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Recognizing emotions in textual data is crucial in a wide range of natural language processing (NLP) applications, from consumer sentiment research to mental health evaluation. The word embedding techniques play a pivotal role in text processing. In this paper, the performance of several well-known word embedding methods is evaluated in the context of emotion recognition. The classification of emotions is further enhanced using a convolutional neural network (CNN) model because of its propensity to capture local patterns and its recent triumphs in text-related tasks. The integration of CNN with word embedding techniques introduced an additional layer to the landscape of emotion detection from text. The synergy between word embedding techniques and CNN harnesses the strengths of both approaches. CNNs extract local patterns and features from sequential data, making them well-suited for capturing relevant information within the embeddings. The results obtained with various embeddings highlight the significance of choosing synergistic combinations for optimum performance. The combination of CNNs and word embeddings proved a versatile and effective approach. [ABSTRACT FROM AUTHOR]

Analysis of the impact of the contextual embeddings usage on the text classification accuracy

Sentiment analysis (SA) involves utilizing natural language processing (NLP) methods to identify the sentiment conveyed by a given text. This study is grounded on the dataset sourced from the internet movie database (IMDB), encompassing evaluations of films and their corresponding positive or negative classifications. Our research experiment aims to ascertain the model with the highest accuracy and generality. Our research utilizes diverse classifiers, comprising unsupervised learning approaches such as Valence Aware Dictionary and sEntiment Reasoner (VADER) and Text Blob, alongside Supervised Learning methods like Naïve Bayes, which encompasses both the Bernoulli NB and Multinomial NB. Several methodologies have been utilized, including the Count Vectorizer, and the Term Frequency-Inverse Document Frequency model (TFIDF) Vectorizer. Subsequently, word embedding and bidirectional LSTM are executed, utilizing various embeddings such as the Long Short-Term Memory (LSTM) base model. Finally, GloVe embeddings achieve the best performance with an accuracy of 90.64% and a sensitivity of 91.07%.

Abstract Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be gathered and analyzed to assess the general public’s opinion. Finding and monitoring comments, as well as manually extracting the information contained in them, is a difficult task due to the vast diversity of ideas on YouTube. Identifying public opinion on war topics is crucial for offering insights to opposing sides based on popular opinion and emotions about the ongoing war. To address the gap, we build a model on YouTube comment sentiment analysis of the Hamas-Israel war to determine public opinion. In this study, we address the gaps by developing a deep learning-based approach for sentiment analysis. We have collected 24,360 comments from popular YouTube News Channels including BBC, WION, Aljazeera, and others about the Hamas-Israel War using YouTube API and Google spreadsheet and labeled them by linguistic experts into three classes: positive, negative, and neutral. Then, textual comments were preprocessed using natural language processing (NLP) techniques, and features were extracted using Word2vec, FastText, and GloVe. Moreover, we have used the SMOTE data balancing technique and used different data splits, but the 80/20 train-test split ratio has the highest accuracy. For classification model building, commonly used classification algorithms LSTM, Bi-LSTM, GRU, and Hybrid of CNN and Bi-LSTM were applied, and their performance is compared. As a result, the Hybrid of CNN and Bi-LSTM with Word2vec achieved the highest performance with 95.73% accuracy for comments classifications.

Sentiment analysis (SA) involves utilizing natural language processing (NLP) methods to identify the sentiment conveyed by a given text. This study is grounded on the dataset sourced from the internet movie database (IMDB), encompassing evaluations of films and their corresponding positive or negative classifications. Our research experiment aims to ascertain the model with the highest accuracy and generality. Our research utilizes diverse classifiers, comprising unsupervised learning approaches such as Valence Aware Dictionary and sEntiment Reasoner (VADER) and Text Blob, alongside Supervised Learning methods like Naïve Bayes, which encompasses both the Bernoulli NB and Multinomial NB. Several methodologies have been utilized, including the Count Vectorizer, and the Term Frequency-Inverse Document Frequency model (TFIDF) Vectorizer. Subsequently, word embedding and bidirectional LSTM are executed, utilizing various embeddings such as the Long Short-Term Memory (LSTM) base model. Finally, GloVe embeddings achieve the best performance with an accuracy of 90.64% and a sensitivity of 91.07%. [ABSTRACT FROM AUTHOR]

Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be gathered and analyzed to assess the general public’s opinion. Finding and monitoring comments, as well as manually extracting the information contained in them, is a difficult task due to the vast diversity of ideas on YouTube. Identifying public opinion on war topics is crucial for offering insights to opposing sides based on popular opinion and emotions about the ongoing war. To address the gap, we build a model on YouTube comment sentiment analysis of the Hamas-Israel war to determine public opinion. In this study, we address the gaps by developing a deep learning-based approach for sentiment analysis. We have collected 24,360 comments from popular YouTube News Channels including BBC, WION, Aljazeera, and others about the Hamas-Israel War using YouTube API and Google spreadsheet and labeled them by linguistic experts into three classes: positive, negative, and neutral. Then, textual comments were preprocessed using natural language processing (NLP) techniques, and features were extracted using Word2vec, FastText, and GloVe. Moreover, we have used the SMOTE data balancing technique and used different data splits, but the 80/20 train-test split ratio has the highest accuracy. For classification model building, commonly used classification algorithms LSTM, Bi-LSTM, GRU, and Hybrid of CNN and Bi-LSTM were applied, and their performance is compared. As a result, the Hybrid of CNN and Bi-LSTM with Word2vec achieved the highest performance with 95.73% accuracy for comments classifications. [ABSTRACT FROM AUTHOR]

Abstract Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology’s reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND. Graphical Abstract

Call centers play a key role in the management of customer relationships in the modern business world. However, the growing demand for their services presents significant challenges, particularly in terms of staffing and handling increasing call volumes. This paper addresses these issues by presenting an AI-driven text classification framework tailored for the Republic of Turkiye Ministry of Trade Call Centre (MTCC), with the aim of automatically routing calls to relevant departments. Using a specific dataset of 20,000 phone call texts collected from the MTCC, the study employs TF-IDF, Word2Vec, and GloVe text vectorization techniques and applies various machine learning algorithms such as K-Nearest Neighbours, Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree and Random Forest for text classification. Through a comprehensive analysis, the study answers key research questions regarding optimal classifiers and vectorization methods. The proposed solution not only improves the efficiency of MTCC's call routing but also provides researchers with practical insights regarding Turkish text classification. The results indicate that a combination of the Random Forest classifier and Word2Vec text vectorization method is the optimal model that can manage to route calls in real-time. [ABSTRACT FROM AUTHOR]

Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology's reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND. [ABSTRACT FROM AUTHOR]

