scholarly journals Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 36
Author(s):  
Weiping Zheng ◽  
Zhenyao Mo ◽  
Gansen Zhao
Keyword(s):  
Multitask Learning ◽  
Classification Error ◽  
Learning Method ◽  
Scene Classification ◽  
Fine Grained ◽  
Learning Framework ◽  
Self Organized ◽  
Similarity Relations ◽  
Hierarchy Construction ◽  
Better Than

Acoustic scene classification (ASC) tries to inference information about the environment using audio segments. The inter-class similarity is a significant issue in ASC as acoustic scenes with different labels may sound quite similar. In this paper, the similarity relations amongst scenes are correlated with the classification error. A class hierarchy construction method by using classification error is then proposed and integrated into a multitask learning framework. The experiments have shown that the proposed multitask learning method improves the performance of ASC. On the TUT Acoustic Scene 2017 dataset, we obtain the ensemble fine-grained accuracy of 81.4%, which is better than the state-of-the-art. By using multitask learning, the basic Convolutional Neural Network (CNN) model can be improved by about 2.0 to 3.5 percent according to different spectrograms. The coarse category accuracies (for two to six super-classes) range from 77.0% to 96.2% by single models. On the revised version of the LITIS Rouen dataset, we achieve the ensemble fine-grained accuracy of 83.9%. The multitask learning models obtain an improvement of 1.6% to 1.8% compared to their basic models. The coarse category accuracies range from 94.9% to 97.9% for two to six super-classes with single models.

scholarly journals When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on a Multitask Learning Framework

2020 ◽  
Vol 12 (20) ◽  
pp. 3276 ◽  
Author(s):  
Zhicheng Zhao ◽  
Ze Luo ◽  
Jian Li ◽  
Can Chen ◽  
Yingchao Piao
Keyword(s):  
Remote Sensing ◽  
Supervised Learning ◽  
Remote Sensing Image ◽  
Multitask Learning ◽  
Natural Image ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Learning Framework ◽  
Continuous Progress ◽  
Image Datasets

In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images. Compared with natural image datasets, however, the acquisition of remote sensing scene images is more difficult, and consequently the scale of remote sensing image datasets is generally small. In addition, many problems related to small objects and complex backgrounds arise in remote sensing image scenes, presenting great challenges for CNN-based recognition methods. In this article, to improve the feature extraction ability and generalization ability of such models and to enable better use of the information contained in the original remote sensing images, we introduce a multitask learning framework which combines the tasks of self-supervised learning and scene classification. Unlike previous multitask methods, we adopt a new mixup loss strategy to combine the two tasks with dynamic weight. The proposed multitask learning framework empowers a deep neural network to learn more discriminative features without increasing the amounts of parameters. Comprehensive experiments were conducted on four representative remote sensing scene classification datasets. We achieved state-of-the-art performance, with average accuracies of 94.21%, 96.89%, 99.11%, and 98.98% on the NWPU, AID, UC Merced, and WHU-RS19 datasets, respectively. The experimental results and visualizations show that our proposed method can learn more discriminative features and simultaneously encode orientation information while effectively improving the accuracy of remote sensing scene classification.

scholarly journals AutoSense Model for Word Sense Induction

2019 ◽  
Vol 33 ◽  
pp. 6212-6219 ◽  
Author(s):  
Reinald Kim Amplayo ◽  
Seung-won Hwang ◽  
Min Song
Keyword(s):  
Latent Variable ◽  
Word Sense ◽  
Name Disambiguation ◽  
Variable Model ◽  
Fine Grained ◽  
Word Sense Induction ◽  
Author Name Disambiguation ◽  
Competing Models ◽  
Word Senses ◽  
Better Than

Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the stateof-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.

Semi-Supervised Aspect-Based Sentiment Analysis for Case-Related Microblog Reviews Using Case Knowledge Graph Embedding

2021 ◽  
pp. 2050012
Author(s):  
Peilian Zhao ◽  
Cunli Mao ◽  
Zhengtao Yu
Keyword(s):  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Opinion Mining ◽  
Data Augmentation ◽  
Training Data ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Learning Framework ◽  
Proposed Model ◽  
Real World Applications

Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly.

scholarly journals Mapping the Space of Chemical Reactions using Attention-Based Neural Networks

2020 ◽  
Author(s):  
Philippe Schwaller ◽  
Daniel Probst ◽  
Alain C. Vaucher ◽  
Vishnu H Nair ◽  
David Kreutter ◽  
...  
Keyword(s):  
Neural Networks ◽  
Reaction Center ◽  
Chemical Reactions ◽  
Chemical Reaction ◽  
Classification Accuracy ◽  
Similarity Searching ◽  
Reaction Space ◽  
Fine Grained ◽  
Visual Clustering ◽  
Better Than

