Word Frequency Vector Research Articles

Recognizing pawing behavior of prepartum doe using semantic segmentation and motion history image (MHI) features

When a doe is approaching delivery, it often exhibits some specific behaviors such as eating less, sleeping less and pawing (scraping the floor with the hoof). Automatic recognition of these behaviors is valuable for farmers to determine whether does are nearing parturition or not. However, it is difficult for humans to recognize the behavior of pawing because movements are subtle and the foreleg is small. In this paper, we proposed a method to recognize the pawing behavior of prepartum does based on semantic segmentation, motion history image (MHI), and multi-scale histogram of oriented gradient (MS-HOG) features. Firstly, the foreleg region of the doe is segmented using YOLO-v5 and Unet. Next, MHI features are extracted from consecutive frames to represent the motion information of foreleg. Since MHI is insufficient to describe the local textures and shape information of the foreleg movements, we extracted MS-HOG based on the MHI features. A support vector machine (SVM) classifier was trained on the extracted features to determine whether or not a given frame contains pawing behavior. Furthermore, a word frequency vector was employed to characterize the proportion of pawing frames in a video episode with a length of one second to facilitate the recognition of pawing behavior. The proposed method was validated using both shorter video episodes and long videos. For the video episodes, our method achieved a classification accuracy of 93.14% and an average error rate of 9.72% in estimating the duration of the behavior. For the long videos, our method achieved a recall of 87.6% and precision of 77.8% when considering a temporal Intersection over Union (tIoU≥0.5). In summary, this study can be further combined with other characteristic behaviors to predict the impending delivery time of the does.

Dilemmas and Breakthroughs in the Legal Regulation of Artificial Intelligence Based on Deep Learning Models

Abstract In this paper, we use big data analysis techniques combined with the TF-IDF algorithm to weigh the frequently occurring word frequency vectors in text and reduce the document length to obtain keywords without destroying the original text feature information. The similarity of text features is combined with a Bayesian algorithm for label classification to facilitate data query and indexing. The results show that the running time of the system is kept around 14s, the recall and accuracy can be close to about 75% and 72% on average, and the number of keywords can reach 5971 with an F1 value of 0.9, which proves the effectiveness of the artificial intelligence legal regulation system based on big data analysis.

Text Classification of Conversational Implicatures Based on Lexical Features

ABSTRACT Following the guiding hypothesis in NLP, similar word frequency vectors may have similar implicatures, but some scholars are more inclined conversational implicatures cannot be obtained only through lexical features. To judge which view is more reasonable and explore the reasons for the divergence between them, whether conversational implicatures can be obtained only through lexical features is verified empirically. Main work of this paper includes: First, based on 600 corpora in the annotated dataset, the values of 20 lexical features of each corpus are obtained by automatic calculation. Second, meta-transformer of logistic regression for selecting features is adopted for feature selection and ranking. Third, after determining the features, the text is classified by the binomial logistic regression with the type of implicatures as labels. Fourth, results are tested for significance to identify relationships between variables. Experiments show that there is a statistical dependence between lexical features and conversational implicatures, and the text classification of implicatures can be performed only based on lexical features. In addition, the results of text classification will not be different due to the difference in context utterance or the type of implicature, and the text classification of implicatures only based on “response utterance” is more efficient.

Application of Deep Learning Technology in the Recommendation System of Constitutional and Constitutional Cases

Due to the huge system in the field of constitution, the content of cases is complicated, and the number of documents related to constitutional cases is huge. It is incredibly tough to find the information that suits your demands in such a large volume of constitutional documents. The artificial neural network-based deep learning method has made significant progress. Deep learning has been used in the disciplines of computer image and video processing, as well as natural language processing, and has outperformed typical machine learning methods. To that purpose, the content-based recommendation algorithm in this work is improved using deep learning approaches. This paper primarily covers the following topics: (1) the current research status of deep learning technology in recommender systems is reviewed, and the theoretical knowledge of deep neural networks is introduced. (2) A text classification model OCCNN based on character-level convolutional neural network is proposed to solve the problem of document classification of massive cases. A semantic similarity calculation model TF-W2V based on word frequency and word vector is also proposed to solve the problem of semantic similarity calculation of cases. (3) The two methods are tested, and the experimental results show that the accuracy is much higher than that of the conventional method. The model can recommend cases that meet the needs of users according to the requirements put forward by users in the process of handling cases.

