Reviewerly/exHarmony huggingface dataset:
SAMR as a Framework for Modeling of Academic Information System in Higher Education Institution toward Education 3.0

The implementation of Education 3.0 in educational institutions, mainly in higher education institutions (HEIs) has been growing from day to day. The implementation of Education 3.0 has brought the institution towards better education. But on the other hand, the implementation also gives new problems to the institution. The problems are increased administrative processes, insufficient mobility access, and unavailability of access for parents and industry. The problems can be solved by using ICT tools that have been used in many educational institutions called academic information system (AIS). AIS can be used as a way out to overcome the problems mentioned above. To be used in support the Education 3.0, AIS must be transformed in accordance with the needs of Education 3.0. In the process of transforming an information system, a model is needed as a guide. This paper will discuss three models namely model-driven architecture (MDA), service-oriented architecture (SOA) and substitution-augmentation-modificationredefinition (SAMR) to find out which model best suits the characteristics of Education 3.0. At the end of the discussion, it can be concluded that SAMR is best suited to Education 3.0 characteristics.
Digital Strategies

The domination of information technology starts with the vision and strategy about specific usage scenarios. And I mean here explicitly business scenarios that benefit from technology. From the technology side, everything is possible. Therefore, to avoid that you drown in a technology lake, I provide some examples of companies and their businesses. In my strong opinion, the Personas who obtain the ultimate benefit from any digitization need to be in the center of all attention. Read here about industry examples from insurance, high tech, fashion, and retail. And learn about specific business process examples such as eCommerce, marketing, and digital boardroom.
To Enhance Phishing Emails Classification using Machine Learning Algorithm

Phishing email becomes more dangers problem in online bank truncation processing problem as well as social networking sites like Facebook, twitter, Instagram. Normally phishing is carrying out by mocking of email or text embedded in email body, which will provoke users to enter their credential. Training on phishing approach is not so much effective because users are not permanently remember their training tricks, warning messages.it is totally depend on the user action which will be performed on certain time on warning messages given by software while operating any URL. In this paper, phishing email classification is enhanced using J48, Naïve Bayes and decision tree on Spam base dataset. J48 does best classification on spam base which is 97%for true positive and 0.025% false negative. Random forest work best on small dataset that is up to 5000 and number of feature are 34.but increase dataset size and reduce feature Naïve Bayes work faster.
A Modified Consensus Algorithm with a Diminutive of Proof of Longevity - Augmenting the Effectuation of Blockchain

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

In latest beyond, it has been visualised in various neural computing applications that the hybridisation of computational intelligence techniques yields better results over traditional methods. This proposed research introduces a new composite version (hybrid) computational model in both real and complex domain that is a unique fusion of revised evolutionary fuzzy clustering along with correlated neural networks. The whole methodology is based on two type of working framework as fuzzy assessment framework and neural categoriser framework. In first framework, suggest styles are disbursed as according to the quantity of clusters on the basis of learning strategy like revised evolutionary fuzzy clustering for generalisation of various evaluated cluster patterns that is totally responsible for selection process of network structure. The second framework of the proposed strategy is to fully based on the correlated neural network evolving the processes of training and subsequent generalisation. The existing real domain analysis is based on first generation neural network (RVNN) and for the analysis of proposed model in second generation neural network (CVNN), Hilbert transformation is used that is available in MATLAB for converting the real datasets in complex form.
A risk analysis and prediction model of electric power GIS based on deep learning

In the distribution and supply system of electric power, the regional-based grids and users are diverse and complicated, which leads to the association between operation of the power systems and its geographic information much more closely. Geographic information systems (GIS) have been becoming an indispensable part of the power information management system (PIMS). By combining the aid from equipment dynamic analysing in GIS and with the deep learning of nonlinear network structure, the complex functional models are able to simulate the situation of power grid equipment more efficiently. Based on this model, we are able to predict the risk of entire power grid and provide decision support for the grids management. We have collected multiple sets of historical grid-runtime data that come from provincial power grid systems as the input of the model, and combined with the prior standard training data to improve the accuracy of the risk prediction model, the methods demonstrate that the model has a highly prediction accuracy and full capability of achieving better results contrasted with other modern optimisation algorithms.
Quadruped Robots

