Semantic consistency evaluation faces two critical challenges: Bi-Encoder models, while efficient, struggle to capture fine-grained interactions between sentence pairs, limiting accuracy; meanwhile, Cross-Encoders and large language models (LLMs), despite superior performance, incur substantial computational costs, hindering practical deployment. This paper proposes a Multi-dimensional Consistency Evaluation Model (MCEM), designed to balance performance and efficiency, enabling precise modeling of sentence pairs and efficient inference across multiple consistency dimensions, including semantic, emotional, and logical aspects. The core innovation of MCEM lies in its integration of gated memory networks with a Multi-Task Mixture-of-Experts (MT-MoE) architecture, which enables fine-grained decomposition and dynamic recombination of tensors to disentangle shared and task-specific features. Furthermore, a multi-granular feature extraction module enhances the model’s ability to capture semantic information at the character, phrase, and sentence levels. Additionally, an uncertainty-aware knowledge distillation mechanism effectively transfers high-confidence knowledge from the Cross-Encoder branch to the lightweight path, significantly improving inference efficiency while maintaining high performance. Experimental results demonstrate that MCEM achieves state-of-the-art (SOTA) performance across multiple consistency benchmarks and exhibits strong generalization capability under both adversarial perturbations and previously unseen data structures.

This article focuses on the joint optimization of beamforming and baseband unit (BBU) computing resource allocation, with the goal of minimizing the network power consumption for the downlink cloud radio access network (C-RAN). To reduce the computational complexity, we split the joint optimization problem into two subproblems for transmission and computation respectively. The subproblem of minimizing power consumption for transmission is a network-wide beamforming design problem. By using positive semi-definite relaxation (SDR) technology, we transform it into a convex positive semi-definite programming (SDP) problem, which can be solved with effect. For the second subproblem, which intends to minimize the power consumption for computation, a computing resource allocation scheme based on the Simulated Annealing algorithm is proposed, which minimizes the active servers in the BBU pool to save power while meeting each user’s computing resource requirement. The simulation results demonstrate that the algorithm proposed in this paper has a better performance compared with the existing algorithms.

This paper proposes POEMS (paper offered environment management service) that can facilitate the activation of various services with a pen and paper based interface. With this tool, meeting participants can control meeting support devices on the same paper that they take notes. Additionally, a meeting participant can also share his/her paper drawings on a selected public display or initiate a collaborative discussion on a selected public display with a page of paper. Compared with traditional interfaces, such as tablet PC or PDA based interfaces, the interface of this tool has much higher resolution and is much cheaper and easier to deploy. The paper interface is also natural to use for ordinary people. With this tool, the screens are automatically started when a pen is pressed on a piece of paper. A presenter can control his/her presentation progress with time-stamped paper taps or drawings. A meeting participant can use the pen to share writing or make annotations on a public display. Users can use the pen to control pointers on public displays. They can use the pen to control a slideshow playback.