Reviewerly/exHarmony huggingface dataset:
Exploring information and communication technology integration among teachers in township public primary schools

Despite significant capability deprivation existing among the teachers in historically disadvantaged schools in the Western Cape province of South Africa, information and communication technologies (ICTs) have had a significant positive effect on teaching and learning. In the case study reported on here we investigated the benefits of using ICTs by public primary school teachers in teaching and learning. Sen’s capability approach was used to frame this study. We employed a qualitative research method in which 8 teachers were involved in lesson observations and semi-structured interviews. The findings reveal that, to some extent, through the teachers’ use of ICTs (commodities) in smart classrooms, the influence of personal conversion factors (ICT skills obtained through a community of practice and university training), had created the capabilities for teachers to use ICTs for curriculum delivery, although to a limited extent. Through this process, teachers benefited with specific vital achievements. From the study we recommend that holistic and annual professional development training programmes should be made mandatory for both novice and seasoned teachers.
Energy Consumption Issues of a Data Center

Nowadays, local or private data centers are a revolution that is developing rapidly. Many companies and educational organizations are building local data centers for security reasons. However, the energy consumption issues of data centers are rapidly increasing which needs to be addressed to develop green data centers. Many methods and techniques had been developed for minimizing the energy consumption of data centers. In this paper, an energy-efficient proposed model has been suggested for East West university’s local data center for upgrading to a green data center. It is found that approximately 20% to 30% of energy will be saved after redesigning the data center.
Use of ICT tools and academic performance of students in the Professional Schools of Mechanical and Electrical Engineering at the Technological National University of Lima Sur, 2023 [Uso de herramientas TIC y rendimiento académico de los estudiantes en las las Escuelas Profesionales de Ingeniería Mecánica y Eléctrica de la Universidad Nacional Tecnológica de Lima Sur, 2023]

The objective of the study was to determine if there is a significant correlation between the use of ICT tools and academic performance. For this reason, research on the use of ICT tools to improve academic performance in higher education was carried out. The type of research is applied, being the level of descriptive and correlational research. The research was cross-sectional; since the survey was carried out only once using the technique of the questionnaire and notes. The sample is simple random 57 students enrolled in the 2023 semester, which constitute the study. It is shown that of the total number of respondents, the medium level predominates, that is, represented by 34.0% (59.65) perceives that the use of ICT tools occupies, 22.81% (13) at the high level. The average of those surveyed towards the application of the use of ICT tools is favorable. Being the most repeated value 3 (favorable). More than 50% of those surveyed are above 2.9474, that is, they have a favorable attitude. On average the subjects are located at 3.0000 (favorable). Likewise, 1.27365 scale units deviate from the average. A minimum score of 1 and a maximum of 5 was observed. Finally, it is concluded that there is an adequate significant relationship between the use of ICT and the academic performance of university students, due to a moderate correlation of 0.785.
The Impact of Data Locality on the Performance of Cluster-Based Under-Sampling

Class-imbalanced classification is one of the most challenging issues in supervised learning. Traditional machine learning classifiers are generally biased toward to the majority class samples and ignore the minority class data. Although a good number of data-sampling methods have been introduced in the last decade, extracting patterns from biased data remains a problematic issue and a major research priority. In this paper, we have analysed the influence of cluster locality on various types of imbalanced datasets employing cluster-based under-sampling. Existing under-sampling methods removes a large portion of majority data to make the dataset balanced, so there is high chance that we may lose the instructive majority class samples. Cluster-based under-sampling technique addresses this issue and applies ensemble learning to boost-up the classification performance. This paper presents an extensive study on cluster-based under-sampling with ensemble learning using 31 imbalanced datasets to figure out which cluster locality is most important for data sampling.
IoT and ML Based Approach for Highway Monitoring and Streetlamp Controlling

Excessive speed and violating traffic rules may cause dangerous road accident. Some reports show that around 3700 people die every day due to road accident. Controlling vehicle speed and proper automated street lighting system may mitigate this problem. This work implements an automated internet of things and machine learning based system to control streetlamps with vehicle speed tracking. The developed machine learning model is capable to guesstimate the speed of the vehicle on the highway and report if there is any excessive speed. The automated streetlamp is integrated with the system that can provide proper illumination considering the environment condition. The proposed system showed good results after practical implementation.
Integrated Music Recommendation System Using Collaborative and Content Based Filtering, and Sentiment Analysis

