Reviewerly/exHarmony huggingface dataset:
Factors influencing hospitality management major college students’ online learning engagement and performance during the COVID-19 pandemic

Higher education institutions are under greater pressure to identify a new pedagogy that can promote students’ motivation and achievement in learning. The unprecedented COVID-19 pandemic has completely changed the educational environment to online learning from face-to-face learning, which potentially raises issues related to the quality of student education. In the online learning environment, it is necessary to examine factors affecting students’ motivation, learning styles, online learning engagement, and their learning outcome because students’ motivation, learning styles, and online learning engagement can be predictors of their critical thinking and learning performance. This study examined how students’ motivation, learning styles, engagement, and critical thinking influenced their online learning outcomes. This study conducted an online survey for university students who majored or minored in hotel and tourism management for two months in September and October 2020 through an online survey by Qualtrics of the United States, and verified the hypothesis using structural equation modeling. The findings suggested students’ motivation was the most important factor in online learning because motivation has a positive impact on both behavioral and emotional engagements, thereby leading positive consequences, such as critical thinking skills, learning outcome, and intention. This study provides recommendations for online education along with suggestions for future research.
User Requirements Analysis

User requirements analysis is a central part of the system development lifecycle. The process of generating requirements can be complex and technical for users and stakeholders, which can result in them not playing an effective part in the process as they should. The application of user-experience (UX) design techniques can help to humanize and enhance the process. The chapter is structured according to the three types of requirements analysis activities: elicitation, recording and analysis. Different components of each of these activities are described alongside particular UX methods that can be employed to enhance them.
A Comprehensive study on Internet of Things Applications and Challenges

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Hybrid Metaheuristic-Based Thresholding and Faster Region-Convolutional Neural Network for Object Detection in Images

The main intent of this proposal is to develop the intelligent image object detection using the modified segmentation and classification models. Initially, the segmentation of the image will be performed by the hybrid metaheuristic-based thresholding with multi-objective function concerning the maximization of between class variance of Otsu method, Kapur’s entropy, and segmentation accuracy. Initially, the segmentation of objects has done optimally, then for detecting, the modified faster region-convolutional neural network (FR-CNN) is applied for detection. In modification stage, the number of hidden neurons of faster R-CNN’s hidden layer nodes are tuned through proposed hybrid metaheuristic algorithm, in such a way that accuracy and precision should attain maximum.
A User-Driven COVID-19 Diseases Contact Tracing System

As the COVID-19 pandemic has captured the nation, innovations and technologies are taking a step forward to prevent the risk and overcome this situation. The health authorities and government bodies in the state play a major role to know all the data/details of the citizens within the state so that they can trace the movement of the citizens at the most granular level and verify with whom they have had contact over the given time. This data/details will be further useful to identify the person who came into the contact with COVID-19 positive patient. This information immediately will be used to ask citizens to get quarantined to break the chain to the people exposed to the disease and minimize the count in high-density areas before a potential outbreak happens. This work focuses on providing independent hardware, which is driven by the concept of self-intervention in contact tracing. The user is notified of his encounters and the symptoms the encountered people are facing. Depending on that information, the user gets a COVID grade which helps him to develop a level of precautions required. This is a basic application which lies in self-driven contact tracing where the user in this pandemic situation can have a detailed view of the encounters throughout the day along with their symptoms answered through a specified set of questions that help in deciding the level of exposure to the virus and the steps to combat it.
Functional interconnection of visual and auditory analyzers

Studies at the level of the cerebral cortex have begun to gain more and more interest because the way it works pro­vides information about a person’s way of thinking or memorizing. It is known that the regions of the cortex are excited differently, so there are action potentials of va­rious sizes, depending on the action performed. From an anatomical and functional point of view, both the visual and the auditory pathways are divided into two important areas, the primary area and the secondary area, also called by the association. Their unitary analysis is necessary be­cause the memorization process is based mainly on the func­tio­nality of the association area, but it is necessary to follow the existence of differences in the primary areas be­cause the individual will naturally select the stimuli that al­low him to acquire. Depending on the neural density pre­sent in the two areas, information can be obtained about a person’s preference to use the visual analyzer to the de­tri­ment of the auditory one, in everyday actions.
Hybridization of Neutrosophic Logic with Quasi-Oppositional Chimp Optimization based Data Classification Model

