Reviewerly/exHarmony huggingface dataset:
An Investigative Study of Long-Term Implication of Extended Reality Applications in Educational Pursuits for Learning-Disabled Population: A Recommendation Approach

Extended Reality is the term that encloses Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR). Virtual Reality experience provide impressiveness and eliminates the physical world from view, In Augmented Reality virtual images overlays the physical world and Mixed Reality presents an altered reality, to engage users to interact with an environment, utilizing virtual objects. With the advent of the Extended Reality various tools and assistive technologies have already been developed to support the disabled learners. These applications increase learner's motivation, provide ease of interaction, develop their cognitive skills, improve short-term memory, and make overall learning interactive, also enhances learning experience of the learners. However, challenges of implementing XR are several, ranging from logistical, technical, high-costs and need for qualified staff and training in electronic competencies. As, XR in educating specially-abled is an unexplored domain. Therefore, this work presents an investigation on various XR applications in promoting education of learning-disabled and outlines their impact on learner. On the basis of the findings, a recommendation approach for use of XR in educational pursuits is presented for the concerned stakeholders, the designers, the developers, the manufacturers, the adopting educational institutions, the special educators and the learners.
Heart of Lorkhan: Procedural Generation in Interactive and Engaging Level Design

This paper concerns the level design for the action-adventure 3rd person 2D top-down dungeon crawler, Heart of Lorkhan. The application of organized generation in designing meaningful levels in randomized manner is the primary focus of the presented paper. Firstly, introductory game concept will be briefly explained in terms of genre, inspiration, control scheme, environments, storyline, beginning and ending walkthroughs, goals and challenges. Literature review and background studies on the appropriation of flow models and design approach will also be conducted in this paper. Next, more in-depth technical exploration of the level design will be elaborated, including details for each level topped with cell diagrams respectively. Further aspects will also be focused on level resources, collision points, PCs and NPCs, and HUD. Lastly, some loose-leaf ideas will also be brought out for extra insight of the proposed level design. Finally, the paper is then concluded with future enhancements for similar yet-to-come studies.
Cloud Bursting: Intelligent Technique in Cloud Computing

Cloud Computing has been a difficult subject in the modern day for the past several years. The goal of cloud computing is to make resources and computation available through the internet. The process of deployment in cloud computing has several obstacles and challenges. This research article is primarily concerned with two topics: load balancing and cloud bursting. The best sort of algorithm covered here is the hybrid load balancing method, which was given by Shu-Ching. In this work, we also attempt to address the issue of high demand for diverse resources from industry and academics. To address this issue, we employ cloud bursting techniques, which allow us to give different servers to different customers and meet their demands.
Speech Difficulties and Clarification: A Systematic Review

Natural language processing helps the computer to process human languages. In general, human language is presented either in the form of text or speech utterances. Speech based computer interaction system helps the computer to understand human language and perform various tasks for verbal instructions and to make intelligent assistive system. Background noise is the most common factor that causes degradation of the quality and intelligibility of speech. The term background noise refers to any unwanted signal that is added to the desired speech signal. In fact, language description is the main motivation behind widely used machine learning (ML) techniques such as Hidden Markov Models, Discriminant Learning, Structured Sequential Learning, Bayesian Learning, and Adaptive Learning. In addition, speech clarification is being used by machine learning as a large-scale, realistic application to assess a particular method's performance and to address new challenges arising due to the naturally sequential and dynamic character of speech. At the other side, although speech clarification occurs for some applications, it remains largely an unexplained issue for most of the applications. This overview article provides readers an insight about modern ML techniques as they are currently used and relevant to future speech explanation systems. Articles are organized according to the most important ML paradigms that are already popular and can contribute significantly to voice recognition.
Develop and Test A Mixed Ledger System using Block Chain

For distributed ledgers, there is variety of consensus mechanism, each with unique strengths and limitations. When compared to conventional intricate fault tolerance procedures, Osa is a new class of Byzantium Fuzzy Inference consensus mechanism that is often used in practise. Several firms are migrating toward a more conventional POA cryptocurrency with Byzantium Fault - tolerant. Without even a broad agreement system, it is exceedingly difficult to stop node assaults. Our goal is to determine if we can create centralized systems with better methods to lessen the danger of assaults on blockchain technology. Utilizing the Pom and POV methods, we generate blocks, add present in the study' vote profile, then update them depending just on voting patterns of verifiers and collect chain rewards. In latest days, the bitcoin has gained enormous popularity, and the underpinning consensus mechanism have also drawn a lot of attention in academic circles. This effort sought to research several bitcoin consensus algorithms to enhance the functionality of bitcoin and construct an universal and adaptable to fit on the majority of systems.
Study of Genetic Algorithm in Multi factor Scheduling for Reduction of Waiting Time in Dock

