scholarly journals Generating Metrically Accurate Homeric Poetry with Recurrent Neural Networks

2020 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Annie K Lamar
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Recurrent Neural Networks ◽  
Future Research ◽  
Expert Evaluation ◽  
Semantic Coherence ◽  
Metrical Analysis

We investigate the generation of metrically accurate Homeric poetry using recurrent neural networks (RNN). We assess two models: a basic encoder-decoder RNN and the hierarchical recurrent encoderdecoder model (HRED). We assess the quality of the generated lines of poetry using quantitative metrical analysis and expert evaluation. This evaluation reveals that while the basic encoder-decoder is able to capture complex poetic meter, it under performs in terms of semantic coherence. The HRED model, however, produces more semantically coherent lines of poetry but is unable to capture the meter. Our research highlights the importance of expert evaluation and suggests that future research should focus on encoder-decoder models that balance various types of input – both immediate and long-range.

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

scholarly journals Randomized SMILES Strings Improve the Quality of Molecular Generative Models

2019 ◽  
Author(s):  
Josep Arús-Pous ◽  
Simon Johansson ◽  
Oleksii Prykhodko ◽  
Esben Jannik Bjerrum ◽  
Christian Tyrchan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Chemical Space ◽  
Cell Types ◽  
Generative Models ◽  
The Other ◽  
Probability Models ◽  
String Representation ◽  
Almost All

Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces of valid and meaningful structures. Herein we perform an extensive benchmark on models trained with subsets of GDB-13 of different sizes (1 million , 10,000 and 1,000), with different SMILES variants (canonical, randomized and DeepSMILES), with two different recurrent cell types (LSTM and GRU) and with different hyperparameter combinations. To guide the benchmarks new metrics were developed that define the generated chemical space with respect to its uniformity, closedness and completeness. Results show that models that use LSTM cells trained with 1 million randomized SMILES, a non-unique molecular string representation, are able to generate larger chemical spaces than the other approaches and they represent more accurately the target chemical space. Specifically, a model was trained with randomized SMILES that was able to generate almost all molecules from GDB-13 with a quasi-uniform probability. Models trained with smaller samples show an even bigger improvement when trained with randomized SMILES models. Additionally, models were trained on molecules obtained from ChEMBL and illustrate again that training with randomized SMILES lead to models having a better representation of the drug-like chemical space. Namely, the model trained with randomized SMILES was able to generate at least double the amount of unique molecules with the same distribution of properties comparing to one trained with canonical SMILES.

scholarly journals QUALITY OF URBAN PUBLIC TRANSPORT WEBSITES ACCORDING TO EXTENT OF PROVIDED INFORMATION

Transport ◽  
2015 ◽  
Vol 30 (2) ◽  
pp. 202-216 ◽  
Author(s):  
Josef Bulíček ◽  
Pavel Drdla
Keyword(s):  
Information Content ◽  
Public Transport ◽  
Future Research ◽  
Expert Evaluation ◽  
Web Information ◽  
Urban Public Transport ◽  
Minimal Information ◽  
Made In

This paper is based on the analysis of websites of Urban Public Transport (UPT) made in different states. The paper proposes a standard of ‘minimal information websites content’ named as ‘Passenger Friendly Web’. Information content is divided into a set of elements. Their weights are determined by expert evaluation with the support of mathematics, especially of Saaty’s Method. Possibilities for future research are also regarded. Results can be applied to all Internet websites of UPT.

A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures

2019 ◽  
Vol 31 (7) ◽  
pp. 1235-1270 ◽  
Author(s):  
Yong Yu ◽  
Xiaosheng Si ◽  
Changhua Hu ◽  
Jianxun Zhang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Cell Structure ◽  
Relevant Information ◽  
Future Research ◽  
Sequential Data ◽  
Learning Capacity ◽  
Research Areas ◽  
Almost All

Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are unable to learn the relevant information of input data when the input gap is large. By introducing gate functions into the cell structure, the long short-term memory (LSTM) could handle the problem of long-term dependencies well. Since its introduction, almost all the exciting results based on RNNs have been achieved by the LSTM. The LSTM has become the focus of deep learning. We review the LSTM cell and its variants to explore the learning capacity of the LSTM cell. Furthermore, the LSTM networks are divided into two broad categories: LSTM-dominated networks and integrated LSTM networks. In addition, their various applications are discussed. Finally, future research directions are presented for LSTM networks.

scholarly journals Predicting the Quality of Short Narratives from Social Media

2017 ◽  
Author(s):  
Tong Wang ◽  
Ping Chen ◽  
Boyang Li
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Large Scale ◽  
Computational Models ◽  
Quality Evaluation ◽  
Future Research ◽  
Automatic Evaluation ◽  
Need To Evaluate ◽  
Difficult Challenge

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.

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