Reviewerly/exHarmony huggingface dataset:
Identifying and Prioritizing Ways to Improve Oman’s Tourism Sector in the Corona Period

Abstract Objectives: To identify the economic, social, and geopolitical impacts of the Corona pandemic on the country’s tourism industry; to identify ways to enhance domestic tourism so as to increase income in the Oman’s tourism industry which has since been declining; to identify specific travel destinations and associated recovery plans; and to suggest recommendations on the best approaches to improving the Oman tourism sector. Methodology: The research involved collection of primary data using interviews where opinions and perceptions of Omani tourism industry experts were collected regarding economic, social, and geopolitical impacts of the Corona pandemic on the country’s tourism industry; the rationale of lifting travel restrictions and the ways of increasing traveller confidence amidst the uncertainties and fears associated with Covid-19 in Oman; ways to enhance domestic tourism so as to increase income in the Oman’s tourism industry which has since been declining; and recommendations on the best approaches to improving the Oman tourism sector. Findings: The main impacts of coronavirus on Omani tourism industry include loss of revenue and reduction in hotel and occupancy rate. These effects resulted into massive job losses, unpaid leave and pay cuts amongst employees in the country’s tourism industry. The measures to be taken to enhance recovery of the tourism industry were categorized into two: government measures and business measures. Value added: With the multiple adverse effects of the coronavirus pandemic on the tourism sector, this study will help in establishing the potential measures of fostering the positive outcomes in the sector. Recommendations: Setting aside COVID-19 recovery financial kitty; Increasing the frequency of marketing the country as the preferred and the best tourist destination in the world in both local and international media; Public-private partnerships; and encouraging domestic tourism by offering discounts and incentives for the locals enjoying the country’s tourism facilities. Offering discounted rates and fees; adopting a flexible booking policy; cancelling or waiving all the fees and fines that were charged on customers who cancelled or amended their bookings as result of COVID-19 pandemic; offering attractive discounts to clients who want to re-book their previous cancelled bookings without conditions; and diversification of operations.
Herb Suggestion Model for Treat Disease by Symptoms Using Decision Tree Techniques

This research paper presents designing and developing model to support a decision making for choosing the traditional herbal medicine to relieve the symptoms in 8 groups of diseases or symptoms and used 38 attributes for filtering the diseases to generate a model. The attributes information related to the diagnosis of disease or symptoms are collected from the knowledge of the herbal medicine books and online medias for use as a learning and testing dataset of 230 records by using the J48 algorithm to create the rule-based from the decision tree model. After that, the model performance is evaluated with k-fold cross validation method and find a suitable accuracy model in the Weka software. From the experimental results, it is found that the data accuracy is 94.40%, a prediction in conjunction with the precision is 0.950, recall is 0.944, and f-measure is 0.938. It is effective to recommend herbs for the symptomatic treatment in alleviating the initial symptoms.
Finding the Better Distortion Function from Prior Scheme for Image Steganography

Steganography is the science and art of concealing the presence of potential communication. However, due to the intricate high dimensional distribution of covers, it is challenging to design more secure schemes in traditional steganography. In this paper, we propose a scheme to find a better steganographic distortion function via a hitting model combined with the prior scheme and an adversarial game. Hitting model is to measure the complexity of the cover elements from the costs given by prior scheme. The adversarial game between steganographic scheme and analyser is implemented as a stackelberg game. The pure and mixed strategies obtained from the hitting model and this game to adjust the costs provide the novel distortion functions. Experiments show that our scheme could find steganographic distortion function with a better security performance compared with the traditional steganographic algorithm against the targeted adversaries.
Formation of faculty members’ professional excellence in the global dimension of democratization of education, revisit

The article formulates the designated area of the strategic goal of research, namely exploration and development of methodological basis to form faculty member’s professional excellence in the global dimension of democratization of education, which envisages substantiation, development, content-methodological and informational- technological filling of the scientific-methodological organizer for the system of formation of faculty members’ professional excellence in institutions of higher education, as well as experimental approbation of its effectiveness in practical organization of the academic process of professional training for future educational elite, with prospects for lifelong employment. It concretizes the local goal of refining and improving the framework of categories and concepts for exploration of the psychological- pedagogical problem of forming faculty members’ professional excellence in the global dimension of democratization of education. The article develops designated tasks for achievement of the designated and strategic goal; namely, it actualizes the contradictions in pedagogical problematics of forming faculty members’ professional excellence in the course of permanent training of future specialists for the field of education, science and innovation theory, in the circumstances of global environmental challenges for further development of civilization and democratization of education. It establishes the methodical framework of scientific knowledge for dealing with the tree of objectives for the strategic goal of the research (a methodical system for analyzing axiological contents of the pedagogical research thesaurus, a systemic quality analysis of theoretical and methodical support of faculty members’ professional excellence for lifelong employment, and a comparative study of means applied for technical regulation in the field of professional and pedagogical implementation of this research). The article highlights the practical importance of the author’s scientific achievements and establishes the role of a faculty member’s professional excellence in ensuring democratization of educational management in the global dimension of sustainable development of our society.
Fuzzy Cognitive Modeling in Coastal Geomorphology