The C# programming language was designed for developing programs for Microsoft’s .NET Framework.
NLP: Rule Based Name Entity Recognition

Named Entity Recognition (NER) is an information extraction task aimed at identifying and classifying words of a sentence, a paragraph or a document into predefined categories of Named Entities (NEs). NEs are terms that are used to name a person, location or organization. They are also used to refer to the value or amount of something. NER is an important tool in almost all NLP application areas out of which it is very essential in Search Engines (Semantic based), Machine Translation, and Question-Answering, Indexing for Information Retrieval and Automatic Summarization systems. This paper presents Rule-based approach for the development of Named Entity Recognition (NER) system for Afan Oromo language.
Customer Satisfaction towards Online Car Insurance at South Tamilnadu a Special Reference with Madurai and Sivaganga District

This paper aims to find customer satisfaction towards online car insurance at south Tamilnadu, especially Madurai and Sivaganga district. According to the various literature reviews, the researcher has found the factors of the study such as trust, service-quality and risk. All the three factors have different variables. In the study used in primary data through questionnaire method, 115 sample size have been collected for analysis of research study. The results evince a strong correlation between trust and service-quality, and positive correlation between trust and risk. All the three factors are considered as a strong impact of online customer satisfaction
Bot Frameworks

Bot frameworks provide a platform for developers to build intelligent conversational agents (bots) and connect them via messaging channels such as Skype, Facebook Messenger, Slack, Telegram, and so forth.
The Technique of Different Semantic Search Engines

Semantic Search is a search technique that improves looking precision through perception the reason of the search and the contextual magnitude of phrases as they show up in the searchable statistics space, whether or not on the net to generate greater applicable result. We spotlight right here about Semantic Search, Semantic Web and talk about about exceptional kind of Semantic search engine and variations between key-word base search and Semantic Search and the benefit of Semantic Search. We additionally provide a short overview of the records of semantic search and its function scope in the world.
Digital Technology in the 4IR and the Future of English Learning From the Perspective of Korean EFL University Students

This study is intended to explore English learners’ experience of using digital technology in the Fourth Industrial Revolution (4IR) and their perceptions of learning English now and in the future. The participants were 579 Korean EFL university students enrolled in mandatory General English courses. A Google Form survey with 25 questions was provided, and the percentages of students’ responses to specific questions were reported. Paired and independent t-tests, two-way mixed and one-way ANOVAs were also administered. Regardless of their experience of using digital technology they were positive about using it to learn English. Additionally, despite little or no statistically significant differences in factors including gender, major, GE course experience, proficiency levels, etc., students in general predicted that English teachers and in-class learning would become less important in the future. Moreover, those experienced and interested in 4IR technology were pessimistic in providing more equal opportunities to learn English and predicted a strong need for private tutoring. Interestingly, those with more experience, interest, and positive perceptions of the 4IR were more negative about how it will affect English learning and education. Based on these findings, preparations for the 4IR need to be made by both teachers and administrators. Detailed implications and limitations are provided.
Interpretation and Translation of American Sign Language for Hearing Impaired Individuals using Image Processing

The growth of technology has influenced development in various fields. Technology has helped people achieve their dreams over the past years. One such field that technology involves is aiding the hearing and speech impaired people. The obstruction between common individuals and individuals with hearing and language incapacities can be resolved by using the current technology to develop an environment such that the aforementioned easily communicate among one and other. ASL Interpreter aims to facilitate communication among the hearing and speech impaired individuals. This project mainly focuses on the development of software that can convert American Sign Language to Communicative English Language and vice-versa. This is accomplished via Image-Processing. The latter is a system that does a few activities on a picture, to acquire an improved picture or to extricate some valuable data from it. Image processing in this project is done by using MATLAB, software by MathWorks. The latter is programmed in a way that it captures the live image of the hand gesture. The captured gestures are put under the spotlight by being distinctively colored in contrast with the black background. The contrasted hand gesture will be delivered in the database as a binary equivalent of the location of each pixel and the interpreter would now link the binary value to its equivalent translation delivered in the database. This database shall be integrated into the mainframe image processing interface. The Image Processing toolbox, which is an inbuilt toolkit provided by MATLAB is used in the development of the software and Histogramic equivalents of the images are brought to the database and the extracted image will be converted to a histogram using the ‘imhist()’ function and would be compared with the same. The concluding phase of the project i.e. translation of speech to sign language is designed by matching the letter equivalent to the hand gesture in the database and displaying the result as images. The software will use a webcam to capture the hand gesture made by the user. This venture plans to facilitate the way toward learning gesture-based communication and supports hearing-impaired people to converse without trouble.
Enhance the Security and Improve the Performance of Web Crawlers using Web Crawling Algorithms

