As mobile devices and sensor technology advance, their role in communication becomes increasingly indispensable. Micro-expression recognition, an invaluable non-verbal communication method, has been extensively studied in human-computer interaction, sentiment analysis, and security fields. However, the sensitivity and privacy implications of micro-expression data pose significant challenges for centralized machine learning methods, raising concerns about serious privacy leakage and data sharing. To address these limitations, we investigate a federated learning scheme tailored specifically for this task. Our approach prioritizes user privacy by employing federated optimization techniques, enabling the aggregation of clients’ knowledge in an encrypted space without compromising data privacy. By integrating established micro-expression recognition methods into our framework, we demonstrate that our approach not only ensures robust data protection but also maintains high recognition performance comparable to non-privacy-preserving mechanisms. To our knowledge, this marks the first application of federated learning to the micro-expression recognition task.

Micro-Expression Recognition (MER) is a challenging task as the subtle changes occur over different action regions of a face. Changes in facial action regions are formed as Action Units (AUs), and AUs in micro-expressions can be seen as the actors in cooperative group activities. In this paper, we propose a novel deep neural network model for objective class-based MER, which simultaneously detects AUs and aggregates AU-level features into micro-expression-level representation through Graph Convolutional Networks (GCN). Specifically, we propose two new strategies in our AU detection module for more effective AU feature learning: the attention mechanism and the balanced detection loss function. With these two strategies, features are learned for all the AUs in a unified model, eliminating the error-prune landmark detection process and tedious separate training for each AU. Moreover, our model incorporates a tailored objective class-based AU knowledge-graph, which facilitates the GCN to aggregate the AU-level features into a micro-expression-level feature representation. Extensive experiments on two tasks in MEGC 2018 show that our approach outperforms the current state-of-the-art methods in MER. Additionally, we also report our single model-based micro-expression AU detection results.

Software quality evaluation is a challenging task in software engineering. A new group decision-making evaluation model is presented in this work. The new model is based on the Vlsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) technique, in which a group regret measurement and a group satisfaction measurement are provided to increase the number of reference criteria in the decision-making process. We choose the median to represent the center of the data. Based on this, an entropy-based weighting method is proposed and used to determine the weights of decision makers. A new normalized projection is explored to measure the closeness between two evaluation matrices in a Pythagorean fuzzy setting. Several experimental analyses demonstrate that the entropy-based weighting method developed in this study is superior to traditional weighting methods. The median-based data center provides support for stable decision outcomes. Four dynamic experiments are reported on in this paper: The first one shows that the decision results remain stable throughout the entire experimental range; the second one demonstrates that the proposed normalized projection measure outperforms traditional projection measure; the third one demonstrates that the newly developed VIKOR method outperforms the traditional VIKOR method; and the last one identifies the optimal range for the three parameters of the proposed comprehensive VIKOR model.