Coastal systems are inherently complex and involve multi-scale feedbacks and interactions that are difficult to quantify. Coastal geomorphologists organize their understanding of these interactions and feedbacks through conceptual models based on their time in the field or laboratory and research networks. While coastal geomorphology is rich in theory and concepts related to time, process, systems and landforms, there have been few attempts to quantitatively evaluate this conceptual knowledge and predict complex spatial patterns. This article reviews the potential for conceptual modeling using Fuzzy Cognitive Maps (FCM), a form of artificial intelligence that merges fuzzy logic and neural computing, to be applied in coastal geomorphology. FCM development, testing, and interpretation is demonstrated using examples for marsh response to sea level rise, eolian transport from the beach to foredune, the establishment of dune-building vegetation in the backshore, barrier island washover and resiliency, and controls on rip current velocity. Additionally, further integration with Cellular-automata (CA) modeling is shown to be an opportunity to develop evolutionary FCMs to predict landscape change. Based on the ability to intuitively incorporate concepts related to physical and social dimensions of coastal change means that FCM can be applied broadly for integrated coastal zone management (ICZM), and as an innovative way to train the next generation of coastal geomorphologists.
Virus Causes Flu: Identifying Causality in the Biomedical Domain Using an Ensemble Approach with Target-Specific Semantic Embeddings

Identification of Cause-Effect (CE) relation is crucial for creating a scientific knowledge-base and facilitate question-answering in the biomedical domain. An example sentence having CE relation in the biomedical domain (precisely Leukemia) is: viability of THP-1 cells was inhibited by COR. Here, COR is the cause argument, viability of THP-1 cells is the effect argument and inhibited is the trigger word creating a causal scenario. Notably CE relation has a temporal order between cause and effect arguments. In this paper, we harness this property and hypothesize that the temporal order of CE relation can be captured well by the Long Short Term Memory (LSTM) network with independently obtained semantic embeddings of words trained on the targeted disease data. These focused semantic embeddings of words overcome the labeled data requirement of the LSTM network. We extensively validate our hypothesis using three types of word embeddings, viz., GloVe, PubMed, and target-specific where the target (focus) is Leukemia. We obtain a statistically significant improvement in the performance with LSTM using GloVe and target-specific embeddings over other baseline models. Furthermore, we show that an ensemble of LSTM models gives a significant improvement ( $$\sim $$ 3%) over the individual models as per the t-test. Our CE relation classification system’s results generate a knowledge-base of 277478 CE relation mentions using a rule-based approach.
Smart Schedule Design for Blended Learning in VNU—University of Education, Hanoi, Vietnam Based on LMS Moodle Online Portal

Based on the current need to innovate the quality of education, the Moodle online learning system is an open source widely used in universities, including the University of Education and Vietnam National University. Currently, most schools are only interested in the organization and quality of training, but not on building an online education management system. Topic “Smart schedule for blended learning in VNU—University of Education, Hanoi, Vietnam on LMS Moodle based” is the theme study on methods of innovation supporting the management of online learning systems to improve the quality and effectiveness of teaching and learning in schools with new teaching models—a blended learning model. After the research, the topic was tested on the event and calendaring feature on Moodle version 3.8, the latest version of Moodle now, with the result that the schedule on Moodle was able to set up. Calendar loop feature and remind notifications sent to email.
Genetic Algorithm and Cloud Computing Platform for SaO2

