scholarly journals 2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4195
Author(s):  
Calvin Janitra Halim ◽  
Kazuhiko Kawamoto
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Short Term Memory ◽  
Markov Models ◽  
Synthetic Data ◽  
Future Research ◽  
Target Sequence ◽  
Temporal Models ◽  
Spatial Aspect ◽  
Future Work

Recent approaches to time series forecasting, especially forecasting spatiotemporal sequences, have leveraged the approximation power of deep neural networks to model the complexity of such sequences, specifically approaches that are based on recurrent neural networks. Still, as spatiotemporal sequences that arise in the real world are noisy and chaotic, modeling approaches that utilize probabilistic temporal models, such as deep Markov models (DMMs), are favorable because of their ability to model uncertainty, increasing their robustness to noise. However, approaches based on DMMs do not maintain the spatial characteristics of spatiotemporal sequences, with most of the approaches converting the observed input into 1D data halfway through the model. To solve this, we propose a model that retains the spatial aspect of the target sequence with a DMM that consists of 2D convolutional neural networks. We then show the robustness of our method to data with large variance compared with naive forecast, vanilla DMM, and convolutional long short-term memory (LSTM) using synthetic data, even outperforming the DNN models over a longer forecast period. We also point out the limitations of our model when forecasting real-world precipitation data and the possible future work that can be done to address these limitations, along with additional future research potential.

LSTM Deep Neural Networks Postfiltering for Enhancing Synthetic Voices

2017 ◽  
Vol 32 (01) ◽  
pp. 1860008 ◽  
Author(s):  
Marvin Coto-Jiménez ◽  
John Goddard-Close
Keyword(s):  
Neural Networks ◽  
Speech Synthesis ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Markov Models ◽  
Natural Speech ◽  
Objective Measures ◽  
Recent Developments ◽  
Small Footprint ◽  
Synthetic Voices

Recent developments in speech synthesis have produced systems capable of producing speech which closely resembles natural speech, and researchers now strive to create models that more accurately mimic human voices. One such development is the incorporation of multiple linguistic styles in various languages and accents. Speech synthesis based on Hidden Markov Models (HMM) is of great interest to researchers, due to its ability to produce sophisticated features with a small footprint. Despite some progress, its quality has not yet reached the level of the current predominant unit-selection approaches, which select and concatenate recordings of real speech, and work has been conducted to try to improve HMM-based systems. In this paper, we present an application of long short-term memory (LSTM) deep neural networks as a postfiltering step in HMM-based speech synthesis. Our motivation stems from a similar desire to obtain characteristics which are closer to those of natural speech. The paper analyzes four types of postfilters obtained using five voices, which range from a single postfilter to enhance all the parameters, to a multi-stream proposal which separately enhances groups of parameters. The different proposals are evaluated using three objective measures and are statistically compared to determine any significance between them. The results described in the paper indicate that HMM-based voices can be enhanced using this approach, specially for the multi-stream postfilters on the considered objective measures.

scholarly journals Better Prediction of Mutation Score

2021 ◽  
Author(s):  
Yossi Gil ◽  
Dor Ma’ayan
Keyword(s):  
Neural Networks ◽  
Real World ◽  
State Of The Art ◽  
Future Research ◽  
World Systems ◽  
Reliable Measurement ◽  
Mutation Score ◽  
Class Level ◽  
Effectiveness Prediction ◽  
Better Than

<div><div><div><p>Mutation score is widely accepted to be a reliable measurement for the effectiveness of software tests. Recent studies, however, show that mutation analysis is extremely costly and hard to use in practice. We present a novel direct prediction model of mutation score using neural networks. Relying solely on static code features that do not require generation of mutants or execution of the tests, we predict mutation score with an accuracy better than a quintile. When we include statement coverage as a feature, our accuracy rises to about a decile. Using a similar approach, we also improve the state-of-the-art results for binary test effectiveness prediction and introduce an intuitive, easy-to-calculate set of features superior to previously studied sets. We also publish the largest dataset of test-class level mutation score and static code features data to date, for future research. Finally, we discuss how our approach could be integrated into real-world systems, IDEs, CI tools, and testing frameworks.</p></div></div></div>

scholarly journals Graph Neural Networks: Taxonomy, Advances, and Trends

2022 ◽  
Vol 13 (1) ◽  
pp. 1-54
Author(s):  
Yu Zhou ◽  
Haixia Zheng ◽  
Xin Huang ◽  
Shufeng Hao ◽  
Dengao Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Research Community ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Review ◽  
Future Research Directions ◽  
Graph Neural Networks ◽  
Low Dimensional

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.