Writing essays is an important skill which enables one to clearly write the ideas and understanding of certain topic with the help of language articulation and examples. Writing essay is a skill so is the grading of those essays. It requires a lot of efforts to grade these essays and the task becomes tedious and repetitive when the student to teacher ratio is high. As with any other repetitive task, the intervention of technology for automated essay grading has been thought of long back. However, the main challenge in automated essay grading lies in the understanding of language construction, word usage and presentation of idea/ argument/ narration. Language complexity makes natural language understanding a challenging task. In this work, we show our experiments with pre-trained static word embeddings like GloVe, fastText and pre-trained contextual model Bidirectional Encoder Representations from Transformers (BERT) for the task of automated essay grading. For the regression task, we have used Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) models under various feature settings framed from the learnt embeddings. The results are shown with the ASAP-AES dataset on all 8 prompts. Our work shows average Quadratic Weighted Kappa (QWK) of 0.81 and 0.71 with SVR and LSTM on in-domain test set essays, respectively. The SVR model shows a better QWK than the human-human agreement of 0.75. To the best of our knowledge, our SVR model with pre-trained BERT embeddings achieve the highest average QWK reported on ASAP-AES data set. We further show the performance of our approach with adversary samples generated using permuted essays and off-topic essays. We experimentally show that our LSTM model though does not show high QWK score with human assigned grade but is robust against the adversarial settings considered. [ABSTRACT FROM AUTHOR]

In the contemporary digital marketplace, the proliferation of online consumer reviews has a pivotal influence on purchasing decisions. Concurrently, the prevalence of spurious reviews poses a substantial risk to the integrity of e-commerce, misleading consumers, and detrimentally impacting businesses. This paper delineates a pioneering methodology for the identification of counterfeit reviews, which is based on the combination of deep learning attributes and aspect-based analytical features. The main contribution of this research is (1) proposing an aspect fusion network based on the hierarchical attention mechanism to address the problem of multiple aspects of representing review content. The aspect fusion network can help select important aspect words and fuse aspect dictionaries with word-level attention weights. (2) We build a cardinality fusion model so that the heuristic can mitigate the negative impact of random weights and intervals on the auxiliary model. The methodology integrates advanced deep learning paradigms with aspect-based sentiment analysis to detect fraudulent reviews. Specifically, the approach encompasses a dual-method strategy: initially utilizing a Convolutional Neural Network (CNN) for the extraction of profound characteristics from review texts, followed by employing aspect-based sentiment analysis tools, including Part-of-Speech (PoS) tagging and GloVe embedding, for the distillation of aspectual features. Subsequently, these split sets of features are synergized and applied in the training of various classifier layers. Eextensive experiments have been conducted on six public review datasets contrasting the previous work on authenticity and aspect analysis. The effectiveness and performance of the proposed authenticity fusion model have been verified by the detailed analyses. The proposed model outperforms the competitors with remarkable improvement on both review authenticity and aspect analysis. This innovative approach was rigorously evaluated using a dataset of Amazon reviews that encompassed both authentic and counterfeit reviews. The empirical results demonstrate that our proposed method attains a remarkable accuracy rate of 97.73%, substantially surpassing existing state-of-the-art methodologies. The study posits that the strategic fusion of deep learning attributes and aspect-based features significantly enhances the efficacy of counterfeit review detection systems, presenting a formidable tool in the arsenal against e-commerce fraud.

Social media platforms have opened avenues for communication, information sharing, and engaging with others online. Automated accounts, known as social media bots, have been observed to engage in harmful activities such as disseminating misinformation, participating in online propaganda and election interference, spreading spam, cyberbullying, and harassing people. This paper proposes a new hybrid architecture based on semantic word embedding and Recurrent Neural Networks (RNNs) to detect social media bots. The research methodology includes the use of Global Vectors (GloVe) for text representation to convert tweets into vectors and combining the Bidirectional Gated Recurrent Units (BiGRU) and Long Short-Term Memory (LSTM) algorithms for semantic text-based classification. Using the proposed architecture, the training process was conducted with two datasets, Cresci-2017 and Twibot-20. The effectiveness of the approach in detecting automated accounts was assessed using five evaluation metrics: Precision, Accuracy, Recall, and F1-score. The proposed approach showed outstanding results in identifying social media bots based only on text-based content, achieving a Precision of 100%, Accuracy of 99.73%, Recall of 99.56%, and F1-Score of 99.63% using the Twibot-20 dataset. Moreover, the proposed architecture surpassed the results obtained by the state-of-the-art approach and showed resilience to overfitting and the ability to detect social media bots effectively in unseen and recent data. This highlights the importance of utilizing deep learning methods and semantic word representations to effectively address issues related to detecting and managing social media bot operations.

Online platforms are fostering social interaction, but unfortunately, this has given rise to antisocial behaviors such as cyberbullying, trolling, and hate speech on a global scale. The detection of hate and aggression has become a vital aspect of combating cyberbullying and cyberharassment. Cyberbullying involves using aggressive and offensive language including rude, insulting, hateful, and teasing comments to harm individuals on social media platforms. Human moderation is both slow and expensive, making it impractical in the face of rapidly growing data. Automatic detection systems are essential to curb trolling effectively. This research deals with the challenge of automatically identifying cyberbullying in tweets from a publicly available cyberbullying dataset. This research work employs robustly optimized bidirectional encoder representations from the transformers approach (RoBERTa), utilizing global vectors for word representation (GloVe) word embedding features. The proposed approach is further compared with the state-of-the-art machine, deep, and transformer-based learning approaches with the FastText word embedding approach. Statistical results demonstrate that the proposed model outperforms others, achieving a 95% accuracy for detecting cyberbullying tweets. In addition, the model obtains 95%, 97%, and 96% for precision, recall, and F1 score, respectively. Results from k-fold cross-validation further affirm the supremacy of the proposed model with a mean accuracy of 95.07%.