This paper deals with a comparative analysis between Collaborative Filtering (CF), Content Based Filtering (CB) and Sentiment Analysis to build a recommendation Engine using Spotify Dataset. Using playlist-song association as an implicit feedback for a user, we implemented CF and we applied the Content Based Filtering using song metadata from spotify dataset. Using lyrics from the dataset, we developed a sentiment analysis model, labeled the song to create a novel dataset and use it to suggest songs based on the mood. K performs better for a smaller value of K, but CF improves as K increases. CF improves as K is increased while K works better for a lower value of K. By comparing all results, TF-IDF outperforms SVM and NB in sentiment analysis.
QPROM: Quantum Nanotechnology for Data Storage Using Programmable Read Only Memory

Quantum computing has the features of parallel and fast processing capability that makes it unique from other conventional computing systems. It has various peculiar properties, like entanglement, that can be leveraged as a resource to build technology. It is a nanotechnology-based qubit programming where different types of electromagnetic interactions are performed for basic operations as qubits and process information as single photons by recreating the quantum state. Quantum computers are more powerful than classical Turing machines because of their coherent superposition of states. Quantum memories would make massive photonic quantum computing systems possible by allowing coherent manipulation, buffering, and retiming of photonic signals. Though traditional PROM (Programmable Read Only Memory) is a slower memory, quantum computing enables the creation of new types of computers capable of operating with qubits as input states, increasing storage capacity. In this paper, a Quantum-based PROM architecture is proposed using algorithms of quantum-based basic operations.
Blockchain Based Services in Education: A Bibliometric Analysis

Blockchain technology promotes immutability, transparency, integrity, and enhanced security. Recently blockchain-based applications and associated technologies have been noticeable in various domains such as finance, healthcare, education, transportation, supply chain, and access control. Blockchain in the education sector is a newly explored topic; the nature of research and the trend should provide useful directions for future research. Motivated by this factor, this work presents a bibliometric analysis of the blockchain in the education sector by investigating 512 research articles from the Scopus database. In particular, we had 335 Conference Papers, 150 Articles, and 27 Book chapters that were published between the year 2016 to 2022. This study provides a comprehensive visual analysis of research trends in subject areas, author contribution, citation, publication channels and venues, country-based research output, and other bibliometric data. To identify the gaps and expand the current scope of knowledge on blockchain technology in the education system, we have used the bibliometric data extracted from the Scopus database. Finally, the investigation concludes with guidelines for future research directions.
Optimal Quantizers for a Nonuniform Distribution on a Sierpiński Carpet

The purpose of quantization for a probability distribution is to estimate the probability by a discrete probability with finite support. In this paper, a nonuniform probability measure P on $$\mathbb R^2$$ which has support on the Sierpiński carpet generated by a set of four contractive similarity mappings with equal similarity ratios has been considered. For this probability measure, the optimal sets of n-means and the nth quantization errors are investigated for all $$n\ge 2$$ .
Estimating Energy Expenditure of Push-Up Exercise in Real Time Using Machine Learning

The Covid-19 pandemic has nearly brought the globe to a standstill. However, we were able to adjust to the circumstance with the aid of computer technology by working remotely from home. Health and fitness have grown to be top priorities during these tumultuous times when people are confined to their homes. By completing certain easy physical activities that don’t require any special equipment and can be done at home, a person can maintain good health and fitness. Furthermore, the detection and recognition of human body motions or gestures is not a new concept when using artificial intelligence. Analysis of human body movement is now quicker and easier because of the development of real-time detection and identification technologies like YOLO. In this study, we have employed YOLO V4 as an AI helper to detect and identify push-ups from a real-time video stream recorded from a webcam or a smartphone camera that may be used to aid with push-ups. In order to keep the system affordable, we are recommending an approach that can identify pushups and estimate energy usage in real-time without the use of additional sensors or other wearable gadgets.
Programmable Logic Array in Quantum Computing

The field of computing known as quantum computing is devoted to creating computer technology that is based on the ideas of quantum theory (which makes sense of the way of behaving of energy and material on the nuclear and subatomic levels). These supercomputers are based on two aspects of quantum physical science: superposition and entanglement. As a result, quantum PCs can handle tasks at speeds that are significantly faster than those of traditional PCs and with a lot less energy consumption. On the other hand, Quantum logic gates are designed to empower this computing field. Programmable Logic Array is a type of PLDs (programmable logic devices) and is mainly used for designing combination logic mutually by sequential logic. It could be a general-purpose logic gadget that’s programmable and a client can configure the gadget as a simple logic gate operation to complex systems on a chip. In this work, a PLA is designed using Quantum-based gates. The features of Quantum-based computing help Quantum-based PLA to perform billions of activities simultaneously and can give immense memory in small space.
A Clustering Based Niching Method for Effectively Solving the 0-1 Knapsack Problem

