scholarly journals Deep Learning-Based Fault Localization with Contextual Information

2017 ◽  
Vol E100.D (12) ◽  
pp. 3027-3031 ◽  
Author(s):  
Zhuo ZHANG ◽  
Yan LEI ◽  
Qingping TAN ◽  
Xiaoguang MAO ◽  
Ping ZENG ◽  
...  
Keyword(s):  
Deep Learning ◽  
Contextual Information ◽  
Fault Localization

scholarly journals Structured (De)composable Representations Trained with Neural Networks

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 79
Author(s):  
Graham Spinks ◽  
Marie-Francine Moens
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Contextual Information ◽  
Learning To Learn ◽  
Class Label ◽  
Impact Performance ◽  
Novel Technique ◽  
Language Data ◽  
Concept Classes ◽  
Generic Representation

This paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to-end deep learning to learn structured and composable representations from input images and discrete labels. The obtained representations are based on distance estimates between the distributions given by the class label and those given by contextual information, which are modeled as environments. We prove that the representations have a clear structure allowing decomposing the representation into factors that represent classes and environments. We evaluate our novel technique on classification and retrieval tasks involving different modalities (visual and language data). In various experiments, we show how the representations can be compressed and how different hyperparameters impact performance.

Automatic offshore oil slick detection based on deep learning using SAR data and contextual information

2021 ◽  
Author(s):  
Emna Amri ◽  
Hermann Courteille ◽  
Alexandre Benoit ◽  
Philippe Bolon ◽  
Dominique Dubucq ◽  
...  
Keyword(s):  
Deep Learning ◽  
Contextual Information ◽  
Oil Slick ◽  
Sar Data ◽  
Offshore Oil

scholarly journals One for All: Neural Joint Modeling of Entities and Events

2019 ◽  
Vol 33 ◽  
pp. 6851-6858 ◽  
Author(s):  
Trung Minh Nguyen ◽  
Thien Huu Nguyen
Keyword(s):  
Deep Learning ◽  
Recent Work ◽  
State Of The Art ◽  
Contextual Information ◽  
Joint Modeling ◽  
Event Extraction ◽  
Event Trigger ◽  
The Individual ◽  
Novel Model ◽  
Argument Roles

The previous work for event extraction has mainly focused on the predictions for event triggers and argument roles, treating entity mentions as being provided by human annotators. This is unrealistic as entity mentions are usually predicted by some existing toolkits whose errors might be propagated to the event trigger and argument role recognition. Few of the recent work has addressed this problem by jointly predicting entity mentions, event triggers and arguments. However, such work is limited to using discrete engineering features to represent contextual information for the individual tasks and their interactions. In this work, we propose a novel model to jointly perform predictions for entity mentions, event triggers and arguments based on the shared hidden representations from deep learning. The experiments demonstrate the benefits of the proposed method, leading to the state-of-the-art performance for event extraction.

scholarly journals A DEEP LEARNING APPROACH TO SOIL MOISTURE ESTIMATION WITH GNSS-R

2021 ◽  
Author(s):  
Max Roberts ◽  
Ian Colwell ◽  
Clara Chew ◽  
Rashmi Shah ◽  
Stephen Lowe
Keyword(s):  
Soil Moisture ◽  
Deep Learning ◽  
Contextual Information ◽  
Additional Information ◽  
Reflection Measurement ◽  
Target Values ◽  
Global Soil ◽  
Data Driven Approach ◽  
Soil Measurements ◽  
Peak Value

