Aspect-based sentiment analysis (ABSA) aims to extract fine-grained opinions from the text by discerning sentiments toward specific aspects. Although large language models (LLMs) perform well in-context learning (ICL), current ICL methodologies typically retrieve semantically similar but structurally redundant examples, failing to capture syntactic and aspect-level cues critical for ABSA. To overcome these limitations, we report Multi-perspective Sequential retrieval with Predictive Feedback (MSPF), a few-shot learning framework that enhances ICL through MSPF, which integrates three complementary perspectives: overall semantic, syntactic relevance, and aspect sentiment alignment. Evaluated on four benchmark datasets (Laptop14, Restaurant14, Books, and Clothing), MSPF achieved F1 scores of 67.03 % (Laptop14), 73.51 % (Restaurant14), 76.07 % (Books), and 81.96 % (Clothing), outperforming standard ICL by +7.06 %, +5.60 %, +25.61 %, and +18.38 %, respectively. These results validated the efficacy of MSPF in improving LLM reasoning for fine-grained sentiment tasks with limited annotations.

Salient object detection (SOD) aims to distinguish salient regions from non-salient ones in an image. In real-world scenarios, factors such as depth variation and surface reflection can interfere with the model's judgment, while illumination uncertainty further intensifies this interference. As a result, the uncertainty in salient boundary detection increases, leading to false or missed detections. To core with uncertainty inherent to the problem, we introduce the concept of shadowed set, which is an effective method to process the uncertainty problem. In this paper, we have designed an illumination-aware feature integration network by conducting dual-input feature integration under the implicit assistance of illumination maps. Firstly, we devised a determination of pixel-level salient area module, which extract illumination maps based on Retinex theory and obtain the main area of salient object based on shadowed set as the implicit feature of illumination. Next, we constructed a dual-modal compression module to solve the problem of feature alignment, which can use the dual-stream structure to process RGB and auxiliary inputs. Finally, multi-stage contextual complementary module can effectively recover fine object edges, and we use the outputs from the last three stages to supervise the training of the entire model. The state originality came from our previous work on illumination maps and shadowed sets, and we creatively combined them with the SOD to process uncertainty of salient area. The experiments demonstrate that our method exhibits excellent performance on multiple RGB-based datasets, at the same time, it also demonstrates unique performance on underwater and challenging scenes.

Occluded person re-identification aims to address the identification challenges posed by pedestrians obscured by other individuals or objects. Existing methods often rely on incorporating pose or semantic information to improve model performance under occlusion. However, such information often depends on external models with inevitably cross-domain gaps, whose stability is limited in complex occlusion environments and prone to false results. In this paper, we propose a Transformer-based uncertainty-driven Gaussian model, termed as UD-Gaussian. Firstly, to enrich the detailed features of pedestrian images, a high-frequency enhancement module is introduced. The high-frequency components of the pedestrian image are extracted by Discrete Haar Wavelet Transform, and Top-K high-frequency patches are extracted to construct a graph Laplacian matrix to achieve high-frequency graph attention, which is fused with features learned from self-attention to enhance the high-frequency feature representation. Given the uncertainty in pedestrian feature learning induced by occlusion makes it challenging to obtain reliable and stable pedestrian features, we propose a probability distribution learning module. This module establishes a memory bank to build Gaussian distributions for each pedestrian identity and the entropy is introduced as a loss function to encourage the model to generate more deterministic and relatively independent probability distributions, thereby enhancing the discriminative ability of the model across different pedestrian identities. The high-frequency enhancement module provides a solid foundation for the probability distribution learning module, alleviating uncertainty caused by pedestrian images themselves. Experimental results on occluded and holistic person re-identification datasets demonstrate the superiority of the proposed method.

Unsupervised Domain Adaptation (UDA) person search aims to adapt models trained on labeled source data to unlabeled target domains. Existing approaches typically rely on clustering-based proxy learning, but their performance is often undermined by unreliable pseudo-supervision. This unreliability mainly stems from two challenges: (i) spectral shift bias, where low- and high-frequency components behave differently under domain shifts but are rarely considered, degrading feature stability; and (ii) static proxy updates, which make clustering proxies highly sensitive to noise and less adaptable to domain shifts. To address these challenges, we propose the Reliable Pseudo-supervision in UDA Person Search (RPPS) framework. At the feature level, a Dual-branch Wavelet Enhancement Module (DWEM) embedded in the backbone applies discrete wavelet transform (DWT) to decompose features into low- and high-frequency components, followed by differentiated enhancements that improve cross-domain robustness and discriminability. At the proxy level, a Dynamic Confidence-weighted Clustering Proxy (DCCP) employs confidence-guided initialization and a two-stage online-offline update strategy to stabilize proxy optimization and suppress proxy noise. Extensive experiments on the CUHK-SYSU and PRW benchmarks demonstrate that RPPS achieves state-of-the-art performance and strong robustness, underscoring the importance of enhancing pseudo-supervision reliability in UDA person search. Our code is accessible at https://github.com/zqx951102/RPPS.

