Reviewerly/exHarmony huggingface dataset:
Quipper School and the Reading Comprehension Performance of Grade 11 Students

The main objective of this study was to determine the effect that Quipper School Instruction may have on the reading comprehension of 52 purposively selected Grade 11 Senior High School students of Jaro National High School enrolled in the course English for Academic and Professional Purposes (EAPP) for the First Semester, School Year 2017-2018. Employing the quasi-experimental design, the six-week study focused on how technology might usher in the development of the following critical reading skills: differentiating academic from non-academic texts, determining text types through their structure, locating the main idea, critical reading strategies like skimming, scanning, and making inferences, paraphrasing, summarizing and outlining. The instruments utilized to gather data were the validated teacher-made reading comprehension test given both as a pretest and a posttest and the participants’ scores in Quipper School (QS) exercises readily provided by the researcher as part of the enrichment tasks. A pretest was given to the participants to determine their initial reading comprehension performance. The experimental group was taught with the aid of Quipper School used by the researcher as an online platform for delivering enrichment activities while the control group was taught using the conventional instruction – the Lecture Method. At the post-experimentation stage, a posttest was given to the participants in both groups. It was found that the respondents in both groups had almost the same performance in reading comprehension, i.e., they were comparable prior to the interventions. This implies that using the two methods for both groups in teaching reading comprehension could yield comparable performance. In terms of the pretest and posttest of the experimental group, no significant difference was found. This means that regardless of the method (instruction with Quipper School) employed by the teacher, the performances of the students in reading comprehension reflected in their pretest and posttest did not significantly improve. However, a significant difference was noted in the pretest and posttest performance in favor of the control group. This means that the students in the control group taught using conventional instruction have significantly improved their performance in reading comprehension. This likewise implies that conventional instruction as a teaching method may yield a better result in teaching reading comprehension to students. In fact, the control group showed a higher mean gain performance than the experimental group. Generally, no significant difference was noted between the performance gain in reading comprehension made by the experimental group over the performance gain made by the control group. Although both groups gained significantly after the intervention, their reading comprehension performances remained comparable, though the control group achieved a higher gain than the experimental group. This further implies that instruction using Quipper School is as beneficial as conventional instruction in teaching reading comprehension.
Pagerank SEO Algorithm : Issues, Complexity and Implementation

Today, the Internet users need an optimal search for information on the web. The display of web pages within a search engine is not a mystery. This implies good mathematical modeling and good knowledge of computer science for its implementation. The web is a directed graph that must be exploited. The matrices of its graph contain a structure of the links and the navigation of the Internet user. Considering the billions of hosted websites and the dynamism of the web, its links can be added at any time. Changing this link structure impacts the PageRank. Thus, for good stability, the algorithms must be improved. PageRank algorithm that displays a good web page Search Engine Optimization (SEO) taking into account the score of each page, pay attention to many researches that make improvements day by day. Even though it is a basic formula, the PageRank algorithm makes a successful business. In this paper, we had not only implemented this algorithm in python but also explain how it works and calculated its complexity
Relationship Between Results of Genexpert RT-PCR and Erythrocyte Sedimentation Rate in Suspected Tuberculosis Patients

Pulmonary tuberculosis (TB) is an infectious disease that attacks the lungs and is caused by the bacterium Mycobacterium tuberculosis. The GeneXpert RT-PCR examination (Xpert MTB / RIF) is a development of a diagnostic tool that can automatically detect the presence of Mycobacterium tuberculosis (MTB) by molecular examination and can be used to detect MTB resistance to rifampicin. Erythrocyte sedimentation rate (ESR) is a test used to determine the rate at which erythrocytes settle in the blood. The purpose of this study was to determine the relationship between GeneXpert RT-PCR results and ESR in suspected tuberculosis patients. This research uses a cross-sectional study. The sample used in this study were 56 new suspected TB patients who had never been treated for ATD and underwent GeneXpert RT-PCR examination. Sampling was carried out by purposive sampling with the TCM Rif Sen group and negative results. A total of 27 patients (96.4%) with Rif Sen's GeneXpert RT-PCR results had high ESR values. 15 patients (53.6%) with negative GeneXpert RT-PCR results had normal ESR values. The results of the Chi Square test (X2) show that p-value = 0.000 OR = 0.032. There is a significant relationship between GeneXpert and ESR values ​​in suspected tuberculosis
Effect of General Characteristics of Entrepreneurship University Students on Self-Efficacy and Major Satisfaction

