Few-shot Named Entity Recognition (NER) systems are designed to identify new categories of entities with a limited number of labeled examples. A major challenge encountered by these systems is overfitting, particularly pronounced in comparison to tasks with ample samples. This overfitting predominantly stems from spurious correlations, a consequence of biases inherent in the selection of a small sample set. In response to this challenge, we introduce a novel approach in this paper: a causal intervention-based method for few-shot NER. Building upon the foundational structure of prototypical networks, our method strategically intervenes in the context to obstruct the indirect association between the context and the label. For scenarios restricted to 1-shot, where contextual intervention is not feasible, our method utilizes incremental learning to intervene at the prototype level. This not only counters overfitting but also aids in alleviating catastrophic forgetting. Additionally, to preliminarily classify entity types, we employ entity detection methods for coarse categorization. Considering the distinct characteristics of the source and target domains in few-shot tasks, we introduce sample reweighting to aid in model transfer and generalization. Through rigorous testing across multiple benchmark datasets, our approach consistently sets new state-of-the-art benchmarks, underscoring its efficacy in few-shot NER applications.

Heterogeneous graphs contain multiple types of entities and relations, which are capable of modeling complex interactions. Embedding on heterogeneous graphs has become an essential tool for analyzing and understanding such graphs. Although these meticulously designed methods make progress, they are limited by model design and computational resources, making it difficult to scale to large-scale heterogeneous graph data and hindering the application and promotion of these methods. In this paper, we propose Restage, a relation structure-aware hierarchical heterogeneous graph embedding framework. Under this framework, embedding only a smaller-scale graph with existing graph representation learning methods is sufficient to obtain node representations on the original heterogeneous graph. We consider two types of relation structures in heterogeneous graphs: interaction relations and affiliation relations. Firstly, we design a relation structure-aware coarsening method to successively coarsen the original graph to the top-level layer, resulting in a smaller-scale graph. Secondly, we allow any unsupervised representation learning methods to obtain node embeddings on the top-level graph. Finally, we design a relation structure-aware refinement method to successively refine the node embeddings from the top-level graph back to the original graph, obtaining node embeddings on the original graph. Experimental results on three public heterogeneous graph datasets demonstrate the enhanced scalability of representation learning methods by the proposed Restage. On another large-scale graph, the speed of existing representation learning methods is increased by up to eighteen times at most.

Large-scale service composition has become an important research topic in Service-Oriented Computing (SOC). Quality of Service (QoS) has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of QoS have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly, the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS_FOA (Web Service composition based on Fruit Fly Optimization Algorithm) for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.

Multiple-Instance Learning (MIL) is used to predict the unlabeled bags’ label by learning the labeled positive training bags and negative training bags. Each bag is made up of several unlabeled instances. A bag is labeled positive if at least one of its instances is positive, otherwise negative. Existing multiple-instance learning methods with instance selection ignore the representative degree of the selected instances. For example, if an instance has many similar instances with the same label around it, the instance should be more representative than others. Based on this idea, in this paper, a multiple-instance learning with instance selection via constructive covering algorithm (MilCa) is proposed. In MilCa, we firstly use maximal Hausdorff to select some initial positive instances from positive bags, then use a Constructive Covering Algorithm (CCA) to restructure the structure of the original instances of negative bags. Then an inverse testing process is employed to exclude the false positive instances from positive bags and to select the high representative degree instances ordered by the number of covered instances from training bags. Finally, a similarity measure function is used to convert the training bag into a single sample and CCA is again used to classification for the converted samples. Experimental results on synthetic data and standard benchmark datasets demonstrate that MilCa can decrease the number of the selected instances and it is competitive with the state-of-the-art MIL algorithms.