive tasks using WSB data.

With the advancement of virtual reality (VR) technology, virtual displays have become integral to how museums, galleries, and other tourist destinations present their collections to the public. However, the current lack of immersion in virtual reality displays limits the user’s ability to experience and appreciate its aesthetics. This paper presents a case study of a creative approach taken by a tourist attraction venue in developing a physical network system that allows visitors to enhance VR’s aesthetic aspects based on environmental parameters gathered by external sensors. Our system was collaboratively developed through interviews and sessions with twelve stakeholder groups interested in art and exhibitions. This paper demonstrates how our technological advancements in interaction, immersion and visual attractiveness surpass those of earlier virtual display generations. Through multimodal interaction, we aim to encourage innovation on the Web and create more visually appealing and engaging virtual displays. It is hoped that the greater online art community will gain fresh insight into how people interact with virtual worlds as a result of this work.

There has been a significant expansion in the use of online social networks (OSNs) to support people experiencing mental health issues. This paper studies the role of Instagram influencers who specialize in coaching people with mental health issues. Using a dataset of 97k posts, we characterize such users' linguistic and behavioural features. We explore how these observations impact audience engagement (as measured by likes). We show that the support provided by these accounts varies based on their self-declared professional identities. For instance, Instagram accounts that declare themselves as Authors offer less support than accounts that label themselves as a Coach. We show that increasing information support in general communication positively affects user engagement. However, the effect of vocabulary on engagement is not consistent across the Instagram account types. Our findings shed light on this understudied topic and guide how mental health practitioners can improve outreach.

Human habitation across multiple planets requires communication and social connection between planets. When the infrastructure of a deep space network becomes mature, immersive cyberspace, known as the Metaverse, can exchange diversified user data and host multitudinous virtual worlds. Nevertheless, such immersive cyberspace unavoidably encounters latency in minutes, and thus operates in a turn-taking manner. This Blue Sky paper illustrates a vision of an interplanetary Metaverse that connects Earthian and Martian users in a turn-based Metaverse. Accordingly, we briefly discuss several grand challenges to catalyze research initiatives for the ‘Digital Big Bang’ on Mars.

User Interaction for NFTs (Non-fungible Tokens) is gaining increasing attention. Although NFTs have been traditionally single-use and monolithic, recent applications aim to connect multimodal interaction with human behavior. This paper reviews the related technological approaches and business practices in NFT art. We highlight that multimodal interaction is a currently under-studied issue in mainstream NFT art, and conjecture that multimodal interaction is a crucial enabler for decentralization in the NFT community. We present a continuum theory and propose a framework combining a bottom-up approach with AI multimodal process. Through this framework, we put forward integrating human behavior data into generative NFT units, as "multimodal interactive NFT." Our work displays the possibilities of NFTs in the art world, beyond the traditional 2D and 3D static content.

Tai Chi is an essential concept of Chinese philosophy which refers to the universe's most primitive state of order. Tai Chi Chuan is a martial art and meditative practice that incorporates the Tai Chi philosophy. With the advent of the digital media age, this traditional martial art is falling into disuse, and most contemporary youths are losing interest. "Meditation in Motion" is an interactive media art installation inspired by the Tai Chi Chuan forms. It aims to convey the central concepts of balance, narrative, and universe of Tai Chi Chuan by breaking down its movements into overlapping circles, building a visual representation of the energy flows in the body.

Mobile cloud gaming enables high-end games on constrained devices by streaming the game content from powerful servers through mobile networks. Mobile networks suffer from highly variable bandwidth, latency, and losses that affect the gaming experience. This paper introduces , an end-to-end cloud gaming framework to minimize the impact of network conditions on the user experience. relies on an end-to-end distortion model adapting the video source rate and the amount of frame-level redundancy based on the measured network conditions. As a result, it minimizes the motion-to-photon (MTP) latency while protecting the frames from losses. We fully implement and evaluate its performance against the state-of-the-art techniques and latest research in real-time mobile cloud gaming transmission on a physical testbed over emulated and real wireless networks. consistently balances MTP latency (<140 ms) and visual quality (>31dB) even in highly variable environments. A user experiment confirms that maximizes the user experience with high perceived video quality, playability, and low user load.

