Existing blockchain systems can quickly respond to verifiable primary key queries based on authenticated indexes. However, many blockchain applications also require high-performance queries on non-primary keys. For example, traders query NFT or tokenized RWA with certain features, e.g., type and return. Therefore, it necessitates authenticated secondary indexes to support efficient verifiable queries on non-primary keys. However, the existing approach to authenticated index design that couples index and authenticated digest together does not adapt well to the phased nature of non-primary key queries, making the most time-consuming process of commitment generation severely block the query process. In this study, we propose the first authenticated secondary index MEST for verifiable non-primary key queries. MEST decouples the data index and authenticated digest, which can parallelize commitment generation on the secondary index and the query processing on the primary index, thus greatly reducing the latency of the non-primary key query. Furthermore, we adopt an Extendible Hash Table to index data and propose a Merkle Growth Tree to generate commitment, which can dynamically adapt to the rapid growth of data and the skew in data access pattern. Extensive experiments on both synthetic and real datasets demonstrate that MEST improves throughput by 3.17×, reduces latency by 59%, and exhibits better scalability than baselines.

Millimeter wave (mmWave) based human activity recognition is important in smart health in terms of studying user lifestyle. In practical health IoT scenarios, fast and accurate human activity recognition is critically important. In this work, we design and implement a lightweight deep learning solution for human activity recognition based on discrete Fourier transformation. The model has a fairly small number of model parameters while offering high accuracy in activity recognition. The core of the solution is a discrete Fourier transform module inside a neural network, which converts the temporal features of mmWave radar activity data into frequency features before activity recognition is performed by a simple classifier. We have extensively evaluated this solution against other traditional deep learning models in mmWave human activity recognition. The evaluation demonstrates that the DFT-based network can achieve the same accuracy as other traditional neural network models, but with a very small computational load.

Human daily activity monitoring has its particular significance in smart health. Human activity recognition based on mmWave has drawn enormous research efforts and achieved significant progress. Most of these solutions, however, work on data that has been manually segmented for each piece to contain only a single activity, which is impractical in reality where the sensor continuously generates data containing a series of activities. To address this challenge, this paper proposes a multi-head attention model that can detect the transition from one activity to another in a stream of mmWave sensor data of various human activities by analyzing the inner correlation of mmWave radar data fragments with a sliding window mechanism. Furthermore, the model then recognizes the new activity type in the data once it detects an activity transition. The solution has been extensively evaluated with a sparse point cloud dataset generated by a mmWave radar, which contains five types of activities. The experiment results show that the solution can achieve an accuracy of 98% in detecting activity transition at its best.

Abstract Creating a high‐quality Chinese vector font library, which can be directly used in real applications is time‐consuming and costly, since the font library typically consists of large amounts of vector glyphs. To address this problem, we propose a data‐driven system in which only a small number (about 10%) of Chinese glyphs need to be designed. Specifically, the system first automatically decomposes those input glyphs into vector components. Then, a layout prediction module based on deep neural networks is applied to learn the layout style of input characters. Finally, proper components are selected to assemble the glyph of each unseen character based on the predicted layout to build the font library that can be directly used in computers and smart mobile devices. Experimental results demonstrate that our system synthesizes high‐quality glyphs and significantly enhances the producing efficiency of Chinese vector fonts.

In this paper, we investigate fundamental throughput and delay tradeoffs in cognitive radio networks (CRN) with a primary user (PU) and a cooperative secondary user (SU). Considering the user fairness, we propose a novel cooperation scheme, where the PU transmits packets with a probability of α instead of always occupying the spectrum, in exchange for the SU's cooperation. The unauthorized SU can sense the environment periodically and get access to the licensed spectrum when the PU is idle. The SU will either transmit its own packets or forward the PU's packets based on a priority factor β. Considering the bursty nature of the sources, Markov chain model is used to characterize the evolution of buffer states. Based on the steady state probabilities, the average buffer length and the system's stable throughput region are obtained. The impacts of the cooperation scheme (α,β) on the throughput tradeoff between the PU and the SU are theoretically analyzed. We also derive the average end-to-end transmission delay of the two users. The throughput-delay tradeoff and the impacts of the link qualities on the system performance are analyzed. The accuracy of the analytical results are validated by simulations.