scholarly journals Capturing the Style of Fake News

2020 ◽  
Vol 34 (01) ◽  
pp. 490-497
Author(s):  
Piotr Przybyla
Keyword(s):  
Neural Network ◽  
High Accuracy ◽  
General Purpose ◽  
Training Data ◽  
Fake News ◽  
Realistic Evaluation ◽  
News Websites ◽  
Text Classifiers ◽  
Automatic Methods ◽  
Online Sources

In this study we aim to explore automatic methods that can detect online documents of low credibility, especially fake news, based on the style they are written in. We show that general-purpose text classifiers, despite seemingly good performance when evaluated simplistically, in fact overfit to sources of documents in training data. In order to achieve a truly style-based prediction, we gather a corpus of 103,219 documents from 223 online sources labelled by media experts, devise realistic evaluation scenarios and design two new classifiers: a neural network and a model based on stylometric features. The evaluation shows that the proposed classifiers maintain high accuracy in case of documents on previously unseen topics (e.g. new events) and from previously unseen sources (e.g. emerging news websites). An analysis of the stylometric model indicates it indeed focuses on sensational and affective vocabulary, known to be typical for fake news.

scholarly journals Robust Mouse Tracking in Complex Environments using Neural Networks

2018 ◽  
Author(s):  
Brian Q. Geuther ◽  
Sean P. Deats ◽  
Kai J. Fox ◽  
Steve A. Murray ◽  
Robert E. Braun ◽  
...  
Keyword(s):  
Neural Network ◽  
Environmental Conditions ◽  
State Of The Art ◽  
High Accuracy ◽  
General Purpose ◽  
Training Data ◽  
Network Architectures ◽  
Complex Environments ◽  
Behavioral Experiments ◽  
Modern Machine

AbstractThe ability to track animals accurately is critical for behavioral experiments. For video-based assays, this is often accomplished by manipulating environmental conditions to increase contrast between the animal and the background, in order to achieve proper foreground/background detection (segmentation). However, as behavioral paradigms become more sophisticated with ethologically relevant environments, the approach of modifying environmental conditions offers diminishing returns, particularly for scalable experiments. Currently, there is a need for methods to monitor behaviors over long periods of time, under dynamic environmental conditions, and in animals that are genetically and behaviorally heterogeneous. To address this need, we developed a state-of-the-art neural network-based tracker for mice, using modern machine vision techniques. We test three different neural network architectures to determine their performance on genetically diverse mice under varying environmental conditions. We find that an encoder-decoder segmentation neural network achieves high accuracy and speed with minimal training data. Furthermore, we provide a labeling interface, labeled training data, tuned hyperparameters, and a pre-trained network for the mouse behavior and neuroscience communities. This general-purpose neural network tracker can be easily extended to other experimental paradigms and even to other animals, through transfer learning, thus providing a robust, generalizable solution for biobehavioral research.

scholarly journals Action detection using a neural network elucidates the genetics of mouse grooming behavior

2020 ◽  
Author(s):  
Brian Q. Geuther ◽  
Asaf Peer ◽  
Hao He ◽  
Gautam Sabnis ◽  
Vivek M. Philip ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Stereotyped Behavior ◽  
General Purpose ◽  
Training Data ◽  
Mouse Strains ◽  
Human Observer ◽  
Grooming Behavior ◽  
Action Detection ◽  
Visual Diversity

AbstractAutomated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming, a prototypical stereotyped behavior, is often used as an endophenotype in psychiatric genetics. Using mouse grooming behavior as an example, we develop a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operate across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We survey grooming behavior in the open field in 2500 mice across 62 strains, determine its heritable components, conduct GWAS to outline its genetic architecture, and perform PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of mechanisms underlying these behaviors.

scholarly journals A Novel Convolutional Neural Network for Classifying Indian Coins by Denomination

2021 ◽  
Author(s):  
Yash Chauhan ◽  
Prateek Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Academic Research ◽  
High Volume ◽  
Classification Problem ◽  
General Purpose ◽  
Training Data ◽  
Slot Machines ◽  
Validation Data ◽  
Starting Point

