Reviewerly/exHarmony huggingface dataset:
Face mask detection application and dataset

The COVID–19 pandemic is, without any doubt, changing our world in ways that are beyond our wildest imagination. In a bid to curb the spiraling negative fallouts from the virus that has resulted in a large number of casualties and security concerns. The World Health Organization, amongst other safety protocols, recommended the compulsory wearing of face masks by individuals in public spaces. The problem with the enforcement of this and other relevant safety protocols, all over the world, is the reluctance and outright refusal of citizens to comply and the inability of relevant agencies to monitor and enforce compliance. This paper explores the development of a CCTV–enabled facial mask recognition software that will facilitate the monitoring and enforcement of this protocol. Such models can be particularly useful for security purposes in checking if the disease transmission is being kept in check. A constructive research methodology was adopted, where a pre-trained deep convolutionary neural network (CNN) (mostly eyes and forehead regions) used and the most probable limit (MPL) was use for the classification process. The designed method uses two datasets to train in order to detect key facial features and apply a decision-making algorithm. Experimental findings on the Real-World-Masked-Face-Dataset indicate high success in recognition. A proof of concept as well as a development base are provided towards reducing the spread of COVID-19 by allowing people to validate the face mask via their webcam. We recommend that the use of the app and to further investigate the development of highly robust detectors by training a deep learning model with respect to specified face-feature categories or to correctly and incorrectly wear mask categories.
Application of Fuzzy Pushdown Automaton on Prediction of Quality Control for Spinning Yarn

In order to perform better recognition, tracking and control for fuzzy and uncertain thing, this paper will design a suitable fuzzy pushdown automaton (FPDA) control method to solve the problem. Firstly, the control design structure of FPDA and the decision reasoning rules in control are given. Secondly, the application of FPDA in prediction of quality control for spinning yarn is discussed in the practical problem. Finally, the comparison of FPDA and other control methods on the target control is given. The simulation results show that the control speed and the average precision of designed FPDA are faster by12ms and higher by 4.98% than that of traditional method, which its control precision is 96.87%.
Sentiment Analysis Of Twitter Data By Using Deep Learning And Machine Learning

In today’s world, social media is viral and easily accessible. The Social media sites like Twitter, Facebook, Tumblr, etc. are a primary and valuable source of information.Twitter is a micro-blogging platform, and it provides an enormous amount of data. Such type of information can use for different sentiment analysis applications such as reviews, predictions, elections, marketing, etc. It is one of the most popular sites where peoples write tweets, retweets, and interact daily. Monitoring and analyzing these tweets give valuable feedback to users. Due to this data's large size, sentiment analysis is using to analyze this data without going through millions of tweets manually. Any user writes their reviews about different products, topics, or events on Twitter, called tweets and retweets. People also use emojis such as happy, sad, and neutral in expressing their emotions, so these sites contain expansive volumes of unprocessed data called raw data. The main goal of this research is to recognize the algorithms by using Machine Learning Classifiers. The study intends to categorize Fine-grain sentiments within Tweets of Vaccination (89974 tweets) through machine learning and a deep learning approach. The study takes consideration of both labeled and unlabeled data. It also detects emojis from tweets using machine learning libraries like Textblob, Vadar, Fast text, Flair, Genism, spaCy, and NLTK.
Utilization of Blockchain Technology Revolution in Electronic ID Card Data Integrity

KTP is a proof of identification issued by the implementing agency that applies throughout the territory of the Unitary State of the Republic of Indonesia. The existence of KTP is recognized as very important in daily life because it is used in all activities or any series of administrative transactions. Field studies found the number of cases of Asphalt ID cards (original but fake) used as a means of media fraud. Because ID cards are very easy to falsify this makes it difficult to identify cases of data misappropriation. The focus of the research was the utilization of Blockchain, by applying distributed consensus and cryptography in improving the administration management system of KTP to identify the existence of fake IDs and double data. The research method used is descriptive analysis method and literature study. By implementing the decentralization and cryptographic system owned by Blockchain, it is expected to minimize the occurrence of identity misappropriation, as well as be able to be a technology capable of contributing to the success of aspects of state administration governance systems.
Fostering Success and Motivating EFL Learners Using Zoom Meeting: A Synchronous Learning Strategy

