scholarly journals Revisiting Text Guide, a Truncation Method for Long Text Classification

2021 ◽  
Vol 11 (18) ◽  
pp. 8554
Author(s):  
Krzysztof Fiok ◽  
Waldemar Karwowski ◽  
Edgar Gutierrez ◽  
Mohammad Reza Davahli ◽  
Maciej Wilamowski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Computational Cost ◽  
Attention Mechanism ◽  
Common Method ◽  
Improve Performance ◽  
Truncation Method ◽  
High Computational Cost ◽  
Naive Approach

The quality of text classification has greatly improved with the introduction of deep learning, and more recently, models using attention mechanism. However, to address the problem of classifying text instances that are longer than the length limit adopted by most of the best performing transformer models, the most common method is to naively truncate the text so that it meets the model limit. Researchers have proposed other approaches, but they do not appear to be popular, because of their high computational cost and implementation complexity. Recently, another method called Text Guide has been proposed, which allows for text truncation that outperforms the naive approach and simultaneously is less complex and costly than earlier proposed solutions. Our study revisits Text Guide by testing the influence of certain modifications on the method’s performance. We found that some aspects of the method can be altered to further improve performance and confirmed several assumptions regarding the dependence of the method’s quality on certain factors.

Using Structural Similarity to Classify Tests in Mutation Testing

2013 ◽  
Vol 378 ◽  
pp. 546-551 ◽  
Author(s):  
Joanna Strug ◽  
Barbara Strug
Keyword(s):  
Edit Distance ◽  
Computational Cost ◽  
Structural Similarity ◽  
Mutation Testing ◽  
Effective Technique ◽  
The Cost ◽  
High Computational Cost

Mutation testing is an effective technique for assessing quality of tests provided for a system. However it suffers from high computational cost of executing mutants of the system. In this paper a method of classifying such mutants is proposed. This classification is based on using an edit distance kernel and k-NN classifier. Using the results of this classification it is possible to predict whether a mutant would be detected by tests or not. Thus the application of the approach can help to lower the number of mutants that have to be executed and so also to lower the cost of using the mutation testing.

scholarly journals An Extended Text Combination Classification Model for Short Video Based on Albert

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Liu ◽  
Yue Zhang ◽  
Haidong Hu ◽  
Xiaodong Liu ◽  
Lun Zhang ◽  
...  
Keyword(s):  
Text Classification ◽  
Rapid Development ◽  
Computational Cost ◽  
Attention Mechanism ◽  
Classification Model ◽  
Classification Methods ◽  
Video Classification ◽  
Short Video ◽  
Text Information ◽  
Key Frames

With the rise and rapid development of short video sharing websites, the number of short videos on the Internet has been growing explosively. The organization and classification of short videos have become the basis for the effective use of short videos, which is also a problem faced by major short video platforms. Aiming at the characteristics of complex short video content categories and rich extended text information, this paper uses methods in the text classification field to solve the short video classification problem. Compared with the traditional way of classifying and understanding short video key frames, this method has the characteristics of lower computational cost, more accurate classification results, and easier application. This paper proposes a text classification model based on the attention mechanism of multitext embedding short video extension. The experiment first uses the training language model Albert to extract sentence-level vectors and then uses the attention mechanism to study the text information in various short video extensions in a short video classification weight factor. And this research applied Google’s unsupervised data augmentation (UDA) method based on unsupervised data, creatively combining it with the Chinese knowledge graph, and realized TF-IDF word replacement. During the training process, we introduced a large amount of unlabeled data, which significantly improved the accuracy of model classification. The final series of related experiments is aimed at comparing with the existing short video title classification methods, classification methods based on video key frames, and hybrid methods, and proving that the method proposed in this article is more accurate and robust on the test set.

scholarly journals Automatic calibration of a functional-structural wheat model using an adaptive design and a metamodelling approach

2021 ◽  
Author(s):  
Emmanuelle Blanc ◽  
Jérôme Enjalbert ◽  
Pierre Barbillon
Keyword(s):  
Experimental Data ◽  
Gaussian Process ◽  
Adaptive Design ◽  
Computational Cost ◽  
Simulated Data ◽  
Calibration Method ◽  
Complex Models ◽  
Competition For Light ◽  
High Computational Cost