Recently, the government is advocating the activation of entrepreneurship to solve the job problem. Recently, the role of education for university students' entrepreneurship is increasing. The purpose of this study was to investigate the effect of entrepreneurship students' self-efficacy on major satisfaction. A survey was conducted on 303 students of four-year entrepreneurship departments located in Daegu, Gyeongbuk, Gunsan, and Gwangju, and statistical analysis was conducted using SPSS22. As a result of the study, self-efficacy showed significant results in cognitive efficacy, social efficacy, and emotional efficacy. We looked at the reasons why students chose their major, and as a result, there was a large difference between cognitive efficacy and social efficacy. Differences in important subjects in education were also examined, and as a result, emotional efficacy, social efficacy, and cognitive efficacy showed significant results. In the survey of differences by grade, there was a significant difference between major satisfaction and class satisfaction. These results confirmed that the higher the self-efficacy, the overall effect on major satisfaction. This study is meaningful in that it comprehensively analyzes the general characteristics of entrepreneurship majors and their effects on self-efficacy and major satisfaction, derives implications, and presents educational directions.
Identification to Fake Messages with Two PKCs

Abstract In this paper, two public key cryptosystems are presented and analyzed. The first cryptosystem is an improvement of RSA public key cryptosystem. The second cryptosystem is a novel public key cryptosystem. The encryption/decryption moduli of the introduced new public key scheme is quite different from widely used encryption/decryption moduli by other public key cryptosystems. The introduced new public key cryptosystem based on the exponentiation congruence and use an interesting, computer-friendly modulus:2 n . These primitives can be used in protocols for authentication, key exchanges and digital signatures. The introduced two public key cryptosystems are more suitable for highly confidential digital transactions. Both of the cryptosystems are strongly IND secure. The most important thing is, that the both of the public key cryptosystems can identify whether a message is fake or not. The introduced new public key cryptosystem is very collaborative with hash functions such as Whirlpool and SHA-512, because of the modulus 2 n .
NFT Mart

Non Fungible Tokens which are commonly known as NFT’s are digital items such as audios, Videos, Photographs etc. NFT’s are unique cryptographic tokens that exist on a blockchain and cannot be replicated. All Non Fungible Tokens have unique identification codes and metadata that differentiate each token from each other.
Watermarking Technique for Verifying Medical Images in IOMT Using Fractal Encoding and Concept of UNET

The Internet of Medical Things, smart healthcare systems are becoming ubiquitous in our daily lives. Patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of electronic health records , medical images such as scanning reports X-rays for successful treatment and management of different ailments. A Multi watermarking method is proposed for medical images based on quantum random walk and optimization algorithm. A logo image is used to verifying medical image integrity is embedding in region of interest and text data are embedded in the region of non interest to conceal private hospital and patient informations. This method improves the authenticity and integrity of medical images. A number of experiments are conducted to validate the security ,robustness and capacity of the proposed multi watermarking scheme.
Social Media and Its Impact on Consumers Behaviour

Social media is becoming one of the most popular and commonly used ways of communication. Individuals use social media to communicate with one another all around the world. In recent years, people have used social media to share their stories. This also includes encounters with a product, service, or platform. These product reviews submitted by social media users are seen by thousands of people every day and have become a source of influencing consumer purchasing behaviour. Corporations have begun to utilise social media to advertise their products and services after discovering the significance of this medium. Social media is now being utilised to efficiently sell products and services to a diverse audience. The purpose of this article is to determine how various features of social media might influence customer purchasing behaviour.
Design and Implementation of a Secure QR Payment Based on Visual Cryptography

In this paper, the design and implementation of a secure payment system based on QR codes will be covered. In recent years, QR codes have been widely used due to their capacity to speed up payments and provide customers with the highest comfort. Yet, as convenient as QR-based online payment systems may seem, they are vulnerable to a variety of attacks. Transaction processing needs to be adequately secure in order to protect the privacy and accuracy of each payment operation. The online payment system must also guarantee the legitimacy of each transaction for both the sender and the recipient. This article offers security for the proposed QR-based system using visual cryptography. The recommended solution consists of a mobile application and a payment gateway server that employs visual cryptography. Customers who use the application may conduct financial transactions in a secure environment thanks to its clear and user-friendly layout.
Uninterrupted Flow of Information on Social Media a Boon or a Bane for the Society? : A Critical Study

