scholarly journals Automatic Generation of Children's Songs Based on Machine Statistic Learning

2018 ◽  
Vol 13 (03) ◽  
pp. 17
Author(s):  
Li Pan
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Language Model ◽  
Automatic Generation ◽  
Target Language ◽  
Generation Process ◽  
Statistical Machine Learning ◽  
Translation Model ◽  
Children's Songs ◽  
Relationship Of

In this paper, the automatic generation of children's songs is studied. First of all, according to the number of key lyrics submitted by the songwriter, statistical machine learning is used to expand and obtain more theme-related lyrics, and then the first sentence is generated through the language model automatically. On this basis, the subsequent sentences are generated through the statistical machine learning translation method. In the process of the generation, the statistical machine learning is used to expand the conception of the song, so as to get richer sentence candidates. The main features and contributions of the study are: Firstly, the statistical machine learning translation is put forward as the theoretical basis, the preceding and next sentence relationship of children's songs are mapped into the relation of the source language and target language in the statistical translation model, and the machine statistics learning translation model is designed with the integration of the domain knowledge of songs. Secondly, the statistical machine learning is used in the generation process to expand the lyrics words, thereby enhancing the theme and conception of the song. The experimental results have confirmed the effectiveness of the proposed method.

A self-learning finite element extraction system based on reinforcement learning

2021 ◽  
pp. 1-29
Author(s):  
Jie Pan ◽  
Jingwei Huang ◽  
Yunli Wang ◽  
Gengdong Cheng ◽  
Yong Zeng
Keyword(s):  
Reinforcement Learning ◽  
Mesh Generation ◽  
Domain Knowledge ◽  
Domain Boundary ◽  
Automatic Generation ◽  
Extraction System ◽  
Generation Process ◽  
High Quality ◽  
Geometric Boundary ◽  
Self Learning

Abstract Automatic generation of high-quality meshes is a base of CAD/CAE systems. The element extraction is a major mesh generation method for its capabilities to generate high-quality meshes around the domain boundary and to control local mesh densities. However, its widespread applications have been inhibited by the difficulties in generating satisfactory meshes in the interior of a domain or even in generating a complete mesh. The element extraction method's primary challenge is to define element extraction rules for achieving high-quality meshes in both the boundary and the interior of a geometric domain with complex shapes. This paper presents a self-learning element extraction system, FreeMesh-S, that can automatically acquire robust and high-quality element extraction rules. Two central components enable the FreeMesh-S: (1) three primitive structures of element extraction rules, which are constructed according to boundary patterns of any geometric boundary shapes; (2) a novel self-learning schema, which is used to automatically define and refine the relationships between the parameters included in the element extraction rules, by combining an Advantage Actor-Critic (A2C) reinforcement learning network and a Feedforward Neural Network (FNN). The A2C network learns the mesh generation process through random mesh element extraction actions using element quality as a reward signal and produces high-quality elements over time. The FNN takes the mesh generated from the A2C as samples to train itself for the fast generation of high-quality elements. FreeMesh-S is demonstrated by its application to two-dimensional quad mesh generation. The meshing performance of FreeMesh-S is compared with three existing popular approaches on ten pre-defined domain boundaries. The experimental results show that even with much less domain knowledge required to develop the algorithm, FreeMesh-S outperforms those three approaches in essential indices. FreeMesh-S significantly reduces the time and expertise needed to create high-quality mesh generation algorithms.

Hierarchical Concept-Driven Language Model

2021 ◽  
Vol 15 (6) ◽  
pp. 1-22
Author(s):  
Yashen Wang ◽  
Huanhuan Zhang ◽  
Zhirun Liu ◽  
Qiang Zhou
Keyword(s):  
Language Model ◽  
Generation Process ◽  
Data Generation ◽  
Modeling Framework ◽  
Long Distance ◽  
Short Text ◽  
Proposed Model ◽  
Scalable Inference ◽  
End To End ◽  
Hidden Layer