In this paper, we highlight the use of Instagram for social activism, taking 2019 Hong Kong protests as a case study. Instagram focuses on image content and provides users with few features to share or repost, limiting information propagation. Nevertheless, users who are politically active offline also share their activism on Instagram. We first evaluate the effect of protests on social media activity for protesters and non-protesters over two significant protests. Protesters’ exposure to protest-related posts is much higher than non-protesters, and their network activity follows the protest schedule. They are also much more active on posts related to the protest that they participate in than the other protest. We then analyze the images posted by the users. Users predominantly use symbols related to protests and share personal thoughts on its primary actors. Users primarily share content to raise their network’s awareness, and the content choice is directly affected by Instagram’s intrinsic interaction modalities.

The computational capabilities of recent mobile devices enable the processing of natural features for Augmented Reality (AR), but the scalability is still limited by the devices' computation power and available resources. In this paper, we propose EdgeXAR, a mobile AR framework that utilizes the advantages of edge computing through task offloading to support flexible camera-based AR interaction. We propose a hybrid tracking system for mobile devices that provides lightweight tracking with 6 Degrees of Freedom and hides the offloading latency from users' perception. A practical, reliable and unreliable communication mechanism is used to achieve fast response and consistency of crucial information. We also propose a multi-object image retrieval pipeline that executes fast and accurate image recognition tasks on the cloud and edge servers. Extensive experiments are carried out to evaluate the performance of EdgeXAR by building mobile AR apps upon it. Regarding the Quality of Experience (QoE), the mobile AR apps powered by EdgeXAR framework run on average at the speed of 30 frames per second with precise tracking of only 1-2 pixel errors and accurate image recognition of at least 97% accuracy. As compared to Vuforia, one of the leading commercial AR frameworks, EdgeXAR transmits 87% less data while providing a stable 30FPS performance and reducing the offloading latency by 50 to 70% depending on the transmission medium. Our work facilitates the large-scale deployment of AR as the next generation of ubiquitous interfaces.

Connected vehicles, whether equipped with advanced driver-assistance systems or fully autonomous, require human driver supervision and are currently constrained to visual information in their line-of-sight. A cooperative perception system among vehicles increases their situational awareness by extending their perception range. Existing solutions focus on improving perspective transformation and fast information collection. However, such solutions fail to filter out large amounts of less relevant data and thus impose significant network and computation load. Moreover, presenting all this less relevant data can overwhelm the driver and thus actually hinder them. To address such issues, we present Augmented Informative Cooperative Perception (AICP), the first fast-filtering system which optimizes the informativeness of shared data at vehicles to improve the fused presentation. To this end, an informativeness maximization problem is presented for vehicles to select a subset of data to display to their drivers. Specifically, we propose (i) a dedicated system design with custom data structure and lightweight routing protocol for convenient data encapsulation, fast interpretation and transmission, and (ii) a comprehensive problem formulation and efficient fitness-based sorting algorithm to select the most valuable data to display at the application layer. We implement a proof-of-concept prototype of AICP with a bandwidth-hungry, latency-constrained real-life augmented reality application. The prototype adds only 12.6 milliseconds of latency to a current informativeness-unaware system. Next, we test the networking performance of AICP at scale and show that AICP effectively filters out less relevant packets and decreases the channel busy time.

Mobile cloud gaming requires a very low end-to-end latency. Edge computing significantly reduces network latency. However, in mobility scenarios, the user will frequently move out of the edge server's coverage area, requiring frequent migration of the game instance. This paper presents Talaria, an in-engine content synchronisation solution for unnoticeable game instance migration between edge servers. Talaria creates a minimal instance with content immediately relevant to the game experience, allowing the client to switch servers in a minimal amount of time. The remaining content is then synchronised according to priority until the game's state is coherent between both instances. Our implementation of Talaria as a Unity engine plugin reduces the game's downtime by 61% compared to one-off server migration, with an average latency below 25 ms for the server migration, and 87 ms for the entire game synchronisation.

