Reviewerly/exHarmony huggingface dataset:
Creative Process for Designing a Hybrid Game for Nutrition Education

In this paper we present and discuss the creative process behind a hybrid game, grounded in the research project “FlavourGame”. The research project is developing a serious game for kids aged 10–12 years old to generate discussion and sense-making around nutrition, more specifically everyday choices of nourishment and food. The main aim is to develop a game design model for hybrid games, making use of digital and physical interactions with fictional worlds to create engaging experiences. In this paper we unravel the creation process of the game narrative, game board, cards and characters while enunciating the mechanics and rules needed to play the game. Additionally, we describe the implementation of the game, the used technological approach, and challenges faced by combining the digital and analog components.
Research and Design of Virtual Simulation Experiment Teaching Platform for Business Income Audit Under the Environment of Big Data

Experiment teaching is an important teaching link of audit practice courses. The economic and employment environment changes produced by big data and informatization require traditional experiment teaching to make adaptive changes, and the demand for virtual simulation experiment teaching arises at the historic moment. In order to overcome the limitations of abstraction of principles and high cost of teaching in audit teaching, the paper analyzes the characteristics of operating income under the environment of big data, studies the changes in the mode of audit work, and designs the plan of audit practice virtual simulation teaching system that adjusts the curriculum teaching arrangements, changing teaching methods and evaluation models to improve the effectiveness of audit practice teaching.
Faceted Search on Amazon Data using Naïve Bayes Approach

Facets can allow the user to interactively search in online webshops, e-commerce websites, and digital libraries. Faceted search in E-commerce websites is a challenging task. After successful identification of facets, the quality of information retrieval results improves drastically. Experiments about faceted search on E-commerce domain by taking into account research done on Amazon webpages corpus data words. The main task is to detect facets using text classification methods namely the Naïve Bayes method. In this paper, the primary focus is on accomplish information retrieval using faceted search for boosting the searching results to find the desired product with the help of large data set of words using K-Means, Term Frequency, Naïve Bayes approach collectively as a hybrid approach. In this work Naïve Bayes technique are used on Amazon text words to develop and detect faceted search results using Python language. To classify the text words random forest, machine learning algorithms have been used for faceted search. Results of experiments show that the use of this hybrid approach of faceted search in Amazon webpages data words using Naïve Bayes will enhance the accuracy by 90.55% on corpus data irrespective of Random Forest model results.
Leveraging Unpaired Image to Image Translation for Generating High Quality Synthetic Data

Modern deep learning applications require huge data to deliver required accuracy. In most cases accumulating the required amount of data of sufficient quality is often very difficult and costly. We propose a GAN based solution to this problem. CycleGAN is a method of unpaired image -to -image translation which transforms the input image to target domain while preserving all the essential features of the input image. We can leverage CycleGAN to generate synthetic data of required quality by choosing the appropriate target domain.
Implementation of Two-Dimensional Image Result Analysis Using Artificial Neural Networks with the Counter propagation Method

Two-dimensional image recognition is one of the activities to check the original image. The method counter propagation used is focused on how to extract data from the available samples. The data extraction step was dividing the sample into several research areas. Then from each region the active pixel value is taken so that numerical data can be obtained as much as the available area. The numerical result data is then normalized by fixed compare, where each numeric data comes from the same data.To prove the method, the writer made a supporting application, namely char-cognition with a basic visual programming language. The final result of the analysis shows that the pattern produced by this method can be recognized well the authenticity of the two-dimensional image. Manual examination is considered inefficient because it faces problems with the eye's eye foresight and accuracy. Two-dimensional images are generally identical but not the same in terms of shape and slope. Automatic two-dimensional image matching via a computer system is essential for better and more accurate identification of two-dimensional images. Artificial neural network method is used for extraction with various data in two-dimensional images.From the five of the simulation can be concluded that the best Learn Rates by the lowest Error Level is 0.1 and the best Learn Rates based on the highest Match Level is 0.3. Otherwise, due to successful rate from this experiment is not measured by low rate of Error Level but from the high of Match Level rate, so that Simulation 2 with Learn Rates=0.3 that will be used in the next experiment.
The Transformation of Relational Database to Wide Column Store Database

