Reviewerly/exHarmony huggingface dataset:
Segmentation of University Students using Clustering and considering a Virtual Cycle

The present work had as purpose the creation of Grouping or Clustering Models for each specialty that the National University of Engineering has, with the purpose of carrying out tutorials focused on students who obtained low academic performance both in the face-to-face period 2019-1 and in the virtual period 2020-1. In this research, the CRISP-DM methodology was used to create the models, in addition, the necessary theory of Clustering is developed. The data used was purely academic. During the data analysis process, some important findings were found, such as the significant improvement in qualifications after taking a virtual cycle. The models used were K-means, DBSCAN, Affinity Propagation and MeanShift. To find the optimal parameters of each model in each specialty, parameter searches were carried out using the Silhouette Coefficient as the Performance Metric. Finally, to choose the best model, the Success Metric was defined as the number of groups generated by each model among students with low academic performance, then it was compared between the models, and it was found that the Affinity Propagation model is the one that performs better for the application of focused tutorials.
Machine learning application in university management: Classification model Dropping out of engineering students in Peru

We apply artificial intelligence to various cases of university management, with a proactive approach. In this study, we apply machine learning to classify whether a student will drop out or not, considering certain variables from the SITUATION DATA, based on the relevant attributes of the students. The results of this study would serve decision markers as a basis of inquiring about the high cost of not finishing the degree and adopting retention strategies. On the other hand, students can perceive the benefits of education, especially in engineering careers by having expectations, and value or assessment of it. The model developed is a neural network with 8 internal layers, in addition to the input and output layers, 225 training iterations have been considered, obtaining as a result an accuracy of 67.10%.
Position Control for a Solar Panel Implemented with a Predictive Control Algorithm.

This article describes the development and implementation of a control system applied to a photovoltaic panel.The objective of the controller is the automatic tracking of sunlight, for which controllers based on PID and MPC are implemented.For the experimental development, a photovoltaic panel, a personal computer and an ARDUINO card were used as a communication interface.For the simulation, we worked in the environment of the MATLAB and SIMULINK platform.In the case of the analysis of the response of the MPC, three scenarios were used.Comparing the results of the PID vs MPC, it can be stated that better follow-up responses were achieved in the case of the control based on the MPC model.
Implementation of Image Processing in Matlab for Prediagnosis in Ophthalmology

This research focuses on the implementation of image processing in Matlab and its role in the pre-diagnosis of eye problems, aiming to automate the process of medical image analysis in this field. The global health crisis caused by the pandemic has significantly impacted teleconsultations, particularly in our country, where the challenge lies in visualizing the patient's eye clearly through a screen. To address this, we propose the application of a single-person booth that will allow real-time capture of the patient's eye and its respective processing, so that it works as a tool that facilitates the accuracy of the doctor's diagnosis. Additionally, an infrared sensor is implemented to ensure contactless operation by detecting people entering the booth. The application of image processing techniques provides clear and accurate images, showcasing the adaptability of the project to specific situations with the integration of mechatronic and biomedical engineering knowledge.
Wearable Object Detection System for People with Blindness and IoT Monitoring

As technology advances, a wide variety of devices that aim to help people who suffer from blindness have been developed, since most of the time when this people leave their homes, they tend to be exposed to accidents if they do not have the necessary aid.Therefore, at present, devices have been created either with the objective of leisure such as products that read texts to individuals, also devices made to facilitate the movement of users such as those that make use of sensors or artificial vision and machine learning, even devices that have the objective of monitoring a blind person in case of emergencies, among others.At the same time, the cost of these new technological products is increasing, leading to an increase in the price of the devices used by blind people.This proposed system allows blind people to have access to a portable and affordable device, which informs them by vibrations if there are obstacles in front using ultrasonic sensors.Through a Wi-Fi module and making use of the IoT, acquaintances of the disabled person will be able to monitor their surroundings in live stream or through pictures.
Automatic Identification of Fake Profiles in Online Social Networks

In the modern era, the social life of individuals has shifted towards becoming connected with online informal communities. These communities have revolutionized the way we pursue our social life, making it easier to connect with them and keep up with their updates. However, with the rapid growth of these communities, there has been a rise in issues such as false profiles and online bullying. There is currently no practical solution to address these issues. To address this issue, we have developed a system with which the programmed identification of false profiles is feasible and efficient. This system uses order methods such as support vector machine, nave bayes and decision trees to classify the profiles into false or true classes. As this is a software-based recognition method, it can be easily connected to online informal organizations that have a large number of profiles whose profiles cannot be physically inspected. In today’s world, users are bombarded with unnecessary information during surfing, which are posted by false users. Research has shown that 20% to 40% of profiles in social networks like Facebook are fake profiles. This detection of fake profiles leads to solution using frameworks. Keywords: Online Social Network, Fake Profiles, Classification, Natural Language Processing.
Special Issue on Security and Privacy in Blockchains and the IoT Volume II