Any dispersed framework should incorporate errand scheduling since it guides occupations to the right assets for execution, like matrices, mists, and distributed networks. Normal scheduling methods incorporate disadvantages such high transient intricacy, deferred input task handling, and broadened program execution periods. On heterogeneous dispersed registering frameworks, investigation based scheduling algorithms focus on positions utilizing various methods, prompting extended execution spans. Task focusing on therefore turns into a bottleneck in such frameworks. It is fitting to utilize speedier algorithms to focus on exercises with the fastest execution times. One developmental strategy used to quickly tackle troublesome issues is the genetic algorithm (GA). This study's primary goal is to utilize a Guide Diminish design to lessen how much time it takes for the undertaking scheduling cycle to finish in general in a distributed computing climate. The GA was utilized related to heuristic strategies to allocate errands to processors in the primary phase of the proposed strategy. Then, at that point, the GA was utilized related to the Guide Decrease structure to dole out positions to processors in the subsequent stage. In our tests, we consider running a task on different assets with different execution times, as well as heterogeneous assets that shift in their ability to complete different errands. The results exhibit that the proposed approach beats different algorithms, including the whale advancement algorithm, the moth-fire improvement algorithm, and the astute water drop algorithm. In the beyond a decade, cross-docking, another capacity practice in planned operations that is utilized broadly all over the planet, has drawn the interest of various researchers. While monetary reasons have been broadly explored in the writing, helping client support quality and putting serious areas of strength for an on consumer loyalty are two of the main measures for progress in the wildly cutthroat worldwide market. In this review, we present a three-level store network three-echelon vehicle steering and scheduling issue with cross-docking and time windows.
Face Mask Detection using Single Shot Multibox Detector and Mobile Net

During the pandemic time government took many safety measures to protect the public at common gathering places. People are insisted on wearing a face mask to protect themselves from COVID. Even then many people were roaming without a mask in public places. The proposed technique to detect the face mask is to identify the person's face with mask and person's face without mask and reporting to the safety officers about the persons without mask for further action. The proposed Face mask detection is developed using the ML technique which can be used to classify the people wearing masks and not wearing masks with the input given to the model. The proposed face mask detector is a one-stage detector that focuses on detecting the face mask alone. This work is implemented using the Tensor flow and Computer vision libraries. NumPy is used for image processing. The data set used in MAFA dataset. The model is trained using this data set to get the accurate results. To enable multiple detection here the single shot with multi box detector is used. The base model used for this process is Mobile Net V2. The proposed model is simple and it can be integrated with several other technologies to provide high accuracy percentage of output in the minimum possible time.
IoT (Internet of Things) Going to Monitor System for Safe Agricultural Production Application

Real-time system management greatly benefits from the use of IoT (Internet of Things) solutions that are well-designed. Wireless sensors were taken into account by Internet of Things technology to control power usage. Smart sensors built on sensor units provide efficient power management system for the smart link. The communications system measures voltage consumption. It is beneficial to push a smart agriculture system above conventional ways by using IoT and other technologies. To obtain all the data about the use and efficacy of IoT, a survey with three main items was administered to 65 participants in this study. The study has provided the debate and analysis as well as the answer to the survey questions. Findings and research showed that the cost of the Basic Network infrastructure for agricultural areas is affordable; but the cost is greatly increased by the incorporation of specific features in the sensing devices. Additionally, the internet connectivity in farms and communities is subpar, which hinders the exchange of information among farmers and agricultural specialists.
IoT Framework for Manufacturing and Image Classification

Real-time industrial process identification and picture classification are now necessary in order to prevent excessive power consumption and, respectively, to identify water contamination. These new requirements were brought about by recent legislative changes. Scholars are studding for an IoT solution that is cost-compensation and productive because implementing automated machines in manufacturing companies is normally an expensive endeavor. The live condition of industrial gear would be detected and measured using this technique. Additionally, the IoT's innovation can be used to recognize images in order to locate the causes of water pollution. In this study, multiple approaches to picture categorization were examined, and the benefits and economics of the Internet of Things (IoT) that is currently in use were presented. In order to acquire meaningful data, a primary quantitative survey approach was adopted, and the opinions of sixty different respondents served as the basis for the study. Since then, the “Discursive” sampling technique has been used to analyze the key details and offer support for an important finding. WSN is a system that has a reduced price level & can be adopted into both smaller and big industrial organizations, based on the findings of the research and analysis. Image classification by IoT has shown to be helpful for identifying contamination of water since texture analysis becomes less costly than spatiotemporal analysis. This is so because the two categories of analysis are identical.
Enhanced Resource Control in Cloud Environment using Optimized VM Allocation in Data Centers