Data classification is the procedure of investigating structured or unstructured data and forming it into distinct classes depending upon file types, size, etc. It assist the organizations to derive important solutions based on the data and helps decision making process. The computational intelligence techniques such as neural computing, fuzzy logic, machine learning, etc. can be used to design effective data classification models. This study offers a Hybridization of Neutrosophic Logic with Quasi-Oppositional Chimp Optimization based Data Classification (HNLQOCO) model. The presented HNLQOCO algorithm aims to integrate the concepts of NL and QOCO algorithm for improved data classification outcomes. Besides, the QOCO algorithm is designed by incorporating the concepts of quasi oppositional based learning (QOBL) with traditional chimp optimization algorithm (COA). Here, the NL is applied to represent various kinds of knowledge and the QOCO algorithm is applied to tune the produced NS rules. The experimental result analysis of the HNLQOCO model is tested using three benchmark medical dataset. The obtained results reported the significant performance of the HNLQOCO model over the other methods.
Exploration in the Teaching Reform of Instrument Analysis in Local Applied Undergraduate Universities

Instrument analysis is a multi-disciplinary cross penetration and interrelated comprehensive course, which has strong theoretical, practical, developmental, and cutting-edge discipline characteristics.It is also one of the professional core courses of many specialties, such as applied chemistry, material chemistry, environmental science, environmental engineering, pharmaceutical engineering in applied undergraduate universities.The teaching reform of the instrument analysis course is significant for the training of high-quality applied talents.This paper puts forward the reform and exploration from course content, teaching method, teaching skill, and experimental training of instrument analysis to provide a reference for further improving the training quality of applied-oriented undergraduate universities.
On Comparative Study of Clustering Using Finite Mixture of Non-Gaussian Distributions

Finite mixture models are broadly used in model-based clustering approaches for heterogeneous data. Model-based clustering techniques attempt to optimize the fit between the given data and some mathematical model. This paper compares finite mixtures of non-normal distributions such as lognormal, geometric skew normal, skew Laplace, skew-t, and skew normal for model-based clustering approaches. The model selection criteria and parameter estimation for the finite mixture of the non-normal distribution are also discussed. The comparative study was carried out based on the new initialization technique for obtaining the initial component parameters in the EM algorithm. This comparative study is illustrated using real datasets.
Mmc-Based Heat Sink Topology Optimization Design for Natural Convection Problems

The optimal design of heat sink using MMC is presented for natural convection problems. Due to the high computational cost, a reduced order model of incompressible Navier-Stokes equations is operated together with convection-diffusion equation. For another, to reduce the computational degrees of freedom, bilinear shape functions are chosen to discretize the governing equations. Stabilization method is introduced to repress oscillation of the solutions. To obtain shape-controlled heat sink with a clear boundary, natural convection problems based on MMC method are studied. Furthermore, some numerical results show that the MMC-based heat sink design is effective and reasonable under natural convection conditions.
Determining perimeter of a letter with limited accuracy using splitted Bezier curves

In light of the growing volume of textual information in the world, the quantitative analysis of texts presented in electronic form is an extremely urgent task of information technology. Quantification of the font design, spatial shape of text, etc. are relevant to problems in attribution, computational semantics, and the similar ones. Until now, no analytical definition of the letter curves perimeter that is unique for practical application has been proposed. The easiest way to do this is to use the mathematical apparatus of the Bezier curves. In this work, we use the splitted Bezier curves at the reduced dimension to define the perimeter of an arbitrary planar shape.
Formation of the Earth - Lesson Plans

This chapter presents the lesson plans for the Formation of the Earth STEM Road map module. The chapter provides a series of three lesson plans  Geologic Time, Human Influences on Natural Processes and The Anthropocene: Exploring and Deliberating a New Epoch in Geologic Time. Moving through the lessons, students work in teams to learn deeply about an Era, then synthesizing their learning with other teams to create a timeline of the history of the Earth. Students use the engineering design process to present models of human influence on the environment, which are then peer-reviewed. Students work culminates in Lesson Plan 3 as they synthesize their learning to gather data related to geologic time scales and apply them to potential boundary events that are best suited to mark the beginning of the Anthropocene Epoch. From this examination, students will present their recommendations to textbook publishers interested in whether and how to include information on this epoch in Earth science textbooks. As such, student groups will be tasked with persuading publishers then writing a textbook entry (including various forms of media) suitable for the intended audience.
A hierarchical deep learning approach for segmentation of glioblastoma tumor niches on digital histopathology

