Wei Shao, Yihang Wang, Gao yu Zhu, Ziqiang Cheng, Lei Yu, Jiafeng Guo, Xueqi Cheng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Yihang Wang, Bin Wu, Yueyang Su, Tianfu Zhang, Yiqi Du, Lei Yu, Jiafeng Guo, Xueqi Cheng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Current blockchain consensus protocols have a triangle of contradictions in aspects of decentralization, security, and energy consumption, and cannot be synchronously optimized. We describe a design of two new blockchain consensus protocols, called “CHB-consensus” and “CHBD-consensus,” based on a consistent hash algorithm. Honest miners can fairly gain the opportunity to create blocks. They do not consume any extra computational power resources when creating new blocks, and such blocks can obtain the whole blockchain network to confirm consensus with fairness. However, malicious miners have to pay massive computational power resources for attacking the new block creation privilege or double-spending. Blockchain networks formed by CHB-consensus and CHBD-consensus are based on the same security assumption as that in Bitcoin systems, so they save a huge amount of power without sacrificing decentralization or security. We analyze possible attacks and give a rigorous but adjustable validation strategy. CHB-consensus and CHBD-consensus introduce a certification authority (CA) system, which does not have special management or control rights over blockchain networks or data structures, but carries the risk of privacy breaches depending on credibility and reliability of the CA system. Here, we analyze the robustness and energy consumption of CHB-consensus and CHBD-consensus, and demonstrate their advantages through theoretical derivation.

Fisher vector has been widely used in many multimedia retrieval and visual recognition applications with good performance. However, the computation complexity prevents its usage in real-time video monitoring. In this work, we proposed and implemented GPU-FV, a fast Fisher vector extraction method with the help of modern GPUs. The challenge of implementing Fisher vector on GPUs lies in the data dependency in feature extraction and expensive memory access in Fisher vector computing. To handle these challenges, we carefully designed GPU-FV in a way that utilizes the computing power of GPU as much as possible, and applied optimizations such as loop tiling to boost the performance. GPU-FV is about 12 times faster than the CPU version, and 50\% faster than a non-optimized GPU implementation. For standard video input (320*240), GPU-FV can process each frame within 34ms on a model GPU. Our experiments show that GPU-FV obtains a similar recognition accuracy as traditional FV on VOC 2007 and Caltech 256 image sets. We also applied GPU-FV for realtime video monitoring tasks and found that GPU-FV outperforms a number of previous works. Especially, when the number of training examples are small, GPU-FV outperforms the recent popular deep CNN features borrowed from ImageNet.

Parallel processing system has been extensively developed and used in numerous commercial servers for large-scale data analysis. However, the issues of scalability, reliability and efficiency cannot be achieved simultaneously. Motivated by this observation, a Multi-level Parallel Database System based on B-tree structure (MPDBS) is designed for large-scale structured data and semi-structured data. Correspondingly, a multi-level index scheme (MLIS) is proposed in this paper. Based on MPDBS framework and MLIS scheme, the system can parallel execute analyzing task and full-text query efficiently, meanwhile reducing the network I/O and disk I/O greatly. The optimal architecture of MPDBS is also derived by mathematical approach. Experimental results show that, given the same hardware configuration and TPC-H benchmark, comparing with Hive using Hadoop Distributed File System (HDFS), the query (i.e., statistical query, keyword query and point query) latency on 200GB commercial data for the proposed MPDBS is declined by 95%.

Inferring a gold-standard ranking over a set of objects, such as documents or images, is a key task to build test collections for various applications like Web search and recommender systems. Crowdsourcing services provide an efficient and inexpensive way to collect judgments via labeling by sets of annotators. We thus study the problem of finding a consensus ranking from crowdsourced judgments. In contrast to conventional rank aggregation methods which minimize the distance between predicted ranking and input judgments from either pointwise or pairwise perspective, we argue that it is critical to consider the distance in a listwise way to emphasize the position importance in ranking. Therefore, we introduce a new listwise approach in this paper, where ranking measure based objective functions are utilized for optimization. In addition, we also incorporate the annotator quality into our model since the reliability of annotators can vary significantly in crowdsourcing. For optimization, we transform the optimization problem to the Linear Sum Assignment Problem, and then solve it by a very efficient algorithm named CrowdAgg guaranteeing the optimal solution. Experimental results on two benchmark data sets from different crowdsourcing tasks show that our algorithm is much more effective, efficient and robust than traditional methods.

On-chip many core architecture is an emerging and promising computation platform. High speed on-chip communication and abundant chipped resources are two outstanding advantages of this architecture, which provide an opportunity to implement efficient synchronization scheme. The practical execution efficiency of synchronization scheme is critical to this platform. However, there are few researches on systematic evaluation method of choice synchronization schemes for on-chip many core processors, and effect of dedicated hardware support in this context. So we focus on the evaluation method and criterion of synchronization scheme on the platform. Firstly, we present several criterions proper to on-chip many core architecture, that is, absolute overhead of synchronization operation, the transferring time between different synchronization operations, overhead caused by load imbalance, and the network congestion caused by synchronization operation. Secondly, we illustrate how to design microbenchmarks which one dedicated to evaluate a performance criterion respectively. Finally, we implement these microbenchmarks and synchronization schemes on an on-chip many core processor with shared level-two cache and AMD Opteron commercial chip multi-processor, respectively. And we analyze effect of dedicated hardware support. Results show that the most overhead of synchronization is caused by load imbalance and serialization on synchronization point. It also shows that synchronization scheme supported with dedicated hardware can improve its performance obviously for chipped many-core processor.