Owing to the attractive properties of decentralization, unforgeability, transparency, and traceability, blockchain is increasingly being used in various scenarios such as supply chain and public services, where massive Spatial-Temporal-Keywords (STK) transactions need to be packaged to the blockchain systems. However, due to the multi-dimensionality of STK transactions and transaction randomness among different blocks, existing solutions fail to enable queries in a verifiable and efficient way for blockchains storing multidimensional transactions. To this end, this paper takes the first step to innovatively propose an authenticated and efficient query approach in hybrid blockchain systems consisting of on-chain and off-chain parts. We first design a new data structure named MRK-Tree in the block body, which organizes STK transactions for efficient nodes pruning of both kNN and range queries. Then we propose an improved block header, which improves the efficient pruning of blocks on the basis of ensuring the authentication of query results. Also, we design a cross-block searching algorithm named Efficient Block Pruning (EBP) and intra-block searching algorithms named Authenticated kNN/Range Query (AKQ/ARQ) to accelerate authenticated queries for multiple MRK-Trees in the hybrid blockchain systems. Authentication mechanisms are proposed to ensure the soundness and completeness of query results. Rigorous security analysis validates the practicability of the proposed approach. We build a blockchain prototype to comprehensively evaluate the performance of proposed query schemes. Extensive evaluation results with real datasets reveal that our approach can ensure authenticated queries, meanwhile improving the time efficiency by up to 36.45x and space efficiency by up to 4 orders of magnitude compared with the well-known benchmark query schemes.

Smart vehicles can cooperate in teams to perform crowdsensing tasks in smart cities. A critical challenge in this regard is to build a secure model for nondeterministic vehicle teams to achieve maximum social welfare. Although several crowdsensing models have been proposed, none of them has focused on real-time vehicle teamwork. In this study, to the best of our knowledge, we propose the first secure model, called Blockchain-based Nondeterministic Teamwork Cooperation(BNTC), for nondeterministic teamwork cooperation in a vehicular crowdsensing system. We model the system as a multi-conditional NP-complete problem by explicitly considering the dynamic features of task issuers and workers. To solve the problem, we propose Winning Teams Selected(WTS) algorithm based on a reverse auction and utilize a knapsack-based method to solve the models. We consider credit of teams for determining the payment. Thus, we propose a Credit-based Team Payment(CTP) algorithm for BNTC to maximize the welfare of the system. We also propose a general blockchain-based framework to address trust issues and security challenges to make the method suitable for use in practical applications. Based on theoretical analyses and extensive simulations, we demonstrate that the proposed model performs better than the baselines and can achieve the maximum social welfare. Implementation with Ethereum suggests our model can operate within a reasonable cost.

MLIR (Multi-Level Intermediate Representation) compiler infrastructure has gained widespread popularity in recent years. It introduces dialects to accommodate various levels of abstraction within the representation. Due to its fundamental role in compiler construction, it is critical to ensure its correctness. Recently, a grammar-based fuzzing technique (i.e., MLIRSmith) has been proposed for it and achieves notable effectiveness. However, MLIRSmith generates test programs in a random manner, which restricts the exploration of the input space, thereby limiting the overall fuzzing effectiveness. In this work, we propose a novel fuzzing technique, called MLIR. As complicated or uncommon data/control dependencies among various operations are often helpful to trigger MLIR bugs, it constructs the operation dependency graph for an MLIR program and defines the associated operation dependency coverage to guide the fuzzing process. To drive the fuzzing process towards increasing operation dependency coverage, MLIR then designs a set of dependency-targeted mutation rules. By applying MLIR to the latest revisions of the MLIR compiler infrastructure, it detected 63 previously unknown bugs, among which 38/48 bugs have been fixed/confirmed by developers.

Self-supervised tasks show significant advantages for node representation learning in recommender systems. This core idea of self-supervised task-based recommender systems depends on data augmentation to generate multi-view representations. However, there are two key challenges that are not well explored in existing self-supervised tasks: i) Restricted by the structure of the graph-based CF paradigm itself, the classical graph comparison learning architecture ignores the global structural information on the user-item interaction graph. ii) In a key part of existing contrast learning-random graph data enhancement schemes can significantly deteriorate model performance. To address these challenges, we propose a new hypergraph collaborative filtering with adaptive augmentation framework(HCFAA). It captures both local and global collaborative relationships on the user-item graph through a hypergraph-enhanced joint learning architecture. In particular, the designed adaptive structure-guided model ignores the noise introduced on unimportant edges, and thus learns the critical node information on the user-item graph. Comprehensive experimental studies on the Amazon dataset show that the method is effective, which provides an optimization scheme with a new perspective for the problems of key node loss in graph data enhancement and loss of higher-order structural information in GNN. The source code of our model can be available on https://github.com/RSnewbie/RS/tree/master/HCFAA.