Examinations are one way of evaluating students. To ensure the production of valid exams, frameworks such as Bloom's taxonomy are utilised when preparing questions. Bloom's taxonomy is a well-known framework that categorises educational objectives into six hierarchical levels of cognitive complexity. However, manually categorising exam questions can be time-consuming and subjective. The extant literature has yet to leverage advanced deep learning methods and state-of-the-art word embedding techniques. This study utilises the effectiveness of Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) models along with GloVe, BERT and TF-IDF for automating the classification of exam questions according to the revised Bloom's taxonomy. The study collected various question types from online sources and multiple state universities in Sri Lanka, resulting in a dataset of 16,584 questions labelled manually with the aid of domain experts. The dataset was cleaned using natural language processing techniques. Three models were proposed: ANN+TF-IDF, LSTM+GloVe, and LSTM+BERT. The results of the ANOVA and post hoc pairwise comparisons using Bonferroni correction indicate that the LSTM+BERT model outperformed the other models significantly. The proposed approach provides a reliable and consistent way of evaluating students, and educators can use it to improve their teaching strategies. The findings of this study have important implications for educational institutions and can lead to more effective and efficient evaluations. [ABSTRACT FROM AUTHOR]

Background: Patients are hospitalized for extended periods, particularly in intensive care units (ICUs). As a result, the saturation probe (pulse oximeter) remains attached for an extended period and microorganisms can grow in the wet environment. If the pulse oximeters are not reprocessed, cross-infection may occur. The literature contaiudies in which gloves were used for the measurement while various SpO (peripheral arterial oxygen saturation) measurements were compared with each other. However, such comparisons have yet to be made with the results of arterial blood gas SpO measurements by pulse oximeter, considered as the gold standard. The present study aimed to compare arterial blood gas values with the fingertip saturation measurement performed by having adult patients wear gloves of different colors, one after the other, on their fingers and determining the effect of the differently colored gloves (transparent, white, black, light blue) on saturation values.Methods: The study was conducted on 54 patients in an ICU. Intra-arerial blood gas SpO results were measured. Oxygen saturatieasured while the patient 1. did not wear gloves and 2. sequentially wore a series of gloves of different colors. Paired t-test, correlation analysis, and Bland Altman charts were used to evaluate the results.Results: The mean SpO% value of the participants’ intra-arterial blood gas measurements was 97.76±2.04. The mean SpO% value obtained from the measurements of the fingers with a transparent glove was 0.43 points lower than the mean SpO% value of the intra-arterial blood gas measurements (t=0.986, p=0.61). The mean SpO% value obtained from the measurements of the fingers with a white glove was 0.9oints lower than the mean SpO% value of the intra-arterial blood gas measurements (t=1.157, p=0.093).Conclusion: Of the measurements performed with a glove, the mean SpO% value obtained from the measurements of the fingers with a transparent glove was more consistent with the mean SpO% the intra-arterial blood gas measurements than measurement of the fingers without a glove.

PURPOSE This study aimed to examine the difficult realities, unresolved problems, and policy proposals of disability sports in Korea through the disability sports movies and . METHODS We conducted a textual analysis from the perspective of critical theory. RESULTS The problems of Korean disabled sports revealed in the films and could be cattegorized into three major issues: 'incorrect prejudice and indifference' of non-disabled people that disabled people will not be able to enjoy sports; 'sports facilities' where non-disabled people are prioritized and disabled people are marginalized, and the 'absence of sports facilities' specialized for disabled people; and the 'lack of leaders' who correctly understand the characteristics of the disabled. These have been pointed out as causes that keep them away from the natural right to enjoy sports. CONCLUSIONS The films (2005) and (2011) were made based on true stories, and despite the fact that more than 10 years have passed since they were made, it remains a sad reality that the problems of Korean disabled sports shown in the films remain unresolved. Improvement measures in various aspects are required to promote sports for the disabled from the perspective of lifelong and adapted sports, such as media education and publicity that can eliminate misunderstanding and prejudice against people with disabilities, building sports facilities tailored to the disabled, and improving the treatment of sports instructors for the disabled.

Twitter is a popular social media platform that allows users to share short message’s opinion and engage in real-time conversations on a wide range of topics known as tweet. However, tweets often have a complicated and unclear context, which makes it difficult to determine the actual emotion. Therefore, sentiment analysis is required to see the tendency of an opinion, whether the opinion tends to be positive, negative, or neutral. Researchers or institutions can find out how the response and emotions of an issue are happening and make good decisions. With the large user of Twitter social media in Indonesia, sentiment analysis will be carried out using deep learning Convolutional Neural Network (CNN), Term Frequency-Inverse Document Frequency (TF-IDF), Robustly Optimized BERT Pretraining Approach (RoBERTa), Synthetic Minority Over-sampling Technique (SMOTE), and Global Vector (Glove). In this research, the dataset used is trending topics with hashtags related to government policies on Twitter social media and obtained through crawling. By using 30.811 data, the result shows the highest accuracy of 95.56% using CNN with a split ratio of 90:10, baseline unigram, RoBERTa, SMOTE, and Top10 corpus tweet with an increase 10.1%.

As the mainstream language of the world, English still occupies an unshakeable prime position, and English translation is one of the essential learning content for students. The semantic network association network model is introduced in this paper, and a text analysis model for English translation is proposed. The model obtains word vectors through the GloVe word embedding model, combines the relevance measure of the Jaccard index, and applies the improved RelArtNet algorithm to calculate semantic relevance in English texts. The translated data from a prominent English test is selected as a sample to analyze the relevance of each feature value of English translation scores. From the three aspects of word surface, conceptual fidelity, and semantic expression, the textual analysis of the experimental corpus by the model yields the comprehensive scoring effect of English translation by the model. The correlation between translation length and English translation scoring is the highest, and the correlation of scoring in both English to Chinese and Chinese to English is more significant than 0.58. The overall model scoring and manual scoring of English translation are closer, with a difference of 1.59 in scoring and a correlation coefficient of greater than 0.8, which verifies the practical value of the model for scoring English translations. The English translation scoring model can help teachers review translations and students practice independently, which has important practical significance.

Due to their geometry and thermal physiology, hands are most vulnerable to cold weather injuries and loss of dexterity. Gloves are the most common for hand protection during exposure to extreme thermal and hazardous environments. Although glove microclimate properties such as area factor, air gap thickness, and contact area play a significant role in thermal protection, identifying local (at individual hand segments) glove microclimate properties is still a research gap. For the first time, the glove-microclimate properties for 16 hand segments at high spatial resolution were analyzed by employing state-of-the-art hand-held 3D scanner and post-processing techniques for different glove types. Our results clearly indicate that the glove area factor for distal phalanges is significantly higher (by 49.8 %) than that for other hand segments, which increases the heat transfer from distal phalanges. In contrast, average air gap thickness was relatively uniform across all hand segments. The glove type had a pronounced effect on glove microclimate properties, e.g., bulky and heavy cold weather protective gloves had a larger average air gap thickness and glove area factor. Regression models are also developed to estimate the glove microclimate properties from simple measurement (i.e., ease allowance). Overall, this study provides essential information for the design and development of protective gloves that can help improve safety, comfort, and dexterity. Methods and mathematical models developed in this study also contribute to facilitating extremity (e.g., hand) focused thermoregulation modeling, hazard simulation, injury prediction, ergonomic design, optimum performance (dexterity and tactility) along with thermal protection.

