Multimodal sentiment analysis leverages various information sources (such as text, audio, video, etc.) to obtain more comprehensive and accurate sentiment information. Recent research has mainly focused on developing fusion mechanisms to bridge the modality gap, but there has been insufficient exploration into the heterogeneity of multimodal data, particularly in modeling the consistency and complementarity features between modalities, as well as the lack of guidance from prior knowledge in the fusion of heterogeneous data. In this paper, we propose a Heterogeneous Contrastive Representation Learning (HCRL) approach for multimodal sentiment analysis, which incorporates both consistency contrastive representation learning and complementarity contrastive representation learning to model cross-modal semantic interactions. This approach allows for the effective learning of inter-modality similarity and difference information. Additionally, by incorporating prior knowledge to enhance the semantic interactions between multimodal features, the model enhances its capability to represent multimodal sentiment information. Experimental results on publicly available datasets CMU-MOSI and CMU-MOSEI demonstrate that our model outperforms state-of-the-art methods, showcasing the effectiveness of our proposed method.

Account shuffling is a crucial method to solve the problems of high cross-shard transaction (TX) ratio and load imbalance in sharded blockchain. However, most existing methods primarily focus on account partitioning, with insufficient attention to account migration, resulting in limited improvements in throughput and latency. Therefore, we propose an efficient account shuffling mechanism based on lightweight migration in sharded blockchain (ASLM-Shard). Specifically, we first propose a migration-aware label propagation algorithm (MA-LPA) to improve the effect of account partitioning by balancing the relationship among account migration overhead, cross-shard TX ratio and load imbalance. Then, we adopt a sparse Merkle tree (SMT) to store account states to support flexible state verification, and propose a transaction-aware lightweight account migration (TLAM) method that leverages a “Lock-Mint” strategy to minimize migration costs while ensuring security. Extensive experimental results show that, compared with the SOTA baseline, ASLM-Shard improves system throughput by up to 27.8% and reduces TX latency by up to 81.1% when the account partitioning strategy is fixed, it also achieves up to 17.9% higher throughput and 23.4% lower latency when the migration method is fixed.

Smart contracts have expanded the range of applications for blockchain, enabling the construction of complex business logic on the chain. However, the execution of smart contracts depends on transaction triggers and cannot operate autonomously, limiting the flexibility of on-chain applications. Despite the emergence of new smart contract automation solutions, there is a general lack of verifiable execution results and native support for multi-chain operations. In this paper, we propose the Specy Network protocol, which implements verifiable task verification results and is the first publicly available automation solution to support multi-chain operations. To this end, Specy Network first defines a specific domain-specific programming language (DSL) for declaring the execution conditions of tasks, and further designs a trusted runtime for this language based on trusted execution environment (TEE). Secondly, it designs the multi-chain automation task flow using cross-chain technology. Lastly, we implemented the protocol and verified its effectiveness through a loan case study.

Neural architecture search (NAS) has emerged as a transformative approach for automating the design of neural networks, demonstrating exceptional performance across a variety of tasks. Numerous NAS methods aim to optimize neural architectures within discrete or continuous search spaces, but each method possesses its own inherent limitations. Additionally, the search efficiency is notably impeded by suboptimal encoding methods, presenting an ongoing challenge. In response to these obstacles, this paper introduces a novel approach, evolutionary neural architecture optimization (ENAO), which optimizes architectures in an approximate continuous search space. ENAO begins with training a deep generative model to embed discrete architectures into a condensed latent space, leveraging unsupervised representation learning. Subsequently, evolutionary algorithm is employed to refine neural architectures within this approximate continuous latent space. Empirical comparisons against several NAS benchmarks underscore the effectiveness of the ENAO method. Thanks to its foundation in deep unsupervised representation learning, ENAO demonstrates a distinguished ability to identify high-quality architectures with fewer evaluations and achieve state-of-the-art result in NAS-Bench-201 dataset. Overall, the ENAO method is a promising approach for optimizing neural network architectures in an approximate continuous search space with evolutionary algorithms and may be a useful tool for researchers and practitioners in the field of NAS.

As traditional spectrum sensing approaches unable to deal with the contradiction between detection accuracy and complexity in cognitive radio network, a novel q-weighed sequential cooperative energy detection method for spectrum sensing in time varying channel is proposed in this paper to achieve better performance with lower complexity. By adding the q- weighted log likelihood ratio (LLR) of the past local observations from previous sensing slots to the current LLR sequentially, cognitive radio nodes can aggregate the current and previous received energy values to yield the improvement of sensing performance. Moreover, we pose a q-weighted K-out of-N voting rule at the fusion center to minimize the total error probability. For different probability of primary signal for turning its state from active to idle, we employ corresponding different weighted value q to make the sensing scheme more flexible and efficient.