Reviewerly/exHarmony huggingface dataset:
A Hypothesis on Cloud Sourcing Sharing Users’ Mobile Devices Through Virtualization

The term Cloud has taken on a significant role in the computer industry and is now recognized as the forerunner of cutting-edge technology that makes possible an enormous variety of services and applications. Cloud computing servers are made up of a large resource pool that combines hardware and software that is readily available and serves users depending on demand, with pay-per-use consumers accessing services. Large amounts of resources that can be consolidated on an active network and cloud servers that can be distant users’ mobile handheld devices are both features of cloud services that may be offered to users. With the use of mobile device resources, this research suggests a novel method of cloud computing that would represent a ground-breaking development for the field. In this situation, virtualization technology may also be leveraged to benefit from the resources of mobile devices. Additionally, mobile devices can function as a backup virtual server for the cloud. Based on the outcomes of the relevant intelligent parameters, a dynamic optimization algorithm may categorize mobile devices that are linked to the cloud and operating as a part of it, and the result log would be stored in the server. Additionally, the log will act as the cloud’s main building block for effectively allocating and scheduling jobs to various types of categorized mobile devices based on the task’s complexity, which results in greater elasticity, flexibility, cost savings, and slick server performance. The potential solution adds a monetary incentive that is advantageous to both the service user and the service provider.
An Extensive Survey on Machine Learning-Enabled Automated Human Action Recognition Models

In recent times, human action recognition (HAR) is the most significant one because of its applications in numerous domains like entertainment, health, intelligent environments, and security and surveillance. A substantial amount of work was carried out on HAR and the authors have used various methods, like device free, wearable, and object tagged for recognizing the activities of humans. The most promising technology to assist elder people’s life is sensor-based HAR, which has enabled massive efficiency in human-centric applications. Studying HAR exhibits that the authors were interested in the day-to-day actions of humans. This paper offers a brief survey of recently developed HAR models available in the literature. The survey begins with a general architectural diagram of HAR system, along with a discussion of major modules. In addition, the design issues associated with the HAR system are elaborated on in detail. Besides, we offer an extensive survey of existing HAR models with their objectives, novelty, merits, and drawbacks. Moreover, a brief result analysis of reviewed approaches is performed. Finally, some open research issues and future scope of the work are discussed in the domain of HAR.
Mineral Resources

The mineral resources of the State are hosted by the metasedimentary rocks which rest over the Anmode Ghat Trondhjemite Gneiss (3.4–3.3 Ga) and the intrusive Canacona Granite (2.9–2.3 Ga) together forming the basement for the supracrustal rocks. The volcano-sedimentary assemblage belongs to the ‘Castlerock Band’ which comprises two greenstone sequences, a lower metavolcanics-dominated sequence comprising the Barcem Group and a clastic-dominated sequence assigned to the Ponda Group. The banded iron formations (BIF) which have supported the State economy for over seven decades occur as bands, reefs and lenses of banded-haematite-quartzite (BHQ) and banded-magnetite-quartzite (BMQ) interstratified within the phyllites of the Bicholim Formation. The associated manganese ores, however, occur within both the quartz-sericite-chlorite schists of the Barcem and the pink ferruginous phyllites of the Ponda Group. The bauxite deposits are largely confined to the Mopa-Verna tableland (palaeoplain) which consists of a thick carapace of laterite capping various other lithologies. The bauxite mineralization occurs within the lower horizons of the laterite profile. The most widespread and better developed soil profiles occur on the coastal plain (< 10 m above MSL) where they have developed over coastal alluvium and granitic gneisses. In southern parts of the State, they are found to occur over laterites that cap the schistose metasediments. Sand extraction is largely ‘in-stream’ extraction confined mainly to the estuaries and in a few cases upstream sections of the major rivers. As for the marine shell deposits, the best and the largest occurrence of shells is located between Chopdem and Tuem in the bed of River Chapora. Two horizons of shells (0.5–0.7 and 0.3 m thick) occur within a shell-siltstone. It is exposed on the tidal/mud flat and extends laterally from 300 to 600 m; however, the continuity needs to be ascertained by pitting. These resources provide a means of livelihood to the local community.
Energy Optimisation in a Cloud Infrastructure Using Ant Colony Optimiser

Cloud services in a few decades have received considerable attention resulting in the quest for an efficient infrastructure to support the high demands from clients. In meeting clients’ needs, there is also the need to manage the substantial financial cost associated with energy consumption in these data centres. In this study, an ant colony optimiser was proposed to manage cloudlets’ scheduling effectively. Implementing the optimiser in a CloudSim comparatively reveals significant improvement in energy consumption over the first come,-first serve algorithm initially proposed by the authors of CloudSim.
Progressive Web App Implementation in Omah Wayang Klaten Website

