Reviewerly/exHarmony huggingface dataset:
A Game or Notes? The Use of a Customized Mobile Game to Improve Teenagers’ Phishing Knowledge, Case of Tanzania

Recently, phishing attacks have been increasing tremendously, and attackers discover new techniques every day to deceive users. With the advancement of technology, teenagers are considered the most technologically advanced generation, having grown up with the availability of the internet and mobile devices. However, as end-users, they are also considered the weakest link for these attacks to be successful, as they still show poor cybersecurity hygiene and practices. Despite several efforts to educate and provide awareness on the prevention of phishing attacks, less has been done to develop tools to educate teenagers about protecting themselves from phishing attacks considering their differences in social-economic and social culture. This research contributes a customized educational mobile game that fits the African context due to the participants’ existing differences in social-economic and social culture. We initially conducted a survey to assess teenagers’ phishing and cybersecurity knowledge in secondary schools categorized as international, private, and government schools. We then developed a customized mobile game based on the African context taking into consideration participants’ differences in social-economic and social culture. We compared the performance of phishing knowledge of teenagers using a game and a traditional teaching method. The traditional teaching method was presented by the reading notes method. The results revealed that teenagers’ phishing and cybersecurity knowledge differs based on their socioeconomic and social culture. For instance, international, private scholars, and those who live in urban areas have better phishing knowledge than those from government schools and those who live in rural areas. On the other hand, participants who had a poor performance in the first assessment improved their knowledge after playing the game. In addition, participants who played the game had retained their phishing knowledge more, two weeks later, than their counterparts who read only notes.
A Review of Various Line Segmentation Techniques Used in Handwritten Character Recognition

Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation.
Comparison of exact and approximate approaches to UAVs mission contingency planning in dynamic environments

This paper presents a novel approach to the joint proactive and reactive planning of deliveries by an unmanned aerial vehicle (UAV) fleet. We develop a receding horizon-based approach to contingency planning for the UAV fleet's mission. We considered the delivery of goods to spatially dispersed customers, over an assumed time horizon. In order to take into account forecasted weather changes that affect the energy consumption of UAVs and limit their range, we propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. These rules are used in the course of designing the contingency plans related to the need to implement an emergency return of the UAV to the base or handling of ad hoc ordered deliveries. Due to the nonlinearity of the environment's characteristics, both constraint programming and genetic algorithm paradigms have been implemented. Because of the NP-difficult nature of the considered planning problem, conditions have been developed that allow for the acceleration of calculations. The multiple computer experiments carried out allow for comparison representatives of the approximate and exact methods so as to judge which approach is faster for which size of the selected instance of the UAV mission planning problem.
Thai digit handwriting image classification with convolution neuron networks

This paper aims to determine the efficiency in classifying and recognizing Thai digit handwritten using convolutional neural networks (CNN). We created a new dataset called the Thai digit dataset. The performance test was divided into two parts: the first part determines the exact number of epochs, and the second part examines the occurrence of overfits in the model with Keras library's EarlyStoping() function, processed through Cloud Computing with Google Colaboratory, and used a Python programming language. The main parameters for the model were a dropout of 0.75, mini-batch size of 128, the learning rate of 0.0001, and using an Adam optimizer. This study found the model's predictive accuracy was 96.88 and the loss was 0.1075. The results showed that using CNN in image classification and recognition. It has a high level of prediction efficiency. However, the parameters in the model must be adjusted accordingly.
Unified approach to the nonlinear Rabi models

Abstract An analytical approach is proposed to study the two-photon, two-mode and intensity-dependent Rabi models. By virtue of the su(1, 1) Lie algebra, all of them can be unified to the same Hamiltonian with <?CDATA ${\mathcal{Z}}_{2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="script">Z</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> symmetry. There exist exact isolated solutions, which are located at the level crossings between different parities and correspond to eigenstates with finite dimension. Beyond the exact isolated solutions, the regular spectrum can be achieved by finding the roots of the G -function. The corresponding eigenstates are of infinite dimension. It is noteworthy that the expansion coefficients of the eigenstates present an exponential decay behavior. The decay rate decreases with increasing coupling strength. When the coupling strength tends to the spectral collapse point g → ω /2, the decay rate tends to zero which prevents the convergence of the wave functions. This work paves a way for the analysis of novel physics in nonlinear quantum optics.
Projection or Reflection? The Pandemic Bookshelf as a Mirror for Self-Image and Personal Identity