Coins recognition systems have humungous applications from vending and slot machines to banking and management firms which directly translate to a high volume of research regarding the development of methods for such classification. In recent years, academic research has shifted towards a computer vision approach for sorting coins due to the advancement in the field of deep learning. However, most of the documented work utilizes what is known as ‘Transfer Learning’ in which we reuse a pre-trained model of a fixed architecture as a starting point for our training. While such an approach saves us a lot of time and effort, the generic nature of the pre-trained model can often become a bottleneck for performance on a specialized problem such as coin classification. This study develops a convolutional neural network (CNN) model from scratch and tests it against a widely-used general-purpose architecture known as Googlenet. We have shown in this study by comparing the performance of our model with that of Googlenet (documented in various previous studies) that a more straightforward and specialized architecture is more optimal than a more complex general architecture for the coin classification problem. The model developed in this study is trained and tested on 720 and 180 images of Indian coins of different denominations, respectively. The final accuracy gained by the model is 91.62% on the training data, while the accuracy is 90.55% on the validation data.

Convolutional Neural Network Considering the Effects of Noise for Bearing Fault Diagnosis

2020 ◽  
Author(s):  
Ilyoung Han ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Taeyun Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
High Accuracy ◽  
Training Data ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
System Noise ◽  
Novel Method ◽  
Maintenance Activities

Abstract Convolutional Neural Network (CNN) is, in general, good at finding principal components of data. However, the characteristic components of the signals could often be obscured by system noise. Therefore, even though the CNN model is well-trained and predict with high accuracy, it may detect only the primary patterns of data which could be formed by system noise. They are, in fact, highly vulnerable to maintenance activities such as reassembly. In other words, CNN models could misdiagnose even with excellent performances. In this study, a novel method that combines the classification using CNN with the data preprocessing is proposed for bearing fault diagnosis. The proposed method is demonstrated by the following steps. First, training data is preprocessed so that the noise and the fault signature of the bearings are separated. Then, CNN models are developed and trained to learn significant features containing information of defects. Lastly, the CNN models are examined and validated whether they learn and extract the meaningful features or not.

An Investigation of Multilingual TDNN-BLSTM Acoustic Modeling for Hindi Speech Recognition

2021 ◽  
Vol 11 ◽  
Author(s):  
Ankit Kumar ◽  
Rajesh Kumar Aggarwal
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
High Accuracy ◽  
Training Data ◽  
Acoustic Modeling ◽  
Training Dataset ◽  
Acoustic Model ◽  
Indian Languages ◽  
Acoustic Models ◽  
Asr System

Background: In India, thousands of languages or dialects are in use. Most Indian dialects are low asset dialects. A well-performing Automatic Speech Recognition (ASR) system for Indian languages is unavailable due to a lack of resources. Hindi is one of them as large vocabulary Hindi speech datasets are not freely available. We have only a few hours of transcribed Hindi speech dataset. There is a lot of time and money involved in creating a well-transcribed speech dataset. Thus, developing a real-time ASR system with a few hours of the training dataset is the most challenging task. The different techniques like data augmentation, semi-supervised training, multilingual architecture, and transfer learning, have been reported in the past to tackle the fewer speech data issues. In this paper, we examine the effect of multilingual acoustic modeling in ASR systems for the Hindi language. Objective: This article’s objective is to develop a high accuracy Hindi ASR system with a reasonable computational load and high accuracy using a few hours of training data. Method: To achieve this goal we used Multilingual training with Time Delay Neural Network- Bidirectional Long Short Term Memory (TDNN-BLSTM) acoustic modeling. Multilingual acoustic modeling has significantly improved the ASR system's performance for low and limited resource languages. The common practice is to train the acoustic model by merging data from similar languages. In this work, we use three Indian languages, namely Hindi, Marathi, and Bengali. Hindi with 2.5 hours of training data and Marathi with 5.5 hours of training data and Bengali with 28.5 hours of transcribed data, was used in this work to train the proposed model. Results: The Kaldi toolkit was used to perform all the experiments. The paper is investigated over three main points. First, we present the monolingual ASR system using various Neural Network (NN) based acoustic models. Second, we show that Recurrent Neural Network (RNN) language modeling helps to improve the ASR performance further. Finally, we show that a multilingual ASR system significantly reduces the Word Error Rate (WER) (absolute 2% WER reduction for Hindi and 3% for the Marathi language). In all the three languages, the proposed TDNN-BLSTM-A multilingual acoustic models help to get the lowest WER. Conclusion: The multilingual hybrid TDNN-BLSTM-A architecture shows a 13.67% relative improvement over the monolingual Hindi ASR system. The best WER of 8.65% was recorded for Hindi ASR. For Marathi and Bengali, the proposed TDNN-BLSTM-A acoustic model reports the best WER of 30.40% and 10.85%.

