Reviewerly/exHarmony huggingface dataset:
Virtual Reality Application for Pain Management: User Requirements

The usage of fully immersive Virtual Reality applications for pain relief is still in an exploratory stage.In consequence there is a need to understand the user perspective and wishes regarding this kind of product.To address this issue, this paper presents quantitative research in order to establish the functional and non-functional requirements of our application.Voluntary response sampling was used for the research (N = 55).The inquiry form contained questions regarding serious game content, eagerness for testing, performance, resource consumption optimization, portability, data security, accessibility.The questionnaire was shared via Google Forms.The answers were collected and interpreted.The study revealed that a significant part of the participants was willing to test the application and that they would use an immersive Virtual Reality application during a normal treatment session if the opportunity is available.As functional requirements, the following were considered important: the presence of animals in game, a bright environment and nature-based background sounds.The following non-functional requirements were considered important: game optimization, portability, data security, accessibility, graphics quality and a short learning curve.
Study on Optimization of Olefin Preparation by Ethanol Coupling Based on Polynomial Regression Model

In this paper, aiming at the problem of ethanol coupling to prepare C4 olefin, the relationship between ethanol conversion, selectivity of C4 olefin and temperature was studied, and the effects of different combination of catalysts and temperature on ethanol conversion and selectivity of C4 olefin were discussed.In this paper, firstly, the polynomial regression model is established, the standard deviation of each order regression model is calculated, and the order with the best goodness of fit is determined.Then the linear regression model and regression coefficients are obtained.Finally, the multiple decision coefficients of each equation are calculated to evaluate the goodness of fit of each equation.At the same time, it is not difficult to find that under different catalyst combinations, the ethanol conversion and the selectivity of C4 olefins will increase with the increase of temperature, showing a positive correlation.Then the binary quadratic polynomial regression model is established after the decision variables are determined by the method of control variables.The obtained model is transformed into linear form, and the linear regression model is obtained.Then the regression coefficients of each equation are calculated and the expressions of all equations are obtained.Finally, the multiple decision coefficients used to evaluate the goodness of fit of each equation are calculated.The visual analysis of the eight sets of regression equations shows that the selectivity of C4 olefins is better when the load of Co is near 1%wt, the content of Co/SiO2 is 200mg, the charge ratio of Co/SiO2 to HAP is about 1.2, and the intake rate of ethanol is near 1.2ml/min.When the Co loading is near 1%wt, the content of Co/SiO2 is 200mg, the loading ratio of Co/SiO2 to HAP is 0.5, and the ethanol intake rate is near 0.5ml/min, the ethanol conversion is better.
Effective Cross Synthesized Methodology for Movie Recommendation with Emotion Analysis through Ranking Score

Providing accurate movie recommendations to a user with limited computing capability is a challenging task.A hybrid system offers a good trade-off between the accuracy and computations needed for such recommendations.Collaborative Filtering and Content-Based Filtering are two of the most widely employed methods of computing such recommendations.In this work, a high-efficient hybrid recommendation algorithm is proposed, which deeds users' contour attributes to screen them into various groups and recommends movie to a user based on rating given by other similar users.Compared to traditional clustering-based CF recommendation schemes, our technique can effectively decrease the time complexity, whereas attaining remarkable recommendation output.This approach mitigates the shortcomings of the individual methods, while maintaining the advantages.This allows the system to be highly reactive to new viewer inputs without sacrificing on the quality of the recommendations themselves.Building on other hybrids of a similar kind, our proposed system aims to reduce the complexity and features needed for calculation while maintaining good accuracy and further enhanced by utilizing Sentiment Analysis to rank the movies and take user reviews into consideration, which traditional hybrids do not take into account.Then analysis was performed on the data set and the results show that the proposed recommendation system outperforms other traditional approaches.
A Distortion Model-Based Pre-Screening Method for Document Image Tampering Localization Under Recapturing Attack

Document images are vulnerable to tampering by image editing tools. Recapturing operation can be employed as a simple but effective counter-forensic measure in hiding the digital forgery trace. In this work, we first study spatial and spectral distortion models in the printing and scanning process. The connection between the spatial halftone parameters and the spectral representation are built. The distortion models are then employed in extracting spectral features in both tampered and untampered regions. The proposed forensic scheme can then be established by comparing the spectral features in both the questioned document image and the reference halftone patterns. To evaluate the performance of our approach, we gather a database of 528 recaptured images and 72 tampered images. The experimental results show that the proposed method can accurate classify recaptured document images with AUC higher than 0.9999 even the training and testing samples are collected by different devices. In the tampering localization experiment, the proposed method can be combined with some generic CNN models to yield a two-stage scheme with high efficiency and accuracy, i.e., F1-score as high as 0.9. Finally, we also show that proposed method is still a feasible solution without the prior knowledge of printer model is unavailable.
Real Power Loss Reduction by Extreme Learning Machine, Chaotic, Quantum and Opposition based California Condor Optimization Algorithms