Due to the rapid advancement of technology, teachers were forced to integrate technological equipment and method in educational strategies, for the main purpose of facilitating the learning experience. Accordingly, this study aimed to answer two main questions namely; 1) How can Zoom Meeting enhance high school students at Salatiga to gain success in ELT? 2) To what extent does use the Zoom Meeting as an asynchronous learning strategy affect the motivation of high school students at Salatiga? For that purpose, the researcher implemented an experimental design with a mixed approach to conduct the current study using a pre-test, and a post-test to measure the change in the levels of EFL learners’ English skills, the researcher also spread questionnaires to examine the attitude of the students towards the Zoom meeting. The participants of the current study were 60 high school students at Salatiga who were divided into two groups namely the control and experimental group. The findings indicated that zoom meeting enhanced EFL learners’ English skills and motivation&#x0D; Keywords: Zoom Meeting, Synchronous Learning Strategy, Zoom Meeting Learning
Blockchain-Based Music Wallet for Copyright Protection in Audio Files

The works produced within the music industry arepresented to their listeners on a digital platform,taking advantage of technology. The problems of thepast, such as pirated cassettes and CDs, have left theirplace to the problem of copyright protection on digitalplatforms today. Block chain is one of the mostreliable and preferred technologies in recent timesregarding data integrity and data security. In thisstudy, a blockzincir-based music wallet model isproposed for safe and legal listening of audio files.The user's selected audio files are converted intoblock chain structure using different techniques andalgorithms and are kept securely in the user's musicwallet. In the study, performance comparisons aremade with the proposed model application in terms ofthe length of time an ordinary audio player can addnew audio files to the list and the response times ofthe user. The findings suggest that the proposedmodel implementation has acceptable differences inperformance with an ordinary audio player.
Boom in Distance Learning During the Coronavirus Pandemic: Challenges and Possibilities

The article highlights brief theoretical aspects of the origin of distance learning tracing the experience of foreign counties as well as Ukraine’s practice in this regard. The paper outlines practical and social aspects of distance education; definition of what successful distance learning should look like; effective solution to the problem of implementing distance or blended learning by the academic staff of the Precarpathian national University. The necessity of engaging both teachers and students in integrating innovative digital technologies in academic process of education institutions to ensure a quality level of practical classes, creating favorable conditions for self-realization and professional development of future primary school teachers is substantiated. Major accent is placed on the efficiency of the use of particular digital tools like Kahoot, Mentimeter, Pear Deck and Flipgrid for innovative organization of education environment, creative presentation of theoretical information, consolidating students’ knowledge, practical skills and abilities, formative and summative assessment of learning outcomes, development of students’ competences within certain courses, as well as systematic professional self-improvement. It was found out that among the difficulties most often faced by the education institutions in working with students was identified as follows: providing equal access to online or blended education under the condition of Coronavirus pandemic in rural, mountain and low-income communities, etc. The authors proved the quality of introduction and application of distance learning at the Pedagogy Faculty of the PNU in the process of teaching/studying the academic courses of the language and literature cycle can be assessed by means of the mentioned in the research digital instruments. This will contribute to the practical orientation of the educational process, active participation of each student in the learning process, increasing motivation and engagement in all forms of cooperation in the education environment.
Handling Out-Of-Vocabulary Problem in Hangeul Word Embeddings

Word embedding is considered an essential factor in improving the performance of various Natural Language Processing (NLP) models.However, it is hardly applicable in realworld datasets as word embedding is generally studied with a well-refined corpus.Notably, in Hangeul (Korean writing system), which has a unique writing system, various kinds of Out-Of-Vocabulary (OOV) appear from typos.In this paper, we propose a robust Hangeul word embedding model against typos, while maintaining high performance.The proposed model utilizes a Convolutional Neural Network (CNN) architecture with a channel attention mechanism that learns to infer the original word embeddings.The model train with a dataset that consists of a mix of typos and correct words.To demonstrate the effectiveness of the proposed model, we conduct three kinds of intrinsic and extrinsic tasks.While the existing embedding models fail to maintain stable performance as the noise level increases, the proposed model shows stable performance.
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment

Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification.While knowledge bases contain a large number of entities in high-resource languages such as English and French, corresponding entities for lower-resource languages are often missing.To address this, we propose Lexical-Semantic-Phonetic Align (LSP-Align), a technique to automatically mine cross-lingual entity lexica from mined web data.We demonstrate LSP-Align outperforms baselines at extracting cross-lingual entity pairs and mine 164 million entity pairs from 120 different languages aligned with English.We release these cross-lingual entity pairs along with the massively multilingual tagged named entity corpus as a resource to the NLP community.
Geometric graphs from data to aid classification tasks with Graph Convolutional Networks

Traditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such as known relations between samples. Here, we show that, even if additional relational information is not available in the dataset, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world datasets from various scientific domains.
Cloud Classroom Design for English Education Based on Internet of Things and Data Mining

The Informa ionization of social life and the globalization of economy have made the importance of English increasingly prominent. Building an information-based teaching platform for supplementary teaching under the network environment has become a mainstream teaching method in various basic schools. How to integrate various types of multimedia teaching resources into English classroom teaching has become the main goal of the current college teaching reform. Aiming at the shortcomings of the current English education classroom, this paper designs and develops an English education cloud classroom based on the Internet of Things and data mining methods. First of all, the system adopts a three-tier B/S model, the development platform chooses, NET, the development language, uses ASP.NET, and the database chooses SQL server. Secondly, the data mining method is used to clean and organize the data in the cloud classroom background to explore the course education status behind the data. Finally, the simulation test analysis verifies the efficiency of the English education cloud classroom established in this article.
A Comparative Review on Object Detection System for Visually Impaired

Vision is one of the key senses allowing citizens to communicate with the natural world. There are about two hundred million blind people globally and visually disabled people obstruct numerous everyday practices. It is also really critical that blind people recognize their world and realize with which items they communicate. This paper review all the method and tool related to camera-based device to enable the blind person interpret text patterns written on items kept in hand. This is the system for helping individuals with visual disability interpret and translate text patterns to the audio output. The framework first suggests the approach to take an image from the camera and the area of the target to retrieve the object from the context and derive a text pattern from that object. Diffrent algorithm is assessed in various scenes. The observed text is linked to the blueprint and translated into the performance of the voice. Localized and binarized text patterns utilising Optical Character Recognition (OCR). The text is translated to an audio output. The voice quality is given to theblind person.&#x0D;
A Heuristic Approach for Telugu Text Summarization with Improved Sentence Ranking

Extracting/abstracting the condensed form of original text document by retaining its information and complete meaning is known as text summarization. The creation of manual summaries from large text documents is difficult and time-consuming for humans. Text summarization has become an important and challenging area in natural language processing. This paper presents a heuristic appraoch to extract a summary of e-news articles of the Telugu language. The method proposes new lexical parameter-based information extraction (IE) rules for scoring the sentences. Event score and Named Entity Score is a novel part in sentence scoring to identify the essential information in the text. Depending on the frequency of occurrence of event/named entites in the sentence and document, sentences are selected for summary. Data is collected from online news sources (i.e., Eenadu, Sakshi,Andhra Jyothi, Namaste Telangana) to experiment. The proposed method is compared with other techniques developed for Telugu text summarization. Evaluation metrics like precision, recall, and F1 score is used to measure the proposed method's performance. An extensive statistical and qualitative evaluation of the system's summaries has been conducted using Recall-Oriented Understudy for Gisting Evaluation (ROUGE), a standard summary evaluation tool. The results showed improved performance compared to other methods.
Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine

Point-Of-Interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the lack of negative samples and the complexities of check-in contexts limit their effectiveness significantly. This paper focuses on the problem of context-specific POI recommendation based on the check-in behaviors recorded by Location-Based Social Network (LBSN) services, which aims at recommending a list of POIs for a user to visit at a given context (such as time and weather). Specifically, a bidirectional influence correlativity metric is proposed to measure the semantic feature of user check-in behavior, and a contextual smoothing method to effectively alleviate the problem of data sparsity. In addition, the check-in probability is computed based on the geographical distance between the user’s home and the POI. Furthermore, to handle the problem of no negative feedback in LBSN, a weighted random sampling method is proposed based on contextual popularity. Finally, the recommendation results is obtained by utilizing Factorization Machine with Bayesian Personalized Ranking (BPR) loss. Experiments on a real dataset collected from Foursquare show that the proposed approach has better performance than others.
Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling

A sufficient amount of annotated data is usually required to fine-tune pre-trained language models for downstream tasks.Unfortunately, attaining labeled data can be costly, especially for multiple language varieties and dialects.We propose to self-train pre-trained language models in zero-and few-shot scenarios to improve performance on data-scarce varieties using only resources from data-rich ones.We demonstrate the utility of our approach in the context of Arabic sequence labeling by using a language model fine-tuned on Modern Standard Arabic (MSA) only to predict named entities (NE) and part-of-speech (POS) tags on several dialectal Arabic (DA) varieties.We show that self-training is indeed powerful, improving zero-shot MSA-to-DA transfer by as large as 10% F 1 (NER) and 2% accuracy (POS tagging).We acquire even better performance in few-shot scenarios with limited amounts of labeled data.We conduct an ablation study and show that the performance boost observed directly results from training data augmentation possible with DA examples via self-training.This opens up opportunities for developing DA models exploiting only MSA resources.Our approach can also be extended to other languages and tasks. 1
Data Augmentation for Hypernymy Detection

The automatic detection of hypernymy relationships represents a challenging problem in NLP.The successful application of stateof-the-art supervised approaches using distributed representations has generally been impeded by the limited availability of high quality training data.We have developed two novel data augmentation techniques which generate new training examples from existing ones.First, we combine the linguistic principles of hypernym transitivity and intersective modifier-noun composition to generate additional pairs of vectors, such as small dogdog or small dog -animal, for which a hypernymy relationship can be assumed.Second, we use generative adversarial networks (GANs) to generate pairs of vectors for which the hypernymy relation can also be assumed.We furthermore present two complementary strategies for extending an existing dataset by leveraging linguistic resources such as Word-Net.Using an evaluation across 3 different datasets for hypernymy detection and 2 different vector spaces, we demonstrate that both of the proposed automatic data augmentation and dataset extension strategies substantially improve classifier performance.
Improvement of RSA Algorithm Using Euclidean Technique

Information Security has become an essential concern in the modern world. Encryption is an effective way to prevent an unofficial person from viewing the digital information with the secret key. RSA encryption is often used for digital signatures which can prove the authenticity and reliability of a message. As RSA encryption is less competent and resource-heavy, it is not used to encrypt the entire message. If a message is encrypted with a symmetric-key RSA encryption it will be more efficient. Under this process, only the RSA private key will be able to decrypt the symmetric key. The Euclidean algorithm is attainably one of the most extensively known algorithms. The Euclidean algorithm is used for finding the greatest common divisor of two numbers. The algorithm can also be defined for more general rings than just the integers. This work is very useful to improve the data security in Smart card and Aadhaar card. In this paper, the RSA algorithm is modified using the Euclidean technique to improve its performance. The proposed algorithm shows its better performance in terms of speed, throughput, power consumption, and the avalanche effect. Experimental results and mathematical justification supporting the proposed method are reported.
Expanding, Retrieving and Infilling: Diversifying Cross-Domain Question Generation with Flexible Templates

Sequence-to-sequence based models have recently shown promising results in generating high-quality questions.However, these models are also known to have main drawbacks such as lack of diversity and bad sentence structures.In this paper, we focus on question generation over SQL database and propose a novel framework by expanding, retrieving, and infilling that first incorporates flexible templates with a neural-based model to generate diverse expressions of questions with guidance of sentence structure.Furthermore, a new activation/deactivation mechanism is proposed for template-based sequenceto-sequence generation, which learns to discriminate template patterns and content patterns, thus further improves generation quality.We conduct experiments on two largescale cross-domain datasets.The experiments show that the superiority of our question generation method in producing more diverse questions while maintaining high quality and consistency under both automatic evaluation and human evaluation.
Discover the Wonderful World of Plants with the Help of Smart Devices

Augmented Reality in Botanical Garden" (ARGB) is the name of the application we developed, which brings visitors closer to the Botanical Garden "Anastasie Fătu" from Iași.Even if they want to search for a plant to know where they can find it or maybe they are interested in knowing the garden and its sections, visitors can just access the app and get all the help they need.More than this, the app provides enhanced features for the people responsible for the maintenance of the garden such as the possibility to generate placards for different plants during different plant exhibitions.