Efficient Uncertainty Quantification and Data Assimilation via Theory-Guided Convolutional Neural Network

SPE Journal ◽  
1900 ◽  
pp. 1-29
Author(s):  
Nanzhe Wang ◽  
Haibin Chang ◽  
Dongxiao Zhang
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Convolutional Neural Network ◽  
Uncertainty Quantification ◽  
Reservoir Simulation ◽  
High Accuracy ◽  
Training Data ◽  
Flow Problems ◽  
Stochastic Parameter ◽  
Reservoir Flow

Summary A deep learning framework, called the theory-guided convolutional neural network (TgCNN), is developed for efficient uncertainty quantification and data assimilation of reservoir flow with uncertain model parameters. The performance of the proposed framework in terms of accuracy and computational efficiency is assessed by comparing it to classical approaches in reservoir simulation. The essence of the TgCNN is to take into consideration both the available data and underlying physical/engineering principles. The stochastic parameter fields and time matrix comprise the input of the convolutional neural network (CNN), whereas the output is the quantity of interest (e.g., pressure, saturation, etc.). The TgCNN is trained with available data while being simultaneously guided by theory (e.g., governing equations, other physical constraints, and engineering controls) of the underlying problem. The trained TgCNN serves as a surrogate that can predict the solutions of the reservoir flow problem with new stochastic parameter fields. Such approaches, including the Monte Carlo (MC) method and the iterative ensemble smoother (IES) method, can then be used to perform uncertainty quantification and data assimilation efficiently based on the TgCNN surrogate, respectively. The proposed paradigm is evaluated with dynamic reservoir flow problems. The results demonstrate that the TgCNN surrogate can be built with a relatively small number of training data and even in a label-free manner, which can approximate the relationship between model inputs and outputs with high accuracy. The TgCNN surrogate is then used for uncertainty quantification and data assimilation of reservoir flow problems, which achieves satisfactory accuracy and higher efficiency compared with state-of-the-art approaches. The novelty of the work lies in the ability to incorporate physical laws and domain knowledge into the deep learning process and achieve high accuracy with limited training data. The trained surrogate can significantly improve the efficiency of uncertainty quantification and data assimilation processes. NOTE: This paper is published as part of the 2021 Reservoir Simulation Conference Special Issue.

Data assimilation of subsurface flow via iterative ensemble smoother and physics-informed neural network

2020 ◽  
Author(s):  
Nanzhe Wang ◽  
Haibin Chang
Keyword(s):  
Neural Network ◽  
Data Assimilation ◽  
Surrogate Model ◽  
Flow Problem ◽  
Subsurface Flow ◽  
High Accuracy ◽  
Training Data ◽  
Model Parameters ◽  
Ensemble Smoother ◽  
Iterative Ensemble Smoother

