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The service life of refractory brick in the slag tapping hole of the entrained flow gasifier was significantly lower than that in other locations. It was critically important to study the corrosion mechanism of refractory brick in the slag tapping hole for guiding industrial production. Considering the complex flow field in the slag tapping hole, the influence of gas velocity and temperature fluctuation on the service life of refractory brick was investigated in this study. The results showed that the slag flow characteristics periodically changed with the gas temperature and velocity fluctuations. The brick corrosion rate increased with the decrease of fluctuation frequency. Compared with the gas-phase velocity fluctuation, the gas-phase temperature fluctuation had a more significant influence on the brick corrosion rate. When the fluctuation frequency was 0.01 Hz and the gas temperature fluctuation amplitude was 200 K, the corrosion rate of refractory bricks increased by 25%. It could be concluded that the fluctuation of gas-phase temperature was the main cause for the low service life of refractory bricks in the tapping hole.

Time-series shapelets are discriminative subsequences, recently found effective for time series classification (tsc). It is evident that the quality of shapelets is crucial to the accuracy of tsc. However, major research has focused on building accurate models from some shapelet candidates. To determine such candidates, existing studies are surprisingly simple, e.g., enumerating subsequences of some fixed lengths, or randomly selecting some subsequences as shapelet candidates. The major bulk of computation is then on building the model from the candidates. In this paper, we propose a novel efficient shapelet discovery method, called bspcover, to discover a set of high-quality shapelet candidates for model building. Specifically, bspcover generates abundant candidates via Symbolic Aggregate approXimation with sliding window, then prunes identical and highly similar candidates via Bloom filters, and similarity matching, respectively. We next propose a $p$p-Cover algorithm to efficiently determine discriminative shapelet candidates that maximally represent each time-series class. Finally, any existing shapelet learning method can be adopted to build a classification model. We have conducted extensive experiments with well-known time-series datasets and representative state-of-the-art methods. Results show that bspcover speeds up the state-of-the-art methods by more than 70 times, and the accuracy is often comparable to or higher than existing works.

Advanced artificial intelligence techniques, such as the federated learning, has been applied to broad areas, e.g., image classification, speech recognition, smart city, and healthcare. Despite intensive research on the federated learning, existing schemes are vulnerable to attacks and cannot meet the security requirement for real applications. The problem of designing a secure federated learning framework to ensure the correctness of training procedure has not been sufficiently studied and remains open. In this paper, we propose VFChain, a verifiable and auditable federated learning framework based on the blockchain system. First, to provide the verifiability, a committee is selected through the blockchain to collectively aggregate models and record verifiable proofs in the blockchain. Then, to provide the auditability, a novel authenticated data structure is proposed for blockchain to improve the search efficiency of verifiable proofs and support a secure rotation of committee. Finally, to further improve the efficiency, an optimization scheme is proposed to support multiple-model learning tasks. We implement VFChain and conduct extensive experiments by utilizing the popular deep learning model and the public real-world dataset. The evaluation results demonstrate the effectiveness of our proposed VFChain system.

The recent years have witnessed the growing popularity of indoor location-based services (LBS) in practice and research. Among others, indoor shortest path query (ISPQ) is of fundamental importance for indoor LBS. However, existing works on ISPQ ignore indoor temporal variations, e.g., the open and close times associated with entities like doors and rooms. In this paper, we define a new type of query called Indoor Temporal-variation aware Shortest Path Query (ITSPQ). It returns the valid shortest path based on the up-to-date indoor topology at the query time. A set of techniques is designed to answer ITSPQ efficiently. We design a graph structure (IT-Graph) that captures indoor temporal variations. To process ITSPQ using IT-Graph, we design two algorithms that check a door's accessibility synchronously and asynchronously. Furthermore, we propose a novel index structure (IT-Index) that extends the state-of-the-art index significantly by storing dynamic door-to-door distances in a compact distance cube associated with tree nodes. When processing ITSPQ using IT-Index, we make use of the distance cube to avoid time-consuming indoor distance computation on-the-fly. We evaluate the proposed techniques using extensive experiments on synthetic and real data. The results show that our IT-Index based method is the most efficient for processing ITSPQ at a modest cost of index memory consumption.