Reviewerly/exHarmony huggingface dataset:
Design of virtual education experiment platform based on artificial intelligence

In order to solve the problem that the digital image processing of traditional virtual education experiment platform is time-consuming and the evaluation result of the platform does not match the actual result, the design method of virtual education experiment platform based on artificial intelligence is proposed. This paper introduces the general framework of virtual education experiment platform based on artificial intelligence, including remote registration, intelligent push of learning courses, establishment of teaching centre website by using artificial intelligence technology, etc. Based on the framework and heap code, the virtual education experiment platform is built by server and client calling service objects. The experimental results show that the accuracy of the proposed method is between 80% and 95%, the time-consuming is less than 0.02 μs, and the fitting range is between 80% and 100%. Compared with the methods based on 3D MAX, the proposed method has obvious advantages.
A Novel Cryptosystem for Files Stored in Cloud using NTRU Encryption Algorithm

The cloud refers to a set of services and infrastructure that are accessed via the internet. the cloud infrastructure is shared by many users each one performing different tasks. in order to prevent data leakage in the cloud the Cloud Service Provider should employ an Encryption Algorithm to protect the data of the users. Since the cloud service providers and large amount of data the Encryption Algorithm should be very efficient in terms of computational cost and time. Current Cloud service providers you son of the following algorithms to encrypt and decrypt the data Advanced encryption standard(AES), Rivest–Shamir–Adleman (RSA) and elliptic curve cryptography(ECC).The encryption for cloud should be chosen such that it is computationally efficient for the Cloud Service Provider and also meets the security requirement of the user
Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data

:Most online customers use cards to pay for their purchases. As charge cards become the most mainstream strategy for installment, instances of misrepresentation relationship with it too increases. The primary goal of this venture is to be ready to perceive false exchanges from non-fake exchanges. In request to do so,primarily,data mining methods are utilized to examine the examples and attributes of deceitful and non-fake transactions.Then,machine learning systems are utilized to foresee the fake and non-fake exchanges automatically. Algorithms LR (Logistic Regression) is used. Therefore, the blend of AI and information mining procedures are utilized to distinguish the fake and non-fake exchanges by learning the examples of the information. Models are made utilizing these calculations and afterward precision,accuracy,recall are determined and an examination is made.
Solar power forecasting using robust kernel extreme learning machine and decomposition methods

This paper proposes empirical mode decomposition (EMD)-based robust kernel extreme learning machine (RKELM) to achieve a precise predicted value of solar power generation in a smart grid environment. The non-stationary historical solar power data is initially decomposed into various intrinsic mode functions (IMFs) using EMD, which are subsequently passed through the proposed robust Morlet wavelet kernel extreme learning machine (RWKELM) for solar power prediction at different time horizons. Further a reduced kernel matrix version of RWKELM is used to decrease the training time significantly without appreciable loss of forecasting accuracy. By implementing the real time data for validation of the proposed method for short term solar power prediction it can be observed that the proposed EMD-based RWKELM outperforms various other methods, in terms of different performance matrices and execution time. The solar power prediction results on experimental data show the lowest error which proves the highest prediction accuracy.
Blockchain

A blockchain is a decentralized system that uses a peer-to-peer network to enforce transactional integrity as well as security and avoids using a third party such as banks for mediation. Blockchains can be considered as decentralized architectures with built-in security to increase the trust and integrity of transactions.
Guest Editor's Introduction: Special Section on Virtualization and Services for Cloud-Based Application Systems

Cloud-based application systems are rapidly deployed worldwide in production use via virtualization and services computing technologies. The scaling demands for these application capabilities to the cloud providers, compound with differentiated requirements on the quality of services, have brought severe technical challenges. This special section focuses on the techniques of virtualization and services for cloud-based application systems, mainly including multi-scale resource management and sharing, elastic scheduling and allocation of computing and network resources, monitoring and diagnosis for cloud-based services, and cloud-based mobile systems. The articles of this special section illustrate recent advances in virtualization and services provisioning for cloud-based application systems. We expect that this special section will provide an integrated view of the state-of-the-art techniques, identify new challenges as well as opportunities, and promote collaboration among researchers in this field. We received 34 submissions and we finally accepted 4 articles. The acceptance rate is as low as 11.8 percent.
A Study of Augmented Reality for the Development of Spatial Reasoning Ability