Speeding, slowing down, and sudden acceleration are the leading causes of fatal accidents on highways. Anomalous driving behavior detection can improve road safety by informing drivers who are in the vicinity of dangerous vehicles. However, detecting abnormal driving behavior at the city-scale in a centralized fashion results in considerable network and computation load, that would significantly restrict the scalability of the system. In this paper, we propose CAD3, a distributed collaborative system for road-aware and driver-aware anomaly driving detection. CAD3 considers a decentralized deployment of edge computation nodes on the roadside and combines collaborative and context-aware computation with low-latency communication to detect and inform nearby drivers of unsafe behaviors of other vehicles in real-time. Adjacent edge nodes collaborate to improve the detection of abnormal driving behavior at the city-scale. We evaluate CAD3 with a physical testbed implementation. We emulate realistic driving scenarios from a real driving data set of 3,000 vehicles, 214,000 trips, and 18 million trajectories of private cars in Shenzhen, China. At the microscopic (road) level, CAD3 significantly improves the accuracy of detection and lowers the number of potential accidents caused by false negatives up to four times and 24 times as compared to distributed standalone and centralized models, respectively. CAD3 can scale up to 256 vehicles connected to a single node while keeping the end-to-end latency under 50 ms and a required bandwidth below 5 mbps. At the mesoscopic (driver-trip) level, CAD3 performs stable and accurate detection over time, owing to local RSU interaction. With a dense deployment of edge nodes, CAD3 can scale up to the size of Shenzhen, a megalopolis of 12 million inhabitant with over 2 million concurrent vehicles at peak hours.

Many factors affect speech intelligibility in face-to-face conversations. These factors lead conversation participants to speak louder and more distinctively, exposing the content to potential eavesdroppers. To address these issues, we introduce Theophany, a privacy-preserving framework for augmenting speech. Theophany establishes ad-hoc social networks between conversation participants to exchange contextual information, improving speech intelligibility in real-time. At the core of Theophany, we develop the first privacy perception model that assesses the privacy risk of a face-to-face conversation based on its topic, location, and participants. This framework allows to develop any privacy-preserving application for face-to-face conversation. We implement the framework within a prototype system that augments the speaker's speech with real-life subtitles to overcome the loss of contextual cues brought by mask-wearing and social distancing during the COVID-19 pandemic. We evaluate Theophany through a user survey and a user study on 53 and 17 participants, respectively. Theophany's privacy predictions match the participants' privacy preferences with an accuracy of 71.26%. Users considered Theophany to be useful to protect their privacy (3.88/5), easy to use (4.71/5), and enjoyable to use (4.24/5). We also raise the question of demographic and individual differences in the design of privacy-preserving solutions.

The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate traffic data collection and interpretation towards better traffic light control and congestion alleviation. Operating within the coverage of the edge servers, DRLE aggregates data from neighboring edge servers for city-scale traffic light control. DRLE decomposes the highly complex problem of large area control into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning and demonstrate its superiority over several state-of-the-art algorithms via extensive evaluations.

The current explosion of video traffic compels service providers to deploy caches at edge networks. Nowadays, most caching systems store data with a high programming voltage corresponding to the largest possible ‘expiry date’, typically on the order of years, which maximizes the cache damage. However, popular videos rarely exhibit lifecycles longer than a couple of months. Consequently, the programming voltage can instead be adapted to fit the lifecycle and mitigate the cache damage accordingly. In this paper, we propose LiA-cache, a Lifecycle-Aware caching policy for online videos. LiA-cache finds both near-optimal caching retention times and cache eviction policies by optimizing traffic delivery cost and cache damage cost conjointly. We first investigate temporal patterns of video access from a real-world dataset covering 10 million online videos collected by one of the largest mobile network operators in the world. We next cluster the videos based on their access lifecycles and integrate the clustering into a general model of the caching system. Specifically, LiA-cache analyzes videos and caches them depending on their cluster label. Compared to other popular policies in real-world scenarios, LiA-cache can reduce cache damage up to 90 perce, while keeping a cache hit ratio close to a policy purely relying on video popularity.

