In recent years, meta-learning-based and contrastive learning-based approaches have achieved promising results in addressing the cold-start problem in recommendation systems by globally modeling user preferences. However, due to the sparse historical interactions between cold-start items and users, it is difficult to accurately capture the hierarchical relationships between users and items. To alleviate the above issue, we propose a dual-space mapping contrastive meta learning for cold start recommendation (DMCM), which simultaneously maps user and item information into both poincaré space and euclidean space to more effectively capture the latent hierarchical structure of user-item interaction information. Through dual-space mapping contrastive learning, the representational capacity of user-item associations is enhanced, and the coverage of user interaction information is expanded. Subsequently, a multi-level meta-learning path dynamic adjustment strategy is employed to further improve the model ability to personalize and adapt to new users and new items. Experiments conducted on three public datasets demonstrate that the proposed framework outperforms baseline methods in both cold-start and non-cold-start scenarios, significantly improving recommendation accuracy.

The adapter technique has significantly improved the performance of fine-tuning pre-trained language models (PLMs) in multi-task settings, especially for small models with limited resources. However, current adapter frameworks typically rely on single-task data, a fixed representation space, and a single training phase. This approach limits their ability to generalize across domains in multi-task scenarios, thus restricting performance improvements for smaller models. To address these challenges, we propose the cross-domain contrastive optimization (CDCO) framework to enhance performance in multi-task learning. CDCO improves model performance by asynchronously co-optimizing across diverse task data sources, representation spaces, and multi-stage structures. Specifically, CDCO introduces innovations in both data sample selection and training strategy. First, CDCO introduces out-of-domain manifold sampling (ODMS), which enhances training diversity by selecting challenging hard-negative samples from out-of-domain datasets through manifold learning. Second, CDCO employs multi-stage asynchronous co-optimization (MAC), mapping samples from ODMS to Euclidean and Poincaré spaces. Then, it constructs a cross-domain contrastive loss based on the spatial properties of these distributions to guide the optimization process. By sequentially optimizing adapter layers across different spatial distributions, CDCO maximizes the potential of the adapter while mitigating overfitting, thus improving the adaptability and stability of small models in multi-task environments. Experimental results demonstrate that CDCO significantly improves performance on in-domain (ID), out-of-domain (OOD), and knowledge-intensive (KI) tasks, confirming its broad applicability and effectiveness.

The Emotion Recognition in Conversations (ERC) task requires models to precisely capture subtle emotional nuances within contextual environments. Presently, the correlation between utterances and emotions is relatively weak, meaning the same utterance might express entirely different emotions. To tackle this challenge, pre-trained language models (PLMs) are typically employed, either through full-parameter fine-tuning or parameter-efficient tuning and learning (PETL) methods. However, these methods incur high computational costs. To address the weak correlation between utterances and emotions under limited computational resources, we propose a feature-task dynamic emotion neural link (DENL), which refines emotional feature representation at a low computational cost and rapidly adapts to ERC tasks. At the feature-learning layer, we embed multiple specialized Emotion Dual-Rank Adapters (EDRA) in parallel, coupled with a gradient-aware dynamic gating mechanism (DeepGate), to avoid propagation costs through the backbone network during backpropagation, thus creating a low-cost emotion neural link to capture emotional features. At the task-learning layer, we utilize a Emotion Space Intervention (ESI) approach, employing a low-rank subspace to manipulate portions of the hidden representations of utterance embeddings, thus guiding the model to rapidly adapt to ERC tasks. Experimental results demonstrate that DENL not only improves the accuracy of fine-grained emotion classification on three benchmark ERC datasets, but also significantly reduces computational cost and parameter size.

CBC mode of 3DES encryption has a wider application and higher security than ECB mode. However, there is a bottleneck of 3DES with CBC mode due to an inherent feedback loop from output to input. In this paper, we propose a method to move XOR gates out of the critical path where two XORs in the junction between two adjacent rounds can be merged into a new XOR gate which then can be absorbed into the precomputation of S-box, thus all the XOR gates are eliminated from the critical path. Our ASIC implementation can achieve a throughput of 2.84Gbps at the cost of 5.84K gates which is superior to others.