Abstract Spatial ability has been reported to be an important predictor for student success in STEM fields (Sorby, 2013). Accordingly, various studies have looked at multiple strategies to help students develop these skills (Ha and Fang, 2016). This study tested the use of augmented reality on smartphones for developing spatial reasoning in the context of a spatial reasoning skills course. The mobile AR app gave students the ability to view digital three-dimensional objects by pointing their phones at a printed marker. They could either move their phones around the marker, or they could move the markers, in order to view the objects from multiple angles. They could manipulate the objects via commands within the app, such as rotations and folding. Various games were implemented in the app to support the development of mental rotation abilities while using the motivational aspects of gamification to increase engagement. To test the effects of this app, approximately 70 freshman early-engineering students at a major Midwestern university who performed poorly on the PSVT:R spatial abilities test had the opportunity to work with this app in the context of a course designed to develop their spatial reasoning abilities. They were compared with approximately 70 students who took the same course minus the augmented reality app. Both sets of students experienced traditional means for teaching spatial reasoning (see Sorby, 2013). Multiple measures will be compared for these groups of students. In particular, we will compare: * Performance on the PSVT:R spatial abilities test at the end of the course * Performance of in-app activities and repeated achievement measures * Student attitudes, including their confidence and enjoyment of spatial abilities tasks * Student comments in follow-up focus groups It is anticipated that the results of this study will demonstrate the efficacy of using the augmented reality app in conjunction with traditional classroom activities for developing spatial reasoning abilities, and that results will align with previous findings regarding beneficial motivational effects and user satisfaction with AR for the purpose of spatial reasoning practice.
An amalgamated prediction model for breast cancer detection using fuzzy features

Input feature processing is required for obtaining meaningful results for cancer prognosis. In this paper, the extended Kalman filter (EKF) and fuzzy K-means clustering algorithms have been combined into a hybrid algorithm with improved functionality, compared to either of the two separately. The proposed hybrid algorithm implements fuzzy K-means with support vector machine (SVM) coupled with an EKF for data filtering, working with from consecutive filtering and prediction cycles. Fuzzy membership functions are then calculated to map the labels with the attributes which is used by K-means to create a new modified set of attributes supplied to the SVM classifier, with lesser number of support vectors. The number of clusters is added into the training process as the input parameter except the kernel parameters and the SVM penalty factor. The approach was tested for various publicly available datasets like UCL, SEER and a real dataset compiled by the authors.
Racism and social media: a study in Indian context

Racism is a kind of hatred behaviour, exhibited in written, verbal or physical form against the ethnicity or physical appearance of a group or an individual. Around the world, problematic behavioural incidents occur and in India, media often interpret it as towards people of colour or blacks. There was a lot of discussion about these incidents both online and offline, some groups consider that India is not free from racism and others view it is of misinformation processing. Therefore, there is a requirement of a system to evaluate and determine the possible scenarios towards racism in India. In this regard, using four different sets of keywords we created Twitter datasets. The data collected from social media are analysed to identify the polarity of content and the amount of racism using the software developed in R programming language. Contents are categorised in different polarities such as racist, non-racist and neutral.
A hybrid algorithm for efficient task scheduling in cloud computing environment

Cloud is a boon to the generation which provides services that can reduce the overhead in maintenance and computational complexities. Scheduling the user's job in the cloud resources plays an important role for the better performance. Task scheduling is an NP-hard problem, since it may have more than one solution to fit in. In this paper a hybrid algorithm is proposed by the amalgamation of artificial bee colony Algorithm and particle swarm optimisation named as ABPS algorithm. The proposed ABPS algorithm optimises the task scheduling on the cloud environment by providing minimised makespan, cost, and maximised resource utilisation and to balance the load. The proposed ABPS algorithm compared with ABC and PSO algorithm have been simulated in the CloudSim simulation tool. The proposed ABPS algorithm based on makespan outperforms ABC and PSO algorithms by 22.07% and 28.12%, respectively, also when compared with cost outperforms ABC and PSO algorithms by 32.41% and 44.49% respectively. ABPS algorithm based on resource utilisation outperforms ABC and PSO algorithms by 49.37% and 48.88% respectively and based on degree of imbalance outperforms ABC and PSO algorithms by 16.21% and 20.51% respectively.
The project canvas: five years evolution of a project management instrument