Due to the popularity of big data applications, such as social networking, e-commerce, etc., companies need to quickly access huge amounts of data. Under such conditions, a relational database (RDB) cannot meet the demand, and more and more companies decide to switch to NoSQL database to maintain competitiveness. It's necessary to convert data from an RDB to a NoSQL database for the companies that have a lot of operation data in an RDB. Therefore, this paper proposes an algorithm to explore how to convert the schema of RDB into the ones of Wide Column Store DB (WCSDB), and how to import actual data from an RDB into a WCSDB.
Development of mobile learning integration with scientific approach in stoichiometry

Abstract This study aims to develop mobile learning on stoichiometry based on the scientific approach. Mobile Learning was tested in two different high schools. Research and Development method by Borg and Gall used by modifying stages of need analysis, development, validation, and product test. The result of need analysis indicated necessary media, which provide a summary, exercise, practical video, animation video, and educational games as learning based on scientific support in stoichiometry. 74% of respondents liked mobile learning. At the development phase, designed mobile learning media and storyboard along with software chosen to make mobile learning. The feasibility test of the media produced 88% with a reliability of 0.77. Feasibility test for topic and language feasibility test produced 88% with a reliability of 0.9. Chemistry teacher’s test acquired 92% as the best criteria. Small-scale student tests acquired 81%, and big-scale student tests acquired 83% considered as the best criteria. According to this, the study concluded that mobile learning media based on scientific in stoichiometry topic is proper to use and suitable for students and teachers needed.
Visual Analytics for Basketball Star Card Market—Under the Background of the NBA Restart

This paper is to analyze the fluctuation of the basketball cards market and to find the price change of the basketball cards during the past three months under the background of the NBA Restart. Besides, the paper makes a comparison between two online trade markets respectively-eBay and Card Hobby. Mixed methods are employed to gather specific data to answer the research questions. Data scraping is used to gain data from the trade platforms. Inferential statistical analyses and data visualization are conducted to examine whether the price and the quantity of the demand and supply of the basketball star cards are influenced by the NBA Restart. Also, the paper tries to dig out the time series data to understand the behaviors of both the suppliers and consumers in the market. The paper finds that the external incidents have big effects on the price change of the basketball star cards. Both supply and demand sides are sensitive to the external incidents. The price of a basketball star card depends on the price fundamentals of the entire star card market and the value of the card itself and the external impact of the star card itself factor.
A novel GA-based Selection Algorithm of Cloud Services

Cloud services, an importance service which are deployed and executed in dynamic cloud environment, play an increasing role in modern life. However, selection of a suitable service composition is non-trivial task for non-experts. Since the number of candidate services is large, it is urgent issue for the cloud service engineering to choose a suitable cloud service composition. In order to tackle with the issue, this paper put forwards a novel algorithm based on multi-objective genetic algorithm. Considering the dynamic feature of cloud service, our approach combines the adaptive mechanism with genetic algorithm, which can adjust the weights of GA operators. To adapt to the dynamic situation of cloud service, it utilizes niche technique and the moving average method which can smooth random fluctuation in Internet. Simulating experiments shows this algorithm can obtain the better selecting performance of service composition under satisfying customer requirements. .
MF - Ensemble Algorithm with threshold to enhance the Supervised Learning Classifier Accuracy on Big Data

The increasing rate of data volume causes an issue in data management and decisions making. Big data analytics furnishes a data analysis in an automated way with the consideration of techniques such as classification. The data classification attained performance in less and computational challenges as high in the directions of big data. The reason behind this issue is focused on high data volume and dimensions. To enhance the performance on the prediction of classification task with the imputes of classification models, the dimensional in advanced linear perspective needs an effective approach to reduce the dimensions of data. An effective approach considered in this research work is the ensemble strategy which aims to identify the optimal set of features. This strategy is to tackle the big data difficulty especially with high dimensionality. Hence, the solution of optimum feature set to improve the classification performance is an of the essence challenge. A novel method i.e. ensemble method has been considered as an efficient conceptualization to better the accuracy of classification.
Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity

Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering (LCF) algorithm, user-based collaborative filtering (UCF) algorithm, and user recommendation algorithm (F2F), the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results.
Circuit Design and Realization of capacitive Atmospheric refractive index Measurement instrument