GNSS reflection measurements in the form of delay-Doppler maps (DDM) from the CYGNSS constellation can be used to complement soil measurements from the SMAP Mission, which has a revisit rate too slow for some hydrological/meteorological studies. The standard approach, which only considers the peak value of the DDM, is subject to a significant amount of uncertainty due to the fact that the peak value of the DDM is not only affected by soil moisture, but also complex topography, inundation, and overlying vegetation. We hypothesize that information from the entire 2D DDM could help decrease uncertainty under these conditions. The application of deep learning based techniques has the potential to extract additional information from the entire DDM, while simultaneously allowing for incorporation of additional contextual information from external datasets. This work explored the data-driven approach of convolutional neural networks (CNNs) to determine complex relationships between the reflection measurement and surface parameters, providing a mechanism to achieve improved global soil moisture estimates. A CNN was trained on CYGNSS DDMs and contextual ancillary datasets as inputs, with aligned SMAP soil moisture values as the targets. Data was aggregated into training sets, and a CNN was developed to process them. Predictions from the CNN were studied using an unbiased subset of samples, showing strong correlation with the SMAP target values. With this network, a soil moisture product was generated using DDMs from 2018 which is generally comparable to existing global soil moisture products, but shows potential advantages in spatial resolution and coverage over regions where SMAP does not perform well.

scholarly journals Challenges of Sarcasm Detection for Social Network : A Literature Review

2020 ◽  
Vol 8 (2) ◽  
pp. 169
Author(s):  
Afiyati Afiyati ◽  
Azhari Azhari ◽  
Anny Kartika Sari ◽  
Abdul Karim
Keyword(s):  
Social Media ◽  
Feature Extraction ◽  
Deep Learning ◽  
Contextual Information ◽  
Unstructured Data ◽  
Learning Models ◽  
Critical Aspect ◽  
Hybrid Approaches ◽  
Data Problem ◽  
The Future

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.

scholarly journals Advancing PICO element detection in biomedical text via deep neural networks

2020 ◽  
Vol 36 (12) ◽  
pp. 3856-3862
Author(s):  
Di Jin ◽  
Peter Szolovits
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Learning Model ◽  
Detection Accuracy ◽  
Clinical Question ◽  
Specific Patient ◽  
Benchmark Datasets ◽  
Deep Learning Model

Abstract Motivation In evidence-based medicine, defining a clinical question in terms of the specific patient problem aids the physicians to efficiently identify appropriate resources and search for the best available evidence for medical treatment. In order to formulate a well-defined, focused clinical question, a framework called PICO is widely used, which identifies the sentences in a given medical text that belong to the four components typically reported in clinical trials: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). In this work, we propose a novel deep learning model for recognizing PICO elements in biomedical abstracts. Based on the previous state-of-the-art bidirectional long-short-term memory (bi-LSTM) plus conditional random field architecture, we add another layer of bi-LSTM upon the sentence representation vectors so that the contextual information from surrounding sentences can be gathered to help infer the interpretation of the current one. In addition, we propose two methods to further generalize and improve the model: adversarial training and unsupervised pre-training over large corpora. Results We tested our proposed approach over two benchmark datasets. One is the PubMed-PICO dataset, where our best results outperform the previous best by 5.5%, 7.9% and 5.8% for P, I and O elements in terms of F1 score, respectively. And for the other dataset named NICTA-PIBOSO, the improvements for P/I/O elements are 3.9%, 15.6% and 1.3% in F1 score, respectively. Overall, our proposed deep learning model can obtain unprecedented PICO element detection accuracy while avoiding the need for any manual feature selection. Availability and implementation Code is available at https://github.com/jind11/Deep-PICO-Detection.

scholarly journals Biomedical named entity recognition using deep neural networks with contextual information

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Hyejin Cho ◽  
Hyunju Lee
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Contextual Information ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Approaches ◽  
Short Term ◽  
Term Memory ◽  
Named Entity ◽  
Long Short Term Memory