Sentiment analysis (SA) has recently developed an automated approach for assessing sentiment, emotion, and these reviews or opinions to extract relevant and subjective information from text-based data. Analyzing sentiment on social networks, such as Twitter, has become a powerful means of learning about the users' opinions and better understanding and satisfaction. However, it consumes time and energy to disperse and collect surveys from clients, often inaccurate and inconsistent, and evaluating and improving the accuracy of the methods in sentiment analysis is being hindered by the challenges encountered in Natural Language Processing (NLP). This paper uses NLP, text analysis, biometrics, and computational linguistics to detect and extract replies, moods, or emotions from Skytrax Airline Customers' Feedback SACF data. This research uses deep learning models to analyze various approaches applied to small SACF to solve sentiment analysis problems. We applied word embedding (Glove embedding models) to improve the sentiment classification performance of a series of datasets extensively utilized for feature extractions. Finally, a comparative study has been conducted on the SACF data analysis utilizing deep learning (DL)for evaluating the performance of the different models and input features, which is Recurrent Neural Networks (RNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU), 1D Convolutional Neural Networks (CONV1D), and Bidirectional Encoder Representations from Transformers (BERT) for application to big datasets in 2019. This approach was assessed using each classification technique; the precision, recall, f1-score, and accuracy metrics for sentiment analysis have been identified. And The results show that LSTM outperforms in classification accuracy; the outcome was 91%. [ABSTRACT FROM AUTHOR]

This study introduces a new wearable fiber-optic sensor glove. The glove utilizes a flexible material, polydimethylsiloxane (PDMS), and a silicone tube to encapsulate fiber Bragg gratings (FBGs). It is employed to enable the self-perception of hand posture, gesture recognition, and the prediction of grasping objects. The investigation employs the Support Vector Machine (SVM) approach for predicting grasping objects. The proposed fiber-optic sensor glove can concurrently monitor the motion of 14 hand joints comprising 5 metacarpophalangeal joints (MCP), 5 proximal interphalangeal joints (PIP), and 4 distal interphalangeal joints (DIP). To expand the measurement range of the sensors, a sinusoidal layout incorporates the FBG array into the glove. The experimental results indicate that the wearable sensing glove can track finger flexion within a range of 0° to 100°, with a modest minimum measurement error (Error) of 0.176° and a minimum standard deviation (SD) of 0.685°. Notably, the glove accurately detects hand gestures in real-time and even forecasts grasping actions. The fiber-optic smart glove technology proposed herein holds promising potential for industrial applications, including object grasping, 3D displays via virtual reality, and human–computer interaction. [ABSTRACT FROM AUTHOR]

The article investigates the possibility of identifying the presence of SKOS (Simple Knowledge Organization System) relations between concepts represented by terms on the base of their vector representation in general natural language models. Several language models of the Word2Vec and GloVe families are considered, on the basis of which an artificial neural network (ANN) classifier of SKOS relations is formed. To train and test the efficiency of the classifier, datasets formed on the basis of the DBPedia and EuroVoc thesauri are used. The experiments performed have shown the high efficiency of the classifier trained using GloVe family models, while training it with use of Word2Vec models looks impossible in the bounds of considered ANN-based classifier architecture. Based on the results, a conclusion is made about the key role of taking into account the global context of the use of terms in the text for the possibility of identifying SKOS relations. [ABSTRACT FROM AUTHOR]

Objectives: Spacesuits are designed to be reliable personal spacecraft that preserve the life and well-being of the astronaut from the extremes of space. However, materials, operating pressures, and suit design requirements often result in a risk of musculoskeletal discomfort and injury to various areas of the body. In particular, this investigation looked at fingernails and their risk of developing onycholysis. Methods: An onycholysis literature review was followed by a retrospective analysis of injury characteristics, astronaut suited training and spaceflight events, hand anthropometry, glove sizing, and astronaut demographics. Multiple logistic regression was used to assess the likelihood of onycholysis occurrence by testing potential risk variables against the dataset compiled from the retrospective data mining. Results: The duration of event exposure, type of glove used, distance (delta) between the fingertip and the tip of the glove, sex, and age were found to be significantly related to occurrence of onycholysis (whether protective or injurious). Conclusion: An initial risk formula (model) for onycholysis was developed as a result of this investigation. In addition to validation through a future study, further improvement to this onycholysis equation and spacesuit discomfort and injury in general can be aided by future investigations that lead to better definition of the threshold between safe and risky exposure for each type of risk factor. Application: This work described a potential method that can be used for EVA spacesuit glove onycholysis injury risk analysis for either iterative glove design or between glove comparisons, such as during a product downselect process. [ABSTRACT FROM AUTHOR]

In the field of natural language processing (NLP), detecting emotions or sentiments can be a challenging task, and sometimes emotions can be more complex than just positive or negative. However, detecting sarcasm in textual data adds another layer of complexity. Despite this, identifying the underlying sarcasm in the text has become a recent area of interest among NLP researchers. Headlines in newspapers often use sarcasm to engage readers, but readers may have difficulty recognizing it, leading to a misinterpretation of the news and spreading misinformation. As a result, there is an urgent need for technology that can automatically identify sarcasm with high accuracy. Recent studies in this domain have revealed a need for a robust and efficient model. Deep learning approaches have proven to be effective in sarcasm detection. In this work, we propose a novel two-stage model that uses a word-embedding technique to select relevant features followed by an advanced deep-learning architecture to classify sarcasm in news headlines. Our proposed method demonstrates promising results in identifying sarcasm in text with an accuracy rate of approximately 97 %. We have fine-tuned the hyper-parameters to increase the precision level, which enhances the efficacy of our model. Our work provides a significant contribution to the field of NLP by presenting a reliable and effective model for sarcasm detection. The comparison of our model with recent advancements indicates that our approach outperforms them. By using our model, readers can avoid misinterpretations and the spreading of misinformation. Therefore, our work can have a positive impact on society, and we believe that it can inspire future research in the field of sarcasm detection. [ABSTRACT FROM AUTHOR]

Unwanted text messages are called Spam SMSs. It has been proven that Machine Learning Models can categorize spam messages efficiently and with great accuracy. However, the lack of proper spam filtering software or misclassification of genuine SMS as spam by existing software, the use of spam detection applications has not become popular. In this paper, we propose multiple deep neural network models to classify spam messages. Tiago’s Dataset is used for this research. Initially, preprocessing step is applied to the messages in the data set, which involves lowercasing the text, tokenization, lemmatization of the text, and removal of numbers, punctuations, and stop words. These preprocessed messages are fed in two different deep learning models with simpler architectures, namely Convolution Neural Network and a hybrid Convolution Neural Network with Long Short-Term Memory Network for classification. To increase the accuracy of these two simple architectures, BUNOW and GloVe word embedding techniques are incorporated with deep learning models. BUNOW and GloVe are popular choices in sentiment analysis, but in this work, these two-word embedding techniques are tried in the context of text classification to improve accuracy. The best accuracy of 98.44% is achieved by the CNN LSTM BUNOW model after 15 epochs on a 70% - 30% train-test split. The proposed model can be used in many practical applications like real-time SMS spam detection, email spam detection, sentiment analysis, text categorization, etc.