By virtue of its its characteristic features social media provides a unique platform to its users to share ideas/information without any hindrance or interruption. From social networking sites like Facebook, Instagram, Twitter and YouTube to messaging apps like WhatsApp, Telegram, Viber and Facebook Messenger, social media covers a broad universe of platforms that facilitate seamless communication to people residing in any part of the globe. Today more than 4.7 billion people use social media, that is equal to roughly 60% of the world’s population. This could be possible because of continuously increasing popularity of mobile technology and large scale penetration of smart phones across the world. The information flow on social media without any gate keeping has two aspects. On the positive side it’s a wonderful gift of science &amp; technology to human beings that has made personal and professional life easy, provided ready-made solution to day to day problems, connected people residing in different corners of globe like family members and democratized the world of communication. But on the negative side, the uninterrupted flow of information on social media is far more dangerous than any evil spirit ever imagined. It can cause social disturbance and communal violence, prove to be an easy tool in the hands of mischief makers, peep into someone’s privacy, fleece innocent people, pose a threat to the national security and bring death and devastation in the society. Against this backdrop a rigorous research was done. The available literature on the topic was reviewed and a sample survey was conducted in Delhi &amp; NCR to delve deep into the topic and find out answer to the research question - if the uninterrupted flow of information on social media is a boon or a bane to the society?
자주적 어휘선택 전략에 의한 L2 어휘학습 및 학습자 견해

Purpose: This study aimed to examine the effects of Vocabulary Self-collection Strategy (VSS) withglossing on L2 word recall and retention as well as learner perspectives on vocabulary knowledgeand word consciousness by the strategy in EFL classroom contexts. Methods: For the study, sixty-four learners were randomly allocated for both VSS accompanied withL1 glossing (VSSL1) and VSS accompanied with L2 glossing (VSSL2) groups. Data were collectedthrough a pre-test for collecting target words, two immediate and two delayed post vocabulary tests,and a retrospective questionnaire. Results: The results revealed that VSSL2 group significantly outperformed in immediate word recalland delayed retention in receptive and productive knowledge than VSSL1 group. In addition, overall,positive perceptions on the strategy were found in vocabulary knowledge and word consciousnessfrom the two groups. It is noteworthy that the VSSL2 group showed significantly higher mean scoreof the statement regarding long-term memory effect by the strategy. Conclusion: The results from VSS L2 group appeared to be attributed to deeper processing for form-meaningconnections of target words with higher involvement load in need to understand, search for themusing L2-L2 dictionary, and evaluation to check proper meanings in the context on their own. Thiscould be strengthened by the cooperative classroom discussions with peers and teacher guidance forfull comprehension of target language input. (Dankook University)
Efficacy of Specific Learning Disorder Manual: A Pretest-Posttest Survey Among Primary School Teachers

Background: Specific learning disorder (SLD) is a neurodevelopmental disorder that are generally typically diagnosed in school going children. The teachers play a pivotal role in identifying SLD. Aim: The present study aimed to develop a manual for primary school teachers on SLD and to investigate the efficacy of the manual by conducting a pre and post-test survey. Method: The developed SLD manual consisted of signs and symptoms, causes, assessment, and rehabilitation. A questionnaire was constructed based on the developed manual which consisted of 34 questions. A pre-test survey was carried out, after which the manual was given to the participants and given two weeks’ time to go through the manual, after which posttest survey was conducted. Results and discussion: The results revealed that primary school teachers had somewhat moderate level of understanding about SLD. They were not aware about symptoms, rehabilitation options and no in-depth knowledge about SLD. Their performance improved significantly after they referred the manual and all of them opined that there is a need for in-depth education for primary school teachers about SLD. To conclude, primary school teachers lack adequate knowledge about SLD. Since teachers are the primary identifiers of academic difficulties of children, their expertise is of utmost significance because it involves the identification of SLD’s at the initial stage. Therefore, a manual about SLD will definitely uplift the knowledge levels of teachers and help in early identification &amp; intervention of SLD. Key words: Specific learning disorder, Awareness, Identification, Early intervention
Extracting and Analyzing Features in Natural Language Processing for Deep Learning with English Language