In order to solve the problem that oxygen saturation can be described during continuous monitoring. We use ARIMA model to describe the change of blood oxygen saturation of each person, and use pca-ga-bp neural network model to represent a person by inputting parameters under the new index system. The network topology is constructed by 33 training samples, and the index of the three test samples is the quantitative value of the sample number of the three test samples. The accuracy of the test model is 100%. Compared with BP neural network system, pca-ga-bp neural network model can more accurately represent a person. Then, the correlation between age and SpO2 was analyzed by data visualization, deterministic coefficient method and regression coefficient. First of all, we add the mean value and variance of 36 groups of SpO2 data to describe the average level and stability of individual SpO2, and correlate them with age, smoking history, gender and BMI to form a series model of SpO2. We get: the correlation between age and mean, variance and BMI is very small, but also affected; age and mean is negatively correlated, age and variance is positively correlated, age and BMI is positively correlated. In other words, compared with young people, the average oxygen saturation of the elderly is lower, the stability of individual oxygen saturation is lower, and they are more likely to gain weight.
Digital transformation of education in the Arctic: case of North-Eastern Federal University

The article presents the case of developing a program for the digital transformation of higher education in the northern territories of the Russian Federation through the example of the North-Eastern Federal University. It describes the principles and mechanisms of digitalization of basic processes in conditions of limited resources and remoteness from the leading scientific, educational and cultural centres. The authors suggest the development of a digital educational ecosystem to provide high quality and accessible education in remote areas.
영문학 수업에 팀 기반 플립러닝 적용해보기

The year 2020 drives both educators and learners to face the un-contact future in educational fields. Since 2010, we have heard about flipped learning as so-called trendy pedagogical term. However, there are so many guesses and misunderstandings to prohibit us from endeavoring flipped learning. This study starts to change the general concept of flipped learning for my research purpose. By forming a working concept of flipped learning as a paradigm, not a specific method, and utilizing team-based learning methods for during-class activities, I came up three research questions: 1) How does flipped learning action sheet function for learners to participate in group discussion?; 2) How does flipped learning action sheet serve for learners’ understanding of text and their satisfaction of the class?; 3) Are flipped learning action sheet and quiz effective tools as a precedent task? Grounded on two questionnaires the result demonstrates that flipped learning action sheet enables the learner to contribute to group discussion easily and increases the level of class satisfaction as well as understanding of the text. With these results, I can suggest that flipped learning action sheet and taking a quiz are useful precedent methods for educator to construct team-based flipped learning paradigm.
Fixed-parameter tractability for the Tree Assembly problem

Calculating the “distance” between two given objects with respect to a designated “editing” operation is a hot research area in bioinformatics, where the “distance” is always defined as the minimum number of the “editing” operations required to transform one object into the other one. One of the famous problems in the area is the Minimum Common String Partition problem, which is the simplified variant of the Minimum Tree Cut/Paste Distance problem. Within the paper, we consider another simplified variant of the Minimum Tree Cut/Paste Distance problem, named Tree Assembly problem, of which the edge-deletion operations are specified. More specifically, the Tree Assembly problem aims to transform a given forest into a given tree by edge-addition operations only. In our investigations, we present a fixed-parameter algorithm with runtime 2O(klog⁡k)nO(1) for the Tree Assembly problem, where k is the number of trees in the given forest, and n is the number of nodes in the given tree and forest. Additionally, we give a polynomial time algorithm for a restricted variant of the problem.
Trends in higher education in the context of the formation of an innovative economy

The emergence of an innovative economy determines the demand for new competencies from future highly qualified specialists, where the university plays a special role in the formation and implementation of innovative potential. The paper analyzes the transformations in higher education, which are determined by large-scale processes affecting axiological attitudes regarding the role of knowledge in social development.
Context in Social Media Question Asking: A Practice Approach

Recent works have highlighted the complementary advantages and disadvantages of web search and social media question asking (SMQA), and there has been a growing interest in merging these two. To make this idea a success, understanding the context of questions in social media is a prime concern. Yet existing works give us a hint that the positivist approach towards the context of questions is not sufficient in achieving this, and we need to revisit the concept of context regarding SMQA. In this paper, we took a practice based lens [1] to examine existing works along with real-life data of queries in a popular social network to see if it explains the broader contextual factors and associated values. Our data highlighted the importance of understanding the complex social relationship that people make within the structure of social networks, and suggest design strategies to support this through iteratively progressing from phenomenological to representational view of practice.
Information Technology, Governance and Insurance in Sub-Saharan Africa