Vibration-based and computer vision-aided nondestructive health condition evaluation of rail track structures

In railway engineering, monitoring the health condition of rail track structures is crucial to prevent abnormal vibration issues of the wheel–rail system. To address the problem of low efficiency of traditional nondestructive testing methods, this work investigates the feasibility of the computer vision-aided health condition monitoring approach for track structures based on vibration signals. The proposed method eliminates the tedious and complicated data pre-processing including signal mapping and noise reduction, which can achieve robust signal description using numerous redundant features. First, the method converts the raw wheel–rail vibration signals directly into two-dimensional grayscale images, followed by image feature extraction using the FAST-Unoriented-SIFT algorithm. Subsequently, Visual Bag-of-Words (VBoW) model is established based on the image features, where the optimal parameter selection analysis is implemented based on fourfold cross-validation by considering both recognition accuracy and stability. Finally, the Euclidean distance between word frequency vectors of testing set and the codebook vectors of training set is compared to recognize the health condition of track structures. For the three health conditions of track structures analyzed in this paper, the overall recognition rate could reach 96.7%. The results demonstrate that the proposed method performs higher recognition accuracy and lower bias with strong time-varying and random vibration signals, which has promising application prospect in early-stage structural defect detection.

A Framework for Semantic Clustering of News Articles Based on Fuzzy

Text mining is a process that uses data mining approaches to extract valuable information held in the hidden form in textual data. In this paper, we are proposing a framework for fuzzy clustering of news articles. These news articles originate on different news portals on the web. The data obtained need to be stored in a central database and then pre-processing reduces the noise. The keyword extraction is used to extract keywords from the text and then word-frequency vector is generated. On these vectors, distance measure or similarity measure function is used to find the similarity between articles. One article may belong to more than one cluster so fuzzy context vector must be generated. Mutual Information can be used to find fuzzy membership values. The threshold values are required for the identification of clusters. The proposed framework shows that fuzzy clustering does not restrict each news article to belong exactly to one cluster. Therefore this framework when applied to information retrieval systems or other application systems, system gives better performance and relevance to the users.

Delta Берроуза для древнегреческих авторов: опыт применения

This paper tests the effectiveness of Burrow’s Delta Method on a corpus of selected prose writings in ancient Greek. When tested on a corpus of fourteen and eight authors, the method yields good results with relatively small samples (1000, 3000, and 5000 words) and different word frequency vectors (100, 200, 500 words), but its performance is worse with texts of similar genres (oratory, historical or medical writings). We conclude that it is the generic proximity that influences the results of classification most. However, in cases where confusion is more likely, such as the writings of Demosthenes and Aeschines, the method proves effective for shortlisting potential authors. Shortlists can give an adequate idea of a sample’s nearest neighbors while leaving some freedom for the researcher in interpreting the results.

A Public Opinion Analysis System Based on Emotion Analysis in Linyi

This article, the official news site of Linyi five services in Shanghai, Yiwu GanZhou Shenzhen national logistics hub of news as the data source, through Word2Vec and construction LSTM emotion classification model, with positive or negative emotion in general categories, calculating to analyze its emotional value, from the emotional category and time series analysis and word frequency vector to Linyi public opinion analysis of logistics hub

Effective hate-speech detection in Twitter data using recurrent neural networks

This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users' tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.

Research of specific field ultra-short text classification based on collaborative filtering algorithm

In some specific fields, there are a lot of ultra-short texts that need to be categorized. This paper proposes an ultra-short text classification method based on collaborative filtering algorithm aiming at the problems such as short text content, short length, sparse features, and large number of categories in certain fields. First, converting ultra-short text into word frequency vector by doing Chinese word segmentation and calculating word frequency; Secondly, combining relevant data in specific fields, defining the ultra-short texts as users, categories as items, and then constructing a user-item recommendation matrix. Finally, calculating text similarity by using cosine similarity method and obtaining the classification results. The experimental results show that the proposed method can well solve the problem of classification of ultra-short texts in specific fields, and the average accuracy is 9.19% and 3.81% higher than vector space model and topic similarity method respectively.

Prediction of virus-host infectious association by supervised learning methods

BackgroundThe study of virus-host infectious association is important for understanding the functions and dynamics of microbial communities. Both cellular and fractionated viral metagenomic data generate a large number of viral contigs with missing host information. Although relative simple methods based on the similarity between the word frequency vectors of viruses and bacterial hosts have been developed to study virus-host associations, the problem is significantly understudied. We hypothesize that machine learning methods based on word frequencies can be efficiently used to study virus-host infectious associations.MethodsWe investigate four different representations of word frequencies of viral sequences including the relative word frequency and three normalized word frequencies by subtracting the number of expected from the observed word counts. We also study five machine learning methods including logistic regression, support vector machine, random forest, Gaussian naive Bayes and Bernoulli naive Bayes for separating infectious from non-infectious viruses for nine bacterial host genera with at least 45 infecting viruses. Area under the receiver operating characteristic curve (AUC) is used to compare the performance of different machine learning method and feature combinations. We then evaluate the performance of the best method for the identification of the hosts of contigs in metagenomic studies. We also develop a maximum likelihood method to estimate the fraction of true infectious viruses for a given host in viral tagging experiments.ResultsBased on nine bacterial host genera with at least 45 infectious viruses, we show that random forest together with the relative word frequency vector performs the best in identifying viruses infecting particular hosts. For all the nine host genera, the AUC is over 0.85 and for five of them, the AUC is higher than 0.98 when the word size is 6 indicating the high accuracy of using machine learning approaches for identifying viruses infecting particular hosts. We also show that our method can predict the hosts of viral contigs of length at least 1kbps in metagenomic studies with high accuracy. The random forest together with word frequency vector outperforms current available methods based on Manhattan and d_{2}^{*} dissimilarity measures. Based on word frequencies, we estimate that about 95% of the identified T4-like viruses in viral tagging experiment infect Synechococcus, while only about 29% of the identified non-T4-like viruses and 30% of the contigs in the study potentially infect Synechococcus.ConclusionsThe random forest machine learning method together with the relative word frequencies as features of viruses can be used to predict viruses and viral contigs for specific bacterial hosts. The maximum likelihood approach can be used to estimate the fraction of true infectious associated viruses in viral tagging experiments.