Glioblastoma (GBM) is a highly aggressive brain tumor and is notoriously known for its intra-tumoral heterogeneity. Diagnosis of GBM is based on histopathology confirmation via tissue samples obtained from intra-cranial biopsies. After surgical intervention, histopathology tissue slides are visually analyzed by neuro-pathologists to identify distinct GBM histological hallmarks. GBMs may be histologically undergraded based on the amount of viable tissue due to sampling errors associated with small tissue samples obtained. Consequently, there is a need for automatic identification of histopathological GBM hallmarks. In this work, we present a hierarchical deep learning strategy to automatically segment distinct GBM niches including necrosis, cellular tumor, and hyperplastic blood-vessels, on H&E digitized histopathology slides. Our approach includes first segmenting necrosis and cellular tumor regions, then identifying hyperplastic blood-vessels within cellular tumor regions.
Analysis of Blockchain Secure Models and Approaches Based on Various Services in Multi-tenant Environment

This research paper is focused on analyzing past proposed papers on using blockchain with cloud computing to enhance the present security system of the cloud. Homomorphic encryption technique is the latest and most secure way of protecting cloud data on the server. It allows computational operations on encrypted data as there is no need to decrypt the data for processing it. Still, we cannot guarantee privacy and data integrity. Here comes the concept of blockchain, it is a decentralized approach as compared to the cloud’s centralized approach. Decentralization means data on the blockchain is stored at different locations or servers which is owned by different organizations. Hence, it is not dependent on any single third party for its execution or processing. The blockchain can be combined with fully or partially homomorphic encryption schemes to build a strong security system in a multi-tenant environment.
On the Requirement Analysis of Industry 4.0 Online Training and Educational Activities: LIFT Learning Factory Ecosystem Case

The competitive manufacturing market is driving the demand for digital technologies in manufacturing companies. As a result, there is a need for upskilling manufacturing employees using digital and Industry 4.0 technologies. The LIFT learning factory ecosystem aims to develop customized training courses to increase manufacturing companies' competitiveness in increasingly digitalized industrial sectors. To determine the needs and operational conditions for supporting the development of training activities, the LIFT ecosystem members collaborate to conduct a requirement analysis. In this study, a hybrid methodology combining traditional literature review and questionnaire-based survey is considered to obtain input from LIFT ecosystem members with diverse pedagogical expertise and experience
Work Balancing vs. Load Balancing in Network Ids Parallelization

Signature-based Network Intrusion Detection Systems (NIDS) is considered the state-of-the-art for precise attack detection. However,available systems are very resource demanding and often not able to cope with the increasing data rates in modern communicationnetworks. Parallelization using multiple instances of NIDS in parallel is considered the most promising solution. This can berealized by (1) distributing the network tra c between multiple NIDS to reduce the network load per system or (2) distributing thesignatures (rules) between mutliple NIDS to reduce the work load per packet. Conceptually, rule and tra c distribution are wellstudied, however, often not in direct comparison and in a thorough and exhaustive way. In this paper, we study distribution strategiestargeting application, transport, and network layer for both tra c and rule distribution approaches. We compare the performance ofrule distribution with tra c distribution for each strategy. In addition, we investigate the importance of considering the processingspeed optimization in the rule development phase. For our experiments, we rely on the very popular open source system Snort. Ourexperiments show that in general tra c distribution performs better in terms of packet drop and alert detection compared to ruledistribution. The network layer distribution strategy shows the contrast between the two distributions at its highest level, detecting34.9% more alerts and dropping 26.5% less packets. We also show that optimizing the rules sets further improves the processingspeed significantly.
The Effect of Human Development and Good Governance on Mobile Diffusion in the Sub-Saharan Region: Evidence from Panel Quantile Regression