In this day and age, data are indispensable commodities and have become an integral part of our daily lives [...]
Endorsed Attributions: eXplainable AI (XAI) with Voting Mechanism with Application in Healthcare

&lt;p&gt;Feature attribution methods have been widely employed in the &lt;u&gt;eXplainable&lt;/u&gt; Artificial Intelligence (&lt;u&gt;XAI&lt;/u&gt;) community and they have been designed with various underlying concepts. As a consequence, these methods sometimes assign different feature importance on the same data. To resolve the possible discrepancies, we introduce \textit{endorsed attributions}, a voting mechanism that integrates the strengths of different feature attribution methods on healthcare datasets: maternal health risk classification, body signal of smoking, &lt;u&gt;fetal&lt;/u&gt; health and heart disease classification. Our methods generate simplified visualization charts as an explainable AI use case in clinical settings, avoiding the need for end users like patients or medical practitioners to focus too deeply on technical nuances. Graph visualizations from our partitioning methods also reveal the differences between correct and wrong predictions. Finally, we demonstrate how the extraction of endorsement cores (EEC) can be used for data pruning, and models trained on the resulting EEC data subset can achieve competitive performance.&lt;/p&gt;
Comparative Analysis of the Compression of Text Data Using Huffman, Arithmetic, Run-Length, and Lempel Ziv Welch Coding Algorithms

The purpose of the study was to compare the compression ratios of file size, file complexity, and time used in compressing each text file in the four selected compression algorithms on a given modern computer running Windows 7. The researcher used the Java programming language on the NetBeans development environment to create user-friendly user interfaces that displayed both the content being compressed and the output generated by the compression. A purposive sampling technique was used to select text files with varying complexities from the Google data store, as well as other text documents developed and manipulated by the researcher to meet the level of complexity required for the research experiment. The results showed that each compression algorithm compressed differently in terms of word count. This is due to the fact that the compression ratios of the number of words in each file changed in each compression algorithm. Furthermore, the complexity of the text in the file had no effect on the algorithms used. This was due to the fact that no significant changes in the various algorithms were observed with the selected files, despite the complexities of some files. Finally, time for compression was found to decrease with decreasing file size, though there were some notable variations across all four algorithms used. Before choosing a compression algorithm for efficient text compression, users must first determine their goals.
Enhancement of S13 Quantum Key Distribution Protocol by Employing Polarization, Secrete Key Disclosure and Non-repudiation

Quantum cryptography is the most convenient resolution for information security systems that presents an ultimate approach for key distribution.Today, the most viable key distribution resolutions for information security systems are those based on quantum cryptography.It is based on the quantum rules of physics rather than the assumed computational complexity of mathematical problems.But, the initial BB84 quantum key distribution protocol which is the raw key exchange of S13 quantum key distribution protocol has weakness of disclosure of large portion of secrete key or eavesdropping.Also, it cannot make use of most of the generated random bit.This paper enhanced S13 quantum key distribution protocol by employing polarization, secrete key disclosure and non-repudiation.The use of biometric or MAC address ensures non-repudiation.The row key exchange part of the S13 quantum key distribution which is the same as BB84 is enhanced by employing polarization techniques to make use of most of the generated random bit.Then, the tentative final key generated at the end of error estimation phase should be divided into blocks, padding, inverting the last bit of each block and XORing the block to generate a totally different key from the tentative one.Also, the random bits will be from biometric or serve MAC address respectively.The enhanced S13 quantum key is evaluated using cryptanalysis which shows that the enhanced protocol ensures disclosures of large portion of secrete key to prevent eavesdropping, utilization of most of the chosen binary strings to generate strong key and safeguarding against impersonation attack.
Digital Reading among Children in Saudi Arabia