The project canvas is an instrument for winning a common understanding of a project, especially (but not only) in interdisciplinary groups. For this, the project canvas has special features, which were developed, tested and proven in the context of multi-year qualitative research. Research has been conducted in the spirit of free and open-source development. In this context, a key role was played by an open global community, in which project managers have free access to data, receive and apply the project canvas for their purposes, and share their experiences, resulting in improvements and enhancements of the instrument. This article describes the research and development of the project canvas from initial ideas to professional maturity. After the introduction, we present the theoretical background, then the research methodology, followed by your results, discussion and conclusion with limitations.
Impact of clustering on quality of recommendation in cluster-based collaborative filtering: an empirical study

In memory nearest neighbour computation is a typical approach for collaborative filtering (CF) due to its high recommendation accuracy. However, this approach fails on scalability; which is the declined performance of the same due to the rapid increase in the number of users and items in archetypal merchandising applications. One of the popular techniques to attenuate scalability issue is cluster-based collaborative filtering (CBCF), which uses clustering approach to group most similar users/items from complete dataset. In this work we present a detailed analysis of the impact of clustering in CF approach. Specifically, we study how the extent of clustering impacts collaborative filtering systems in terms of quality of predictions, quality of recommendations, throughput and coverage. Based on the empirical results obtained from two datasets, Movielens100K and Jester; we conclude that with increasing number of clusters the quality of predictions, the quality of recommendations and the throughput are enhanced but the coverage provided by clustered subsystems declines.
Empowered Pedagogy: Catching Up with the Future

Purpose - Recently, a layered and lateral framework that provides opportunities for students to design the pedagogy that creates a broad based, flexible and personalized learning is introduced by the Taylorâ€™s University School of Engineering. This approach creates avenue for the students to design their own curriculum within the framework that is developed with inputs from the industry. The framework, the key components, and the tracking of the performance to align the set outcomes through assessment tool is presented. Methodology - My Study Plan (MySP) a macro-enabled software is used to develop a four-year tier model for the students to design the curriculum with the help of a mentor at the start of the programme. Studentâ€™s learning is monitored on a semester-based learning analytics process implementing the Programme Outcomes (PO) Scorecard. The August 2018 cohort of the Electrical and Electronic Engineering programme is used to pilot this pedagogy. Findings - Currently the PO measurement is done for the graduating student and focus only on the effectiveness of the programme. The Programme Outcome Assessment Review (POAS) used in this study to monitor the MySP the continual monitoring each semester rather towards the end of graduation. Initial simulation of existing PO attainment record of students suggest that this can generate interest towards the learning progress on a semester basis. Significance - The successful adoption sees an effectiveness in the long run in developing graduates through the continual monitoring of the programme outcomes. The graduating students carry the PO scorecard that complements a second transcript for value addition for their studies. Prospective employers can use the PO scorecard as a metric for initial recruitment purposes, a measure for the soft skills achievements of the applicant.
Fast and Economical Object Tracking using Raspberry Pi 3.0

This paper proposes a way to construct a financially cheap and fast object tracking using Raspberry Pi3. Multiple object detection is an important step in any computer vision application. Since the number of cameras included is more these gadgets are compelled by expense per hub, control utilization and handling power. We propose a tracking system with low power consumption. The framework is completely designed with python and OpenCV. The tracking quality and accuracy is measured using publicly available datasets.
Conclusions

This concluding chapter provides a synthesis and commentary pertaining to the studies presented in this collection and it highlights some of the key opportunities and issues associated with mobile language learning. Five notable themes running through the case studies are identified, namely breaking down barriers; unfettered flow of information; frequent interaction and reflection; enjoyment and perception of personal gains; and finally, multiplicity of technologies, modalities and methods. These themes highlight key strengths of mobile approaches to teaching and learning, with special reference to out-of-class environments where teachers and researchers often have more scope to try out something new. They also draw attention to how these strengths play into lifelong learning. The chapter highlights some emerging trends and possible developments in mobile and ubiquitous learning in a rapidly changing world and in relation to known barriers to the uptake of innovations.
The Impact of HRM Practices on Job Satisfaction in the select Star Hotels of Coimbatore