In this paper Extreme Learning Machine, Chaotic, Quantum and Opposition based California condor Optimization algorithms are applied to solve the power loss lessening problem.California condor Optimization (CO) algorithm is modelled based on the deeds of California condor.In normal environs, numerous California condors can be substantially alienated into dual clusters, in which the procedure principally computes the fitness rate for elucidations to split California condor's into classes.In the design period, in order to alleviate hunger, presumptuous that the poorest solution in the populace is the feeblest and ravenous, the California condor's attempt to preserve their expanse from the poor and emanated up with the preeminent solution.In California condor Optimization (CO) algorithm, twofold of the preeminent solutions are measured as the robust and preeminent California condor's, and the other California condor's attempt to approach the preeminent one.In iterations, the complete population is recomputed.In the proposed Extreme Learning Machine based California condor Optimization (ELMCO) Algorithm, CO approach enhances Extreme Learning Machine features to determine an optimal skeleton of Extreme Learning Machine for enhanced canons.In ELMCO Algorithm principally all elements don't own any info about the explication area.Chaotic sequences are integrated into the California condor Optimization (CO) algorithm and it called as -Chaotic based California condor Optimization (CCO) algorithm.This integration will augment the Exploration and Exploitation.Tinkerbell chaotic map engendering standards are implemented.Quantum mechanics has been combined with California condor Optimization (CO) algorithm and it entitled as Quantum based California condor Optimization (QCO) algorithm.In quantum method, features emulate the analogous performance with the certain stage as they route in a credible powdered of median.California condor Optimization (CO) algorithm, even though the initiative of contestant explications slants to touch an optimal solution, yet several are get entombed and not adept of emotive in the route of the dominant solution.It significances to snare in local optima and it accordingly enforces into primary and slow convergence.Subsequently Opposition based California condor Optimization (OCO) algorithm employs Laplace distribution to enhance the exploration skill.Proposed Extreme Learning Machine, Chaotic, Quantum and Opposition based California condor Optimization algorithms are corroborated in IEEE 30 bus system and IEEE 14, 30, 57, 118, 300 bus test systems without considering the voltage constancy index.True power loss lessening, voltage divergence curtailing, and voltage constancy index augmentation has been attained.
Identification of Data-Intensive Systems Requirements using Semantic Similarity Search

The phenomenal growth of big data in social applications and IT software platforms over the last few decades has emphasized the significance of a systematic requirement engineering strategy for analyzing the requirements of dataintensive systems, that deliver valuable insights to business entities.Classification of data-intensive requirements can aid in the development of a more systematic and transparent requirements engineering process, resulting in increased requirement compliance and software project completion.As a result, this paper provides a unique approach Word2Vector based Fast Similarity Search (WV-FaSS) for improving the process of software requirement categorization for dataintensive systems in two phases.Word2Vec begins by taking a corpus of software requirements as input and producing a well-trained high-dimensional vector space.Following that, the vectors that were extracted semantically are indexed.The query vector is then used to look for the most similar vectors within the index, and lastly, similar documents are obtained.Experiments on two benchmark datasets, PURE and WARC, as well as a dataset from the private IT industry, demonstrated that our model outperformed state-of-the-art techniques with precision, recall, and F1 values of 0.91, 0.9, and 0.9, respectively.Thus, the proposed model WV-FAISS enables developers to rapidly search for embeddings of similar requirements that are similar to one another, while also increasing the scalability of similarity search methods.
Problems of the Development and Use of VR Simulators in the Teacher Training System