Progressive Web Apps (PWAs) are websites that provide mobile browser users with an app-like experience. This article describes our efforts to improve the Omah Wayang Klaten (OWK) website's user experience by integrating Progressive Web Application (PWA) technology. As the existing website was built with WordPress, the API was utilized to implement the change. In redesigning the OWK website and using PWA technology, we employed Prototyping Method as the development strategy. Afterward, the planned system was evaluated using the User Experience Questionnaire (UEQ). UEQ is a complete user experience tool comprised of six components: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. Thirty (30) responders committed to the UEQ evaluation process prior to and following the PWA. We used Google's Lighthouse as a web-automated measuring tool and manual assessment to determine the quality of the PWA. The analysis revealed that the OWK website has successfully implemented the WordPress API for PWA. In the most recent version of UEQ and Lighthouse, analysis of results before and after deployment yielded higher scores.
Detecting Phishing Websites Based on Machine Learning Techniques

Phishing is one of the most common threats to cybersecurity in the modern world. It is to this regard many look to emergent technologies such as artificial intelligence and machine learning as preventative measures. By using such preventative measures many hope to reduce the potential for data breaches and other harmful occurrences linked to phishing. To that extent this paper investigates creating a Manifest V3 extension that can work alongside a trained machine learning model to give users more information about which sites are potentially hazardous.
Identification of Face by Using CNN and Deep Learning Algorithms

The development of advanced computers and upgraded cameras have propelled research toward designing facial recognition systems for various facial representation system in a variety of implementations. Depending on the utility, the facial recognition systems may employ real-time input or offline records. This work proposes the designing and evaluating a CNN-based actual facial detection system. Modern AT&T datasets are used for the initial evaluation of the proposed layout and later extended again for layout of an actual system. Moreover, specifics on how CNN settings are adjusted to judge and improve the suggested system's recognition and reliability have been presented. It is also suggested to tune the parameters using a scientific method to improve the computer's performance. The suggested approach yields maximum recognition accuracies of 98.75% and 98.00% using well-known datasets and real-time inputs, respectively.
Ubiquitous Learning Environment with Augmented Reality to Stimulate Motor Coordination

This research on ubiquitous learning environments under augmented reality technology, which allows the stimulation of motor coordination, aims to analyze the use of this technology in students to stimulate motor coordination, taking into consideration that augmented reality produces brain waves, the same ones that improve memory, likewise allows for better coordination between the head, trunk, and extremities; in the same way, in the coordination of fine movements such as the fingers of the hands, these activities can now be matured more frequently by children aged 8 years. The applied methodology is exploratory experimental type, where augmented reality activities were included to improve the development of motor coordination; it was done through interventions both inside and outside the classroom as activities in contact with the teacher as well as autonomous tasks. The approach used is by parts to determine both the qualitative part of the study and the numerical results where it can be shown that this type of activity improved motor coordination in the students. Finally, it is established that reality increases as a learning strategy in ubiquitous learning environments are quite necessary, which must be implemented in the development of motor coordination.
Convolutional Neural Networks Based Classification of Mammograms

This paper presents the application of convolutional neural networks (CNNs) for developing a classification system of X-Ray images into three classes: Normal, Benign, and Malignant. For training convolutional neural network models, we use nearly 9000 Vietnamese mammograms, which were collected and annotated by the radiologists of the Vietnam National Cancer Hospital. Our experiments were conducted with the 2 phases with two different data sets. The first phase experimented with multiple training options, as with the weighted loss function, the gray-level pre-processing, and the same number of samples of classes. The results of experiments in the first phase would form the basis for the second phase for developing a mammogram classification system based on the CNN with ResNet 18. The evaluation of system performance using the testing set achieves a macAUC of 0.828247, an average sensitivity of 0.64738, and an average specificity of 0.825670. In particular, the system accuracy achieves an AUC of 0.894291 in predicting the presence of cancer. The evaluation of system performance achieved a macAUC of 0.828247 which is higher than a macAUC of 0.754 for the classifying images into BI-RADS 045, BI-RADS 1, BI-RADS 23 categories when using 7912 breast cancer X-Ray images collected by Vietnam Hanoi Medical University Hospital for ResNet 50 training.
Cloud Resources Usage Prediction Using Deep Learning Models