Understanding relations between surrounding socio-political factors for mobile diffusion, such as human development and governance, has long been a challenge. Researchers urge that human development factors should be included as a critical factor when studying mobile diffusion of the sub-Saharan region, and the variables should reflect localized contexts of the region. Yet, the inclusion of various socio-political factors has been rarely considered due to technological determinism. This study aims to provide associational evidence between mobile diffusion and various qualitative indicators for human development and good governance through social constructivists’ perspectives. To do so, we address the gap first by implementing various composite indices measuring human development and governance and second by conducting panel quantile regression with fixed effect. Our statistical results show that the effect of the independent variables on mobile diffusion is heterogeneous across quantiles. Specifically, the effect of human development is positively associated with all quantiles, which supports the legitimacy of inclusion of human development factors when understanding mobile diffusion of the sub-Saharan region. The results also show that government capacity to create a better environment for business on mobile diffusion is clearly heterogeneous, and the effects are mixed.
Text mining for opinion analysis: the case of recent floods in Iran on Twitter

The sentiment analysis relates to the study and understanding of emotions and beliefs in a particular text. This analysis gives us a lot of information. Twitter is a popular social network in recent years, in which users express their opinions and feelings about various topics in the Twitter social media operating system. By analysing this information, we can get an overview of public opinion about any particular topic. The classification of information is effective in understanding information and we cluster information. In this article, we are trying to analyse the status of Twitter on the monitoring and emotions of people about the recent flood events in Iran.
Design and Implementation of Digital Campus System Based on Improved Genetic Algorithm

The digital campus system is based on a computer network and can collect and process existing information to achieve the purpose of modernization of school education, teaching and management. The genetic algorithm does not need to establish a mathematical model and input a large amount of auxiliary information when it is optimized to solve the problem, and it can manage the school well. For this reason, this paper studies and improves the application of genetic algorithm in the design of digital campus system to improve the accuracy of the system. This article mainly uses the experimental method and statistical method to summarize and sort the data formed by the system test, and provide a feasible scheme for the design of the digital campus system. Experimental research shows that the accuracy of the system can reach more than 90% after applying the improved genetic algorithm, which is a design that meets the requirements of the system.
A Survey on Neural Recommender Systems: Insights from a Bibliographic Analysis

In recent years, deep learning has gotten a lot of attention, notably in fields like Computer Vision and Natural Language Processing. With the growing amount of online information, recommender systems have shown to be an effective technique for coping with information overload. The purpose of this article is to provide a comprehensive overview of recent deep learning-based recommender systems. Furthermore, it provides an experimental assessment of prominent topics within the latest published papers in the field. Results showed that explainable AI and Graph Neural Networks are two of the most attractive topics in the field to this day, and that the adoption of deep learning methods is increasing over.
Standard Cloud Solution for Control and Supervision in Russia

The introduction of cloud computing technologies in the field of public administration has become a global trend. In Russia the prospects of using various solutions and services to optimize the activities of control and supervision bodies are actively being considered. Since 2017, as part of the Administrative Reform, the issue of using unified cloud services for control and supervision bodies has been continuously discussed. Since 2018, within the framework of this initiative, a unified information system is being implemented – a Standard Cloud Solution for control and supervisory activities. The article discusses the content of program and legal documents regulating the implementation of this Standard Cloud Solution, as well as the current practice of its implementation from the point of view of existing trends in the use of cloud computing technologies for the needs of public administration in the world. In conclusion, it is stated that it is still impossible to unambiguously assess the effectiveness of these digital solutions. At the same time, it could be admitted that the necessary legal framework for their application is appeared and it is high time to consider more attentively issues on security and openness of such information systems.
Blockchain and its Dependent Technology's Ethical Implications

This paper will address blockchain and cryptocurrency’s ethical issues, the stakeholders involved, the impact of the ethical issues, the various viewpoints surrounding the issues, why these topics are important in Information technology, and the ideal outcomes of these issues in the future. The first of the problems discussed in this paper is whether blockchain is truly decentralized and the ethical implications. The second set of problems investigated are the criminal use of cryptocurrency, the growing concerns of electronic waste, and the power consumption associated with crypto. I believe this study is important due to the growing market share and user base associated with blockchain and cryptocurrency. More and more organizations are getting involved with these technologies and there are valid concerns regarding the viability, the criminal use of, and the environmental impact of these technologies. It is my hope that this research draws attention to these issues and that positive change will come about as a result.