Question-and-answer (Q&A) websites are one of the latest evolutions in crowdsourced knowledge aggregation. Q&A websites provide more diverse opinions, as they involve the entire community. Quora made its reputation out of enhancing the traditional Q&A model with popular aspects of social media and incites its users to provide their names, locations, and references. This model allows higher quality control - including anonymous content, but more importantly, it leads users to form communities based on other criteria (e.g. profession, city) than similar interests. In this paper, we study the interactions among Quorans to unveil how such communities emerge. We perform both quantitative and qualitative analysis on the user-generated content and relate this content to social and demographic features. We show that being anonymous significantly affects the answers' length and subjectivity. On the other hand, most of the user interactions relate to their geographic locations.

Social networks are effective platforms to study the real-life behavior of users. In this paper, we study users' political polarization during the times of crisis and its relation to nationalism. To this purpose, we focus on the reaction of Indian and Pakistani Twitter users during February 2019 crisis and the ensuing Indian General Elections in 2019. We show that a national crisis affects the polarization and discourse in both countries. Also, we show that user activities increase during a national crisis, and political discourse strengthens while polarization decreases on critical days. Finally, we highlight the links between this crisis and the Indian elections and show how the political parties discussed the crisis in their campaigns.

Smartwatches and other wearables are characterized by small-scale touchscreens that complicate the interaction with content. In this paper, we present Force9, the first optimized miniature keyboard leveraging force-sensitive touchscreens on wrist-worn computers. Force9 enables character selection in an ambiguous layout by analyzing the trade-off between interaction space and the easiness of force-assisted interaction. We argue that dividing the screen's pressure range into three contiguous force levels is sufficient to differentiate characters for fast and accurate text input. Our pilot study captures and calibrates the ability of users to perform force-assisted touches on miniature-sized keys on touchscreen devices. We then optimize the keyboard layout considering the goodness of character pairs (with regards to the selected English corpus) under the force-based configuration and the users? familiarity with the QWERTY layout. We finally evaluate the performance of the trimetric optimized Force9 layout, and achieve an average of 10.18 WPM by the end of the final session. Compared to the other state-of-the-art approaches, Force9 allows for single-gesture character selection without addendum sensors.

The advancement of artificial intelligence and wearable computing triggers the radical innovation of cognitive applications. In this work, we propose VIMES, an augmented reality-based memory assistance system that helps recall declarative memory, such as whom the user meets and what they chat. Through a collaborative method with 20 participants, we design VIMES, a system that runs on smartglasses, takes the first-person audio and video as input, and extracts personal profiles and event information to display on the embedded display or a smartphone. We perform an extensive evaluation with 50 participants to show the effectiveness of VIMES for memory recall. VIMES outperforms (90% memory accuracy) other traditional methods such as self-recall (34%) while offering the best memory experience (Vividness, Coherence, and Visual Perspective all score over 4/5). The user study results show that most participants find VIMES useful (3.75/5) and easy to use (3.46/5).

Throughout the past decade, numerous interaction techniques have been designed for mobile and wearable devices. Among these devices, smartglasses mostly rely on hardware interfaces such as touchpad and buttons, which are often cumbersome and counterintuitive to use. Furthermore, smartglasses feature cheap and low-power hardware preventing the use of advanced pointing techniques. To overcome these issues, we introduce UbiPoint, a freehand mid-air interaction technique. UbiPoint uses the monocular camera embedded in smartglasses to detect the user's hand without relying on gloves, markers, or sensors, enabling intuitive and non-intrusive interaction. We introduce a computationally fast and light-weight algorithm for fingertip detection, which is especially suited for the limited hardware specifications and the short battery life of smartglasses. UbiPoint processes pictures at a rate of 20 frames per second with high detection accuracy - no more than 6 pixels deviation. Our evaluation shows that UbiPoint, as a mid-air non-intrusive interface, delivers a better experience for users and smart glasses interactions, with users completing typical tasks 1.82 times faster than when using the original hardware.