Employees are the greatest asset for any organization and they are pivotal to organization’s effectiveness. Satisfied employees can contribute more to the attainment of organizational goals (Maimako, 2016). According to literature survey(Jeet, 2014; Hassan,2013) the high level of employee job satisfaction will lead to lower turnover, increase in production, increase in customer service, cost reduction in recruitment and selection,lower training cost and improvement in team work.Oyeniyi,Afolabiand Olayanju(2014) and Maimako, (2016)state thatgood Human Resource Management (HRM) practices like training and development, performance appraisal, compensation and safety and health have direct impact on employee job satisfaction and this leads to increase in satisfaction level. For this research, five HRM practices (Staffing; Training and Development, Performance Appraisal, Performance Rewards, and Internal Communication System) were taken to find theirimpact on Job satisfaction. Data collected from ten star hotels in Coimbatore city show that human resource management practices have positive impact on job satisfaction. The moderation effects of gender and salary also have had animpact on employee job satisfaction.
Improved triple generative adversarial nets

Generative Adversarial Nets (GANs) have shown excellent performance in image generation and Semi-Supervised Learning (SSL). However, existing GANs have three problems: (1) the generator G and discriminator D tends to be optimal out of sync, and are not good at processing labelled data. (2) the generator G easy to generate chaotic semantics; and (3) the GANs cannot learn the inverse mapping (projecting data back into the latent space which is benefit to feature representation of related semantics). The problems caused by the limitation of two-player mode, where D can only share incompatible roles of identifying fake samples and predicting labels and distinguishes the data without label. To solve the problems, we propose an Improved Triple Generative Adversarial Nets (ITGANs) which consists of four parts-a generator G, a classifier C, an encoder EN and a discriminator D. The G and the C characterise the conditional distributions between images and labels, the discriminator distinguishes whether a pair of data (x, y) comes from the true distribution, and the EN maps data x to latent representations z. The experimental results show that the ITGANs achieve the state-of-the-art classification results among deep generative models, which demonstrate that the additional encoder can enhance the classification accuracy effectively.
Recognition of Badminton Action Using Convolutional Neural Network

Deep learning approach has becoming a research interest in action recognition application due to its ability to surpass the performance of conventional machine learning approaches. Convolutional Neural Network (CNN) is among the widely used architecture in most action recognition works. There are various models exist in CNN but no research has been done to analyse which model has the best performance in recognizing actions for badminton sport. Hence, in this paper we are comparing the performance of four different pre-trained models of deep CNN in classifying the badminton match images to recognize the different actions done by the athlete. Four models used for comparison are AlexNet, GoogleNet, VggNet-16 and VggNet-19. The images used in this experimental work are categorized into two classes: hit and non-hit action. Firstly, each image frame was extracted from Yonex All England Man Single Match 2017 broadcast video. Then, the image frames were fed as the input to each classifier model for classification. Finally, the performance of each classifier model was evaluated by plotting its performance accuracy in form of confusion matrix. The result shows that the GoogleNet model has the highest classification accuracy which is 87.5% compared to other models. In a conclusion, the pre-trained GoogleNet model is capable to be used in recognizing actions in badminton match which might be useful in badminton sport performance technology.
A uniqueness-driven similarity measure for automated competitor identification

Uniqueness is an important source of competitive advantage and a salient aspect for firms identifying competitors and market structure. While marketing research often includes uniqueness as an important aspect of product positioning and product strategy, the existing literature has offered little guidance on operationalising this notion for use in the competitor identification process. This paper proposes a probabilistic similarity measure to quantify a competitive landscape where uniqueness is a key driver of competition. The proposed measure, when used with readily available data and combined with existing clustering algorithms, enables automation of the competitor identification process. Empirical experiments are used to validate the proposed measure. These experiments show that marketers can use readily available data, including social media tags and geographical proximity data, to reveal the same insight as is gathered when using the more laborious and time-consuming approach of traditional consumer surveys.
Grey relational classification algorithm for software fault proneness with SOM clustering

The estimation by the human judgment to deal with the inherent uncertainty of software gives a vague and imprecise solution. To cope with this challenge, we propose a new hybrid analogy model based on the integration of grey relational analysis (GRA) classification with self-organising map (SOM) clustering. In this paper, a new classification approach is proposed to distribute the data to similar groups. The attributes are selected based on GRC values. In the proposed, the similarity measure between reference project and cluster head is computed to determine the cluster to which target project belongs. The fault-proneness of reference project is estimated based on the regression equation of the selected cluster. The proposed algorithm gives resilience to users to select features for both continuous and categorical attributes. In this study, two scenarios based on the integration of proposed classification with regression have been proposed. Experimental results show significant results indicating that proposed methodology can be used for the prediction of faults and produce conceivable results when compared with the results of multilayer-perceptron, logistic regression, bagging, naïve Bayes and sequential minimal optimisation (SMO).