Reviewerly/exHarmony huggingface dataset:
Efficacy of BASA Booklet Innovation as Reading Difficulty Intervention Material for Grade 1 Learners in Taba-Ao Elementary School

The study focuses on the efficacy of the BASA booklet as a reading difficulty intervention material for Grade 1 learners in Taba-ao Elementary School, and the study used a qualitative action research method. All the Grade One learners at Taba-ao Elementary School are the respondents of this study. The researcher used the standardized test set by the Department of Education’s Division of Cebu Province Memo No. 425, s. 2021, the usage of the Early Grade Reading Assessment Test Toolkit, which is also where the BASA Booklet was patterned after. This study used t-testing in order to explain the statistical data and analysis that shows a significant effect of the positive benefit of the BASA Booklet to those learners who are struggling readers. Thus, the researcher-innovator encourages teachers to use the BASA booklet as an intervention material to aid the learner’s reading difficulty.
Investigating the Interaction between Data and Algorithms

Research in computer vision is centered on algorithmic improvements, for example, by developing better models.Thereby, the data is considered fixed.This is in contrast to many real-world applications of computer vision systems in which algorithms and data co-evolve.To address this shortcoming of previous research, we study the properties of the data and their interaction with deep learning algorithms.Thereby, we investigate the size of the data, the share of mislabels, class imbalance and the presence of unlabeled data which can be leveraged using semi-supervised learning.In experiments on 100 classes from ImageNet, we show that a tiny network architecture outperforms a much more powerful one it if has access to only a little bit more data.Only if vast amounts of data are available so that adding even more images has little effect on performance, large architectures dominate smaller ones.If little data is provided, adding a few labeled images has a huge effect on accuracy.Once accuracy saturates, massive amounts of additional data are needed to achieve even small improvements.Furthermore, we find that mislabels severely reduce performance.To fix that, we propose a cost-efficient way of identifying mislabels which is especially beneficial if many images are already available.Conversely, if little data is available, labeling more images is more advantageous than cleaning existing annotations.In the case of imbalanced data, we illustrate that labeling more instances from rare classes has a much greater effect on performance than only increasing dataset size.Moreover, we show that leveraging unlabeled images by semi-supervised learning offers a consistent benefit even if the labeled subset contains significant label noise.
Towards Deep Learning: A Comprehensive Overview on PSO with Machine Learning

In this era of deep learning where AI and machine learning models are used in well-known applications such as speech to text, real-time translation, image recognition, requires a large amount of data for the training.To process a large amount the data, model efficiency is a major concern, for this, there are few optimizations' methods being developed by a different researcher.In our research, we briefly discussed the famous optimization method such as PSO and their implementation with different machine learning models and perform experiments with different volumes of textual data and overview the performance with or without PSO algorithm, besides we also utilize a similar approach with neural networks.Experimental results show the proposed overview significance on data volume.
Video Analysis Tool with Template Matching and Audio-Track Processing

In the last few decades, we have observed the rapid advancement of multimedia analysis tools, and video analysis is one of such application domains.While much effort has been put into the analysis of business and professional videos (e.g., films, professional sports, security cameras) by utilizing advanced image processing algorithms, many of these approaches often do not work well with raw videos that are recorded with a single, consumer-level camera (e.g., a mobile phone) by non-professional videographers.These "amateur" videos typically do not have multiple view-angles and often contain low-resolution and noisy images, making it more difficult to apply certain algorithms compared to cases with videos that are professionally recorded with multiple high-quality cameras and that are properly edited.In this paper, we discuss a prototype interactive video image analysis tool that combines both the image and audio analysis of such videos.The tool provides multiple channels of data analysis visualizations that presumably complement each other for the users to understand the video content effectively and more easily.
Archival Handwritten Digits Identification Through Deep Learning Models Johnson

This research is inspired by the work of climate scientists to analyse archival handwritten documents and predict reliably changes in the climate.The aim of this work is to better understand recognition of handwritten documents especially focusing on archival maritime logs.Indeed, OCR (Optical Character Recognition) has existed for many years.The shortcomings of this widely used method, however, are manifesting in frequent confusion of digits and letters when it comes to archival handwritten documents.The conversation of perfecting the methods of automated recognition of handwritten characters has been evolving in recent years.With the advent of deep learning methods, new tools are considered within the problem space.In this extension of thesis work, two such methods are put to the test -convolutional and long-short term memory (LSTM) neural networks (NN).The applicability of several state-of-the-art models is considered with detailed experiments and comparative analysis.A compound model of convolutional NN followed by LSTM is also considered.While all models register high accuracy, it is observed that the compound model performs faster with accuracy above the lone CNN.
Covert Communications via Spotify Playlists