Cloud computing is the most revolutionary technology of the last decade since it makes a new generation of computer resources accessible to consumers via IaaS, PaaS, and SaaS administrations. Infrastructure as a service (IaaS) allows a VM to be deployed, launched, and managed over a cluster of servers. Efficient VM assignment is often regarded as one of the most challenging issues for CPs to conquer because to the inherent tension between the goals of improving customer demand fulfilment while cutting costs and maximising asset efficiency. The purpose of this work is to provide a novel approach to allocating virtual machines to meet the needs of individual customers by using the modified emperor penguin optimization method algorithm. Relying on established optimization methods for efficient VM allocation further demonstrates the value of the suggested approach. This application was built using the Java programming language and deployed to the Netbeans IDE 12.4.
Sign Language Recognition System using Convolutional Neural Network

Sign language is the common communication language for the hearing and speech-impaired community. It is hard for most people to communicate in sign language without an interpreter. Sign language refers to the tracking and identification of meaningful human expressions made with the hands, arms, fingers, heads, etc. The method used in this case converts the sign language movements into a spoken language that the listener may easily understand. The communication using sign language is useful for the peoples depend on gestural sign language but it is more complex for the other publics. The existing systems are not efficient since they are struggling with skin tone detection. But, adding a filter symbol can be recognized regardless of skin tone. In this work, primarily focused on analyzing convolutional neural networks (CNN). There are four kinds of layers: convolution layers, fully connected layers, pooling/subsampling layers and nonlinear layers for learning new characteristics.
Search of Orphanages to Use The Formula Factor

By having provided a 2 different messaging platform for such adoption agency to publish its necessities while also allowing the benefactor to view how anything accurately is needed by that of the children's home to make beneficial charitable contributions toward the street children, the objective is to develop a site web software that allows charitable contributions both of finances and necessary materials. By precisely locating the neighbouring schools using the Haar wavelet equations, researchers have reduced the amount of human labour required of the material donor and resources recipient. In addition to calling the orphan directly to give the required items, modules for making payments for contributions online have really been introduced. There is a search filter added to display the charities according to the stated criteria. An operator element was added to ensure that any orphanages being established in our web service is legitimate and has the necessary evidence and documents. When opposed to other applications and websites, the idea of utilising an app to give and accept contributions will be approached more broadly and with less dela.
MOOC Teaching Platform System Based on Application of Artificial Intelligence

In recent years, artificial intelligence technology has developed more and more rapidly, and has been gradually applied to intelligence, automation and other aspects in teaching. This paper designs an interactive learning platform based on artificial intelligence information technology. Firstly, this paper introduces the concept, characteristics and development process of MOOC platform. Then, this paper studies the artificial intelligence information technology, designs the MOOC teaching platform system based on the algorithm, and tests the performance of the system. Finally, the test results show that the operation time and delay time of MOOC teaching platform based on artificial intelligence information are less than 3 seconds, the occupied memory is also low, and the CPU utilization is also low. This shows that the performance of the platform meets the basic needs of users and can realize the communication between teachers and students to a certain extent.
“Secure Architecture for Providing Data Authenticity in IoT Enabled Devices”

The Internet of Things (IoT) is a young technology that is rapidly evolving. because of importance of IoT within the destiny, it will be extremely important to create adequate security for IoT infrastructure. Via this existing article, the architecture of IoT and their security dimensions are addressed after presenting and exploring the securities and demanding scenarios of various IoT layers. The use of sensitive information that needs to be kept secret from third parties will increase as a result of the development of modern private technologies. In order to assess and mitigate many IoT privacy issues and support the design and deployment of cosy and touchy systems, present methodologies are neither sufficient nor powerful. This study suggests a data authentication architecture in this situation. In particular, there may be a drive toward adopting secure IoT architectures that rely on physically unreproducible capabilities and in-depth research to guarantee the privacy of retrieved IoT data.
Comparative Study on Real-Time Vehicle Classification