<div><div><div><p>Organic reactions are usually assigned to classes grouping reactions with similar reagents and mechanisms. Reaction classes facilitate communication of complex concepts and efficient navigation through chemical reaction space. However, the classification process is a tedious task, requiring the identification of the corresponding reaction class template via annotation of the number of molecules in the reactions, the reaction center and the distinction between reactants and reagents. In this work, we show that transformer-based models can infer reaction classes from non-annotated, simple text-based representations of chemical reactions. Our best model reaches a classification accuracy of 98.2%. We also show that the learned representations can be used as reaction fingerprints which capture fine-grained differences between reaction classes better than traditional reaction fingerprints. The unprecedented insights into chemical reaction space enabled by our learned fingerprints is illustrated by an interactive reaction atlas providing visual clustering and similarity searching. </p><p><br></p><p>Code: https://github.com/rxn4chemistry/rxnfp</p><p>Tutorials: https://rxn4chemistry.github.io/rxnfp/</p><p>Interactive reaction atlas: https://rxn4chemistry.github.io/rxnfp//tmaps/tmap_ft_10k.html</p></div></div></div>

scholarly journals Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 290
Author(s):  
Kai Ma ◽  
Ming-Jun Nie ◽  
Sen Lin ◽  
Jianlei Kong ◽  
Cheng-Cai Yang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Insect Pests ◽  
Accurate Identification ◽  
Technical Application ◽  
Network Mining ◽  
Fine Grained ◽  
Feature Expression ◽  
The Internet Of Things ◽  
Different Levels ◽  
Better Than

Accurate identification of insect pests is the key to improve crop yield and ensure quality and safety. However, under the influence of environmental conditions, the same kind of pests show obvious differences in intraclass representation, while the different kinds of pests show slight similarities. The traditional methods have been difficult to deal with fine-grained identification of pests, and their practical deployment is low. In order to solve this problem, this paper uses a variety of equipment terminals in the agricultural Internet of Things to obtain a large number of pest images and proposes a fine-grained identification model of pests based on probability fusion network FPNT. This model designs a fine-grained feature extractor based on an optimized CSPNet backbone network, mining different levels of local feature expression that can distinguish subtle differences. After the integration of the NetVLAD aggregation layer, the gated probability fusion layer gives full play to the advantages of information complementarity and confidence coupling of multi-model fusion. The comparison test shows that the PFNT model has an average recognition accuracy of 93.18% for all kinds of pests, and its performance is better than other deep-learning methods, with the average processing time drop to 61 ms, which can meet the needs of fine-grained image recognition of pests in the Internet of Things in agricultural and forestry practice, and provide technical application reference for intelligent early warning and prevention of pests.

Identifying Genetic related Neuroimaging Biomarkers of Alzheimer's Disease via Diagnosis-Guided Group Sparse Multitask Learning Method

2021 ◽  
Vol 16 ◽  
Author(s):  
Anshi Lin ◽  
Wei Kong ◽  
Shuaiqun Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Regression Model ◽  
Multivariate Regression ◽  
Prior Information ◽  
Diagnostic Information ◽  
Multitask Learning ◽  
Learning Method ◽  
Pathological Characteristics ◽  
Different Types

Background: Advances in brain imaging and high-throughput genotyping techniques have provided new methods for studying the effects of genetic variation on brain structure and function. Traditionally, a variety of prior information has been added into the multivariate regression method for single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) to improve the accuracy of prediction. In previous studies, brain regions of interest (ROIs) with different types of pathological characteristics (Alzheimer's Disease/Mild Cognitive Impairment/healthy control) can only be randomly dispersed in test cases, greatly limiting the prediction ability of the regression model and failing to obtain optimal global results. Objective: This study proposes a multivariate regression model informed by prior diagnostic information to overcome this limitation. Method: In the prediction model, we first consider traditional prior information and then design a new regularization form to integrate the diagnostic information of different sample ROIs into the model. Results: Experiments demonstrated that this method greatly improves the prediction accuracy of the model compared to other methods and selects a batch of promising pathogenic SNP loci. Conclusion: Taking into account that ROIs with different types of pathological characteristics can be employed as prior information, we propose a new method (Diagnosis-Guided Group Sparse Multitask Learning Method) that improves the ability to predict disease-related quantitative feature sites and select genetic feature factors, applying this model to research on the pathogenesis of Alzheimer's disease.