Mobile Augmented Reality (MAR) applications employ computationally demanding vision algorithms on resource-limited devices. In parallel, communication networks are becoming more ubiquitous. Offloading to distant servers can thus overcome the device limitations at the cost of network delays. Multipath networking has been proposed to overcome network limitations but it is not easily adaptable to edge computing due to the server proximity and networking differences. In this article, we extend the current mobile edge offloading models and present a model for multi-server device-to-device, edge, and cloud offloading. We then introduce a new task allocation algorithm exploiting this model for MAR offloading. Finally, we evaluate the allocation algorithm against naive multipath scheduling and single path models through both a real-life experiment and extensive simulations. In case of sub-optimal network conditions, our model allows reducing the latency compared to single-path offloading, and significantly decreases packet loss compared to random task allocation. We also display the impact of the variation of WiFi parameters on task completion. We finally demonstrate the robustness of our system in case of network instability. With only 70% WiFi availability, our system keeps the excess latency below 9 ms. We finally evaluate the capabilities of the upcoming 5G and 802.11ax.

Touchscreen interfaces are shrinking and even dis-appearing on mobile headsets. The existing approaches for text acquisition on mobile headsets, for instance, speech commands and hand gestures, are cumbersome and coarse. In this paper, we show the feasibility of interaction on a miniature area as small as 12 * 13 mm2that offers an input alternative on small form-factor devices such as smartwatches, smart rings, or the spectacles frames of mobile headsets. To this end, we propose and implement two interaction approaches, namely FRS and DupleFR, for acquiring textual contents on mobile headsets. Both approaches leverage force-assisted interaction on a miniature-size interface. They enable the user to acquire textual content with various granularities such as characters, words, sentences, paragraphs, and the entire text. After 8 sessions, 22 participants with FRS and DupleFR achieve the peak performance of respectively 11.455 and 10.611 seconds per textual acquisition with accuracy rates of 91.41% and 94.95%. Although FRS and DupleFR as indirect manipulations are disadvantageous, they are at least 37.06% faster than the commercial standards designated to direct manipulation on touchscreens.

Smartglasses mostly rely on hardware interfaces such as touch-pad and buttons, which are often cumbersome and counter-intuitive to use. Furthermore, smartglasses feature cheap and low-power hardware preventing the use of advanced pointing techniques. To overcome these issues, we introduce TiPoint, a freehand mid-air interaction technique. TiPoint uses the monocular camera embedded in smartglasses to detect the user's hand, enabling intuitive and non-intrusive interaction. We introduce a light-weight algorithm for fingertip detection, which is especially suited for the limited hardware specifications and the short battery life time of smartglasses. Our evaluation shows that TiPoint as a mid-air non-intrusive interface delivers a better experience for users and smart glasses interactions, with users completing typical tasks 1.82 times faster than when using the original hardware.

Most smart glasses have a small and limited field of view. The head-mounted display often spreads between the human central and peripheral vision. In this paper, we exploit this characteristic to display information in the peripheral vision of the user. We introduce a mobile peripheral vision model, which can be used on any smart glasses with a head-mounted display without any additional hardware requirement. This model taps into the blocked peripheral vision of a user and simplifies multi-tasking when using smart glasses. To display the potential applications of this model, we implement an application for indoor and outdoor navigation. We conduct an experiment on 20 people on both smartphone and smart glass to evaluate our model on indoor and outdoor conditions. Users report to have spent at least 50% less time looking at the screen by exploiting their peripheral vision with smart glass. 90% of the users Agree that using the model for navigation is more practical than standard navigation applications.

Real time interactive cloud-based mobile applications such as augmented reality and cloud gaming require low and stable latency, especially in urban areas. These conditions are difficult to meet with the traditional single carrier LTE network access and consolidated server deployment in a cloud. Yet, with multiple SIM/multiple radio devices, latency can be kept under a given threshold through dynamic selection among multiple carriers and server deployment at network edge. To this end, it is necessary to understand how mobile network latency changes over time during a session with different carriers and how the server placement affects the latencies. In this paper, we present results from a measurement study of mobile network latency and jitter in 4G networks of Hong Kong and Helsinki, two very different cities in terms of population density and mobile infrastructure. Based on the results, we introduce a lightweight carrier selection algorithm that displays latencies 10 to 20% lower than single carrier operation.