Modern cluster management systems have effectively evolved to deal with the increasing and diverse cloud computing demands. However, several challenges including low resource utilization, high power consumption are still present that can be solved with a precise real-time usage prediction. This prediction problem is complicated since the cloud workloads vary dynamically and there are nonlinear relationships between the usage, duration and jobs characteristics. Therefore, non-linear feature extraction methods including logarithm, encoder and several feature extraction methods were used in the past studies. Our study utilized several regression models and deep learning models including GRU, LSTM in univariate and multivariate settings to explore and extract highly-dimensional and highly-nonlinear relationship. Our experiments used Google Cluster Trace data v3 to perform prediction on duration, CPU and memory utilization.
Machine Learning Technique for Data Fusion and Cognitive Load Classification Using an Eye Tracker

Advanced Driver Assistance Systems (ADAS) and other human-machine automation systems should be able to accurately recognize and adapt to the cognitive load of the user. For effective human-machine automation, it is important to develop techniques to automatically predict the cognitive load, based on data from non-invasive and low-cost sensors, such as eye-trackers. In this paper, we investigate the use of machine learning (ML) to classify the cognitive load of a participant performing n-back tasks. The ML models are trained using a large number of raw eye-tracking metrics. Our results demonstrate that tree-based algorithms are able to quickly predict cognitive load with a high degree of accuracy compared to other methods, indicating their potential usefulness for real-time applications.
An Artificial Intelligence Peer-To-Peer Car Repair Platform

Auto service industry is a very extensive field and there are many chances for utilizing technology in offering better services. In current model of service provider, finding the righteous place for repairing car can be a tedious job. In the Kingdom of Saudi Arabia, vehicle repairing becomes a challenging task since vehicle workshops are usually located in distant areas and randomly distributed. This can ravage customers’ time to find the right workshop, especially, with the increasing traffic and accidents in the Kingdom of Saudi Arabia. With smart phones applications, reporting and accepting services become easy and fast. Therefore, finding the best deal with transparent pricing is a desirable service. Time saving and preventing commercial fraud are among the goals of using such an application. The proposed system aims to enable the user to upload the image of the vehicle then, the image will be analyzed, and an estimate of repairing cost is given. The novelty in this application is grounded on a shared platform between customer and service provider with pricing estimate of service needed and the request is shared among the available shops. Liability and usability of the app was measured with error free application and 88% application usability survey. This system will contribute to the work of a strategy intended to improve business and customer satisfaction when it comes to vehicle repair processes.
AC Motor Monitoring and Controlling Using IoT

The design of IOT technology is shown to monitor and diagnose the performance of a three-phase induction motor and record crucial operational characteristics. Today's technology is rapidly tied to the Internet of Things (IOT), keeping things connected efficiently. For the purpose of gathering and processing induction motor parameters, the solutions given include an IOT-based platform. The parameters are made up of sensors, including humidity, temperature, voltage, and current sensors. With the help of the pocket IOT application, this data may be shown on a smartphone, accessed via web sites, and stored in a cloud platform. It will be promptly informed if performance limits are exceeded. To prevent motor downtime, an induction motor can be checked and immediate action taken can save money and time. Utilising IOT to monitor induction motors has several benefits, including notification of problem alerts and historical data for preventative maintenance. Recent technological advancements have greatly improved the quality, speed, and ease of our lives. This article explains how to manage and control induction motors (IOT) using the Internet of Things. The IOT is more effective and convenient for controlling systems because it can be used from anywhere via Wi-Fi. This intelligent system's main objective is to prevent induction motor failure by taking preventive measures. Because of its many benefits, including their self-starting nature, low cost, high power factor, and robust construction, induction motors are utilised in a wide range of applications, including those for electric vehicles, businesses, and agricultural areas. In order to maximise motor efficiency and assure safe and reliable operation, it is crucial to identify defects in motors as soon as possible using the best smart protection approach currently available. Remote monitoring is possible for the induction motor's speed, voltage, current, temperature, humidity, and other electrical, mechanical, and environmental parameters because errors in these areas seriously damage the motors and have an impact on the induction motor's ability to function in other applications. In this system for monitoring and controlling Induction using IOT, a number of sensors are employed to acquire the motor data in real-time and a relay is used to control the motor. The proposed system will collect and analyse induction motor parameters in real-time using an IOT-based platform.
A Power Efficient Solution to Determine Red Blood Cell Deformation Type Using Binarized DenseNet