Purpose – This study investigates the role of ICT in modulating the effect of governance on insurance penetration in 42 sub-Saharan African countries using data for the period 2004-2014.Design/methodology/approach – Two insurance indicators are used in the analysis, namely: life insurance and non-life insurance. The three ICT modulating dynamics employed include: mobile phone penetration, internet penetration and fixed broadband subscriptions. Six governance channels are also considered, namely: political stability, “voice & accountability”, regulation quality, government effectiveness, the rule of law and corruption-control. The empirical evidence is based on generalized method of moments.Findings – The following main findings are established. First, mobile phone penetration does not significantly modulate governance channels to positively affect life insurance while it effectively complements “voice & accountability” to induce a positive net effect on non-life insurance. Second, internet penetration complements: (i) governance dynamics of political stability, government effectiveness and rule of law to induce positive net effects on life insurance: and (ii) corruption-control for an overall positive effect on non-life insurance. Third, the relevance of fixed broadband subscriptions in promoting life insurance is apparent via governance channels of regulation quality, government effectiveness and the rule of law while fixed broadband subscriptions do not induce significant overall net effects on non-life insurance though the conditional effects are overwhelmingly significant.Orginality/value – To the best our knowledge, studies on the relevance of ICT in promoting insurance consumption through governance channels are sparse, especially for a region such as sub-Saharan Africa where insurance penetration is low compared to other regions of the world.
Product Recommendation using User Preference and Online Opinion Reviews

This paper focuses on product recommendation using user preference and online opinion mining. Multiple features of a product are analyzed to get user specific product. In this paper we have analyzed user review data of mobiles sold in online markets and then predicted the most suitable mobile for the user based on preference list provided by the user. To analyze the review, the textual data is processed in multiple stages like preprocessing, feature extraction, categorizing reviews based on features, and then predicting the polarity of the review data using several machines learning model like Logistic Regression, Support Vector Machines, Naive Bayes Classifier and Multi-Layer Perceptron Classifier. After this user entered preferences are analyzed to give user specific products.
Class-related graph convolution for weakly supervised semantic segmentation

Semantic segmentation with deep learning has achieved remarkable progress in classifying the pixels in the image. Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. Image-level label-based weakly supervised semantic segmentation (WSSS) aims to adopt image-level labels to train semantic segmentation models, saving vast human labors for costly pixel-level annotations. A typical pipeline for this problem is to adopt Class Activation Maps (CAMs) with image-level labels to generate pseudo-masks (a.k.a. seeds) and then use them for training segmentation models. The main difficulty is that seeds are usually sparse and incomplete. In recent years, GCNs have made great strides in various fields. GCN can perform global modeling and reasoning on the relationship between regions, which is beneficial for many computer vision tasks. This is our motivation to combine these two aspects. Therefore we propose the module of class-related graph convolution. Because there are differences between classes, our GCN is parallel. Each GCN can learn the classrelated region extension strategy. To enable GCN to learn more authentic relationships, we also introduce the attention mechanisms. We conduct lots of experiments on the public PASCAL VOC dataset, and our model yields state-of-the-art performance.
Research on Slope Patrolling of Intelligent Car Based on Memory Algorithm

Abstract There are many problems in the intelligent car driving on the slope, such as pause, tire slip and so on. This system takes msp432p401r single chip microcomputer as the control core, combines with motor differential, photoelectric cell array, and controls through memory algorithm and PID speed control algorithm, designs an intelligent electric car, and realizes the function of ramp driving and accurate stop. At the same time, in the process of experimental analysis, it can memorize the track, extract effective information from various working conditions, and then complete the accurate control System. After the final debugging, the system has realized the effect of non-stop and non-skid driving on the 0-35° slope surface, and can accurately arrive at the parking point and stop at the set time.
An Innovative Authentication Model for the Enhancement of Cloud Security

Cloud computing provides different types of services deployed in different models; thus, its security has become a paramount concern in the IT field. In this paper, a conceptual framework is proposed to mitigate various authentication threads by introducing an encrypted certificate and token build-up using the user’s geographical location in such a way to enhance the security and ensure the users against the data loss, unauthorized access over unauthorized users and hackers.
Jointly Subspace Hashing for Medical Image Retrieval

The explosive growth of medical images brings about massive amounts of high-dimensional images. The medical image retrieval technique is effective in selecting some similar medical images for medical analysis. A vast majority of existing medical image retrieval methods aim to extract a single feature to represent a medical image. Although these methods have improved the performance, they ignore that different characteristics express different information and they are an indispensable part of content information. Moreover, conventional features consume much storage, which is not conducive to data storage. Therefore, to reduce the loss of information and save storage space, we propose a novel jointly subspace hashing (JSSH) method for medical image retrieval. We solve the high-dimensionality of medical by the simplest image segmentation technology, and then study different subspace projection matrices. Finally, we integrate these matrices into the hash learning model, and build an objective function and learn a series of discriminative binary codes. By conducting comprehensive experiments on three medical image benchmark datasets, we demonstrate the effectiveness of our proposed JSSH.