- Background and Aims Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost. In this paper, we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. - Methods We used a Bayesian calibration method to estimate the values of 5 parameters of the WALTer model by fitting the model outputs to tillering dynamics data. The method presented in this paper is based on the Efficient Global Optimisation algorithm. It involves the use of Gaussian process metamodels to generate fast approximations of the model outputs. To account for the uncertainty associated with the metamodels approximations, an adaptive design was used. The efficacy of the method was first assessed using simulated data. The calibration was then applied to experimental data. - Key Results The method presented here performed well on both simulated and experimental data. In particular, the use of an adaptive design proved to be a very efficient method to improve the quality of the metamodels predictions, especially by reducing the uncertainty in areas of the parameter space that were of interest for the fitting. Moreover, we showed the necessity to have a diversity of field data in order to be able to calibrate the parameters. - Conclusions The method presented in this paper, based on an adaptive design and Gaussian process metamodels, is an efficient approach for the calibration of WALTer and could be of interest for the calibration of other functional-structural plant models .

scholarly journals Iterative machine learning applied to annotation of text datasets

2021 ◽  
Author(s):  
Thiago Abdo ◽  
Fabiano Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Computational Cost ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Techniques ◽  
The Creation ◽  
The Impact ◽  
High Computational Cost

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

scholarly journals Application of an emotional classification model in e-commerce text based on an improved transformer model

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247984
Author(s):  
Xuyang Wang ◽  
Yixuan Tong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Text Classification ◽  
Rapid Development ◽  
Attention Mechanism ◽  
Mobile Internet ◽  
Sentiment Classification ◽  
Classification Model ◽  
Test Model ◽  
Transformer Model

With the rapid development of the mobile internet, people are becoming more dependent on the internet to express their comments on products or stores; meanwhile, text sentiment classification of these comments has become a research hotspot. In existing methods, it is fairly popular to apply a deep learning method to the text classification task. Aiming at solving information loss, weak context and other problems, this paper makes an improvement based on the transformer model to reduce the difficulty of model training and training time cost and achieve higher overall model recall and accuracy in text sentiment classification. The transformer model replaces the traditional convolutional neural network (CNN) and the recurrent neural network (RNN) and is fully based on the attention mechanism; therefore, the transformer model effectively improves the training speed and reduces training difficulty. This paper selects e-commerce reviews as research objects and applies deep learning theory. First, the text is preprocessed by word vectorization. Then the IN standardized method and the GELUs activation function are applied based on the original model to analyze the emotional tendencies of online users towards stores or products. The experimental results show that our method improves by 9.71%, 6.05%, 5.58% and 5.12% in terms of recall and approaches the peak level of the F1 value in the test model by comparing BiLSTM, Naive Bayesian Model, the serial BiLSTM_CNN model and BiLSTM with an attention mechanism model. Therefore, this finding proves that our method can be used to improve the text sentiment classification accuracy and effectively apply the method to text classification.

scholarly journals Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sunil Kumar Prabhakar ◽  
Harikumar Rajaguru ◽  
Dong-Ok Won
Keyword(s):  
Deep Learning ◽  
Hybrid Model ◽  
Language Processing ◽  
Text Classification ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Attention Mechanism ◽  
Learning Models ◽  
High Classification Accuracy

Over the past few decades, text classification problems have been widely utilized in many real time applications. Leveraging the text classification methods by means of developing new applications in the field of text mining and Natural Language Processing (NLP) is very important. In order to accurately classify tasks in many applications, a deeper insight into deep learning methods is required as there is an exponential growth in the number of complex documents. The success of any deep learning algorithm depends on its capacity to understand the nonlinear relationships of the complex models within data. Thus, a huge challenge for researchers lies in the development of suitable techniques, architectures, and models for text classification. In this paper, hybrid deep learning models, with an emphasis on positioning of attention mechanism analysis, are considered and analyzed well for text classification. The first hybrid model proposed is called convolutional Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism and output (CBAO) model, and the second hybrid model is called convolutional attention mechanism with Bi-LSTM and output (CABO) model. In the first hybrid model, the attention mechanism is placed after the Bi-LSTM, and then the output Softmax layer is constructed. In the second hybrid model, the attention mechanism is placed after convolutional layer and followed by Bi-LSTM and the output Softmax layer. The proposed hybrid models are tested on three datasets, and the results show that when the proposed CBAO model is implemented for IMDB dataset, a high classification accuracy of 92.72% is obtained and when the proposed CABO model is implemented on the same dataset, a high classification accuracy of 90.51% is obtained.