The opportunity for display of books is a frequently cited reason for choosing print reading over e-reading. But the desired display is not always intended for others. For many, the most important observer of a personal bookshelf is the person to whom it belongs: a physical shelf as means to access, commemorate, and honour reading history and reading identity. This makes it essential to consider bookshelves in the era of COVID-19 not only in terms of the Zoom feed, beaming snapshots of collections to the outside world, but also Zoom “self view”: the digital selfportrait hovering in the corner of our device screens. Drawing on qualitative data from surveys, focus groups and interviews conducted between 2014 and 2021, this paper will explore the phenomenon of the backdrop as mirror, and examine curated bookshelves - including manipulated, misattributed, or appropriated bookshelves - in terms of self- as well as public image.
Development of a Mobile-Based Hostel Location and Recommendation Chatbot System

A Chabot is a conversational intelligent agent that has the capability of engaging in human-like interaction with its users. A lot of chatbots have been developed, but to the best of our knowledge, there are few or no chatbots that have been developed for hostel location integrated with a recommendation component to ease the cost, time, and stress of identifying suitable hostels for students, especially at higher institutions of learning. Therefore, this work develops a location-based chatbot system enhanced with recommendation capabilities to allow students to locate hostels that satisfy their needs in an easy and efficient way. The chatbot system was designed as a cross platform compatibility application with different tools and technologies which include Python, HTML and CSS with JavaScript to enhance the interactivity and attractiveness of the system. PHP provides access to MySQL database. The chatbot system provides good experience to its users in terms of loading speed, user friendliness, interface appearance, platform compatibility and recommendation accuracy as it allows them identify suitable hostel speedily and as well provides personalized recommendation of hostels to them.
Framework Model to Enhance the Effectiveness of Blockchain Technology through the Knowledge Management Processes

This study topic is essential since it highlights the challenges facing the effective integration of blockchain technology and the necessary measures to improve their performance through knowledge management processes. The primary aim of this study was to develop a framework model for advancing the effectiveness of blockchain technology in the Middle East and the Gulf region. For this study, the researchers collected secondary data by conducting a deep systematic review of all the information found online on the topic. The researchers selected essential studies by integrating an inclusion and exclusion criteria. This research exclusively included articles released post-2019 to guarantee that they contained relevant information. In addition, studies had to be in English to ensure clarity for all the researchers involved. The authors collected the secondary data through the GovInfo.gov database as well as the Google Scholar search engine. After the comprehensive reviewing of all the relevant articles that fit the inclusion and exclusion criteria, fifteen articles were included in the research. Out of the fifteen, eight focused on the Middle East and the rest on the Gulf. Conclusion: The development of blockchain technology in the Middle East and the Gulf region has witnessed tremendous improvements over the years. Many sectors like healthcare and banking industries have dedicated efforts to applying the technology in their processes. However, the technology has faced numerous challenges that need immediate address to ensure the ultimate success of this technology. The concerned policymakers should research on the topic and develop appropriate policies that guarantee the effectiveness of the technology.
Cloud-Base Height Estimation Based on CNN and All Sky Images

Among several meteorological parameters, Cloud-Base Height is employed in many applications to provide operational and real-time cloud-base information to the aviation industry, to initialize Numeric Weather Prediction models and to validate climate models. Moreover, Cloud-Base Height is also useful in the nowcasting (very short-term forecasting) of solar radiation. As cloud movements mainly affect the solar irradiance availability, their characterization is extremely important for solar power applications; an accurate estimation of the ground shadowing requires the knowledge of cloud height and extent. In the present work, the Cloud-Base Height value is estimated starting from sky images acquired from a single All Sky Imager. In order to fulfill this task, a Convolutional Neural Network model is chosen and developed.
impact of training for IoT technology awareness program In the Saudi Arabian Universities