Text Mining in Cybersecurity

2021 ◽  
Vol 54 (7) ◽  
pp. 1-36
Author(s):  
Luciano Ignaczak ◽  
Guilherme Goldschmidt ◽  
Cristiano André Da Costa ◽  
Rodrigo Da Rosa Righi
Keyword(s):  
Neural Networks ◽  
Text Mining ◽  
Literature Review ◽  
Text Classification ◽  
Real World ◽  
Classification Performance ◽  
Unstructured Data ◽  
Future Research ◽  
Level Of Automation ◽  
Data Volume

The growth of data volume has changed cybersecurity activities, demanding a higher level of automation. In this new cybersecurity landscape, text mining emerged as an alternative to improve the efficiency of the activities involving unstructured data. This article proposes a Systematic Literature Review ( SLR ) to present the application of text mining in the cybersecurity domain. Using a systematic protocol, we identified 2,196 studies, out of which 83 were summarized. As a contribution, we propose a taxonomy to demonstrate the different activities in the cybersecurity domain supported by text mining. We also detail the strategies evaluated in the application of text mining tasks and the use of neural networks to support activities involving unstructured data. The work also discusses text classification performance aiming its application in real-world solutions. The SLR also highlights open gaps for future research, such as the analysis of non-English content and the intensification in the usage of neural networks.

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 Better Prediction of Mutation Score

2021 ◽  
Author(s):  
Yossi Gil ◽  
Dor Ma’ayan
Keyword(s):  
Neural Networks ◽  
Real World ◽  
State Of The Art ◽  
Future Research ◽  
World Systems ◽  
Reliable Measurement ◽  
Mutation Score ◽  
Class Level ◽  
Effectiveness Prediction ◽  
Better Than

<div><div><div><p>Mutation score is widely accepted to be a reliable measurement for the effectiveness of software tests. Recent studies, however, show that mutation analysis is extremely costly and hard to use in practice. We present a novel direct prediction model of mutation score using neural networks. Relying solely on static code features that do not require generation of mutants or execution of the tests, we predict mutation score with an accuracy better than a quintile. When we include statement coverage as a feature, our accuracy rises to about a decile. Using a similar approach, we also improve the state-of-the-art results for binary test effectiveness prediction and introduce an intuitive, easy-to-calculate set of features superior to previously studied sets. We also publish the largest dataset of test-class level mutation score and static code features data to date, for future research. Finally, we discuss how our approach could be integrated into real-world systems, IDEs, CI tools, and testing frameworks.</p></div></div></div>

scholarly journals Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series

2020 ◽  
Vol 34 (06) ◽  
pp. 10334-10341
Author(s):  
Tan Zhi-Xuan ◽  
Harold Soh ◽  
Desmond Ong
Keyword(s):  
Time Series ◽  
Missing Data ◽  
State Space ◽  
Real World ◽  
Markov Models ◽  
State Space Models ◽  
Multimodal Fusion ◽  
Real World Data ◽  
Forward Algorithm ◽  
Temporal Models

Integrating deep learning with latent state space models has the potential to yield temporal models that are powerful, yet tractable and interpretable. Unfortunately, current models are not designed to handle missing data or multiple data modalities, which are both prevalent in real-world data. In this work, we introduce a factorized inference method for Multimodal Deep Markov Models (MDMMs), allowing us to filter and smooth in the presence of missing data, while also performing uncertainty-aware multimodal fusion. We derive this method by factorizing the posterior p(z|x) for non-linear state space models, and develop a variational backward-forward algorithm for inference. Because our method handles incompleteness over both time and modalities, it is capable of interpolation, extrapolation, conditional generation, label prediction, and weakly supervised learning of multimodal time series. We demonstrate these capabilities on both synthetic and real-world multimodal data under high levels of data deletion. Our method performs well even with more than 50% missing data, and outperforms existing deep approaches to inference in latent time series.

scholarly journals Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

Biomimetics ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Michelle Gutiérrez-Muñoz ◽  
Astryd González-Salazar ◽  
Marvin Coto-Jiménez
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Computational Cost ◽  
Real Life ◽  
Fixed Number ◽  
Training Procedure ◽  
Statistical Validation ◽  
Significant Drop ◽  
Training Time ◽  
Important Solution

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality.

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