Natural Language Processing (NLP) is a field of study that develops software capable of interpreting human speech for mechanical use.Words are the building blocks of advanced grammatical and semantic analysis, and word segmentation is often the first order of business for natural language processing.This paper introduces the feature extraction method of deep learning and applies the ideas of deep learning to multi-modal feature extraction in order to address the practical problem of huge structural differences between different data modalities in a multi-modal environment.In this study, we present a neural network that can process information from several sources at once.Each mode is represented by a separate multilayer sub-neural network structure.It's purpose is to transform features from one mode to another.In order to solve the issues of current word segmentation techniques not being able to ensure long-term reliance on text semantics and lengthy training prediction time, a hybrid network English word segmentation processing approach is presented.This approach uses the BI-GRU (Bidirectional Gated Recurrent Unit) to segment English words and the CRF (Conditional Random Field) model to sequentially annotate sentences, which eliminates the long-distance dependency of text semantics and reduces the time needed to train the network and predict its performance.Compared to the BI-LSTM-CRF (Bidirectional-Long Short Term Memory-Conditional Random Field) model, the experimental results reveal that this technique achieves equivalent processing effects on word segmentation, while also boosting processing efficiency by a factor.
Object Detection with Voice Guidance to Assist Visually Impaired Using Yolov7

Abstract: Object recognition technology has revolutionized various domains such as autonomous vehicles, industrial facilities, and many more. However, the visually impaired individuals, who are most in need of this technology, have not been able to utilize it effectively. Therefore, this paper presents a deep learning-based object detection system that is specifically designed for the blind community. The system incorporates the YOLOV7 (You Only Look Once) algorithm for object recognition and uses text-to-speech (TTS) technology to provide a voice-guidance technique that conveys information about the objects around them. The main objective of the proposed system is to empower visually challenged individuals to independently identify objects in a particular space without relying on external assistance. The system's efficiency and performance are thoroughly scrutinized through experiments to validate its accuracy and effectiveness. The proposed system employs technical terms such as deep learning, object recognition, YOLOV7 algorithm, and also image classification techniques for feature identification and extraction of the video frames and categorize them into the respective classes. It uses COCO dataset, which consist of around 123,287 hand-labeled images classified into 80 categories, has been utilized to train the proposed system. In conclusion, the suggested system for object detection uses cutting-edge deep learning techniques along with voice guidance methods to assist individuals who are visually impaired in identifying objects independently. The performance of the system is evaluated through experiments, demonstrating its effectiveness and potential for real-world applications
BUILDING A CHATBOT FOR HEALTHCARE USING NLP

&lt;p&gt;A chatbot or conversational agent is a software that can communicate with a person using natural language. Designing dialogue is one of the key challenges in artificial intelligence and natural language processing. Creating effective chatbots has been the most difficult problem for artificial intelligence since its beginning. While chatbots can perform a variety of tasks, their primary objective is to comprehend human language and provide appropriate responses. In the past, chatbot architectures were created using simple statistical methods or manually crafted templates and rules. However, since around 2015, end-to-end neural networks have replaced these models due to their greater learning capabilities. The encoder-decoder recurrent model, which was originally developed for the neural machine translation field and proved highly successful, now dominates conversation modeling. Several enhancements and modifications have been made to chatbots to significantly enhance their conversational abilities up to this point.&lt;/p&gt;
BenCoref: A Multi-Domain Dataset of Nominal Phrases and Pronominal Reference Annotations

Coreference Resolution is a well studied problem in NLP. While widely studied for English and other resource-rich languages, research on coreference resolution in Bengali largely remains unexplored due to the absence of relevant datasets. Bengali, being a low-resource language, exhibits greater morphological richness compared to English. In this article, we introduce a new dataset, BenCoref, comprising coreference annotations for Bengali texts gathered from four distinct domains. This relatively small dataset contains 5200 mention annotations forming 502 mention clusters within 48,569 tokens. We describe the process of creating this dataset and report performance of multiple models trained using BenCoref. We expect that our work provides some valuable insights on the variations in coreference phenomena across several domains in Bengali and encourages the development of additional resources for Bengali. Furthermore, we found poor crosslingual performance at zero-shot setting from English, highlighting the need for more language-specific resources for this task.
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains

Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space. However, in the presence of unpredictable noise, the fitness and descriptor of the same solution can differ significantly from one evaluation to another, leading to uncertainty in the estimation of such values. Given the elitist nature of QD algorithms, they commonly end up with many degenerate solutions in such noisy settings. In this work, we introduce Archive Reproducibility Improvement Algorithm (ARIA); a plug-and-play approach that improves the reproducibility of the solutions present in an archive. We propose it as a separate optimization module, relying on natural evolution strategies, that can be executed on top of any QD algorithm. Our module mutates solutions to (1) optimize their probability of belonging to their niche, and (2) maximize their fitness. The performance of our method is evaluated on various tasks, including a classical optimization problem and two high-dimensional control tasks in simulated robotic environments. We show that our algorithm enhances the quality and descriptor space coverage of any given archive by at least 50%.