The rapid growth of blockchain-based decentralized applications (DApps) highlights blockchain's potential to enhance application security. However, expensive on-chain data storage limits the deployment of DApps with large datasets. Additionally, DApp development tools, such as Ethereum smart contracts, lack support for complex queries, further hindering dataintensive DApps. To address the challenges of expensive storage and inability of complex queries, we propose ContractDB, a framework that integrates external verifiable databases (VDBs) with blockchain DApps. ContractDB offloads data storage and processing to VDBs, thereby reducing on-chain storage costs and enhancing data handling capabilities. Existing VDBs incur high verification costs and lack support for public verifiable update. In this paper, we propose a novel VDB design using authenticated dictionaries and authenticated set operations to reduce verification cost and enable verifiable updates. Performance evaluations show that ContractDB can verify the results of 6-condition conjunction (with mixed equivalent and range) queries on a 220-line data table within 2.4 million gas cost, with potential for optimization. For comparison, storing those data in contracts requires over 36 billion gas and still cannot support range or multi-condition queries. Therefore, the proposed ContractDB makes it feasible to support DApps with large datasets.

Due to the advantages of device ubiquity, natural interaction and privacy preservation, acoustic-based gesture input has received widespread attention. Researchers have proposed various techniques for different applications. However, the existing work has shortcomings of heavy data-collection overhead, non-continuous input, and performance degradation in crossuser scenarios. To overcome these shortcomings, we propose UltraWrite, an acoustic-based gesture input system that only needs extremely low data-collection overhead, supports continuous input, and achieves high cross-user recognition accuracy. The key idea of our solution is to synthesize training data of continuous gestures from isolated ones, build a lightweight continuous gesture recognition model based on connectionist temporal classification (CTC) mechanism, and design a novel decoupled model training strategy to improve its cross-user recognition capability. We have implemented prototype systems on commercial devices and conducted comprehensive experiments to evaluate their performance. The results show that UltraWrite achieves an average top-1 word accuracy of 99.3% and top-1 word error rate of 0.34%. In addition, we have also evaluated UltraWrite's robustness to the sensing distance, angle, background noise, and device. The results reveal that UltraWrite possesses strong robustness to these factors.

Recent research studies have made significant progress in acoustic-based gesture recognition. However, existing methods lack the capability to expand to customized gestures and adapt to different practical environments. We propose a highly scalable gesture recognition system called EchoGest which integrates a well-designed feature-wise transformation layer into prototypical network framework, and accomplishes unseen gesture recognition with a device’s built-in speaker and microphone. Our key insight involves gauging the similarity between query sample representations and class prototypes in the embedding space, and thus enabling the scalability to unseen gestures. Meanwhile, we introduce a feature transformation layer to linearly adjust feature maps and propose an efficient two-stage training strategy to obtain regularized parameters for this layer. Specifically, this layer employs affine transformation to enhance intermediate feature activations and yield more diverse feature distributions for cross-domain recognition, and it improves recognition accuracy by 10% in 1-shot cases. We train the system with a collected a letter gestures (i.e., writing ’A’ to ’Z’) dataset and test it on a digit gestures (i.e., writing ’0’ to ’9’) dataset with 10 volunteers. The results show that EchoGest can recognize unseen digit gestures with an accuracy of 93.7% in 2-shot cases, and 93.2% in the leave-one-user-out testing setting. We also explore a semi-supervised clustering approach in which each user’s data can be used to update his or her prototypes for personalized customization. The comprehensive experiments also verify that EchoGest remain good performance across various environments, age groups, and different devices.