An Unsupervised Aspect-Aware Recommendation Model with Explanation Text Generation

2022 ◽  
Vol 40 (3) ◽  
pp. 1-29
Author(s):  
Peijie Sun ◽  
Le Wu ◽  
Kun Zhang ◽  
Yu Su ◽  
Meng Wang
Keyword(s):  
Data Privacy ◽  
Auxiliary Information ◽  
Generation Process ◽  
Text Generation ◽  
Generation Task ◽  
Auxiliary Data ◽  
Fine Grained ◽  
Aspect Extraction ◽  
Learning Framework ◽  
Real World Datasets

Review based recommendation utilizes both users’ rating records and the associated reviews for recommendation. Recently, with the rapid demand for explanations of recommendation results, reviews are used to train the encoder–decoder models for explanation text generation. As most of the reviews are general text without detailed evaluation, some researchers leveraged auxiliary information of users or items to enrich the generated explanation text. Nevertheless, the auxiliary data is not available in most scenarios and may suffer from data privacy problems. In this article, we argue that the reviews contain abundant semantic information to express the users’ feelings for various aspects of items, while these information are not fully explored in current explanation text generation task. To this end, we study how to generate more fine-grained explanation text in review based recommendation without any auxiliary data. Though the idea is simple, it is non-trivial since the aspect is hidden and unlabeled. Besides, it is also very challenging to inject aspect information for generating explanation text with noisy review input. To solve these challenges, we first leverage an advanced unsupervised neural aspect extraction model to learn the aspect-aware representation of each review sentence. Thus, users and items can be represented in the aspect space based on their historical associated reviews. After that, we detail how to better predict ratings and generate explanation text with the user and item representations in the aspect space. We further dynamically assign review sentences which contain larger proportion of aspect words with larger weights to control the text generation process, and jointly optimize rating prediction accuracy and explanation text generation quality with a multi-task learning framework. Finally, extensive experimental results on three real-world datasets demonstrate the superiority of our proposed model for both recommendation accuracy and explainability.

Pengaruh Metode Pembelajaran dan Kecerdasan Linguistik Terhadap Kemampuan Bahasa Arab Siswa Kelas II SDIT Anak Sholeh

2016 ◽  
Vol 18 (2) ◽  
pp. 77
Author(s):  
Ayif Royidi
Keyword(s):  
Hypothesis Testing ◽  
Experimental Methods ◽  
Arabic Language ◽  
Learning Ability ◽  
Learning Method ◽  
Tukey Test ◽  
Student Ability ◽  
And Linguistics ◽  
Better Than ◽  
Arabic Students

The purpose of this research is to find out the influence of Three-ber method and linguistic intelligence implementation on Arabic students learning Ability of Arabic language. The research is comparative quantitative with the experimental methods and 2 x 2 by level design .A test is the instrument, used to gather the linguistics data intelligence and student Ability of Arabic language. ANAVA is applied for hypothesis testing two lanes continued to Tukey Test .The results of the study (1) .The Students who learn Arabic trough Three-ber method achieve better than the students who is being taught conventionally(2) There is an interaction between learning method and linguistics intelligence(3)The students whose high linguistic intelligence and learn using Three-ber method achieve better than the students who learn conventionally(4). Arabic students learning Ability of Arabic language whose low linguistic intelligence and learn using Three-ber method achieve lower than student who is being taught conventionally.

scholarly journals Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 298 ◽  
Author(s):  
Linyi Liu ◽  
Yingying Dong ◽  
Wenjiang Huang ◽  
Xiaoping Du ◽  
Juhua Luo ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Transfer Learning ◽  
Kappa Coefficient ◽  
Learning Method ◽  
Training Process ◽  
Wheat Powdery Mildew ◽  
Field Samples ◽  
Monitoring Accuracy ◽  
Sample Data ◽  
Better Than

In order to monitor the prevalence of wheat powdery mildew, current methods require sufficient sample data to obtain results with higher accuracy and stable validation. However, it is difficult to collect data on wheat powdery mildew in some regions, and this limitation in sampling restricts the accuracy of monitoring regional prevalence of the disease. In this study, an instance-based transfer learning method, i.e., TrAdaBoost, was applied to improve the monitoring accuracy with limited field samples by using auxiliary samples from another region. By taking into account the representativeness of contributions of auxiliary samples to adjust the weight placed on auxiliary samples, an optimized TrAdaBoost algorithm, named OpTrAdaBoost, was generated to map regional wheat powdery mildew. The algorithm conducts this by: (1) producing uncertainty associated with each prediction based on the similarities, and calculating the representativeness contribution of all auxiliary samples by taking into account the overall uncertainty of the wheat powdery mildew map; (2) calculating the errors of the weak learners during the training process and using boosting to filter out the unreliable auxiliary samples by adjusting the weights of auxiliary samples; (3) combining all weak learners according to the weights of training instances to build a strong learner to classify disease severity. OpTrAdaBoost was tested using a dataset with 39 study area samples and 106 auxiliary samples. The overall monitoring accuracy was 82%, and the kappa coefficient was 0.72. Moreover, OpTrAdaBoost performed better than other algorithms that are commonly used to monitor wheat powdery mildew at the regional level. Experimental results demonstrated that OpTrAdaBoost was effective in improving the accuracy of monitoring wheat powdery mildew using limited field samples.

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