Mobile Augmented Reality (MAR) drastically changes our approach to computing and user interaction. Web browsing, in particular, is impractical on AR devices as current web design principles do not account for three-dimensional display and navigation of virtual content. In this paper, we propose Mobile to AR (M2A), the first framework for designing web pages for AR devices. M2A exploits the visual context to display more content while enabling users to locate relevant data intuitively with minimal modifications to the website. To evaluate the principles behind the framework, we implement a demonstration application in AR and conduct two user-focused experiments. Our experimental study reveals that participants with M2A are 5 times faster to find information on a web page compared to a smartphone, and 2 times faster than a traditional AR web browser. Furthermore, users consider navigation on M2A websites to be significantly more intuitive and easy to use compared to their desktop and mobile counterparts.

Text input is a very challenging task in Augmented Reality (AR). On non-touch AR headsets, virtual keyboards are counter-intuitive and character keys are hard to locate inside the constrained screen real estate. In this paper, we present the design, implementation and evaluation of HIBEY, a text input system for smartglasses. HIBEY provides a fast, reliable, affordable, and easy-to-use text entry solution through vision-based freehand interactions. Supported by a probabilistic spatial model and a language model, a three-level holographic environment enables users to apply fast and continuous hand gesture to pick characters and predictive words in a keyboardless interface. Through the pilot study and a thorough evaluations lasting 8 days, we show that HIBEY leads to a mean text entry rate of 9.95 word per minute (WPM) with 96.06% accuracy, which is comparable to other state-of-the-art approaches. After 8 days, participants can achieve an average of 13.19 WPM. In addition, HIBEY only occupies 13.14% of the screen real estate at the edge region, which is 62.80% smaller than the default keyboard layout on Microsoft Hololens.

With the explosion of the Internet of Things (IoT), the number of devices to identify grew up exponentially in recent years and solutions such as visual markers or radio technologies are starting to show their limits. Visible light markers have been proposed to overcome these limitations. However, visible light communication (VLC) is very sensitive to interferences. As such, if two light markers are overlapping, the corresponding signals cannot be recovered. In smart homes, this situation is likely to occur as devices can be placed in very close proximity. In this paper, we design a protocol based on orthogonal codes to detect and isolate adjacent light markers, and individually identify several contiguous objects. We implement the algorithms within an Android application and evaluate their effect both analytically and experimentally. We demonstrate the robustness of our protocol in different conditions, both at the transmitter and the receiver side. Light markers are correctly recovered with low error rates (under 5%), at a pace of 5 frames per second, enough for object identification in most IoT scenarios.

Mobile augmented reality (MAR) applications are gaining popularity due to the wide adoption of mobile and especially wearable devices. Such devices often present limited hardware capabilities while MAR applications often rely on computationally intensive computer vision algorithms with extreme latency requirements. To compensate for the lack of computing power, offloading data processing to a distant machine is often desired. However, if this process introduces new constrains in the application, especially in terms of latency and bandwidth. If current network infrastructures are not ready for such traffic, we envision that future wireless networks such as 5G will rapidly be saturated by resource hungry MAR applications. Moreover, due to the high variance of wireless networks, MAR applications should not rely only on the evolution of infrastructures. In this article, we analyze MAR applications and justify their need for accessing external infrastructure. After a review of the existing network infrastructures and protocols, we define guidelines for future real-time and multimedia transport protocols, with a focus on MAR offloading.

Data Pendulum [1] designates the packet backlog switch from one side of a bottleneck link to the other in presence of two antiparallel TCP connections. This paper completes the explanation of the Data Pendulum dynamics by elucidating on which side the backlog grows, while identifying the relevant parameters in the process. We also explain why and when the data backlog may switch sides without any packet loss. We propose a model for the dynamics of antiparallel TCP connections that effectively explains the throughput observed in real conditions. Moreover, the model is applicable to a more realistic case in which buffers are dimensioned in packets rather than bytes, the case that previous models could not capture.