242 research outputs found
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% across
various settings of two FSDLRE benchmarks.Comment: Accepted to EMNLP 202
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