Early exiting is an effective paradigm for improving the inference efficiency of pre-trained language models (PLMs) by dynamically adjusting the number of executed layers for each sample. However, in most existing works, easy and hard samples are treated equally by each classifier during training, which neglects the test-time early exiting behavior, leading to inconsistency between training and testing. Although some methods have tackled this issue under a fixed speed-up ratio, the challenge of flexibly adjusting the speed-up ratio while maintaining consistency between training and testing is still under-explored. To bridge the gap, we propose a novel Consistency-Oriented Signal-based Early Exiting (COSEE) framework, which leverages a calibrated sample weighting mechanism to enable each classifier to emphasize the samples that are more likely to exit at that classifier under various acceleration scenarios. Extensive experiments on the GLUE benchmark demonstrate the effectiveness of our COSEE across multiple exiting signals and backbones, yielding a better trade-off between performance and efficiency.

Early exiting has demonstrated great potential in accelerating the inference of pre-trained language models (PLMs) by enabling easy samples to exit at shallow layers, eliminating the need for executing deeper layers. However, existing early exiting methods primarily rely on class-relevant logits to formulate their exiting signals for estimating prediction certainty, neglecting the detrimental influence of class-irrelevant information in the features on prediction certainty. This leads to an overestimation of prediction certainty, causing premature exiting of samples with incorrect early predictions. To remedy this, we define an NSP score to estimate prediction certainty by considering the proportion of class-irrelevant information in the features. On this basis, we propose a novel early exiting method based on the Certainty-Aware Probability (CAP) score, which integrates insights from both logits and the NSP score to enhance prediction certainty estimation, thus enabling more reliable exiting decisions. The experimental results on the GLUE benchmark show that our method can achieve an average speed-up ratio of 2.19× across all tasks with negligible performance degradation, surpassing the state-of-the-art (SOTA) ConsistentEE by 28%, yielding a better trade-off between task performance and inference efficiency. The code is available at https://github.com/He-Jianing/NSP.git.

Multigranularity data analysis has recently become an active research topic in the intelligent computing and data mining fields. Feature selection via multigranularity data analysis is an effective tool for characterizing hierarchical data and enhancing the accuracy of the results. Although the multigranularity data analysis method has been widely adopted for feature selection, existing studies still present one prevalent disadvantage: multigranularity data analysis mostly focuses on information presented at a single granularity while ignoring the hierarchical structure of multigranularity data, which is contrary to the nature of multigranularity. Hence, this article proposes a multigranularity data analysis with a zentropy uncertainty measure for efficient and robust feature selection. Specifically, a consistent degree is first introduced to obtain optimal granularity combinations and establish an efficient neighborhood model for multigranularity information processing. Then, a novel and robust uncertainty measure is developed by integrating the multigranularity information, namely the zentropy-based measure. Considering its accuracy among uncertainty measures, two important measures are further designed and applied to feature selection. Extensive experiments demonstrate that the proposed method can achieve better robustness and classification performance than other state-of-the-art methods.

The feature selection based on the rough set (RS) theory has been an active research topic in data mining and knowledge discovery. Fuzzy RSs (FRSs), an efficient tool to process the inconsistency between features and decisions, have attracted attention to the problems of feature selection. However, most FRSs-based feature selection methods pay much attention to the approximation space while ignoring the interaction between different levels. Note that the single-level feature selection method, depending on the boundary objects, is easily influenced by the noise data and cannot integrate multiple granular levels to evaluate features accurately. Therefore, this article proposes an efficient and robust feature selection approach based on the neighborhood-aware model and zentropy measure. Specifically, we first define a neighborhood-aware FRS (NAFRS) with weighted fuzzy relation to improve the antinoise ability of FRSs. Then, we propose a fuzzy granule zentropy (FGZE) measure based on zentropy by analyzing the granular level relation in NAFRS. Moreover, a significance measure with FGZE is designed and applied to feature selection. Finally, the experimental results of our method on 22 datasets by comparing it with 12 representative feature selection methods demonstrate the antinoise and the classification ability of the proposed method.