The 0-1 knapsack problem (01-KP) is a NP-hard combinatorial optimization problems (COPs) with several applications. Because of its non-convexity, its search space contains several local and/or global optimum solutions. In this situation, both the classical and metaheuristic global optimization approaches often failed to locate the optimal solution to the 01-KP. Therefore, this research develops a clustering-based niching (CBN) method for maintaining and locating multiple solutions within an optimization run, increasing the possibility of locating the global optimal solution to the 01-KP. To do this, CBN method divides the population individuals into a number of clusters (similar to niches in a biology or ecology system) by measuring the Hamming distance between individuals. During the optimization, the individuals in the formed clusters independently explore different regions of the combinatorial search space in order to find promising solutions. For simplicity, the proposed CBN method is implemented using a modified binary PSO (lbest-BPSO) method. The numerical results show that the CBN method is superior than the well-known metaheuristic methods on the basis of solution quality, with a better success rate and average function evaluations, over widely used sets of 0-1 knapsack instance. This guarantees that CBN method is a suitable optimization method for determining the optimal solution to combinatorial optimization problems.
A Hybrid Cloud System for Power-Efficient Cloud Computing

Around our world clients needs services that are informative and technologically advanced. Advanced technologies like cloud computing allow the clients or the consumer to pay an efficient amount of money according to the service that they are getting. It permits any application for being hosted in a research or corporational structure. The included networked computers, cables, power supply, etc. in the data center is the main bone of cloud computing. The data centers consume a great amount of power to fulfill their work process which increases the cost and also affects the environment of the work by increasing the carbon footprint. To keep the carbon emission to check it is very necessary to check the electricity and power consumption. Keeping the energy in check we have solved the issue of efficient cloud computing.
A Belief Rule Based Expert System to Diagnose Schizophrenia Using Whole Blood DNA Methylation Data

Schizophrenia is a severe neurological disease where a patient’s perceptions of reality are disrupted. Its symptoms include hallucinations, delusions, and profoundly strange thinking and behavior, which make the patient’s daily functions difficult. Despite identifying genetic variations linked to Schizophrenia, causative genes involved in pathogenesis and expression regulations remain unknown. There is no particular way in life sciences for diagnosing Schizophrenia. Commonly used machine learning and deep learning are data-oriented. They lack the ability to deal with uncertainty in data. Belief Rule Based Expert System (BRBES) methodology addresses various categories of uncertainty in data with evidential reasoning. Previous researches showed the association of DNA methylation (DNAm) with risk of Schizophrenia. Whole blood DNAm data, hence, is useful for smart diagnosis of Scizophrenia. However, to our knowledge, no previous studies have investigated the performance of BRBES to diagnose Schizophrenia. Therefore, in this study, we explore BRBES’ performance in diagnosing Schizophrenia using whole blood DNAm data. BRBES was optimized by gradient-free algorithms due to the limitations of gradient-based optimization. Classification thresholds were optimized to yield better results. Finally, we compared performance to two machine learning models after 5-fold cross-validation where our model achieved the highest average sensitivity (76.8%) among the three.
Early Planning of Virtual Machines to Servers in Cloud Server Farms is an Approach for Energy-Efficient Resource Allocation

Carbon dioxide emissions are a significant source of pollution in the atmosphere. The innovative hardware plays a fundamental part in the carbon release. This is huge in light of the fact that power utilization by mechanical apparatus has surpassed 40 k TWh in 2021 from 10 k TWh in 2000 and is expanding step by step. Server farms in the Cloud processing climate have involved a significant imperative situation in this class of mechanical hardware. In Cloud computing, computational assets are leased staying away from gigantic ventures on the business part. Because of this alluring contribution, reception and sending of Cloud computing have become exceptionally famous among ventures as well as in the exploration local area. Increased use of Cloud computing, on the other hand, has resulted in higher energy consumption and carbon emissions into the atmosphere. A server farm is a collection of servers with a big number of them, and there are a lot of them all over the world. In the modern era of Cloud computing, energy consumption is currently viewed as one of the most significant evaluation challenges. Service-Level Agreements are contracts between a customer and a vendor in which the vendor agrees to particular service qualities such as quality, availability, and accountability. One of the major challenges identified is reducing the amount of power consumed by server farms without affecting the Quality of Service (QoS). Consequently, it is proposed to optimize resource allocation and minimize energy consumption for the Cloud environment. This is completed by limiting dynamic servers in a server farm without compromising the exhibition of undertakings and client prerequisites. To verify the efficiency of the suggested calculations, CloudSim is used in conjunction with verifiable responsibility data obtained from over 1,000 virtual computers from Planet Lab.
A Novel Texture Descriptor Evaluation Window Based Adjacent Distance Local Binary Pattern (EADLBP) for Image Classification