In this paper, we utilize a music streaming platform called Spotify to create two distinct covert channels.The usage for these covert channels is based on the ability for various users to access publicly available playlists.Users can use these playlists to send and receive covert messages.The first method uses the first letter of an album name to construct a message, and the second uses song's explicit tags to create binary messages.We concluded that these covert channels were successful.This is because they could send messages with adequate obscurity that outside actors would not be privy to the covert message contained.
Interactive Design of the Shopping Process Using User Experience in the Food Section of Supermarket

The design of supermarkets in Korea is uniform, and the competition between supermarkets is mainly a price war.The purpose of this study is to combine interaction design concepts to suggest improvements to supermarkets and create a better user experience.Firstly, the theory of supermarkets, the current situation of hypermarkets, interaction design, and persona is summarized through literature research.Then an observational survey was conducted on the user characteristics and behaviors of supermarkets, etc., and a 5-point Likert scale was used to analyze the user experience satisfaction at each behavioral stage.Based on the observation results, a middle-aged female teacher was set as a persona, and the user experience analysis was conducted for each of the three cases through the six elements of interaction design to find out the inconvenience and problems of the users in the food section of the supermarket.In the conclusion section, a proposal is made based on the results of the analysis through the six elements of interaction design.In future research, the interaction design study will include the analysis of relevant aspects of the self-checkout system.We have high hopes for the application and development of interaction design in supermarkets.
UNIVERSITY EXTENSION AND INNOVATIVE UNIVERSITY: AN EXPLORATORY REVIEW IN THE UFSC INSTITUTIONAL REPOSITORY

This research aims to analyze the academic production on the themes of innovative university and university extension, in the institutional repository of UFSC. It is a qualitative study, with a bibliographic approach, using the Systematic Search Flow (SSF) method. As a result, university management is crucial for extension projects in promoting results for society; it was found that the term entrepreneurial university is more used than innovative university, meaning acting in teaching-research-extension, which is understood as innovation and entrepreneurship, with the transfer of knowledge to the market, using mechanisms of protection, becoming an intangible asset. The emergence of the innovative/entrepreneurial university is evident in the innovation ecosystem, considering the university-industry-government cooperation since the teaching-research-extension triad represent the pillars for the scope of the university’s actions in society.
Currency Recognition System Using Image Processing: Libyan Banknote as a Case Study

Banknote recognition in the field of image processing is used to distinguish between banknotes of different countries and determine their values. One of the problems that emerged with this process is the recognization of the banknote. The recognition operations for Myanmar currency denominations comprise six stages: image capturing, preprocessing, target region extraction, number segmentation, currency value recognition, and output with screen displaying and voice. The currency recognition system is implemented by comparing the similarity of each banknote value template via image processing steps in the Matlab environment. To prove the effectiveness of the proposed system, the number of banknote images with two types (new and medium) from each of the four types of denominations were tried
REVIEW ON SCENE SEMANTICS EXTRACTION FOR DECISION MAKING SYSTEM IN AUTONOMOUS VEHICLES

It is a worldwide witnessed fact that traditional manual driving mechanism will be superseded by Autonomous Vehicles [AVs] in coming years. Autonomous vehicles are going to be most foreseen development in the automotive industry. That would require Decision Making System which will enable AVs to intuitively interpret the real-time situations around. Most importantly scene recognition on streets &amp; extracting relevant semantics from the scene is challenging task. So, image classification &amp; object detection techniques using Deep Convolutional Neural Networks [DCNN] are going to play vital role in every other methodology designed for scene semantics extraction. As per the extracted scene semantics DMS actuates the necessary devices which control the speed of vehicle &amp; steering angel. So for that matter information extraction from road scene images covering all aspects to take intuitive decisions has huge concern with overall performance of the AV’s.
A REVIEW ON OPTIMIZATION TECHNIQUE FOR FAULT TOLERANCE IN CLOUD COMPUTING