A sample of parents in Saudi Arabia was surveyed to find out the reading technologies that children under the age of 12 use, children’s digital reading habits and interests, parents’ roles in encouraging the children to read digitally, the effects of digital reading on children’s reading ability, and to compare digital reading before, during and after the Pandemic. Survey results showed that all the children in the sample use a smart phone to access apps, games, cartoons, and YouTube videos. About 41% use an iPad or tablet and few use their parents or older siblings’ laptops. None of the children in the sample uses an e-reader such as Kindle. 5% do not like to use an iPad/tablet and prefer to use their parents’ smart phones. Children below the age of 6 use touch screen devices in reading the English and Arabic letters, numeracy and words. They enjoy reading on touch screens. 36% of the children in grades 1-3 use touch screen devices in learning to read and 64% use them for games and entertainment. Children in grades 4-6 mainly use touch screen devices to play games, soccer, car races and watch movies mostly in English and do not use those devices for reading purposes. Older children feel that educational and language learning and reading apps are boring. During the pandemic, children used technology intensively due to remote teaching and learning, i.e., more than before and after the Pandemic. About half of the parents do not share, nor supervise reading from touch screen devices with their children whether during, before or after the pandemic. Despite the advancements in digital reading, most parents and children in Saudi Arabia still prefer print books and stories. Mobile audiobooks, electronic reading games, storybooks, picture books and glossy magazines, reading lessons with a digital, human-like character, WhatsApp remote reading, online book clubs, and children's digital libraries are not used. Therefore, this study recommends the integration of digital reading in the school curriculum, raising parents and teachers’ awareness of digital reading devices, reading apps and websites and designing mobile reading apps with interactive features to motivate older children to read Arabic fiction and short stories.
Mammogram Pre-processing Using filtering methods for Breast Cancer Diagnosis

Cancer is the second most found disease, and Breast cancer is the most common in women.Breast cancer is curable and can reduce mortality, but it needs to be identified early and treated accordingly.Radiologists use different modalities for the identification of Breast cancer.The superiority of Mammograms over other modalities is like minor radiation exposure and can identify different types of cancers.Therefore, mammograms are the most frequently used imaging modality for Breast Cancer Diagnosis.However, noise can be added while capturing the image, affecting the accuracy and analysis of the result.Therefore, using different filtering techniques to pre-process mammograms can enhance images and improve outcomes.For the study, the MIAS dataset has been used.This paper gives a comparative study on filters for Denoising and enhancement of mammograms.The study focuses on filters like Box Filter, Averaging filter, Gaussian Filter, Identical Filter, Convolutional 2D Filter, Median Filter, and Bilateral Filter.Performance measures used to compare these filters are Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), and Peak Signal-to-noise Ratio (PSNR).All Performance measures are evaluated for all images of MIAS dataset and compared accordingly.Results show that Gaussian Filter, Median Filter, and Bilateral Filter give better results than other filters.
A Stylistic Study on the Selected Poems of Rupi Kaur’s “Milk and Honey”

The emergence of cyberspace and digital means of communicating has resulted in the flourishing of writers who became famous by publishing their literary works online. One of the successful writers with this humble beginning is Rupi Kaur whose style is subjectively described as bizarre, peculiar, an illusion of profoundness, lacks nuance, and unconventional. To avoid jeopardizing the aesthetics and genuine meaning of her poetry, this research aims to explore the objective analysis and interpretation of her 12 micro poems selected from her book “Milk and Honey”. It specifically seeks to stylistically investigate the presence of foregrounded parts found in her poems and their linguistic forms. The obtained verbal data are analyzed through the lens of Geoffrey Leech’s foregrounding theory which focuses on deviation and parallelism. The study reveals that the foregrounded parts in terms of deviation are enjambment, omission of syllable sounds, hyperbaton, the personification of abstract nouns, and metaphorization which are linguistically categorized as graphological, phonological, grammatical, and semantic. In terms of parallelism, they are the repetition of sounds, lexeme, words, and phrases manifested through anadiplosis, epanalepsis, epistrophe, anaphora, and conduplicatio which are lexical, phonological, and syntactic. The foregrounded parts relay Kaur’s message on the importance of equalness among gender and women's empowerment as she retains her sense of heritage, and culture very evident from the interdependence of various semiotic modes that she used in her poetry. It is recommended that the objectivity of literary analysis and interpretation should be discussed intensively for the awareness of language/literature teachers and learners.
Effectiveness and perception of augmented reality in the teaching of structured programming fundamentals in university students