Introduction. In the era of digitalization, the tasks of developing and using pedagogical simulators, as well as analyzing the effectiveness of the use of virtual technologies in teacher training, are becoming increasingly important. It is important to understand what exactly and how will be practiced with their help. What exactly and how will be measured with their help? Research objectives. The aim of the study is to determine the degree of study of the feasibility and methodology for introducing digital simulations of pedagogical activity into the teacher training system; determine the priority tasks facing thedevelopers of virtual simulators of pedagogical activity; to determine the prospects for scientific research on the use of digital simulations of pedagogical activity for educational and diagnostic purposes. Methodology, methods and techniques. The descriptive method and the classification method were used at the first stage of the search for publications in the e-library, Scopus, Web of Science databases. At the same time, a critical analysis of the articles was carried out for the presence in them, first of all, of the authors’ exit to the problematic level of understanding the issue of introducing digital simulations of pedagogical activity into teacher training. At the second stage, the method of comparative and aspect analysis of the selected materials was applied. The method of constructive problematization was used at the third stage in order to formulate the problem of developing a digital simulator of pedagogical activity to identify and/ or eliminate pedagogical deficits. Results. It was found that scientific research on the possibility of using digital simulators of teacher activity for educational or diagnostic purposes in our country and abroad has not been carried out. Scientific novelty. We have identified and systematized the problems that have arisen in connection with the attempts made around the world to develop digital virtual simulators of pedagogical activity. We also state a new problem of conceptualization of digital simulation of pedagogical activity used to identify and eliminate teachers’ pedagogical deficits. Practical significance. With the solution of this problem, the obstacles to the creation of pedagogical simulators for teacher training and their use for educational and measuring (diagnostic) purposes will be removed.
Multiple-Image Encryption Using Phase Jump Factor-Based Oam Multiplexing Holography

Orbital angular momentum (OAM) has been widely implemented in information encryption as an independent degree of freedom. In this paper, we propose using phase gradient controllable channel as an independent degree of freedom for further boosting OAM information carrying capacity. Multiple images encoded in different phase gradients setting by the phase jump factors even with the same integral or fractional topological charge (TC) can be simultaneously encrypted free of crosstalk. Herein, the safety and the multiplexing capacity are greatly enhanced while keeping the TC resolution of 0.1. In the decryption process, the encoded information can only be reconstructed under the incident vortex beam illumination with inverse TC at correct focus distances and accurate phase jump factors. The feasibility and the security of proposed method are both verified in the experiments and the numerical simulations. This opens up a new avenue for high security and practical information encryption systems.
Architecture

Abstract Machine learning excels at inducing mappings from data, but struggles to induce causal hierarchies. In contrast, symbolic reasoning (in particular, when considered as an expression language ) can represent any form of domain knowledge and can index into code or data via pattern matching.
A k-interpolation Model Clustering Algorithm based on Kriging Method

In this work, a -interpolation model clustering algorithm is proposed based on Kriging method, aim to partition data according to the relationship between the response of interest and input variables.Kriging method is used to describe the relationship between the response of interest and input variables.For each datum, the estimation errors of the interpolation models of the clusters are used to decide its assignment.An optimization strategy is proposed to obtain the final clustering results.The key factors of the proposed algorithm on its performance are studied through the synthetic and realworld datasets.The results show that the proposed algorithm is able to cluster the data according to the response of interest and input variables, and provides competitive clustering performance compared with the other clustering algorithms.
The Development of Spring Encyclopedia in Klaten as Social Studies Learning Media for Middle and Islamic Middle School students

The research and development were carried out at Islamic Middle school 5 Klaten.The school has never used the surrounding environment as a learning resource and has not had a learning media such as an encyclopedia for social studies learning activities in the classroom.The research and development aimed to develop and determine the feasibility level of the spring encyclopedia in Klaten Regency as social studies learning media for students of Islamic Middle school 5 Klaten.The research was conducted using the ADDIE development model.The data collection instrument used a questionnaire as a validation sheet.The validation sheet was intended to material experts, media experts, two social studies subject educators who became the validator team, and product trials for 14 students at 8 th grade students of Islamic Middle school 5 Klaten.The validation sheet was useful to determine the feasibility level of the spring encyclopedia in Klaten Regency.Data were analyzed using a stratified scale for product assessment and a Guttman scale for product trials.The feasibility assessment of encyclopedia products by material experts was 82.22%, media experts were 85%, two social studies educators were 97.18%, and product trials are 98.01%.Based on this assessment, the product of the spring encyclopedia in Klaten Regency was very feasible to be used as social studies learning media for middle or Islamic middle school students.
Role of Medical Libraries for Implementing Knowledge Management During Covid-19 Pandemic: An Empirical Study