It is the outcome of the emergence of on demand service in big scale distributed computing models. Adaptable technology because it delivers software and resources that can be flexibly scaled up and down. These systems have varying degrees of reliability. Tolerance to unexpected hardware or software failures is measured by a system's fault tolerance. As a result of cloud computing’s inability to handle a wide range of errors, its reputation has been tarnished. Tolerating Byzantine errors is difficult since they typically go unnoticed at the beginning and can quickly spread to other virtual machines before being discovered. Thus, vital applications such as air traffic control, online baking, and so on are still avoiding the cloud because of these concerns. As well as some existing models, the goal of this research is to better understand and compare fault tolerance strategies utilised for fault tolerance in cloud systems. The purpose of this work is to evaluate a cloud -based fault-optimization strategy that will address the shortcomings of existing algorithms.
ELEVATING INFORMATION SECURITY PRACTICES WITHIN SUDANESE HEALTHCARE ESTABLISHMENTS' STAFF

: In today’s digital era healthcare establishments find information technologies invaluable in daily tasks. The paper strongly based upon a research that is currently being conducted by the author and according to the results from the research survey only 20% of healthcare establishments in Sudan has security initiatives for their employees and make use of electronic security systems or even physical instruments to protect their assets and patients’ information, whilst on the contrary, 80% have no security measures or any security policies. This is due to there is a lack of academic and professional literature about information security management and information security culture. Moreover, as grew to the best knowledge of the author and extracted from the results, healthcare community thought their role is to heal patients and they do not have any responsibility to protect patients’ information, whereas they deem such role belong to the computer department, even if, these security breaches computer related (e.g. viruses), or socially motivated (e.g. theft of equipment). Therefore, the overall aim of this paper is to identify factors that assist the implementation of information security culture and practices within healthcare establishments, and assist when conducting awareness programmers, so it discusses the need to promote information security issues within contemporary Sudanese healthcare establishments and the consequent need for appropriate training and awareness schemes. In addition, the paper highlights sequence basic elements that healthcare establishments should consider when construct a training and awareness program.
Improved Method of Blockchain Cross-Chain Consensus Algorithm Based on Weighted PBFT

Aiming to solve the problems of low fault tolerance, low throughput, and high delay in traditional methods, an improved method of the blockchain cross-chain consensus algorithm based on weighted PBFT is proposed. This article constructs a blockchain cross-chain exchange model based on cluster centers and divides the nodes in the blockchain system into consensus service nodes, cross-chain exchange nodes, and application nodes to improve the performance of consensus computing services. On this basis, according to the weighted PBFT consensus mechanism, the blockchain consensus environment is set up, and the distribution of nodes in the consensus domain and the blockchain signature scheme are obtained. Therefore, the blockchain cross-chain consensus optimization algorithm is designed to reduce throughput and delay and optimize the consensus effect. The experimental results show that the proposed method can effectively improve the shortcomings of traditional methods, with high throughput and low latency, and strong security. It shows that it is a low resource consumption and secure consensus method.
IMPLEMENTATION OF CYBER SECURITY ATTACKS AND STRATEGIC MITIGATION MECHANISMS

Cyber threats have increased drastically in the recent years and the most common targets are organisation applications or systems for data theft, disrupting the operations or any other malicious use. Incorporating website security prevents these sorts of attacks on the system. It is the act/practice of protecting websites from unauthorized access, use, modification, destruction, or disruption. A web application will be created and tested on various attacks such as Brute Force Dictionary attack, Denial-of-Service attacks, Cross Site Scripting (XSS) attack, NoSQL injections and WebSocket attacks. The vulnerabilities will be analysed, and resolved to ensure that the confidentiality, integrity, and authenticity of the user data is not compromised. To improve the website security and privacy, measures will be taken to add security features and the code of the website will be modified.
Development and Evaluation Efficacy Enhancement Module for Teaching English among Early Childhood Teachers