Video Copy Detection Based on Fusion of Spatio-Temporal Features

A video copy detection method based on fusion of spatio-temporal features is proposed in this paper. Firstly, trajectories are built and lens boundaries are detected by SURF features analyzing, then normalized histogram is used to describe spatio-temporal behavior of trajectories, the bag of visual words is constructed by trajectories behavior clustering, word frequency vectors and SURF features with behavior labels are extracted to express spatio-temporal content of lens, finally, duplicates are detected efficiently based on grade-match. The experimental results show the performance of this method is improved greatly compared with other similar methods.

A unified supervised codebook learning framework for classification

In this paper, we investigate a discriminative visual dictionary learning method for boosting the classification performance. Tied to the K-means clustering philosophy, those popular algorithms for visual dictionary learning cannot guarantee the well-separation of the normalized visual word frequency vectors from distinctive classes or large label distances. The rationale of this work is to harness sample label information for learning visual dictionary in a supervised manner, and this target is then formulated as an objective function, where each sample element, e.g., SIFT descriptor, is expected to be close to its assigned visual word, and at the same time the normalized aggregative visual word frequency vectors are expected to possess the property that kindred samples shall be close to each other while inhomogeneous samples shall be far away. By relaxing the hard binary constraints to soft nonnegative ones, a multiplicative nonnegative update procedure is proposed to optimize the objective function along with theoretic convergence proof. Extensive experiments on classification tasks ( i.e., natural scene and sports event classifications) all demonstrate the superiority of this proposed framework over conventional clustering based visual dictionary learning.

Improving Authorship Attribution: Optimizing Burrows' Delta Method*

Burrows' Delta Method (Burrows, 2002) is a leading method of authorship attribution. It can be used to shortlist potential authors from a list or to even identify potential authors. The technique has been extended by Hoover (2004a, 2006). In this investigation, we look at the choice of words for the word vector used, the size of the word vector, the similarity measure and the impact of corpus choice on the accuracy of text classification. Our results show a word frequency vector of between 200 and 300 words give the most accurate results (Aldridge, 2007). We also demonstrate a dramatic improvement in accuracy by adapting Burrows' Delta to the cosine similarity measure. Additionally, our results indicate areas where the word vector can be optimized still further for more accurate results.

Automatic multimedia indexing: combining audio, speech, and visual information to index broadcast news

This paper describes an indexing system that automatically creates metadata for multimedia broadcast news content by integrating audio, speech, and visual information. The automatic multimedia content indexing system includes acoustic segmentation (AS), automatic speech recognition (ASR), topic segmentation (TS), and video indexing features. The new spectral-based features and smoothing method in the AS module improved the speech detection performance from the audio stream of the input news content. In the speech recognition module, automatic selection of acoustic models achieved both a low WER, as with parallel recognition using multiple acoustic models, and fast recognition, as with the single acoustic model. The TS method using word concept vectors achieved more accurate results than the conventional method using local word frequency vectors. The information integration module provides the functionality of integrating results from the AS module, TS module, and SC module. The story boundary detection accuracy was improved by combining it with the AS results and the SC results compared to the sole TS results

A fast document copy detection model

Text similarity measure is a common issue in Information Retrieval, Text Mining, Web Mining, Text Classification/Clustering and Document Copy Detection etc. The most popular approach is word frequency based scheme, which uses a word frequency vector to represent a document. Cosine function, dot product and proportion function are regular similarity measures of vector. But they are symmetric similarity measures, which cannot find out the subset copies. In this paper we present the concepts of asymmetric similarity model and heavy frequency vector (HFV). The former can detect subset copies well, and the latter can save a great resources and CPU time. We have developed two new asymmetric measures: heavy frequency vector (HFM) and Heavy inclusion proportion model HIPM. The HFM and HIPM are derived from cosine function and proportion function by combining asymmetric similarity concept with HFV. The HFV is to truncate the original full frequency vector to a short vector. We can adjust the parameter of HFV to balance the model’s performance. The paper illustrates the aspects of asymmetric similarity and HFV models by several experiments.