Abstract Background In biomedical text mining, named entity recognition (NER) is an important task used to extract information from biomedical articles. Previously proposed methods for NER are dictionary- or rule-based methods and machine learning approaches. However, these traditional approaches are heavily reliant on large-scale dictionaries, target-specific rules, or well-constructed corpora. These methods to NER have been superseded by the deep learning-based approach that is independent of hand-crafted features. However, although such methods of NER employ additional conditional random fields (CRF) to capture important correlations between neighboring labels, they often do not incorporate all the contextual information from text into the deep learning layers. Results We propose herein an NER system for biomedical entities by incorporating n-grams with bi-directional long short-term memory (BiLSTM) and CRF; this system is referred to as a contextual long short-term memory networks with CRF (CLSTM). We assess the CLSTM model on three corpora: the disease corpus of the National Center for Biotechnology Information (NCBI), the BioCreative II Gene Mention corpus (GM), and the BioCreative V Chemical Disease Relation corpus (CDR). Our framework was compared with several deep learning approaches, such as BiLSTM, BiLSTM with CRF, GRAM-CNN, and BERT. On the NCBI corpus, our model recorded an F-score of 85.68% for the NER of diseases, showing an improvement of 1.50% over previous methods. Moreover, although BERT used transfer learning by incorporating more than 2.5 billion words, our system showed similar performance with BERT with an F-scores of 81.44% for gene NER on the GM corpus and a outperformed F-score of 86.44% for the NER of chemicals and diseases on the CDR corpus. We conclude that our method significantly improves performance on biomedical NER tasks. Conclusion The proposed approach is robust in recognizing biological entities in text.

scholarly journals Recognizing Human Daily Activity Using Social Media Sensors and Deep Learning

2019 ◽  
Vol 16 (20) ◽  
pp. 3955 ◽  
Author(s):  
Junfang Gong ◽  
Runjia Li ◽  
Hong Yao ◽  
Xiaojun Kang ◽  
Shengwen Li
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Human Activity ◽  
Daily Activity ◽  
Low Cost ◽  
Contextual Information ◽  
Learning Sequence ◽  
Proposed Model ◽  
Application Fields ◽  
Access Data

The human daily activity category represents individual lifestyle and pattern, such as sports and shopping, which reflect personal habits, lifestyle, and preferences and are of great value for human health and many other application fields. Currently, compared to questionnaires, social media as a sensor provides low-cost and easy-to-access data sources, providing new opportunities for obtaining human daily activity category data. However, there are still some challenges to accurately recognizing posts because existing studies ignore contextual information or word order in posts and remain unsatisfactory for capturing the activity semantics of words. To address this problem, we propose a general model for recognizing the human activity category based on deep learning. This model not only describes how to extract a sequence of higher-level word phrase representations in posts based on the deep learning sequence model but also how to integrate temporal information and external knowledge to capture the activity semantics in posts. Considering that no benchmark dataset is available in such studies, we built a dataset that was used for training and evaluating the model. The experimental results show that the proposed model significantly improves the accuracy of recognizing the human activity category compared with traditional classification methods.

Quantify pixel-level detection of dam surface crack using deep learning

2022 ◽  
Author(s):  
Bo Chen ◽  
Hua Zhang ◽  
Yonglong Li ◽  
Shuang Wang ◽  
Huaifang Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Surface Crack ◽  
Crack Detection ◽  
State Of The Art ◽  
Contextual Information ◽  
Semantic Segmentation ◽  
Quantitative Information ◽  
Cross Entropy ◽  
Detection Methods ◽  
Water Conservancy

Abstract An increasing number of detection methods based on computer vision are applied to detect cracks in water conservancy infrastructure. However, most studies directly use existing feature extraction networks to extract cracks information, which are proposed for open-source datasets. As the cracks distribution and pixel features are different from these data, the extracted cracks information is incomplete. In this paper, a deep learning-based network for dam surface crack detection is proposed, which mainly addresses the semantic segmentation of cracks on the dam surface. Particularly, we design a shallow encoding network to extract features of crack images based on the statistical analysis of cracks. Further, to enhance the relevance of contextual information, we introduce an attention module into the decoding network. During the training, we use the sum of Cross-Entropy and Dice Loss as the loss function to overcome data imbalance. The quantitative information of cracks is extracted by the imaging principle after using morphological algorithms to extract the morphological features of the predicted result. We built a manual annotation dataset containing 1577 images to verify the effectiveness of the proposed method. This method achieves the state-of-the-art performance on our dataset. Specifically, the precision, recall, IoU, F1_measure, and accuracy achieve 90.81%, 81.54%, 75.23%, 85.93%, 99.76%, respectively. And the quantization error of cracks is less than 4%.

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