In some real-world situations, Pythagorean fuzzy sets are more powerful and effective than intuitionistic fuzzy sets to describe vague and uncertain information, and there are many Pythagorean fuzzy information systems for conflicts in which attitudes of agents on issues are depicted by Pythagorean fuzzy numbers. In this paper, we first provide the concepts of positive, neutral and negative alliances with two thresholds and employ examples to illustrate how to compute positive, neutral and negative alliances in Pythagorean fuzzy information systems for conflicts. Then we focus on three-way conflict analysis based on Bayesian minimum risk theory and explore examples to show how to compute the positive, neutral and negative alliances with a Pythagorean fuzzy loss function given by an expert. Finally, we study how to calculate positive, neutral and negative alliances with group decision theory and take examples to demonstrate how to construct the positive, neutral and negative alliances with a group of Pythagorean fuzzy loss functions given by more experts.

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications.

Prompt learning stands out as one of the most efficient approaches for adapting powerful vision-language foundational models like CLIP to downstream datasets by tuning learnable prompt vectors with very few samples. However, despite its success in achieving remarkable performance on in-domain data, prompt learning still faces the significant challenge of effectively generalizing to novel classes and domains. Some existing methods address this concern by dynamically generating distinct prompts for different domains. Yet, they overlook the inherent potential of prompts to generalize across unseen domains. To address these limitations, our study introduces an innovative prompt learning paradigm, called MetaPrompt, aiming to directly learn domain invariant prompt in few-shot scenarios. To facilitate learning prompts for image and text inputs independently, we present a dual-modality prompt tuning network comprising two pairs of coupled encoders. Our study centers on an alternate episodic training algorithm to enrich the generalization capacity of the learned prompts. In contrast to traditional episodic training algorithms, our approach incorporates both in-domain updates and domain-split updates in a batch-wise manner. For in-domain updates, we introduce a novel asymmetric contrastive learning paradigm, where representations from the pre-trained encoder assume supervision to regularize prompts from the prompted encoder. To enhance performance on out-of-domain distribution, we propose a domain-split optimization on visual prompts for cross-domain tasks or textual prompts for cross-class tasks during domain-split updates. Extensive experiments across 11 datasets for base-to-new generalization and 4 datasets for domain generalization exhibit favorable performance. Compared with the state-of-the-art method, MetaPrompt achieves an absolute gain of 1.02% on the overall harmonic mean in base-to-new generalization and consistently demonstrates superiority over all benchmarks in domain generalization.

Knowledge discovery of heterogeneous data is an active topic in knowledge engineering. Feature selection for heterogeneous data is an important part of effective data analysis. Although there have been many attempts to study the feature selection for heterogeneous data, there are still some challenges, such as the unbalanced problem between the stability and validity of the designed model. Hence, this paper focuses on how to design an effective and robust heterogeneous feature selection method, namely a zentropy-based uncertainty measure for heterogeneous feature selection(Ze-HFS). Different from other entropy-based uncertainty measures, the proposed method does not consider single-level information measures but systematically analyzes and integrates the information between different granular levels, which has an obvious advantage in the study of heterogeneous data knowledge discovery. Specifically, a heterogeneous distance metric is first introduced to construct heterogeneous neighborhood granules and heterogeneous neighborhood rough sets(HNRS). Then, the zentropy-based uncertainty measure is developed by analyzing the granular level structure in the HNRS model. Finally, two significant measures based on the above research are designed for heterogeneous feature selection. Compared with other state-of-the-art methods, the experimental results on 18 public datasets demonstrate the robustness and effectiveness of the proposed method.

Outliers carry significant information to reflect an anomaly mechanism, so outlier detection facilitates relevant data mining. In terms of outlier detection, the classical approaches from distances apply to numerical data rather than nominal data, while the recent methods on basic rough sets deal with nominal data rather than numerical data. Aiming at wide outlier detection on numerical, nominal, and hybrid data, this paper investigates three-way neighborhood characteristic regions and corresponding fusion measurement to advance outlier detection. First, neighborhood rough sets are deepened via three-way decision, so they derive three-way neighborhood structures on model boundaries, inner regions, and characteristic regions. Second, the three-way neighborhood characteristic regions motivate the information fusion and weight measurement regarding all features, and thus, a multiple neighborhood outlier factor emerges to establish a new method of outlier detection; furthermore, a relevant outlier detection algorithm (called 3WNCROD, available at https://github.com/BELLoney/3WNCROD ) is designed to comprehensively process numerical, nominal, and mixed data. Finally, the 3WNCROD algorithm is experimentally validated, and it generally outperforms 13 contrast algorithms to perform better for outlier detection.