Nowadays, Internet of Things (IoT) has been making drastic changes in e-learning &amp; other fields of our daily lives such as the sectors like healthcare, agriculture, transportation, business &amp; trade and various engineering services. It is predicted that fifty billion IoT devices and six billion mobile devices will be on this earth in 2020 itself. Industries around the world are planning for implementing IoT as it reduces the expenses on energy by fifty percent and eventually increases profit. Therefore, academic institutions must also work on IoT enabled services to compete at the global level. IoT devices are connecting people to the web thus everyone comes closer to each other. Job sector would become more competitive from now on as many people opt for e-learning to obtain easily affordable degrees and required skills while continuing their professional commitments. This research attempts as preliminary step to assess the basic training of IoT to students in order to enhance their motivation to use IoT based equipment which is the prime requirement for an IoT enabled campus. T-test had been conducted to calculate the effectiveness of training as the universities in Saudi Arabia are seeking to launch IoT programs in their various departments, are motivated as well for trainings to prepare their teaching staff and students for IoT based courses.
Leveraging Textual Information for Knowledge Graph-oriented Machine Learning: A Case Study in the Construction Industry

The proven power of Knowledge Graphs (KGs) to effectively represent lexical and semantic information about numerous and heterogeneous entities and their interconnectedness has led to the growing recognition of their potential in engineering disciplines. Meanwhile, a greater focus has been placed on graph embedding techniques to derive dense vector representations of KGs. Such representations could enable the use of conventional machine learning techniques over the content of KGs. However, in the context of building engineering, the quality of the graph embeddings could be problematic, mainly due to the relatively small size of the KGs that are created for individual buildings. This paper aims to investigate the effectiveness of applying KG embedding methods when the elements of the building are described narrowly within the KG. The results of our experiments confirm that proper use of data transformation techniques can significantly improve the quality of the feature representation for downstream tasks.
Qr-Code-Based Mathematics Contextual Comic Module for Elementary School Students

This research is based on the results of a preliminary study to teachers and students of class V Elementary School regarding the analysis of the need for Mathematics teaching materials. Initial findings indicate that students' interest in learning mathematics is low, as can be seen from students who pay less attention to when the teacher explains. It is also known that there is no use of other teaching materials other than textbooks. The purpose of this study was to determine the need for appropriate teaching materials in fostering students' interest in learning mathematics. The method used in this research is descriptive qualitative. The subjects of this study were teachers and students of class V, which was held at SDN 2 Pasuruan, Penengahan, Kalianda, South Lampung. Data collection techniques are observation, interviews and questionnaires. The data were analyzed using the Miles and Huberman model, namely: data reduction, data presentation and drawing conclusions/verification. The results of this study indicate that there are still many students whose learning interest is low when studying mathematics but gets a positive response when the teacher uses QR-Code-based illustrated teaching materials so that students become enthusiastic and have an effect on increasing their interest in learning. Specifically on the scale material, the researcher found data that there were still many students who had difficulty understanding it, such as using comparisons on a scale and how to change units of length. Based on the results of the needs analysis, conclusions can be drawn: 1) The use of teaching materials as a learning resource in mathematics is not optimal, 2) Teaching materials that need to be developed in learning mathematics with scale materials, one of which is QR-Code-based comics
HISTORICAL-PEDAGOGICAL AND THEORETICAL DISCOURSES

The peculiarities of distance learning and its technologies in historical perspective, pedagogical methods and techniques used in distance learning are considered in the article. The purpose of the article is to analyze the historical and pedagogical aspects and theoretical foundations of distance learning and its introduction by higher educational institutions of Ukraine in modern conditions. Methodology of the article. Consideration of the essence of the concepts of “distance education” and “distance learning”. It is found out that distance learning involves the interaction between teacher and students at a distance with the use of information and communication technologies that allow to provide it in individual and group forms. It is revealed that in historical retrospect the stages of distance learning development are identified with the use of one-way information transmission (radio, television, video), case studies, two-way communication (consultations, correspondence, etc.), broadcast learning (conferences, forums, computer technology and educational platforms, etc.). The advantages and disadvantages of synchronous and asynchronous learning are demonstrated. The theoretical basis of distance learning is presented with the help of didactic teaching methods, scientific and methodological support, principles of didactics. Results. Analysis of historical stages of distance learning allowed to identify the peculiarities of the use of didactic models, methods, computer technologies (online learning, online interactive learning, e-mail, Web, multimedia programs, games, studying platforms, etc.). It is proved that the effectiveness of distance learning depends on taking into account the individual characteristics of the subject of studying, adherence to the appropriate rate of presentation and control of student’s acquired knowledge and skills, professional orientation of studying, essence of studying, development of individual and group creativity and competencies declared in the academic program. Practical significance of the obtained results is in the fact that they allow a teacher to choose time-tested methods and technologies of distance learning, take into account their advantages and disadvantages in the educational process. Key words: didactic principles of distance learning, transmission of information by means of communication, online services, advantages and disadvantages of distance learning, stages of development of modern distance learning.
An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning

Owing to the loss of effective information and incomplete feature extraction caused by the convolution and pooling operations in a convolution subsampling network, the accuracy and speed of current speech processing architectures based on the conformer model are influenced because the shallow features of speech signals are not completely extracted. To solve these problems, in this study, we researched a method that used a capsule network to improve the accuracy of feature extraction in a conformer-based model, and then, we proposed a new end-to-end model architecture for speech recognition. First, to improve the accuracy of speech feature extraction, a capsule network with a dynamic routing mechanism was introduced into the conformer model; thus, the structural information in speech was preserved, and it was input to the conformer blocks via sequestered vectors; the learning ability of the conformed-based model was significantly enhanced using dynamic weight updating. Second, a residual network was added to the capsule blocks, thus, the mapping ability of our model was improved and the training difficulty was reduced. Furthermore, the bi-transformer model was adopted in the decoding network to promote the consistency of the hypotheses in different directions through bidirectional modeling. Finally, the effectiveness and robustness of the proposed model were verified against different types of recognition models by performing multiple sets of experiments. The experimental results demonstrated that our speech recognition model achieved a lower word error rate without a language model because of the higher accuracy of speech feature extraction and learning using our model architecture with a capsule network. Furthermore, our model architecture benefited from the advantage of the capsule network and the conformer encoder, and also has potential for other speech-related applications.
PENGEMBANGAN LKPD BERBASIS AJARAN TAMANSISWA TRI-N MUATAN BAHASA INDONESIA KELAS II DI SD N PUREN

This descriptive study aims to describe the development and feasibility of LKPD based on the teachings of Tamansiswa Tri-N content in Indonesian for class II at SD N Puren. The research method used in this study is the Borg and Gall model development method which has been modified by Hasyim and is only limited to seven research and development steps, namely: 1) potential and problems, 2) data collection, 3) product design, 4) product validation. , 5) product revision, 6) product trial, 7) product revision. Data collection techniques in the form of observation, interviews and questionnaires with a set of written statements or questions to respondents to answer. The results of this development research indicate that the LKPD product based on the teachings of Tamansiswa Tri-N Class II Indonesian language content at SD N Puren has been successfully developed. The feasibility of the LKPD product obtained an average feasibility score of 93.3% by material experts and 80% by media experts. Then the results of the questionnaire that was tested in the field to determine the response of the school, namely the second grade teacher obtained an average score of 99.09%. While the test of student response questionnaires obtained an average score of 99.70%. Thus, the development of LKPD products based on the teachings of Tamansiswa Tri-N containing Indonesian class II at SD N Puren has good quality to help students learn.
Effect Measurement for Human Motion Rehabilitation Training Using Improved Deep Reinforcement Learning and IoT Networks

Measuring the effect of human motion rehabilitation training is important to help persons develop motion rehabilitation training plans. The current human motion rehabilitation training effect measurement algorithm has the problems of too large gap between the smoothness of the motion speed curve and the reality, high key frame extraction error rate, low measurement accuracy, long measurement time, and low satisfaction. Therefore, this paper proposes a human motion rehabilitation training effect measurement algorithm using improved deep reinforcement learning and Internet of Things (IoT) networks using IoT network technology to collect human motion rehabilitation training videos. The key frames of the human motion rehabilitation video data are extracted according to the interframe distance, and the metalearning method is used to improve the deep reinforcement learning network, and the obtained key frames are input into the improved deep reinforcement learning network to obtain the motion speed curve of human motion rehabilitation training. The smoothness of the motion velocity curve is calculated, and high smoothness indicates a good human motion rehabilitation training effect, while low smoothness indicates a poor human motion rehabilitation training effect, so as to complete the measure of human motion rehabilitation training effect. The results show that the smoothness of the motion speed curve of the proposed algorithm is closer to reality, the average error rate of key frame extraction is 1.45%, the measurement accuracy of rehabilitation training is more than 90%, the measurement time is controlled below 2.1 s, and the maximum user satisfaction is 93.1, which shows that the practical application of the algorithm is good.