Vehicle detection and classification play an important role in various fields such as traffic management and traffic surveillance systems. In this paper, we have compared the two most popular machine learning models, SVM, and YOLO for the detection and classification of vehicles on the same dataset which was collected from the GTI vehicle database. SVM(Support Vector Machine) is employed with HoG(Histogram of Gradient) to extract the HoG features and the Sliding window technique to detect the presence of a vehicle within a frame. On the other hand, YOLO, a regression-based algorithm that functions by separating the image into a matrix and looks for predicting classes and bounding boxes rather than interesting regions of the image. After successfully applying both the algorithms we analyzed that YOLO is preferable over SVM for real-time applications as the processing speed of YOLO is 45fps which is much higher than SVM i.e 2fps.
Survey on Sentiment Analysis using Deep Learning

The text mining is technique that extract data from the semi structured or unstructured text and finds pattern it thought as knowledge discovery from the text. Deep learning is most powerful machine learning technique that is used to extract the feature of the text and from the feature which is identified gives the prediction for the opinion from the various text. The opinion which is mined that helps to decide the social people opinion and sentiments that will be used as an input to various business decision. This article provides overview of sentiment analysis and deep learning approach.
Ability to Apply Mathematical Modeling on Account of DBDT Algorithm

In the era of big data, more and more information is shared, which provides sufficient resources for the development of social science and technology. All these dynamic developments are inseparable from mathematical models. In the information environment, students' modeling ability and consciousness are at the lower middle level. Improving this ability is a huge task to be solved. It is a good breakthrough to cultivate students' mathematical modeling application ability from the perspective of algorithm. Try to use algorithm theory to solve the suspicious and difficult points of mathematical modeling application ability. The research on improving the application ability of mathematical modeling on account of DBDT algorithm is a subject of contemporary significance, which must be strengthened. This paper studies the definition, concept and related knowledge on account of DBDT algorithm, and points out a series of knowledge and theories to improve the application ability of mathematical modeling on account of DBDT algorithm. In the text, the data is tested, and the results show that the improvement of mathematical modeling application ability on account of DBDT algorithm achieves 82.20%, 90.54%, 93.05% and 98.12% high efficiency in terms of system feasibility, innovation, fault tolerance and self-optimization performance.
Performance Analysis of Deep Learning based Face Recognition Model for Video Surveillance

Face detection, face alignment, and face recognition processes are all included in a full-fledged face recognition system. Due to many unconstrained elements, such as pose fluctuation, lighting, aging, partial occlusion, and low resolution, facial identification has proven to be difficult. A resilient and robust solution is currently required because existing face recognition approaches are only partially effective at identifying faces in unrestricted contexts. Therefore, the authors suggested a modified VGG19 model for face recognition using deep transfer learning. The model is trained to utilize the two-phase training approach employing a one-cycle policy's differential learning rate to enhance the recognition accuracy of the model. State-of-the-art (SOTA) competent outcomes are produced in the suggested rectification by concatenating global max pooling and global average pooling, batch normalization, and dropout layers in the classification layers. The experiments are conducted on three benchmark datasets such as Georgia Tech (GT) Face database, Labelled Faces in the Wild (LFW), and YouTube Faces (YTF) to illustrate the supremacy of the proposed work. The proposed work achieved 99.6 % recognition accuracy on the GT face database, approximately 1.35% improvement of recognition accuracy in comparison to other deep learning models on LFW, and 100% recognition accuracy on YTF.
Detection of Malicious Domains in the Cyberspace using Machine Learning &amp; Deep Learning: A Survey

People have daily access to a wide range of services through the World Wide Web, which serves as a platform for global information exchange. Businesses uses web as a low-cost communication channel for their business promotion and communications. As a result, millions of domains are created and registered every day. This immense development allows cyber criminals to play as well. Cybercriminals use a variety of methods to exploit the web security. Recent cyber security reports states that security breaches are 17% higher in 2021 than in 2020. Cybercriminals use URLs (domain name) as an easy tool to carry out malicious activities. Through email, SMS, and social media sites like Face book and Twitter, malicious URLs are disseminated. As soon as victims click on the URL, fraudsters will steal their personal data or place malware on their computer to get access to other systems. It is very important to detect such malicious domain names in advance using AI based techniques. Researchers have proposed several methods to classify malicious domain names and malicious domain names. Recently they preferred to use machine learning and deep learning techniques for automated classification. This paper presents a recent research work in this field and research issues. This will serve as source of new research in this domain.