The aim of this study was to examine the state of Knowledge Management (KM) in medical libraries in Bangladesh. It also explores the awareness and viewpoints of the doctors, researchers, students and librarians regarding KM during the pandemic. The questionnaire survey was adopted for this study. The Data gathering procedure was performed through Google forms. The questionnaire was distributed using email and social networking sites. Among 29 public medical colleges, this quantitative research was conducted in three medical college libraries in Bangladesh. The selected medical libraries were: Dhaka medical college library, Chittagong medical college library and Sylhet MAG Osmani medical college library. This study explored the barriers to KM in medical libraries and the possible implementation areas of KM. The study found that participants are aware of the concept of KM. They are also aware of the necessary KM tools and the probable KM implementation areas in the medical libraries. Present paper also revealed that lack of institutional policy, inadequate support from top management, scarcity of resources as barriers towards implementing KM in the medical libraries in Bangladesh. Further studies should be conducted on the same issues in other sectors as present study was limited to three medical libraries. This study is novel, in the sense that it is the first of its kind to examine KM implementation in medical libraries during pandemic situations in Bangladesh.
Crowdsourcing classification and causality to power a search-and-innovation engine

Durham Zoo (DZ) is a project to create a search-and-innovation engine for science and technology.The engine has been designed to capture the knowledge of experts from different areas of expertise via the classification of the literature.The architecture combines the higher-level cognition of humans and their powers of language, abstraction, of inference and analogy, with the storage and processing power of computers.The system is adapted for searching both what already exists, and novel solutions to problems.To be built and operated by the community, the goal is to democratize innovation whilst funding societal causes such as climate-change mitigation or the search for new antibiotics.The original design, first published in Absalom and Absalom (2012), relied upon fuzzy and faceted classification.The fuzziness related to the similarity of concepts in each of the facets.A search query would be matched with the literature in multiple facets to retrieve holistically similar literature, or to suggest a solution to a problem from elsewhere in technology or the natural world.The facets used to describe a concept in science and technology included a problem and a solution.A recent reappraisal of the project design recognised the potential of causality for modelling and matching problems.This paper proposes a design compatible with the crowdsourced classification. The original motivation for the projectThe initial challenge was how to crowdsource the classification of the literature in support of patent searching.Patent offices still use classification by experts as a cornerstone to searching the prior art.For whilst information retrieval and artificial intelligence are making great strides, the human brain is still class leading at identifying and understanding concepts.It is still best able to work through imperfect language, abstraction, jargon, and terminology to distill the essence of a disclosure.The essence of analogous concepts is encoded with a same classification code.In the patent world there is the added complexity of the legal nature of a patent: things are often described in broad terms so as not to restrict the scope of protection.As an example, a
Detection of Chaos Using Reservoir Computing Approach

Detection of chaos in time series is of utmost importance in many scientific fields. Indeed, the presence of chaos and its significance, especially in multidimensional systems, plays an essential role in the control and analysis of such systems, and in their practical use in a variety of applications. In this paper, we demonstrate a new methodology for the detection of chaos in time series using a reservoir computing (RC) paradigm called conceptor-driven network (ConDN). Case studies on the known chaotic attractors (i.e. Lorenz, Rossler, Chua) of integer (conventional) and non-integer (fractional-order) orders, as well as a physically simulated and designed spintronic device (NCVO) are used in this study to validate the proposed chaos detection approach. The proposed chaos detection approach is tested on clean and noisy time series of the mentioned attractors. It outperforms the 0-1 chaos detection test and the largest Lyapunov exponent (LLE) estimation approach especially in the high noise-level conditions. In addition, the proposed approach is capable of differentiating the time series generated by the systems whose dynamics is at the edge of chaos. The simplicity of use of the proposed chaos detection approach can be counted, as well, as one of its main advantages over traditional chaos detection methods.
Examination of the Factors of E-learning Acceptance in Egyptian Primary and Preparatory Schools during Covid-19

During COVID-19, the social distance became an obligatory fact which forced the whole world to rely on e-learning for all levels of education. E-learning is becoming the main challenge for many developing countries specific. Technologies are not fully accepted and used in Egyptian primary and preparatory schools before. However, the successful usage of e-learning depends on understanding the acceptance factors as well as the main challenges that face it. In this regard, this study aims to examine the factors of e-learning acceptance. The Unified Theory of Acceptance and Use of Technology (UTAUT) model is used as a conceptual framework. An empirical study was carried out using online survey research among 507 participants. The study results show that most of the participants are satisfied with e-learning as a good solution to continue education during global health conditions. However, the responses to the government T.V. channels and websites were very weak. The main obstacles were the technical issues such as the internet speed and traffic, weak technology skills for learners and teachers, in addition to the social preference for the traditional method. The study concluded that the acceptance depends on its ease of use, its usefulness, its social influence, its price value, the facilitating conditions, and its motivations. The school type and the parents' education level were proved to be effective moderators on many constructs