Acoustic-based human gesture recognition (HGR) applications have drawn increasing academic attention in order to overcome the shortcomings of conventional interaction methods on tiny devices. Existing techniques following a learning-based routine requires collecting massive application-specific training data. What is worse, the cross-domain problem induces additional retraining overhead to enable the systems recognize unseen gestures in different environments. This obviously decreases their scalability and prevent them from real-world deployment. Although some recent works propose different few-shot learning solutions to deal with the cross-domain problem in HGR, they possess shortcomings of being application-specific, high training overhead, and/or incapability to recognize unseen gestures. In this paper, we propose PreGesNet, a few-shot acoustic gesture recognition framework based on task-adaptive pretrained networks whose novelty lies in three aspects: i) leveraging pretrained feature extractor which captures generic knowledge of our collected and open-source large-scale gesture datasets; ii) designing task-specific parameter adaptation mechanism to efficiently update the feature extractor to adapt the pretrained feature extractor to each target task; iii) discovering suitable distance metric and task generation strategy which fit HGR application. According to the experiments, when the model is trained with 10 digit gestures, its recognition accuracies of 26 kinds of letter gestures and 8 kinds of other hand gestures can be up to 80.5% and 93.4% with only two shots, respectively. In addition, the average recognition latency of PreGesNet is less than 0.4 second.

World-scale augmented reality (AR) is a form of AR where users move around the real world, viewing and interacting with 3D models at specific locations. However, given the geographical scale of world-scale AR, pre-fetching and storing numerous high-quality 3D models locally on the device is infeasible. For example, it would be impossible to download and store 3D ads from all the storefronts in a city onto a single device. A key challenge is thus deciding which remotely-stored 3D models should be fetched onto the AR device from an edge server, in order to render them in a timely fashion - yet with high visual quality - on the display. In this work, we propose a 3D model retrieval framework that makes intelligent decisions of which quality of 3D models to fetch, and when. The optimization decision is based on quality-compression tradeoffs, network bandwidth, and predictions of which 3D models the AR user is likely to view next. To support our framework, we collect real-world traces of AR users playing a world-scale AR game, and use this to drive our simulation and prediction modules. Our results show that the proposed framework can achieve higher visual quality of the 3D models while missing fewer display deadlines (by 20%) and wasting fewer bytes (by 10x), compared to a baseline approach of pre-fetching models within a fixed distance of the user.

Augmented reality (AR) devices perform visual simultaneous localization and mapping (SLAM) to map the real world and localize themselves in it, enabling them to render the virtual holograms appropriately. Current multi-user AR platforms fall short in that they only allow asymmetric sharing of this SLAM information, resulting in multiple "secondary" devices viewing holograms placed by a single "primary" device, instead of equal participation. The goal of this work is to enable all AR devices to participate equally, by constructing a common global map to which all AR devices can contribute. However, doing so with low latency and high accuracy is challenging on resource-constrained mobile devices. This work proposes an appropriate partitioning between clients and a server to achieve high-throughput, low latency, multi-user SLAM. In our system, SLAM-Share, the edge server performs the complex SLAM computations so that the client devices need only perform lightweight operations. The server utilizes shared memory and efficient map merging to build and update a global map from different clients. It also exploits the parallelism of GPU processing to achieve high-performance tracking. Evaluations show that SLAM-Share is able to achieve significant tracking speedups (up to 50% reduction compared to alternative approaches), maintain good localization accuracy, and merge and update maps within 200 ms.

Emotions show high significance on human health. Automatic emotion recognition is helpful for monitoring psychological disorders, mental problems and exploring behavioral mechanisms. Existing approaches adopt costly and bulky specialized hardware such as EEG/ECG helmet, possess privacy risks, or with low accuracy and user experience. With the increasing popularity of wearables, people tend to equip multiple smart devices, which provides potential opportunity for emotion perception. In this paper, we present a pervasive and portable system called MW-Emotion to recognize common emotional states with multi-modal wearable devices. However, ubiquitous wearable devices perceive shallow information which is not obviously related to human emotions. MW-Emotion excavates intrinsic mapping relationship between emotions and sensing data. Our experiments show that MW-Emotion can recognize different emotion states with a relatively high accuracy of 83.1%.