Tianhao Niu, Ziyu Han, Qiguang Chen, Shiqi Zhou, Baocai Shan, Hengjie Fang, Qingfu Zhu, Wanxiang Che. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

As a fundamental task in graph data analysis, degree statistic estimation serves as the foundation for many complex tasks. Local differential privacy (LDP) preserves the privacy inherent in raw degrees without a trusted third party. Existing methods struggle to balance different types of degree statistics. They either introduce excessive noise when estimating degree distribution due to not fully leveraging the properties of edge LDP, or are limited to polynomial statistic estimation only. We design a locally private framework for degree statistic estimation (LFS), using Laplace mechanism to provide appropriate privacy protection under edge LDP. LFS can estimate three types of degree statistics: polynomial, distribution and single-point. According to degrees with Laplace noise, we transform degree distribution estimation into a linear regression problem, then post-process the estimated distribution to mitigate the excessive smoothing introduced by the regularization term. We also achieve single-point statistic estimation considering the degree distribution and properties of Laplace noise. Systematic experiments on five datasets demonstrate that LFS consistently outperforms existing methods in four utility metrics.

Facial expression recognition has emerged as a critical research area in health monitoring, enabling healthcare professionals to assess patients' emotional and psychological states for timely intervention and personalized care. However, existing methods often struggle to balance computational accuracy with energy efficiency. To address this challenge, this paper proposes FE-SpikeFormer - a high-accuracy, low-energy, and deployment-friendly Spiking Neural Network (SNN) for facial emotion recognition. The proposed architecture comprises three key components: the initial convolution module, the spiking extraction block, and the spiking integration block. These three modules collectively support detailed and contextual feature extraction, promote spatial feature integration, and strengthen the representational capacity of spiking signals. Meanwhile, a joint verification is conducted in both controlled laboratory settings and real-world hospital scenarios. Experimental results demonstrate that FE-SpikeFormer achieves top-three recognition accuracy among state-of-the-art methods, while utilizing only 6.93 million parameters. Moreover, it exhibits strong robustness against various noise conditions, underscoring its potential for practical deployment in healthcare environments.

Counting butterflies in bipartite graphs is a fundamental task that has gained increasing attention. Estimating butterfly counts with edge local differential privacy for privacy protection is meaningful. Existing methods struggle to balance estimation accuracy, running efficiency, user synchronization and communication cost. We propose 2-path-based butterfly counting (PBC), which accurately and efficiently counts the number of butterflies in bipartite graphs in a non-interactive manner. PBC enhances running efficiency by transforming the butterfly count into a function of the 2-path count, and improves estimation accuracy with an unbiased estimation of the butterfly count. Additionally, PBC includes privacy budget allocation that minimizes the variance of the 2-path count. We evaluate PBC on four real-world datasets, demonstrating it's superior estimation accuracy and running efficiency compared to existing methods.

In service composition, quality‐of‐service (QoS) represents a crucial indicator for the policy adoption. Existing composition strategies rarely address the influence of the environment, which may influence QoS and thus lead to sub‐optimal composition policies in a dynamic environment. In this study, a model‐based service composition approach is proposed. Given the user request, it is possible to first find a set of matching abstract web services (AWSs), and then pull relevant concrete web services (CWSs) based on the AWSs. The set of CWSs can be modelled as a Markov decision process (MDP). In addition, the authors model the environment as a fully probabilistic system, capturing changes of environment probabilistically. The environment model can be further composed of the MDP from the service models, obtaining a monolithic MDP. They demonstrate how the probabilistic verification techniques can be used to find the optimal service selection strategy against their QoS and the environment change. A distinguishing feature of their approach is that the QoS, as well as the dynamic of environment change, is made parametric so that the formal analysis is adaptive to the environment which is of paramount importance for autonomous and self‐adaptive systems. Examples and experiments confirm the feasibility of their approach.