This study aimed to develop and evaluate the Efficacy Enhancement Module for Teaching English / PEMBI Module among early childhood teachers.This study used the Design and Developmental Research (DDR) method with three phases of the study.Phase I was the need analysis phase where a total of 90 KEMAS Preschool teachers were given the questionnaire and interviewed.Phase II was the phase of module design and development where the validity of the module content was assessed by nine expert panels.Phase III which was the implementation and evaluation phase was conducted on 66 KEMAS Preschool teachers who were divided into treatment group (n=33) and control group (n=33) at random.Module content development used the Self Efficacy Theory, Keller ARCS model and Androgogy Learning Theory.The instruments consisted of the English Teaching Efficacy Level Questionnaire and the English Teaching Anxiety Level Questionnaire.The findings of the Phase I study showed that teachers believe they are weak in managing their emotional development (M=3.47,SD=6.55), lack self-modelling (M=3.38,SD=0.47), lack knowledge in teaching English (M=3.17,SD=0.43) as well as have relatively low self-assessment (M=2.59,SD=4.62).Therefore, they supported the need for a built module to improve the self-efficacy of teaching English (M=4.04,SD=0.62).The findings of the Phase II study showed that the level of experts' agreement on the usability of the module exceeded 84.6 percent.The findings of the Phase III study showed that there was a significant difference between the mean scores of pre-test and post-test in terms of self-efficacy belief [t(32) = -7.66;p<0.05]; self-efficacy practice [t(32) = -8.58;p<0.05]; and anxiety level [t(32) = 4.128; p<0.05].The results of the interview found some strengths of the module, namely the suitability of the module material, improvement of knowledge and skills, self-change and the quality of teaching.The main disadvantage of the module is that it lacks of practical activities and time.In conclusion, this PEMBI Module is effective in improving the knowledge, skills and self-efficacy to teach English.The implication of the study shows that this module can be used for teacher professional development training to improve the quality of English teaching especially from the psychological aspects of teachers including self-efficacy and anxiety of teaching English.
Inter-sentence Entity Relation Extraction based on GNN of Message Propagation

Abstract Compared with sentence-level relation extraction, it is more common that entities involving relationships exist in multiple sentences in actual scenarios. Therefore, document-level relation extraction has gradually become a research hotspot in the field of information extraction in recent years. In order to make full use of the contextual information and structure information of the document, this paper combine embedding representation of entities and the explicit structural relationships between entities to study extraction of document-level inter-sentence relations. Firstly, document is encoded using BiLSTM, while document graph is established using the explicit structure of document; Secondly, relational graph convolutional neural network based on message propagation is used to update nodes in the graph, thus local information of document is integrated into node embedding representation, using which edge embedding is updated; Finally, an iterative algorithm is used to complete the reasoning of edge information, and entity relation is predicted using a feed-forward network based on edge embedding. Experimental results show that, compared with the EoG model, the F1 values of inter-sentence relation are increased by 2.6% and 0.8% on the CDR and GDA datasets, respectively.
APPLYING OPINION MINING ALGORITHMS TO ANALYZE THE USER SENTIMENTS FROM USER REVIEWS TO RATE THE RESTAURANTS IN SULTANATE OF OMAN

Humans now spend the bulk of their time on the Internet, thanks to the huge rise of social media and other applications. Organizations and individuals have a shared platform to express their thoughts and ideas on any entity in the globe. Every website has a massive amount of information about products and services. Through social media tools, we, as humans, may remark on any subject or incident. Most of the time, these public opinions are considered by organizations and individuals to better corporate operations and strategies, or in the decision-making process. Although these user opinions are practical in decision-making processes, manually analyzing and summarizing them is a difficult effort. It is a lengthy process. As a result, having automated opinion mining techniques to analyze user sentiments is critical. Since the beginning of social media, sentiment analysis has been a hot research area. Many studies have been conducted in various fields. The tourism sector is highlighted as one of the important topics for diversification in Oman Vision 2040. This study suggests an analysis of user reviews of Omani restaurants, categorizing them as positive or bad, and ranking the restaurants according to the reviews. The reviews are analyzed using opinion mining methods
Moving Koha library catalogue into linked data using the LODRefine

Purpose The purpose of this paper is to investigate connected data through the use of open-source technology. It demonstrates the transformation process from library bibliographic data to linked data, which allows for easy searching across numerous collections of information. Design/methodology/approach In generating this file, a high-level operating system such as Ubuntu, which is based on the LAMP architecture, is used. It is required to use open-source strategies in building the relevant information. LODRefine is being used to convert all of Koha's bibliographic data into linked data that is now available on the Web. This framework has been conceptualized and formulated based on linked data principles and search algorithms accordingly. Findings Linked data services have been made publicly available to library users by using a variety of different forms of data. Information may be sought quickly and easily using this interface built on numerous search structures. Aside from that, it also meets the needs of users who use the linked data search mechanism to find information. Through modern scripts and algorithms, it is now possible for library users to easily search the linked data enables services. Originality/value This paper demonstrates how quickly and easily related data from bibliographic details may be developed and generated using a spreadsheet. The entire procedure culminates in the presence of specialists in the library setting. A further advantage of the SPARQL system is that it allows visitors to group distinct concepts and aspects using independent URIs and URLs instead of the SPARQL endpoint.