<p>Subsurface flow problems usually involve some degree of uncertainty. For reducing the uncertainty of subsurface flow prediction, data assimilation is usually necessary. Data assimilation is time consuming. In order to improve the efficiency of data assimilation, surrogate model of subsurface flow problem may be utilized. In this work, a physics-informed neural network (PINN) based surrogate model is proposed for subsurface flow with uncertain model parameters. Training data generated by solving stochastic partial differential equations (SPDEs) are utilized to train the neural network. Besides the data mismatch term, the term that incorporates physics laws is added in the loss function. The trained neural network can predict the solutions of the subsurface flow problem with new stochastic parameters, which can serve as a surrogate for approximating the relationship between model output and model input. By incorporating physics laws, PINN can achieve high accuracy. Then an iterative ensemble smoother (ES) is introduced to implement the data assimilation task based on the PINN surrogate. Several subsurface flow cases are designed to test the performance of the proposed paradigm. The results show that the PINN surrogate can significantly improve the efficiency of data assimilation task while maintaining a high accuracy.</p>

scholarly journals Klasifikasi Data Berat Bayi Lahir Menggunakan Weighted Probabilistic Neural Network (WPNN) (Studi Kasus di Rumah Sakit Islam Sultan Agung Semarang)

2017 ◽  
Vol 10 (1) ◽  
pp. 61
Author(s):  
Hasbi Yasin ◽  
Dwi Ispriyansti
Keyword(s):  
Neural Network ◽  
Low Birthweight ◽  
Probabilistic Neural Network ◽  
High Accuracy ◽  
Training Data ◽  
World Health ◽  
Model Accuracy ◽  
Health Organization ◽  
Very High ◽  
Length Of Gestation

Low Birthweight (LBW) is one of the causes of infant mortality. Birthweight is the weight of babies who weighed within one hour after birth. Low birthweight has been defined by the World Health Organization (WHO) as weight at birth of less than 2,500 grams (5.5 pounds). There are several factors that influence the BWI such as maternal age, length of gestation, body weight, height, blood pressure, hemoglobin and parity. This study uses a Weighted Probabilistic Neural Network (WPNN) to classify the birthweight in RSI Sultan Agung Semarang based on these factors. The results showed that the birthweight classification using WPNN models have a very high accuracy. This is shown by the model accuracy of 98.75% using the training data and 94.44% using the testing data.Keywords:Birthweight, Classification, LBW, WPNN.

scholarly journals Action detection using a neural network elucidates the genetics of mouse grooming behavior

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Brian Q Geuther ◽  
Asaf Peer ◽  
Hao He ◽  
Gautam Sabnis ◽  
Vivek M Philip ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Stereotyped Behavior ◽  
General Purpose ◽  
Training Data ◽  
Mouse Strains ◽  
Human Observer ◽  
Grooming Behavior ◽  
Action Detection ◽  
Visual Diversity

Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endophenotype in psychiatric genetics. Here, we used mouse grooming behavior as an example and developed a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operating across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We surveyed grooming behavior in the open field in 2457 mice across 62 strains, determined its heritable components, conducted GWAS to outline its genetic architecture, and performed PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of behavioral mechanisms.

Semi-supervised Multi-task Network For Image Aesthetic Assessment

2020 ◽  
Vol 2020 (8) ◽  
pp. 188-1-188-7
Author(s):  
Xiaoyu Xiang ◽  
Yang Cheng ◽  
Jianhang Chen ◽  
Qian Lin ◽  
Jan Allebach
Keyword(s):  
Neural Network ◽  
Semantic Content ◽  
Training Data ◽  
Score Distribution ◽  
Generalization Ability ◽  
Subjective Preference ◽  
Ranking Tasks

Image aesthetic assessment has always been regarded as a challenging task because of the variability of subjective preference. Besides, the assessment of a photo is also related to its style, semantic content, etc. Conventionally, the estimations of aesthetic score and style for an image are treated as separate problems. In this paper, we explore the inter-relatedness between the aesthetics and image style, and design a neural network that can jointly categorize image by styles and give an aesthetic score distribution. To this end, we propose a multi-task network (MTNet) with an aesthetic column serving as a score predictor and a style column serving as a style classifier. The angular-softmax loss is applied in training primary style classifiers to maximize the margin among classes in single-label training data; the semi-supervised method is applied to improve the network’s generalization ability iteratively. We combine the regression loss and classification loss in training aesthetic score. Experiments on the AVA dataset show the superiority of our network in both image attributes classification and aesthetic ranking tasks.

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