The accurate measurement of atmospheric refractive index can greatly improve the radar detection accuracy, communication equipment communication distance, and effectively predict the generation of atmospheric waveguide.The common atmospheric refractive index instruments include the five-nine sonde and the high precision microwave refractive index instrument.According to the principle that the oscillation frequency of the gas will change when the gas in the environment is changed, this paper designs the hardware module composition and software circuit of the light-duty capacitive atmospheric parameter tester, and the light-duty capacitive refractive index tester with LC oscillator as the main element.Finally, experiments show that the fitting degree and accuracy of the capacitive instrument data curve are second.
Case Studies of Women in Electronic Music: The Early Pioneers

Louis Niebur’s chapter 13, ‘Case Studies of Women in Electronic Music: The Early Pioneers’, considers a range of the earliest ground-breaking women working with electronic music, including Daphne Oram and Delia Derbyshire, at the BBC’s Radiophonic Workshop in the UK, and Wendy Carlos, Pauline Oliveros, and Suzanne Ciani, in the US.
TCP Incast Solutions in Data Center Networks: Survey

In recent years, Data Centers are considered as the backbone of the Cloud Computing systems and are meant to deliver large-scale cloud-based services. The Transmission Control Protocol (TCP) is the most used one in DCNs wherein congestion occurs when multiple senders send data to one receiver at the same time, this phenomenon is the TCP incast. In this paper, we survey the most recent solutions of TCP incast problems in DCNs, we provided a comprehensive comparison between them according to a set of criteria to discuss their strengths and weakness. Finally, we outline some challenges in mitigating TCP incast in DCNs.
Enhanced End-to-End System for Autonomous Driving Using Deep Convolutional Networks

The emergence of autonomous cars in today’s world makes it imperative to develop superlative steering algorithms. Deep convolutional neural networks are widely adopted in vision problems for their adept nature to classify images. End-to-end models have acted as an excellent substitute for handcrafted feature extraction. This chapter’s proposed system, which comprises of steering angle prediction, road detection, road centering, and object detection, is a facilitated version of an autonomous steering system over just considering a single-blind end-to-end architecture. The benefits of proposing such an algorithm for the makeover of existing cars include reduced costs, increased safety, and increased mobility.
Demand Quality Evaluation of Satellite Mobile Communication System based on Cloud Model

The satellite mobile communication system is one of the important means of modern communication and has wide demand space. The accurate assessment of the quality of demand is an important prerequisite for improving the quality of the demand of satellite mobile communication systems. By constructing the system requirement quality evaluation index system of satellite mobile communication system, the connotation and meaning of each index system are analyzed, and the applicability of cloud model in the quality evaluation of demand is clear. Based on the evaluation method of cloud model, the quality evaluation process of demand quality is clarified, and the evaluation results are obtained, and the Suggestions for the improvement of the quality of the demand quality of the satellite mobile communication system are provided.
HILNER: A Hindi Language Named Entity Recognition System Based on Hybrid Approach

The work described in this paper is related to named entity recognition (NER) for Hindi language. In this paper, an efficient and effective framework for extracting Hindi Named Entities (NEs) from Hindi text using a Hybrid Model comprising of Rule-based approach and Conditional Random Fields (CRF) based Machine Learning (ML) approach, has been proposed. Main goal of this research work is to exploit the advantages of both the approaches so as to come up with a more efficient hybrid model and study the effects on resultant NER System. The experiments conducted show that hybrid approach can result in higher performance compared with the performance of independent rule-based system or independent machine learning system for NER task. This paper also focuses on the task to identify specifications of the Hindi language, and hence prepare an efficient and appropriate feature set, which when used for training statistical models give better accuracy. The proposed methods have been evaluated by carefully designed experiments, following the standard practice in this field. The system shows overall recall, precision, and f-score values of 98.21%, 99.51%, and 98.82%, respectively.
On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets

Transfer Learning has been shown to be a powerful tool for Natural Language Processing (NLP) and has outperformed the standard supervised learning paradigm, as it takes benefit from the pre-learned knowledge. Nevertheless, when transfer is performed between less related domains, it brings a negative transfer, i.e. hurts the transfer performance. In this research, we shed light on the hidden negative transfer occurring when transferring from the News domain to the Tweets domain, through quantitative and qualitative analysis. Our experiments on three NLP taks: Part-Of-Speech tagging, Chunking and Named Entity recognition, reveal interesting insights.
A New Model for Co-Ratings Based Similarity in Collaborative Filtering