Transferability of Deep Learning Models for Focus Quality Assessment in Digital Pathology

2021 ◽  
Author(s):  
Adyn Miles ◽  
Mahdi S. Hosseini ◽  
Sheyang Tang ◽  
Zhou Wang ◽  
Savvas Damaskinos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Quality Assessment ◽  
Computational Cost ◽  
Digital Pathology ◽  
Low Complexity ◽  
Fine Tuning ◽  
Neural Architecture ◽  
Clinical Diagnoses ◽  
High Computational Cost ◽  
Whole Slide Images

Abstract Out-of-focus sections of whole slide images are a significant source of false positives and other systematic errors in clinical diagnoses. As a result, focus quality assessment (FQA) methods must be able to quickly and accurately differentiate between focus levels in a scan. Recently, deep learning methods using convolutional neural networks (CNNs) have been adopted for FQA. However, the biggest obstacles impeding their wide usage in clinical workflows are their generalizability across different test conditions and their potentially high computational cost. In this study, we focus on the transferability and scalability of CNN-based FQA approaches. We carry out an investigation on ten architecturally diverse networks using five datasets with stain and tissue diversity. We evaluate the computational complexity of each network and scale this to realistic applications involving hundreds of whole slide images. We assess how well each full model transfers to a separate, unseen dataset without fine-tuning. We show that shallower networks transfer well when used on small input patch sizes, while deeper networks work more effectively on larger inputs. Furthermore, we introduce neural architecture search (NAS) to the field and learn an automatically designed low-complexity CNN architecture using differentiable architecture search which achieved competitive performance relative to established CNNs.

scholarly journals A Dynamic Emotional Session Generation Model Based on Seq2Seq and a Dictionary-Based Attention Mechanism

2020 ◽  
Vol 10 (6) ◽  
pp. 1967
Author(s):  
Qiangqaing Guo ◽  
Zhenfang Zhu ◽  
Qiang Lu ◽  
Dianyuan Zhang ◽  
Wenqing Wu
Keyword(s):  
Deep Learning ◽  
Emotional Response ◽  
Large Scale ◽  
Attention Mechanism ◽  
Generation Model ◽  
Open Domain ◽  
Dialogue System ◽  
Output Sequence ◽  
Emotional Factors

With the development of deep learning, the method of large-scale dialogue generation based on deep learning has received extensive attention. The current research has aimed to solve the problem of the quality of generated dialogue content, but has failed to fully consider the emotional factors of generated dialogue content. In order to solve the problem of emotional response in the open domain dialogue system, we proposed a dynamic emotional session generation model (DESG). On the basis of the Seq2Seq (sequence-to-sequence) framework, the model abbreviation incorporates a dictionary-based attention mechanism that encourages the substitution of words in response with synonyms in emotion dictionaries. Meanwhile, in order to improve the model, internal emotion regulator and emotion classifier mechanisms are introduced in order to build a large-scale emotion-session generation model. Experimental results show that our DESG model can not only produce an appropriate output sequence in terms of content (related grammar) for a given post and emotion category, but can also express the expected emotional response explicitly or implicitly.

An Alternate Algorithm for (3x3) Median Filtering of Digital Images

2012 ◽  
Vol 2 (1) ◽  
pp. 7-9 ◽  
Author(s):  
Satinderjit Singh
Keyword(s):  
Median Filter ◽  
Computational Cost ◽  
Spatial Coherence ◽  
General Purpose ◽  
Median Filtering ◽  
Basic Algorithm ◽  
Temporal Complexity ◽  
Filter Kernel ◽  
One Step ◽  
High Computational Cost

Median filtering is a commonly used technique in image processing. The main problem of the median filter is its high computational cost (for sorting N pixels, the temporal complexity is O(N·log N), even with the most efficient sorting algorithms). When the median filter must be carried out in real time, the software implementation in general-purpose processorsdoes not usually give good results. This Paper presents an efficient algorithm for median filtering with a 3x3 filter kernel with only about 9 comparisons per pixel using spatial coherence between neighboring filter computations. The basic algorithm calculates two medians in one step and reuses sorted slices of three vertical neighboring pixels. An extension of this algorithm for 2D spatial coherence is also examined, which calculates four medians per step.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

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