For guiding natural language generation, many semantic-driven methods have been proposed. While clearly improving the performance of the end-to-end training task, these existing semantic-driven methods still have clear limitations: for example, (i) they only utilize shallow semantic signals (e.g., from topic models) with only a single stochastic hidden layer in their data generation process, which suffer easily from noise (especially adapted for short-text etc.) and lack of interpretation; (ii) they ignore the sentence order and document context, as they treat each document as a bag of sentences, and fail to capture the long-distance dependencies and global semantic meaning of a document. To overcome these problems, we propose a novel semantic-driven language modeling framework, which is a method to learn a Hierarchical Language Model and a Recurrent Conceptualization-enhanced Gamma Belief Network, simultaneously. For scalable inference, we develop the auto-encoding Variational Recurrent Inference, allowing efficient end-to-end training and simultaneously capturing global semantics from a text corpus. Especially, this article introduces concept information derived from high-quality lexical knowledge graph Probase, which leverages strong interpretability and anti-nose capability for the proposed model. Moreover, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence concept dependence. Experiments conducted on several NLP tasks validate the superiority of the proposed approach, which could effectively infer meaningful hierarchical concept structure of document and hierarchical multi-scale structures of sequences, even compared with latest state-of-the-art Transformer-based models.

scholarly journals “Es gibt nur eine Sprache”: The ‘Task of the Translator’ in Rosenzweig’s Idea of Language and Redemption. Its Conceptual Homologies and Expansions

Naharaim ◽  
2020 ◽  
Vol 14 (2) ◽  
pp. 153-171
Author(s):  
Massimiliano De Villa
Keyword(s):  
Target Language ◽  
Poetic Language ◽  
Translation Model ◽  
Source Language

AbstractThe concurrence of different languages is one of the tenets of Rosenzweig Sprachdenken and of his translation activity which finds its main theoretical explication in the afterword to his ‘Zweiundneunzig Hymnen und Gedichte des Yehuda Halevi’ (Konstanz, Wöhrle, 1924). In the afterword to the translation of ha-Levi’s lyrical corpus, Rosenzweig outlines a translation model which, trying to convey all the morphological, syntactic and lexical traits of the source language into the target language, gives way to a real linguistic fusion which defies the limits and boundaries of expression and opens onto a redemptive perspective. On the basis of this concluding note and of some passages from ‘The Star of Redemption’, the article tries to analyse Rosenzweig’s idea of language and of its nexus with the idea of redemption with reference to Walter Benjamin’s famous essay ‘The Task of the Translator’ and, as a point of convergence, with Paul Celan’s conception of poetic language.

Machine Learning-Assisted Sampling of SERS Substrates Improves Data Collection Efficiency

2021 ◽  
pp. 000370282110345
Author(s):  
Tatu Rojalin ◽  
Dexter Antonio ◽  
Ambarish Kulkarni ◽  
Randy P. Carney
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Domain Knowledge ◽  
Collection Efficiency ◽  
Point Of Care ◽  
Automated Analysis ◽  
Downstream Processing ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Expert User

Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples. While much recent work has established sophisticated automation routines using machine learning and related artificial intelligence methods, these efforts have largely focused on downstream processing (e.g., classification tasks) of previously collected data. While fully automated analysis pipelines are desirable, current progress is limited by cumbersome and manually intensive sample preparation and data collection steps. Specifically, a typical lab-scale SERS experiment requires the user to evaluate the quality and reliability of the measurement (i.e., the spectra) as the data are being collected. This need for expert user-intuition is a major bottleneck that limits applicability of SERS-based diagnostics for point-of-care clinical applications, where trained spectroscopists are likely unavailable. While application-agnostic numerical approaches (e.g., signal-to-noise thresholding) are useful, there is an urgent need to develop algorithms that leverage expert user intuition and domain knowledge to simplify and accelerate data collection steps. To address this challenge, in this work, we introduce a machine learning-assisted method at the acquisition stage. We tested six common algorithms to measure best performance in the context of spectral quality judgment. For adoption into future automation platforms, we developed an open-source python package tailored for rapid expert user annotation to train machine learning algorithms. We expect that this new approach to use machine learning to assist in data acquisition can serve as a useful building block for point-of-care SERS diagnostic platforms.

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