Pedagogical analysis of the process of pianism in China

The article considers the origins and the process of pianism in China. The relevance of the presented research is due to the contradiction that has developed in musical performance in China between the interest of pianists of various specializations, the attention of teacher-methodologists to the process of the formation of playing the piano, and the lack of awareness of musicians about the research in this area. The main emphasis is placed on the period from the middle of the 20th century to the beginning of the 21st century. The leading problem of piano culture in China in the period under study has been revealed: the discrepancy between the high level of development of technical skills and the level of development of the musical thinking of pianists. The characteristics of the activities of the most famous composers - representatives of the piano art of China have been offered. The article analyzes the influence of the piano work of European composers and musicians on the development of piano Pedagogy in China.The purpose of the research is to obtain the necessary scientific data for the further development of practical recommendations for studying the process of pianism in China. In the research the following methods were used: a holistic, structural analysis of the associative manifestations of the rich imaginative structure of the music of Chinese composers; analysis of various methods of learning to play the piano. As a result, the main trends in piano teaching in China have been revealed; actual recommendations for the development of the culture of piano performance have been formulated. The key findings. Learning to play the piano in China as a system for the development of musical thinking is built taking into account the ideas of the ancient philosophy of China. At the same time, there is a combination of national techniques in the musical pedagogy of China, and European “teaching techniques” of teaching playing the piano

The Study of the Relationship Between Corporate Social Responsibility and Corporate Value

Abstract With the deepening economic globalization and rapid development of China's economy, profit pursuit is accompanied by workers' rights, product quality, environmental pollution and many other issues, corporate social responsibility has become an irresistible trend. The dissertation tries to construct corporate social responsibility evaluation system from the perspective of stakeholder theory, and attempts to use this system to analyse the relationship between corporate social responsibility and corporate value. In order to achieve the purpose of enhancing corporate social responsibility awareness and encourage enterprises fulfill social responsibility actively. Firstly, the dissertation described the background and significance of the study, reviewed corporate social responsibility, corporate value and stakeholder theory, summarized the literature of the relationship between corporate social responsibility and corporate value. Secondly, established corporate social responsibility evaluation system from shareholders. And analyzed impact on the company value which takes action to fulfill responsibility through using the samples companies as the basis and SPSS 17.0 for data analysis, to explore the relationship between company value and corporate social responsibility based on the stakeholder theory. Through the study it can be found that enterprises fulfill their social responsibility has a positive impact on the corporate value. The dissertation proposed policy recommendations and suggestions to the manufacturing companies in China on the basis of empirical study conclusion. Keywords: Corporate Social Responsibility, Corporate Value, Stakeholde

Additional Positive Enables Better Representation Learning for Medical Images

This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability. Unlike conventional BYOL which relies on only one positive pair generated by two augmented views of the same image, we argue that information from different images with the same label can bring more diversity and variations to the target features, thus benefiting representation learning. To identify such pairs without any label, we investigate TracIn, an instance-based and computationally efficient influence function, for BYOL training. Specifically, TracIn is a gradient-based method that reveals the impact of a training sample on a test sample in supervised learning. We extend it to the self-supervised learning setting and propose an efficient batch-wise per-sample gradient computation method to estimate the pairwise TracIn to represent the similarity of samples in the mini-batch during training. For each image, we select the most similar sample from other images as the additional positive and pull their features together with BYOL loss. Experimental results on two public medical datasets (i.e., ISIC 2019 and ChestX-ray) demonstrate that the proposed method can improve the classification performance compared to other competitive baselines in both semi-supervised and transfer learning settings.Comment: 8 page

Hyperbolic Face Anti-Spoofing

Learning generalized face anti-spoofing (FAS) models against presentation attacks is essential for the security of face recognition systems. Previous FAS methods usually encourage models to extract discriminative features, of which the distances within the same class (bonafide or attack) are pushed close while those between bonafide and attack are pulled away. However, these methods are designed based on Euclidean distance, which lacks generalization ability for unseen attack detection due to poor hierarchy embedding ability. According to the evidence that different spoofing attacks are intrinsically hierarchical, we propose to learn richer hierarchical and discriminative spoofing cues in hyperbolic space. Specifically, for unimodal FAS learning, the feature embeddings are projected into the Poincar\'e ball, and then the hyperbolic binary logistic regression layer is cascaded for classification. To further improve generalization, we conduct hyperbolic contrastive learning for the bonafide only while relaxing the constraints on diverse spoofing attacks. To alleviate the vanishing gradient problem in hyperbolic space, a new feature clipping method is proposed to enhance the training stability of hyperbolic models. Besides, we further design a multimodal FAS framework with Euclidean multimodal feature decomposition and hyperbolic multimodal feature fusion & classification. Extensive experiments on three benchmark datasets (i.e., WMCA, PADISI-Face, and SiW-M) with diverse attack types demonstrate that the proposed method can bring significant improvement compared to the Euclidean baselines on unseen attack detection. In addition, the proposed framework is also generalized well on four benchmark datasets (i.e., MSU-MFSD, IDIAP REPLAY-ATTACK, CASIA-FASD, and OULU-NPU) with a limited number of attack types

Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing

In the context-dependent Text-to-SQL task, the generated SQL statements are refined iteratively based on the user input utterance from each interaction. The input text from each interaction can be viewed as component modifications to the previous SQL statements, which could be further extracted as the modification patterns. Since these modification patterns could also be combined with other SQL statements, the models are supposed to have the compositional generalization to these novel combinations. This work is the first exploration of compositional generalization in context-dependent Text-to-SQL scenarios. To facilitate related studies, we constructed two challenging benchmarks named \textsc{CoSQL-CG} and \textsc{SParC-CG} by recombining the modification patterns and existing SQL statements. The following experiments show that all current models struggle on our proposed benchmarks. Furthermore, we found that better aligning the previous SQL statements with the input utterance could give models better compositional generalization ability. Based on these observations, we propose a method named \texttt{p-align} to improve the compositional generalization of Text-to-SQL models. Further experiments validate the effectiveness of our method. Source code and data are available.Comment: Accepted to ACL 2023 (Findings), Long Paper, 11 page

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

How to identify semantic relations among entities in a document when only a few labeled documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial for addressing the pervasive data scarcity problem in real-world scenarios. Metric-based meta-learning is an effective framework widely adopted for FSDLRE, which constructs class prototypes for classification. However, existing works often struggle to obtain class prototypes with accurate relational semantics: 1) To build prototype for a target relation type, they aggregate the representations of all entity pairs holding that relation, while these entity pairs may also hold other relations, thus disturbing the prototype. 2) They use a set of generic NOTA (none-of-the-above) prototypes across all tasks, neglecting that the NOTA semantics differs in tasks with different target relation types. In this paper, we propose a relation-aware prototype learning method for FSDLRE to strengthen the relational semantics of prototype representations. By judiciously leveraging the relation descriptions and realistic NOTA instances as guidance, our method effectively refines the relation prototypes and generates task-specific NOTA prototypes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches by average 2.61% $F_1$ across various settings of two FSDLRE benchmarks.Comment: Accepted to EMNLP 202