The article investigates the possibility of identifying the presence of SKOS (Simple Knowledge Organization System) relations between concepts represented by terms on the base of their vector representation in general natural language models. Several language models of the Word2Vec and GloVe families are considered, on the basis of which an artificial neural network (ANN) classifier of SKOS relations is formed. To train and test the efficiency of the classifier, datasets formed on the basis of the DBPedia and EuroVoc thesauri are used. The experiments performed have shown the high efficiency of the classifier trained using GloVe family models, while training it with use of Word2Vec models looks impossible in the bounds of considered ANN-based classifier architecture. Based on the results, a conclusion is made about the key role of taking into account the global context of the use of terms in the text for the possibility of identifying SKOS relations.

Background. hands are used in all areas of everyday life, from simple tasks, such as the selection of an object, to complex ones, such as communication between people with speech and hearing defects. Therefore, it is necessary to find a way not only to track the movements and gestures of human hands, but also to integrate them, and gesture recognition is especially important for people who use their hands to communicate. Materials and methods. A method of organizing communication between people with hearing loss and speech defects is proposed, including the use of a robotic glove, which allows, in the future, to use this proposal for when the standard sign language is exhausted and its addition is required. Results. The connection scheme of industrially manufactured boards has been worked out: the HC-05 Bluetooth module and the Arduino Nano debugging board together with a rough, simplified version of the human robotic brush layout with fixed strain-resistant sensors. Conclusions. The proposed technique, together with the technology of robotic gloves, allows communication not only between people with speech and hearing defects, but also eliminates misunderstandings when communicating people with speech and hearing impairments with ordinary, healthy citizens.

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Many real-world scenarios can be naturally modeled as heterogeneous graphs, which contain both symmetry and asymmetry information. How to learn useful knowledge from the graph has become one of the hot spots of research in artificial intelligence. Based on Metapath2vec algorithm, an improved Metapath2vec algorithm is presented, which combines Metapath random walk, used to capture semantics and structure information between different nodes of a heterogeneous network, and GloVe model to consider the advantage of global text representation. In order to verify the feasibility and effectiveness of the model, node clustering and link prediction experiments were conducted on the self-generated ideal dataset and the MOOC course data. The analysis of experimental data on these tasks shows that the Metapath–GloVe algorithm learns consistently better embedding of heterogeneous nodes, and the algorithm improves the node embedding performance to better characterize the heterogeneous network structure and learn the characteristics of nodes, which proves the effectiveness and scalability of the proposed method in heterogeneous network mining tasks. It is also shown through extensive experiments that the Metapath–GloVe algorithm is more efficient than the non-negative matrix decomposition algorithm (NMF), and it can obtain better clustering results and more accurate prediction results in the video recommendation task. [ABSTRACT FROM AUTHOR]

The digital transformation of various systems has brought great convenience to our daily lives, but it has also increased the level of cyberattacks. As the number of cyberattacks has increased, so has the number of reports analyzing them, MITRE publishes the ATT&CK Matrix which analyzes the tactics and techniques of attacks based on real-world examples. As the flow of attacks has become more understandable through TTP information, researchers have been using it with deep learning models to detect or predict attacks, which makes embedding essential to train the model. In previous studies on embedding TTPs, embedding is limited to simple statistical methods such as one-hot encoding and TF-IDF. Such methods do not consider the order of TTPs and the conceptual similarity between TTPs, therefore do not capture the rich information that TTPs contain. In this paper, we propose embedding TTP with GloVe, a method using a co-occurrence matrix. To properly evaluate the semantic embedding performance of TTP, we also propose a measurement called Tactic Match Rate (TMR). In the experimental results, 8 out of 14 tactics showed a TMR of more than 0.5. Especially the “TA0007 (Discovery)” tactic showed the highest TMR of 0.87. Through correlation analysis, the experimental result shows that the reason for the different embedding performances of the tactic is affected by the frequency of the technique in the same tactic, with at most a 0.96 score. We also experimentally demonstrated that the neutrality of TTP affects learning performance.

The tremendous proliferation of Multi-Modal data and the flexible need of users has drawn attention to the field of Cross-Modal Retrieval (CMR), which can perform image-sketch matching, text-image matching, audio-video matching and near infrared-visual image matching. Such retrieval is useful in many applications like criminal investigation, recommendation systems and person reidentification. The real challenge in CMR is to preserve semantic similarities between various modalities of data. To preserve semantic similarities, existing deep learning-based approaches use pairwise labels and generate binary-valued representation. The generated binary-valued representation provides fast retrieval with low storage requirement. However, the relative similarity between heterogeneous data is ignored. So, the objective of this work is to reduce the modality-gap by preserving relative semantic similarities among various modalities. So, a model named "Deep Cross-Modal Retrieval (DCMR)" is proposed, which takes triplet labels as the input and generates binary-valued representation. The triplet labels locate semantic similar data points nearer and dissimilar points far in the vector space. Extensive experiments are performed and the result is compared with deep learning-based approaches, which shows that the performance of DCMR increases by 2% to 3% for Image→Text retrieval and by 2% to 5% for Text→Image retrieval in mean average precision (mAP) on MSCOCO, XMedia, and NUS-WIDE datasets. So, the binary-valued representation generated from triplet labels preserve better relative semantic similarities than pairwise labels.