Red Blood Cells (RBCs) play an important role in the welfare of human being as it helps to transport oxygen throughout the body. Different RBC-related diseases, for example, variants of anemias, can disrupt regular functionality and become life-threatening. Classification systems leveraging CNNs can be useful for automated diagnosis of RBC deformation, but the system can be quite resource-intensive in case the CNN architecture is large. The proposed approach provides an empirical analysis of the application of 28 and 45-layer Binarized DenseNet for identifying RBC deformations. According to our investigation, the accuracy of the 45-layer binarized variant can reach 93–94%, which is on par with the results of the conventional variant, which also achieves 93–94% accuracy. The 23-layer binarized variant, while not on par with the regular variant, also gets very close in terms of accuracy. Meanwhile, the 45-layer and 28-layer binarized variant only requires 9% and 11% storage space respectively to that of regular DenseNet, with potentially faster inference time. This optimized model can be useful since it can be easily deployed in resource-constrained devices, such as mobile phones and cheap embedded systems.
Entropy in Fuzzy k-Means Algorithm for Multi-view Data

Multi-view data clustering plays a crucial role in various real-world applications. This kind of data from various domains can exhibit a range of distributions, making it challenging for algorithms to uncover robust patterns. This paper extends the fuzzy k-means clustering algorithm to cluster multi-view data. The objective function includes two additional matrixes to measure the compactness of each view and the importance of individual features. The objective function also includes entropy weights. Experiments on real-life data indicate that the proposed algorithm outperforms current state-of-the-art algorithms. These set of algorithms comprises of clustering techniques that incorporate variable weighting, such as W-k-means [11], LAC [9], and EWKM [13], along with a multiview clustering algorithm called TW-k-means [6]. The evaluation of the algorithms involves measuring their accuracy, as well as comparing their respective running times. A comprehensive discussion on the proposed algorithm’s properties was conducted, where all its parameters were fine-tuned and analyzed in detail.
A Comparison of LSTM and GRU for Bengali Speech-to-Text Transformation

This paper represents an approach to speech-to-text conversion in the Bengali language. In this area, we have found most of the methodologies were focused on other languages rather than Bengali. We started with a novel dataset of 56 unique words from 160 individual subjects was prepared. Then in this paper, we illustrate the approach to increasing accuracy in a speech-to-text over the Bengali language where initially we started with Gated Recurrent Unit(GRU) and Long short-term memory (LSTM) algorithms. During further observation, we found that the output of the GRU failed to give any stable output. So, we moved completely to the LSTM algorithm where we achieved 90% accuracy on an unexplored dataset. Voices of several demographic populations and noises were used to validate the model. In the testing phase, we tried a variety of classes based on their length, complexity, noise, and gender variant. Moreover, we expect that this research will help to develop a real-time Bengali speak-to-text recognition model.
Augmented Reality for Cognitive Impairment and Dementia

Dementia is part of the normal ageing process and there are very few Augmented Reality (AR) technologies to help people with dementia. The implementation of these technologies is not fully successful due to limitations in the accuracy of image processing and interactive instruction. This paper aims to improve the accuracy and interactivity of the system to perform tasks in context-specific scenarios. Our objective is to increase the accuracy of video and the interactive instruction of the system by minimizing blurry image processing and overlaying the 3D images over 2D objects respectively. The recommended system involves Optional Frame Selection Algorithm (OFSA), with an 8D descriptor vector in speed up robust features (ESURF) to increase the accuracy of image processing, and overlay interactive 3D images to the original image to make the augmented instruction more effective. The result shows that the purposed (ESURF) algorithm utilizes more details and features in the descriptor vector to improve the regular speed up robust features (SURF) algorithm that comes from filtered OFSA. This result of performance shows that the ESURF algorithm outperforms regular SURF detectors in accuracy for matching objects. A usability study suggested that augmented interactive 3D images are more effective than just providing text as instruction. The proposed system emphasises on reducing blurry images for processing, maximising matching descriptors to increase accuracy during image processing, and to increase the interactivity instruction of the system, which provides dementia patients with more of a sense of the real environment and more effective instruction.
Open Voting Client Architecture and Op-Ed Voting for Secret Ballot Elections

To essentially allow voters to cast ballots using their personal devices (clients), we design a novel voting system architecture in this paper that we refer to as Open Voting Client Architecture. Then, we present details about one particular implementation of our architecture called Op-Ed Voting. Lastly, we assess the Open Voting Client Architecture system’s security. This work indicates that Op-Ed Voting can potentially increase usability and accessibility for voters while simultaneously meeting security criteria for electronic voting. Op-Ed Voting uses voters’ personal devices in an end-to-end verified protocol.