In this research, we suggested a novel texture descriptor distance-based Adjacent Local Binary Pattern AdLBP based on the adjacent neighbor window and the relationships among the sequential neighbors pixel value with a given distance parameter. The suggested technique calculates the neighbor and extracts the binary code from the adjacent neighborhood window and surrounding sub-image window in order to improve the adjacent neighbor information and change the conventional LBP thresholding schema. Additionally, we expanded this adjacent distance-based local binary pattern AdLBP and combined it with the evaluation window-based local binary pattern EwLBP to create a texture descriptor for texture classification that is more robust texture descriptor against noise. Finally combine AdLBP And EwLBP using encoding strategy to propose an Evaluation window based on Adjacent Distance Local Binary Pattern EADLBP descriptor for Image Classification. These descriptors are tested with the KTH-TIPS, KTH-TIPS2b to the applicability of the proposed method. In comparison, the proposed EADLBP approach is more robust against noise and consistently out- perform all of the fundamental methods.
Face Recognition-Based Mass Attendance Using YOLOv5 and ArcFace

Manually recording attendance of a mass gathering like a classroom or seminar is very difficult and error-prone. To make the attendance process trouble-free, in this paper, we have explored the application of face recognition in the automated attendance system that can be used for educational and organizational purposes. Face recognition is a computer vision technology that can be applied for different purposes in various practical applications. The first task of face recognition is to detect faces from an image or real-time video input. Different state-of-the-art Deep Neural Network-based face detection and face recognition models are available in computer vision. We have studied popular face detection models like HaarCascade, SSD, MTCNN, and YOLOv5. We have also analyzed LBPH, FaceNet, ArcFace, and DeepFace models for the face recognition tasks. For this study, we have collected an image dataset of faces using a real-life classroom environment. Based on the result of our study we have proposed an automated attendance system using the YOLOv5 and ArcFace model for mass attendance, that is, recording the attendance of all persons in a room at a time.
Improving Learning Effectiveness by Leveraging Spaced Repetition (SR)

The academic efficiency and knowledge retention of students can be improved by practising active recall testing and implementing spaced repetition techniques. The process of trying to recall information previously learned with the aim of increasing the chance of committing the information to long-term memory is called active recall. Spaced repetition is a technique which can help students to memorize and learn information by outspreading reviews of the topics over larger range of time revising the same topic multiple times in a single session. A qualitative method has been followed in this paper which takes a grounded theory approach while evaluating literature on different memory models, memory creation, and retrieval processes. Based on the literature review, an algorithm has been proposed with the aim of improving learning effectiveness by leveraging spaced repetition techniques.
Matching Job Circular with Resume Using Different Natural Language Processing Based Algorithms

When looking for a job, the job seeker registers in various job portals to get notifications or updates on different types of job circulars. Candidates from all types of job circulars can look for jobs according to their qualifications. To make this system faster and time-efficient, we set up a proposed system where a job seeker finds the similarity score of a Job Post with their resume after uploading it. Based on this resume, the candidate will be able to apply for the job post very quickly as the similarity score will be higher in the job post. This system matches the candidate’s resume skills, projects, and previous job responsibilities, with the properties of job posts like job description, job responsibilities, requirements, and measures similarity score. To measure the similarity score of Job Circular with this resume, we implement different models of NLP. The GloVe model gives the best accuracy which is 79.2% to all other models. We calculate this similarity through cosine similarity and euclidean distance. In this way, the employer will also be able to check these similarity scores and filter the candidates very quickly. Therefore we can say that our proposed system acts as a bridge between the employer and the job seeker.
Performance Analysis of ASUS Tinker and MobileNetV2 in Face Mask Detection on Different Datasets

The world has faced a massive health emergency due to the prompt transmission of corona-virus (COVID-19) over the last two years. Since there is no specific treatment for COVID-19, infections have to be limited through prevention methods. Wearing a face mask is an effective preventive method in public areas. However, it is impractical to manually implement such regulations on big locations and trace any infractions. Automatic face mask detection facilitated by deep learning techniques provides a better alternative to this. This research introduced an automatic face detection system using ASUS Tinker single-board computer and MobileNetV2 model. As most of the publicly available face mask detection dataset was artificially generated, in this work, a real face mask detection dataset was first created consisting of a total of 300 images. The ASUS Tinker board’s model training and testing performance and training time have been assessed for this dataset and a publicly accessible dataset of 1376 images. The recommended system reached 99% of test accuracy, precision, recall, and f1-score for the newly collected dataset and 100% of test accuracy, precision, recall, and f1-score for the publicly available dataset.