Collaborative Filtering recommender systems use the history of user ratings to construct a usually very sparse user-item rating matrix and then use some similarity measurement algorithm to find similar user/items for prediction purpose. Conventional similarity measures such as PCC or Cosine works only on co-rated items and ignore the rating of items that are not rated commonly. Besides, the accuracy of these measures is not very high. Advanced similarity measures such as NHSM and Bhattacharya Co-efficient give comparable accuracy but ignores the weightage of the number of co-rated items. In this paper, we aim to use not only the rating values of co-rated items but also consider the weightage of the number of co-rated items. Moreover, in our improved similarity measure, named Improved_CF we take into account the ratings of non-co-rated items also. Experiments on four publically available datasets (i.e. CiaoDVD, Filmtrust, MovieLens-100K, Epinions) are used to establish the effectiveness of our proposed model. Both prediction and classification accuracy metrics are used as evaluation parameters.
The Fate of Virtual Currencies in India - Regulation and Redefinition

The advent of Virtual Currencies (VCs) in the global financial landscape has been a cause for concern for money market regulators and governments. This is due to the fact that VCs were designed to operate without the need for a centralized authority and consequently, regulating VCs has posed a challenge for most jurisdictions across the world. Further, due to the volatile nature of VCs and their ability to destabilize the market in the long run, most regulators have tried to nip the problem in the bud by not allowing VCs to operate in a manner similar to fiat currency. However, while VCs do not have legal tender status which is guaranteed by a centralized authority, they operate in a manner that is very similar to generic currency. They may be used as a medium of exchange, a unit of account, a store of value, and may be used to as a method for the discharge of debts.That being said, VCs have been restrictively defined by regulators to ensure that they may not function as money to ensure that they cannot turn into a valid medium of exchange that replaces the traditional fiat currency. In India, a similar trend is reported with the RBI prohibiting the entities regulated by it to enter into or facilitate transactions that involve VCs. Furthermore, the legislature is also considering a law to prohibit the use, issuance, transfer, mining, generation, disposal or sale of crypto-currencies within the territory of India.The present paper seeks to determine and analyze the true nature of VCs from a legal viewpoint. To that end, Part I introduces the main issues that are to be dealt with in the present paper; Part II of the paper is a cross-jurisdictions analysis of how VCs have been treated and defined by regulators, legislators and courts across the world; Part III deals with the treatment of VCs in India by the Government and the RBI, with a special focus on the Supreme Court’s judgement in Internet and Mobile Association of India v. Reserve Bank of India to determine the legal definition and status of VCs in India; and Part IV concludes.
Secure Pull Printing with QR Codes and National eID Cards

With more systems becoming digitised, enterprises are adopting cloud technologies and outsourcing non-critical services to reduce the pressure on IT departments. In this process, it is crucial to achieving the right balance between costs, usability and security; prioritising security over the rest when handling sensitive data. Considering the print management, often off-premise, many enterprises report at least one print-related security incident that led to data loss in the past year. This problem can damage the enterprise business, especially considering the fines prescribed by current regulations or its reputation. Focusing on securing enterprise printing, pull printing is the set of technologies and processes that allow the release of print jobs according to specific conditions; typically user authentication and proximity to a printer. We design a software-oriented pull printing infrastructure that supports a print release mechanism using QR codes and electronic IDentity cards as a second-factor authenticator. Our solution addresses the costs, as any medium-size organisation can adopt our open-source solution without additional devices or access badges; and the user experience, as we offer a driverless print environment and a user-friendly mobile application.
Interactive Learning Tool for Image Compression Using Singular Value Decomposition

Nowadays, technology has become an essential part of our daily life. It has proven its effectiveness in many areas. One of these areas is education, where interactive educational tools have appeared to facilitate many complex scientific concepts. Meanwhile, singular value decomposition (SVD) is a linear transformation method used in many applications such as image processing. This paper aims to propose an interactive learning tool-based Graphical User Interface (GUI) using MATLAB for SVD as a technique for applying the image compression. The tool provides the user a step by step illustration of the SVD technique for image compression and allows him to try the application by compressing an image. The tool developed using MATLAB 2019b. The interactive tool enables visualization of the image compression, and it is tested with ten students to evaluate the traditional learning method against the interactive learning method. The results showed that the tool improved the learning since the treatment group used it to get better or equal results in the theoretical questions. Also, they get much better results in the practical question. The tool could motivate the students to learn the topic since the treatment group was more satisfied than the control group.