BackgroundWearing anti-vibration gloves is a simple and effective way to prevent hand-arm vibration disease. The requirements for vibration damping gloves are varied by types of operations exposed to vibration.ObjectiveTo study the vibration attenuation and dexterity of different types of protective gloves, and to provide reference for scientific wearing of vibration damping gloves for people working with vibration exposure.MethodsNine kinds of common protective gloves (A and B were dipping gloves; C, D, and E were rubber gloves; F and G were textile and fabric gloves; H was cotton gloves; I was leather gloves) used by workers exposed to vibration in 28 factories in Guangdong Province were selected as research objects by typical case sampling method, and the basic parameters of included protective gloves were investigated and measured. According to ISO 10819:2013, a glove vibration transmissibility (GVT) test system was used to detect the vibration transmissibility values and analyze vibration attenuation characteristics of the subjects wearing different protective gloves. The dexterity was tested by Minnesota Manual Dexterity Test. Pearson test was used to analyze the correlations among glove thickness, vibration transmissibility, dexterity score, and grip strength score.ResultsFor rubber gloves (C, D, and E), the associated average adjusted vibration transmissibility at middle and low frequencies \begin{document}$ {\overline T _{\text{M}}} $\end{document} and average adjusted vibration transmissibility at high frequency \begin{document}$ {\overline T _{\text{H}}} $\end{document} were lower than those of other gloves (0.89-0.91 and 0.59-0.80 respectively), the vibration transmissibility values of 50-200 Hz frequency band was 0.81-0.97, and the vibration transmissibility values of 315-1250 Hz frequency band decreased with the increase of frequency (the minimum value was 0.13). For other types of gloves (A, B, F, G, H, and I), the \begin{document}$ {\overline T _{\text{M}}} $\end{document} and \begin{document}$ {\overline T _{\text{H}}} $\end{document} were 0.95-0.98 and 1.03-1.11 respectively, the vibration transmissibility values of 50-200 Hz frequency band was 0.96-1.02, and the vibration transmissibility values of 400-1250 Hz frequency band increased (the maximum value was 1.29). The \begin{document}$ {\overline T _{\text{M}}} $\end{document}, \begin{document}$ {\overline T _{\text{H}}} $\end{document}, and vibration transmissibility values of 40-1250 Hz frequency band of rubber gloves with double-layer protective materials (C, D, and E) were significantly lower than those of gloves with single-layer protective materials. But the \begin{document}$ {\overline T _{\text{M}}} $\end{document} and \begin{document}$ {\overline T _{\text{H}}} $\end{document} of gloves of other types with double-layer materials (F, H, and I) were still greater than 0.9 and 1.0 respectively. Compared with single-layer protective materials, the gloves of other types with double-layer materials showed no significant changes in the vibration transmissibility values of 25-200 frequency band (0.91-1.06), and an increase in the vibration transmissibility values of 250-630 Hz frequency band (the maximum value was 1.22). The dexterity scores and grip strength scores of dipping gloves (A and B) were the lowest. Rubber gloves C had the highest dexterity score and grip strength score. The thickness of protective gloves was negatively correlated with the vibration transmissibility values, and positively correlated with the dexterity score and the grip strength score (P < 0.05). The vibration transmissibility value was negatively correlated with the dexterity score and the grip strength score (P < 0.05).ConclusionAmong the 9 kinds of gloves, cotton gloves and leather gloves have no damping effect. Rubber gloves have certain vibration reduction effect, and the vibration reduction effect on high frequency band is better than that on low frequency band. The thicker the damping material is, the better the damping effect is, but the less the dexterity is. Appropriate damping gloves should be selected according to actual vibration operations.

Academic plagiarism has become a serious concern as it leads to the retardation of scientific progress and violation of intellectual property. In this context, we make a study aiming at the detection of cross-linguistic plagiarism based on Natural language Preprocessing (NLP), Embedding Techniques, and Deep Learning. Many systems have been developed to tackle this problem, and many rely on machine learning and deep learning methods. In this paper, we propose Cross-language Plagiarism Detection (CL-PD) method based on Doc2Vec embedding techniques and a Siamese Long Short-Term Memory (SLSTM) model. Embedding techniques help capture the text's contextual meaning and improve the CL-PD system's performance. To show the effectiveness of our method, we conducted a comparative study with other techniques such as GloVe, FastText, BERT, and Sen2Vec on a dataset combining PAN11, JRC-Acquis, Europarl, and Wikipedia. The experiments for the SpanishEnglish language pair show that Doc2Vec+SLSTM achieve the best results compared to other relevant models, with an accuracy of 99.81%, a precision of 99.75%, a recall of 99.88%, an f-score of 99.70%, and a very small loss in the test phase. [ABSTRACT FROM AUTHOR]

Irrelevant search results for a given topic end up wasting search engine users' time. A learning focused web crawler downloads relevant URLs for a given topic using machine‐learning algorithms. The dynamic nature of the web is a challenge in related computation for focused web crawlers. Studies have shown that the learning focused crawler utilizes term frequency‐inverse document frequency (TF‐IDF) to compute the relevance between a web page and a given topic. The TF‐IDF detects similarity of the given topic to its co‐occurrence on the web page. The necessity of efficient mechanism to compute the relevance of URLs syntactically and semantically has led to the proposal of this paper with a word embedding approach to compute the relevance of the web page. The global vector representation cosine similarity is calculated between a topic and the web page contents. The calculated cosine similarity is provided as input to the trained random forest classifier to predict the relevancy of the web page. The evaluation results proved that the proposed crawler produced an average hrate of 0.41 and prate of 0.59, which outperformed other learning‐focused crawlers on support vector machines, Naive Bayes and artificial neural networks. [ABSTRACT FROM AUTHOR]

Considering the ever-growing volume of electronic documents made available in our daily lives, the need for an efficient tool to capture their gist increases as well. Automatic text summarization, which is a process of shortening long text and extracting valuable information, has been of great interest for decades. Due to the difficulties of semantic understanding and the requirement of large training data, the development of this research field is still challenging and worth investigating. In this paper, we propose an automated text summarization approach with the adaptation of static and contextual representations based on an extractive approach to address the research gaps. To better obtain the semantic expression of the given text, we explore the combination of static embeddings from GloVe (Global Vectors) and the contextual embeddings from BERT (Bidirectional Encoder Representations from Transformer) and GPT (Generative Pre-trained Transformer) based models. In order to reduce human annotation costs, we employ policy gradient reinforcement learning to perform unsupervised training. We conduct empirical studies on the public dataset, Gigaword. The experimental results show that our approach achieves promising performance and is competitive with various state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Phishing is a type of cyber-attack that aims to deceive users, usually using fraudulent web pages that appear legitimate. Currently, one of the most-common ways to detect these phishing pages according to their content is by entering words non-sequentially into Deep Learning (DL) algorithms, i.e., regardless of the order in which they have entered the algorithms. However, this approach causes the intrinsic richness of the relationship between words to be lost. In the field of cyber-security, the innovation of this study is to propose a model that detects phishing attacks based on the text of suspicious web pages and not on URL addresses, using Natural Language Processing (NLP) and DL algorithms. We used the Keras Embedding Layer with Global Vectors for Word Representation (GloVe) to exploit the web page content's semantic and syntactic features. We first performed an analysis using NLP and Word Embedding, and then, these data were introduced into a DL algorithm. In addition, to assess which DL algorithm works best, we evaluated four alternative algorithms: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). As a result, it can be concluded that the proposed model is promising because the mean accuracy achieved by each of the four DL algorithms was at least 96.7%, while the best performer was BiGRU with 97.39%. [ABSTRACT FROM AUTHOR]

