As AI agents act on behalf of users, designers increasingly combine explainability (XAI) and anthropomorphism to build trust. Yet, whether these cues create synergy or interference remains a critical, open question. Our online experiment (N=900) revealed a counterintuitive interference effect: anthropomorphism reduced trust in an explainable agent. A preregistered lab study with eye-tracking (N=57) reversed this finding: under controlled conditions, the combined design elicited the highest trust. Eye-tracking reveals the mechanism: XAI promotes deeper cognitive engagement (e.g., longer fixations), which primes users to allocate attention to social cues (e.g., avatars). Our findings show that trust depends on cognitive engagement moderating social cue processing, yielding a critical design insight: effectively pairing explanatory and anthropomorphic interfaces requires first securing the user’s cognitive engagement to avoid undermining trust.

As a core problem in unsupervised learning, anomaly detection focuses on identifying abnormal patterns in datasets, thereby providing support for uncovering potential problems and extracting valuable information. However, most existing methods fail to extract sufficient information in feature interactions when dealing with heterogeneous datasets. To address this challenge, a novel anomaly detection method based on multi-sequence fuzzy feature interaction is proposed. Firstly, we propose multi-sequence features based on joint fuzzy information entropy to capture complex feature interactions and to quantify the interdependencies among features. Secondly, forward and reverse multi-sequence feature subset pairs are constructed to characterize the correlation between features from different angles, enhancing the accuracy of representing complex interactions in heterogeneous data and improving the ability to identify potential anomalies. Subsequently, an uncertainty measure based on multi-sequence information fusion is introduced, and anomaly scores are accumulated by incorporating instance weights, thereby ensuring stable detection performance in heterogeneous datasets. Finally, an anomaly detection algorithm based on multi-sequence fuzzy feature interaction (ADMSFI) is proposed. The experimental results demonstrate that the proposed algorithm ADMSFI significantly outperforms 13 existing algorithms in terms of performance and flexibility in 24 datasets.

Feature selection is a crucial step for data preprocessing, and it is widely applied in machine learning. It can eliminate features that are redundant or irrelevant from data, thereby improving performance and reducing runtime. The uncertain nature of labels produces unique challenges for high-dimensional data with label ambiguity, which is still an open problem; the structural information of the data is not utilized fully. In this article, we sufficiently consider the structural information of the data, including relevancy between labels and features, redundancy among features, and positive regions, and set up a novel label ambiguity feature selection model via weighted label-fuzzy relevancy and redundancy. Specifically, we first transform the non-label distribution annotations to label distribution annotations by using a label enhancement model. Second, we use a fuzzy similarity relation to quantify how similar samples are in label space. Third, a general label-fuzzy rough set model is created, and then, a novel feature evaluation measure based on weighted label-fuzzy relevancy and redundancy is defined. In this model, general label-fuzzy rough sets are employed to process label ambiguity problems, and the label-fuzzy relevancy and redundancy are weighted with the feature significance with the positive region as the focus. Finally, a feature selection algorithm for label ambiguity that follows the idea of weighted label-fuzzy relevancy and redundancy is proposed. Extensive experiments are conducted on 12 label distribution annotation datasets and 8 multi-label annotation datasets. The results indicate the advantages of our proposed algorithm over state-of-the-art algorithms.

Feature selection, as an effective dimensionality reduction technique, is favored in preprocessing data. However, most existing algorithms are solely liable for labeled or unlabeled data, whereas a limited portion of real-world data is annotated with labels. In this article, we therefore propose a novel scheme named SemiFREE, i.e., semisupervised feature selection with fuzzy relevance and redundancy. First, both labeled and unlabeled samples are assigned with fuzzy decisions that allow class membership to naturally express the fuzziness or uncertainty in data labeling. Second, sample similarities in feature space and fuzzy decision are captured to induce fuzzy information measures for redefining the feature relevance and redundancy. Finally, adhering to the principle of relevance-maximization and redundancy-minimization, SemiFREE leverages the forward sequential searching strategy to identify qualified features progressively. Extensive experiments demonstrate the superiority of SemiFREE in the presence of partially labeled data against some other well-established feature selection algorithms.

The main goal of most information quality (IQ)-based measures is to combine data provided by multiple information sources to enhance the quality of information essential for decision makers to perform their tasks. However, there is few work to fuse multisource information from the perspective of possibility distribution (PD) and use IQ as the evaluation criteria for feature selection. The PD is one of important concepts in the possibility theory, which is a generally acknowledged method for describing a kind of uncertain knowledge. In this article, we propose a novel representation model of PDs based on FMs, namely, a possibility distribution information system (PDIS). Then, several IQ measures are defined in the PDIS, including Gini entropy, compatibility, conflict, credibility, and separability degrees. In view of this, a minimal-separability-minimal-uncertainty-based unsupervised feature selection algorithm (UmSMU) is designed. The proposed UmSMU can sufficiently fuse multiple possibilistic information. Meanwhile, the selected features maintain as much information as possible while minimizing the uncertainty of information. The experimental results show that the proposed algorithm performs well, especially when it comes to selecting fewer features and improving performance.