Feature selection and entropy theory are two efficacious data analysis tools for investigating uncertainty information processing in artificial intelligence. The fruitful marriage of the two has been an active research topic in knowledge discovery. Currently, most feature selection methods via entropy theory mainly focus on the information measures at a single granular level. However, it ignores the interaction between granular levels, which leads to the poor stability and accuracy of related methods. Hence, this article proposes a novel zentropy-based uncertainty measure to design a feature selection method by exploiting the granular level structure in knowledge space. Subsequently, by analyzing the granular level structure in decision data, the zentropy-based uncertainty measure and its properties are designed and analyzed to depict the uncertainty knowledge from whole and internal. Moreover, two importance measures are defined to evaluate features based on the designed uncertainty measure, and then a corresponding feature selection algorithm is developed. Finally, some experiments are carried out on public datasets to demonstrate that the proposed method can achieve state-of-the-art performance among methods, especially regarding stability and classification accuracy.

Person re-identification (ReID) typically encounters varying degrees of occlusion in real-world scenarios. While previous methods have addressed this using handcrafted partitions or external cues, they often compromise semantic information or increase network complexity. In this paper, we propose a new method from a novel perspective, termed as OAT. Specifically, we first use a Transformer backbone with multiple class tokens for diverse pedestrian feature learning. Given that the self-attention mechanism in the Transformer solely focuses on low-level feature correlations, neglecting higher-order relations among different body parts or regions. Thus, we propose the Second-Order Attention (SOA) module to capture more comprehensive features. To address computational efficiency, we further derive approximation formulations for implementing second-order attention. Observing that the importance of semantics associated with different class tokens varies due to the uncertainty of the location and size of occlusion, we propose the Entropy Guided Fusion (EGF) module for multiple class tokens. By conducting uncertainty analysis on each class token, higher weights are assigned to those with lower information entropy, while lower weights are assigned to class tokens with higher entropy. The dynamic weight adjustment can mitigate the impact of occlusion-induced uncertainty on feature learning, thereby facilitating the acquisition of discriminative class token representations. Extensive experiments have been conducted on occluded and holistic person re-identification datasets, which demonstrate the effectiveness of our proposed method.

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.

Principal curves arising as an essential construct in dimensionality reduction and data analysis have recently attracted much attention from theoretical as well as practical perspective. In many real-world situations, however, the efficiency of existing principal curves algorithms is often arguable, in particular when dealing with massive data owing to the associated high computational complexity. A certain drawback of these constructs stems from the fact that in several applications principal curves cannot fully capture some essential problem-oriented facets of the data dealing with width, aspect ratio, width change, etc. Information granulation is a powerful tool supporting processing and interpreting massive data. In this paper, invoking the underlying ideas of information granulation, we propose a granular principal curves approach, regarded as an extension of principal curves algorithms, to improve efficiency and achieve a sound accuracy-efficiency tradeoff. First, large amounts of numerical data are granulated into C intervals-information granules developed with the use of fuzzy C-means clustering and the two criteria of information granulation, which significantly reduce the amount of data to be processed at the later phase of the overall design. Granular principal curves are then constructed by determining the upper and the lower bounds of the interval data. Finally, we develop an objective function using the criteria of information confidence and specificity to evaluate the granular output formed by the principal curves. We also optimize the granular principal curves by adjusting the level of information granularity (the number of clusters), which is realized with the aid of the particle swarm optimization. A number of numeric studies completed for synthetic and real-world datasets provide a useful quantifiable insight into the effectiveness of the proposed algorithm.

It is difficult but crucial for minutiae extraction and pseudo minutiae deletion of low quality fingerprint images in auto fingerprint identification systems. Traditional methods based on thinning images or gray-level images are, however, susceptible to noise. Reference 14 indicated that principal curves based fingerprint minutiae extraction was feasible to overcome the drawback, but the extended polygonal line (EPL) principal curves algorithm used in the paper extracted the principal curves ineffectively. As the fingerprint data sets are usually large, the original EPL principal curves algorithm is time-consuming. Meanwhile, scattered fingerprint data lead to the deviation of fingerprint skeleton. In this paper, the algorithm is modified, and a fingerprint minutiae extraction and pseudo minutiae detection method based on principal curves is proposed. Experimental results show that the modified EPL principal curves algorithm outperforms the original EPL algorithm both in efficiency and quality, and the proposed minutiae extraction method outperforms the methods proposed by Miao under noise conditions.

A great number of biclustering algorithms have been proposed for analyzing gene expression data. Many of them assume to find exclusive biclusters whose subsets of genes are co-regulated under subsets of conditions without intersection. This is not consistent with a general understanding of biological processes that many genes participate in multiple different processes. Therefore nonexclusive biclustering algorithms are required. In this paper we present a novel approach (ROB) to find potentially overlapping biclusters in the framework of generalized rough sets. Our scheme mainly consists of two phases. First, we generate a set of highly coherent seeds (original biclusters) based on two-way rough k-means clustering. And then, the seeds are iteratively adjusted (enlarged or degenerated) by adding or removing genes and conditions based on a proposed criterion. We illustrate the method on yeast gene expression data. The experiments demonstrate the effectiveness of this approach.