With the proliferation of the internet, social networking sites have become a primary source of user-generated content, including vast amounts of information about medications, diagnoses, treatments, and disorders. Comments on previously used medicines, contained within these data, can be leveraged to identify crucial adverse drug reactions, and machine learning (ML) approaches such as sentiment analysis (SA) can be employed to derive valuable insights. However, given the sheer volume of comments, it is often impractical for consumers to manually review all of them before determining a purchase decision. Therefore, drug assessments can serve as a valuable source of medical information for both healthcare professionals and the general public, aiding in decision making and improving public monitoring systems by revealing collective experiences. Nonetheless, the unstructured and linguistic nature of the comments poses a significant challenge for effective categorization, with previous studies having utilized machine and deep learning (DL) algorithms to address this challenge. Despite both approaches showing promising results, DL classifiers outperformed ML classifiers in previous studies. Therefore, the objective of our study was to improve upon earlier research by applying SA to medication reviews and training five ML algorithms on two distinct feature extractions and four DL classifiers on two different word-embedding approaches to obtain higher categorization scores. Our findings indicated that the random forest trained on the count vectorizer outperformed all other ML algorithms, achieving an accuracy and F1 score of 96.65% and 96.42%, respectively. Furthermore, the bidirectional LSTM (Bi-LSTM) model trained on GloVe embedding resulted in an even better accuracy and F1 score, reaching 97.40% and 97.42%, respectively. Hence, by utilizing appropriate natural language processing and ML algorithms, we were able to achieve superior results compared to earlier studies. [ABSTRACT FROM AUTHOR]

A novel wearable vision system is proposed in this article. The wearable system contains a vision glove (EyeGlove) and a wearable display unit, which assists operators with inspection and measurement in confined environments. Three microcameras and an artificial fiducial marker are integrated on the index and middle fingers of the EyeGlove with disjointed camera configurations. Operators can achieve complex camera configurations and movements by using their dexterous hands while wearing the EyeGlove. The wearable vision system distinguishes itself by having two different functions, of which the inspection function uses one or two cameras to inspect in confined environments. For the measurement function, all three cameras and the artificial marker are used to identify object dimensions. Two of the three cameras form a stereovision system with disjointed camera configurations and variable baselines. The third camera and the artificial marker are used to determine the variable baseline in real-time. Inspection and measurement results are presented by the wearable display unit onto a virtual display. To make the wearable vision system adapt to dark environments, three auxiliary LED lights are integrated adjoining the three cameras. Experiments are conducted in both bright and dark environments to verify the measurement accuracy with an average relative error ≤ 5.0%. Furthermore, experiments are also conducted in the confined turbine engine combustor to verify its potential application in industrial environments. [ABSTRACT FROM AUTHOR]

Introduction: Glove-induced dermatoses are frequently seen among healthcare workers (HCWs) and are often mistakenly defined as latex allergy. Objectives: To determine the prevalences of (i) the symptoms of immediate type hypersensitivity reactions, (ii) the symptoms of hand eczema, (iii) latex sensitization detected using SPT, and (iv) contact hypersensitivity to rubber additives or glove pieces detected using patch test, in Turkish HCWs. Methods: Ninety-eight HCWs were included in the study. All subjects completed a questionnaire. All participants were skin prick tested for latex, and foods previously identified as concomitant allergens in latex-sensitive individuals; patch tested for 7 rubber additives, 3 additional haptens, and glove pieces. Results: The mean age was 32.1 (± 9.4) years, and 71 (72.4%) participants were nurses. Eighty-four (85.7%) subjects had a history of mucocutaneous symptoms of immediate-type hypersensitivity occurring within the first 24 hours after latex glove contact, while 9 (9.2%) subjects demonstrated SPT positivity for latex. Eighty (81.6%) subjects had a history of glove-induced hand eczema symptoms, while patch test positivity for the rubber additives or glove pieces was in 17.3%. Conclusions: About one-tenth of those with a history of glove-induced type I hypersensitivity symptoms had true latex allergy, and one-quarter of those with a history of glove-related hand eczema symptoms had contact hypersensitivity to glove products. Therefore, rote avoidance of latex use is generally ineffective in the management of glove-related skin complaints. Individual measures should focus on reducing the use of soaps and disinfectants, and promoting the use of moisturizers, rather than glove choice.

The Sign Language Translator is a glove that translates sign language to text and speech in real time. The glove is outfitted with an Arduino Lilypad, an Accelerometer, Flex Sensors and Contact Sensors. Values from these sensors are sent via bluetooth to an iPhone application which predicts and displays the translated gesture. Through the application, users are able to calibrate the glove and view the translation.Team Advisor: Professor Pooria M. YaghiniDepartment of Electrical  Engineering and Computer ScienceWebsite: http://projects.eng.uci.edu/projects/asl-communications-device

Background: Estimating the effort required for software engineering tasks is incredibly tricky, but it is critical for project planning. Issue reports are frequently used in the agile community to describe tasks, and story points are used to estimate task effort. Aim: This paper proposes a machine learning regression model for estimating the number of story points needed to solve a task. The system can be trained from raw input data to predict outcomes without the need for manual feature engineering. Method: Hierarchical attention networks are used in the proposed model. It has two levels of attention mechanisms implemented at word and sentence levels. The model gradually constructs a document vector by grouping significant words into sentence vectors and then merging significant sentence vectors to create document vectors. Then, the document vectors are fed into a shallow neural network to predict the story point. Results: The experiments show that the proposed approach outperforms the state-of-the-art technique Deep-S which uses Recurrent Highway Networks. The proposed model has improved Mean Absolute Error (MAE) by an average of 16.6% and has improved Median Absolute Error (MdAE) by an average of 53%. Conclusion: An empirical evaluation shows that the proposed approach outperforms the previous work.

