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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%.

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.

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]

Hate speech, characterized by intentional expressions of dissatisfaction, is a prevalent phenomenon on social media platforms, including Twitter. Its continual occurrence can foster divisions, misunderstandings, and even acts of violence between individuals and groups, particularly due to the resulting prejudice. This study investigates the occurrence of hate speech within Indonesian content on Twitter, employing a deep learning approach to detect and analyze such expressions. The Long Short-Term Memory (LSTM) method, coupled with the GloVe word embedding technique, is utilized on a dataset comprising 13,169 Indonesian tweets flagged for hate speech. Four distinct model architectures were developed through the integration of LSTM and GloVe. The findings reveal model 1 to exhibit superior performance, achieving a precision of 89%, a recall of 99%, an F-1 score of 94%, and an overall accuracy of 94.24%. It is suggested that future research explore the potential deployment of this model in web or mobile platforms for real-time analysis, thereby enhancing the capacity for immediate hate speech detection and mitigation. [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.

In this paper, we propose a method exploiting syntactic structure, semantic relations and word embeddings for recognizing textual entailment. The sentence pairs are analyzed using their syntactic structure and categorization of sentences in active voice, sentences in passive voice and sentences holding copular relations. The main syntactic relations such as subject, verb and object are extracted and lemmatized using a lemmatization algorithm based on parts-of-speech. The subject-to-subject, verb-to-verb and object-to-object similarity is identified using enhanced Wordnet semantic relations. Further similarity is analyzed using modifier relation, number relation, nominal modifier relation, compound relation, conjunction relation and negative relation. The experimental evaluation of the method on Stanford Natural Language Inference dataset shows that the accuracy of the method is 1.4% more when compared to the state-of-the-art zero shot domain adaptation methods. [ABSTRACT FROM AUTHOR]

Background: Semipermeable membranes might be suitable for glove liners or comfort gloves in individuals with irritant contact dermatitis (ICD). Objectives: To evaluate the effects of different glove materials on inflammation and epidermal barrier impairment after experimental skin irritation. Methods: Nine test areas on the volar forearms of 24 healthy volunteers were irritated with sodium lauryl sulfate (1%) and afterward covered for 6 days (6 or 8 h/day) with semipermeable Sympatex (SYM), vinyl (OCC), combinations of vinyl with Sympatex (SYM/OCC) or cotton (COT/OCC), or left uncovered (CON). Up to day 10, measurements of transepidermal water loss (TEWL), erythema (a*), skin humidity (SH) and visual scoring (VS) were applied. Results: No significant differences in skin parameters were found between COT/OCC and SYM/OCC as well as between each of the combinations and CON. SYM, COT/OCC and SYM/OCC led to better results for most skin parameters than OCC alone. Conclusions: Occlusive material has a negative impact on skin barrier recovery and inflammation after skin irritation whereas SYM is not inferior to uncovered areas indicating good tolerability. Altogether, the data suggest that SYM is a useful alternative to COT as material for glove liners and comfort gloves in ICD patients. [ABSTRACT FROM AUTHOR]

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Copyright of International Journal of Engineering Research & Development (IJERAD) is the property of International Journal of Engineering Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Japanese Journal of Swine Science / Nihon Yoton Gakkaishi is the property of Japanese Society of Swine Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

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.

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.

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 utilization of text vectorization techniques has become essential for numerous classification tasks in present-day natural language processing. Word embedding methods commonly used today, such as Word2Vec, GloVe, etc., are based on the semantic similarity of words. WordNet, as a lexical database of words, provides a rich source of semantic information. In our article, we propose a text vectorization technique using extended text data with the data augmentation method, specifically by replacing words with their synonyms obtained from WordNet. The results obtained from text classification tasks using multiple classifiers demonstrate that expanding the corpus with this method leads to improved vector representations of words. [ABSTRACT FROM AUTHOR]

Despite the plethora of data generated on Arabic social media, research dedicated to this language remains comparatively scarce. Sentiment analysis, an extensively studied field in various languages, has seen limited development in Arabic. Existing approaches to Arabic sentiment analysis primarily employ machine learning, wherein word vector representations serve as features for model training. A significant challenge encountered in this approach is the substantial volume and sparsity of the matrix representation, attributable to the extensive vocabulary of the Arabic language. This paper proposes a novel word embedding that amalgamates the Bag of Roots (BoR) technique with Global Vector distributional representations (GloVe). This innovation is inspired by the characteristic of the Arabic language, where it is rare to find two or more words sharing the same root but conveying different sentiments. The impact of this innovative word embedding technique is highlighted through an evaluation using sentiment analysis. This involves the implementation of conventional classifiers, specifically Support Vector Machines (SVM) and Logistic Regression (LR). The results obtained demonstrate promising precision, recall, and F1-score metrics. Additionally, a significant reduction in processing time is observed when compared to other approaches referenced in literature. Thus, this paper contributes to the advancement of Arabic sentiment analysis, offering a potential pathway to overcoming the challenges associated with the large vocabulary and complex structure of the Arabic language. [ABSTRACT FROM AUTHOR]

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]

There are several international standards that address the resistance of chemical protective clothing materials to the penetration by liquids. The hydrostatic pressure has been documented to discriminate between protective clothing material performance and correlates with visual penetration results that are obtained with human factors validation. The same methodology, based on hydrostatic pressure equipment, is referenced also in other standards addressing penetration resistance of protective clothing and glove materials against synthetic blood or blood-borne pathogens. In this study, we present an automated hydrostatic penetration testing that integrates testing procedures from several standards to evaluate the resistance of materials to penetration by liquids under pressure. The automated control system allows the user to select a specific test method and automatically sets a stepped pressurization protocol to test the material. A pass or a fail result is produced at a certain time and pressure. As an example of application, the penetration of synthetic blood was assessed through gloves made from different materials with ISO 16603, method B, one of the five possible penetration methods and protocols available in the test equipment. The results indicate that the developed system facilitates the application of test methods used to evaluate the barrier effectiveness against liquids of materials used for protective clothing and gloves and show up that the characteristics of the retention grid used have a decisive influence on the test results. In some of the tested glove materials, holes were intentionally performed with needles with different gauges. The capacity of pinhole detection in gloves was evaluated according to the test method selected and compared with results obtained with the classic water leak test method for gloves described in EN ISO 374-2. [ABSTRACT FROM AUTHOR]

Online reviews play an integral part in making mobile applications stand out from the large number of applications available on the Google Play store. Predominantly, users consider posted reviews for appropriate app selection. Manual categorization of such reviews is both inefficient and time-consuming. Therefore, automatic analysis of the sentiments of such reviews provides fast suggestions for new users and facilitates their selection of the appropriate app. However, data imbalance is a major challenge for performing class prediction of such reviews as their distribution is sparse and often leads to low accuracy. This work proposes a framework to overcome this limitation. Extensive experiments are performed using the original and balanced data with the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Additionally, deep learning and machine learning models are evaluated using FastText, FastText Subword, global vector (GloVe), and their combinations for word representation. Baseline machine learning models, including random forest, extra tree classifier, gradient boosting, Naive Bayes, logistic regression (LR), stochastic gradient descent (SGD), and voting classifier (VC) that combines LR and SGD, are used for comparison. The outcomes show that the convolutional neural network using a combination of word embedding techniques produces the most accurate results. [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]