INTRODUCTION: Even while studying programming languages is essential for science and technology education, some students, especially novices, may find it challenging. One reason might be that these pupils are unable to comprehend programming basics, notably the usage of selective and repeated structures (loops), which are too complex and abstract for them to comprehend. OBJECTIVES: Programming structured applications requires understanding the relationship between variable-operators and declarations, so a more intuitive and practical visualization technique is needed. In view of this, this article presents an augmented reality (AR) learning system using a DF-RA mobile application that offers visual representation and interactivity to help college students in entry-level computer science-related majors learn to program structured applications using dynamic and interactive flowcharts. METHODS: In order to examine the influences of said Augmented Reality-enhanced system on student learning, an experiment will be carried out within the group with 34 university students. All students used both an augmented reality-enhanced version and a conventional paper version (classic methodology with paper flowcharts). RESULTS: The expected results is that the augmented reality version through the DF-RA mobile application made students have a better learning efficiency than the traditional paper system. In addition, the system enhanced with Augmented Reality also made students have improved perceptions in terms of system usability, flow experience, and usage perception. CONCLUSION: Experimental findings were analyzed to demonstrate that the augmented reality learning system increases students' motivation to study structured programming fundamentals and their practical competence.
"Impact of Artificial Intelligence on software development: Challenges and Opportunities"

This research paper explores the impact of artificial intelligence (AI) on software development, examining the challenges and opportunities it presents to the industry.The paper investigates how AI technologies, such as machine learning and natural language processing, are revolutionizing various aspects of the software development lifecycle, including requirements engineering, code generation, testing, and maintenance.Additionally, it discusses the ethical implications and potential risks associated with the integration of AI in software development.By analyzing real-world case studies and industry trends, this paper aims to provide insights into the future of software development in the era of AI.
Geotechnical and Geochemical Properties of Residual and Termite Reworked Soils within the Premises of the Polytechnic Ibadan, Southwestern Nigeria

Three representative samples each from residual and termite reworked soils were collected within the premises of The Polytechnic Ibadan, southwestern Nigeria. Their suitability for construction purposes was determined by assessing their geotechnical and geochemical properties. Geotechnical tests carried out include moisture content, specific gravity, Atterberg limits, particle size distribution, compaction, UCS test and CBR. AAS was used to analyze the oxides composition of the soils. Both soil types were classified as low – intermediate plasticity clays with liquid limit between 25.5%-43.1%. The CBR values ranged from 80% – 85% and 20% – 50% for termite reworked soils and residual soils respectively. UCS ranged from 212.1KPa – 226.3 KPa and 191.7KPa – 200.0 KPa for the termite reworked and residual soils respectively. The OMC and MDD values for termite reworked soil ranged from 5.8% – 8.4% and 1968 kg/m3 – 2020kg/m3 respectively and between 12% – 15.2% and 1800 kg/m3-1940 kg/m3 for residual soils. Geochemistry result shows that the termite reworked soils contained a mean of 60.1% SiO2, 31.05% Al2O3, 1.7% CaO and 4.9% Fe2O3 while the residual soils contained a mean of 59.8% SiO2, 28% Al2O3, 1.6% CaO and 4.6% Fe2O3. This accounts for the better geotechnical properties exhibited by the termite reworked soils.
Ready Player Viz: Player-Created Data Visualizations for Video Games

We present a survey of the design space of player-created strategic visualizations for video games, to better understand how players make sense of complex game mechanics and incorporate feedback from fellow players. We present several examples of these visualizations, and contrast them to developer-created visualizations, both in information presentation and purpose. We find that there is a rich community-building aspect to visualization development within game 'fandoms', facilitated by cross-platform exchange and iterative development, including: social media, informational 'wikis', and in-game modifications ('modding'). Finally, we consider player-created visualizations in the context of a broader imagining about the future of visualization development for non-game but analogous strategic applications. We maintain a collection of tagged and categorized examples of player-created video game visualizations.
The Influence of Customer Knowledge Management on Sustainable Promotion

This research has investigated the effect of the customer knowledge management CKM in sustainable promotion SP. The research conducted a quantitative method on a sample of employees in the Al-Furat State Company for Chemical Industries affiliated / the Ministry of Industry and Minerals in Iraq. The research’s problem presented a set of questions, one of the most important was (is there a relation and impact between the dimensions of customer knowledge management and sustainable promotion). The aim of the research is to identify the extent to which customer knowledge management activities are applied in understudy organization. This research adopted the questionnaire as a main instrument to collect information from (140) participants in various administrative levels (top management, middle, and executive). One of the most important results of the research was the existence of a relation and effect between the dimensions of customer knowledge management and sustainable promotion. Also, the findings of the current research revealed a great interest in customer knowledge management and its dimensions (knowledge from the customer, about the customer, of the customer, and joint construction) with sustainable promotion. According to these findings, the research recommended supporting CKM in the researched company through promoting activities related to understanding the needs and desires of customers and providing them with sustainable products.