A figurative language expression known as sarcasm implies the complete contrast of what is being stated with what is meant, with the latter usually being rather or extremely offensive, meant to offend or humiliate someone. In routine conversations on social media websites, sarcasm is frequently utilized. Sentiment analysis procedures are prone to errors because sarcasm can change a statement's meaning. Analytic accuracy apprehension has increased as automatic social networking analysis tools have grown. According to preliminary studies, the accuracy of computerized sentiment analysis has been dramatically decreased by sarcastic remarks alone. Sarcastic expressions also affect automatic false news identification and cause false positives. Because sarcastic comments are inherently ambiguous, identifying sarcasm may be difficult. Different individual NLP strategies have been proposed in the past. However, each methodology has text contexts and vicinity restrictions. The methods are unable to manage various kinds of content. This study suggests a unique ensemble approach based on text embedding that includes fuzzy evolutionary logic at the top layer. This approach involves applying fuzzy logic to ensemble embeddings from the Word2Vec, GloVe, and BERT models before making the final classification. The three models' weights assigned to the probability are used to categorize objects using the fuzzy layer. The suggested model was validated on the following social media datasets: the Headlines dataset, the "Self-Annotated Reddit Corpus" (SARC), and the Twitter app dataset. Accuracies of 90.81%, 85.38%, and 86.80%, respectively, were achieved. The accuracy metrics were more accurate than those of earlier state-of-the-art models. [ABSTRACT FROM AUTHOR]

A vast majority of cyber security information is in the form of unstructured text. A much-needed task is to have a machine-assisted analysis of such information. Named Entity Recognition (NER) provides a vital step towards this conversion. However, cyber security named entities are not restricted to classical entity types like people, location, organisation, miscellaneous etc but comprise a large set of domain-specific entities. Word embedding has emerged as the dominant choice for the initial transfer of semantics to downstream NLP tasks and impacts performance. Though several word embeddings learned using general purpose large corpus like Google News, Wikipedia etc. are available as pre-trained embeddings and have shown good performance on NER tasks; this trend is not consistent when it comes to domain-specific NER. This work explores the relative performances and suitability of prominent word embeddings for cyber security NER task. Embeddings considered include both general-purpose pre-trained word embeddings (non-contextual and contextual) available in the public domain and task-adapted embedding generated by fine-tuning these pre-trained embeddings on a task-specific supervised dataset. The results indicate that when it comes to using pre-trained embeddings for cyber security NER, fastText performs better than GloVe and BERT. However, when embeddings are further fine-tuned for the cyber-NER task, the performance of all the fine-tuned embeddings improved by +2-7%. Further, BERT embedding fine-tuned using position-wise FFN (Feed Forward Network) produced the state-of-the-art 0.974 F1-Score on the cyber security NER dataset. [ABSTRACT FROM AUTHOR]

This work describes the way in which serious games are used in virtual reality for fine motor rehabilitation in an upper limb, as a possible rehabilitation tool for patients who have suffered a cerebrovascular accident. A glove equipped with flexible sensors was built, which allows to move a virtual hand on the computer. A virtual environment was implemented in Unity 3D where the patient is registered, and where his rehabilitation is stimulated through four games under the supervision of the therapist. The four implemented games allow developing the spherical, cylindrical and pincer grasp, as well as the global mobility of the fingers. The system was tested with 12 users. It is expected that the project developed can become an additional tool for the therapist to schedule various rehabilitation sessions for the patient. [ABSTRACT FROM AUTHOR]

Background: Estimating the effort required for software engineering tasks is incredibly tricky, but it is critical for project planning. Issue reports are frequently used in the agile community to describe tasks, and story points are used to estimate task effort. Aim: This paper proposes a machine learning regression model for estimating the number of story points needed to solve a task. The system can be trained from raw input data to predict outcomes without the need for manual feature engineering. Method: Hierarchical attention networks are used in the proposed model. It has two levels of attention mechanisms implemented at word and sentence levels. The model gradually constructs a document vector by grouping significant words into sentence vectors and then merging significant sentence vectors to create document vectors. Then, the document vectors are fed into a shallow neural network to predict the story point. Results: The experiments show that the proposed approach outperforms the state-of-the-art technique Deep-S which uses Recurrent Highway Networks. The proposed model has improved Mean Absolute Error (MAE) by an average of 16.6% and has improved Median Absolute Error (MdAE) by an average of 53%. Conclusion: An empirical evaluation shows that the proposed approach outperforms the previous work. [ABSTRACT FROM AUTHOR]

This study introduces a new wearable fiber-optic sensor glove. The glove utilizes a flexible material, polydimethylsiloxane (PDMS), and a silicone tube to encapsulate fiber Bragg gratings (FBGs). It is employed to enable the self-perception of hand posture, gesture recognition, and the prediction of grasping objects. The investigation employs the Support Vector Machine (SVM) approach for predicting grasping objects. The proposed fiber-optic sensor glove can concurrently monitor the motion of 14 hand joints comprising 5 metacarpophalangeal joints (MCP), 5 proximal interphalangeal joints (PIP), and 4 distal interphalangeal joints (DIP). To expand the measurement range of the sensors, a sinusoidal layout incorporates the FBG array into the glove. The experimental results indicate that the wearable sensing glove can track finger flexion within a range of 0° to 100°, with a modest minimum measurement error (Error) of 0.176° and a minimum standard deviation (SD) of 0.685°. Notably, the glove accurately detects hand gestures in real-time and even forecasts grasping actions. The fiber-optic smart glove technology proposed herein holds promising potential for industrial applications, including object grasping, 3D displays via virtual reality, and human–computer interaction.

Sign language is the predominant mode of communication for the Hearing impaired community. For the millions of people who suffer from hearing loss around the world, interaction with people who have the ability to hear and do not suffer from hearing impairment or loss is considered as complicated. In line with this issue, technology is perceived as a crucial factor in being an enabler of providing solutions to enhance the quality of life of the hearing impairment by increasing accessibility. This research aims to review and analyze articles related to sign language recognition based on the sensor- based glove system, in order to identify academic motivations, challenges, and recommendations related to this field. The search for the relevant review materials and articles was performed on four major databases ranging from 2017 to 2022: Science Direct, Web of Science, IEEE Xplore, and Scopus. The articles were chosen based on our inclusion and exclusion criteria. The literature findings of this paper indicate the dataset size to be open issues and challenges for hand gesture recognition. Furthermore, the majority of research on sign language recognition based on data glove was performed on static, single hand, and isolated gestures. Moreover, recognition accuracy typically achieved results higher than 90%. However, most experiments were carried out with a limited number of gestures. Overall, it is hoped that this study will serve as a roadmap for future research and raise awareness among researchers in the field of sign language recognition.

Twitter is one of the popular social media to channel opinions in the form of criticism and suggestions. Criticism could be a form of hate speech if the criticism implies attacking something (an individual, race, or group). With the limit of 280 characters in a tweet, there is often a vocabulary mismatch due to abbreviations which can be solved with word embedding. This study utilizes feature expansion to reduce vocabulary mismatches in hate speech on Twitter containing Indonesian language by using Global Vectors (GloVe). Feature selection related to the best model is carried out using the Logistic Regression (LR), Random Forest (RF), and Artificial Neural Network (ANN) algorithms. The results show that the Random Forest model with 5.000 features and a combination of TF-IDF and Tweet corpus built with GloVe produce the best accuracy rate between the other models with an average of 88,59% accuracy score, which is 1,25% higher than the predetermined Baseline. The number of features used is proven to improve the performance of the system.