World Wide Web (WWW) is a massive resource of information that contains data in zeta bytes. Large number of users surfing Internet for various purposes need a quick response from web. Search engines are preferable, easily available and reliable source to retrieve data very rapidly with minimum resources. Search engine uses Web Crawler to search abundant data from web. It is a program that automatically passes through hypertext structure of the web and access webpage and hyperlink within it. Web crawling is a technique that focuses on obtaining relevant and fruitful links. To make the traversing accurate and productive, a properly designed web crawling algorithm is required. Web crawler and crawling techniques are kept secret as it is a hidden formula for the success of any search engine. In this paper, we have presented various web crawling algorithms along with their pros and cons to be utilized towards web search. Also we have proposed a novel web crawling algorithm to enhance the security and improving the performance of web crawler using single threaded and multi-threaded crawling algorithms.
Mining patterns in open source software using software metrics and neural network models

As the complexity of a system increases, the need to develop a prototype for the problems becomes more and more prominent. To take care of different plan issues, it is seen that structure design finds a superior answer for a large number of repetitive plan issues. For the most part, design-level documents are indicated utilising semi-formal documentation, for example, unified modelling language (UML) diagrams. In any case, these kinds of semi-formal documentation lead to ambiguities and irregularities. In this paper, design level documents are retrieved by using the idea of program metrics. The proposed methodology retrieves the reusable documents from the open-source software such as PMD and JUnit. The presented method uses three kinds of neural network models to analyse the effectiveness of the pattern retrieval approach.
A Sublime Technique to Solve DNA-Repair Model

Deoxyribonucleic acid (DNA) is an essential macromolecule for all known varieties of life and its damage is a life-threatening structure. The DNA double-strand breaks (DSBs) are considered as one of the most rigorous kinds of DNA damage and an error in its repairing mechanism stimulates cancer and also causes lethality in cells with various genetic disorders. In this article, we have exhibited a numerical solution of an ordinary differential equation based biological model to overcome the error for estimating the average number of DSBs per cell time. This model is called DNA repair Model (DRM) and to solve this model a Modified Adomian decomposition method with new polynomials (MADMNP) is applied. The convergence of the aforesaid method is established and the order of error is also dissertated. To solve DRM, this method provides an improved scheme to estimate the average number of DSBs per cell time in comparison to Adomain decomposition method (ADM) and Laplace ADM with Pad`e approximation (LADM-Pad`e). In this respect, a comparison table and a two-dimensional comparison graph are provided by considering a numerical example.
An Analyisis of Twitter Discourse on Xenophobia in South Africa

In this paper the researcher will report on the history of Xenophobia in post-apartheid South Africa.This will be followed by a Twitter analysis of the discourse around the outbreak of Xenophobic attacks in September 2019.The nVivo12 software package was implemented to conduct a thematic and discourse analysis on 3784 unique tweets.The tweets were mostly condemning the attacks with many calling for a peaceful solution.Another trend was the relatively low number of tweets posted by the traditional media.
Classification

This chapter introduces machine classification in the context of an authorship attribution problem. Various methods of text pre-processing are combined here to generate a corpus of 430 text samples. These samples are then used for training and testing a support vector machines supervised learning model.
Information hiding using LSB replacement technique and adaptive image fusion

Steganography is a branch of information hiding which allows people to communicate in a secure way. To increase confidentiality in sending information, our paper combines two techniques: 1) the LSB replacement technique for hiding text messages in an image; 2) an iterative image fusion algorithm. The fusion algorithm aims at choosing an optimal carrier image to be fused with an input image containing the hidden text. The results show an increase in the PSNR value for the final image. Also, a comparative analysis is done in three ways: 1) PSNR values before and after fusion; 2) effects of using an optimum carrier versus a random carrier image; 3) effects of varying lengths of input text messages and the corresponding changes in PSNR values. From the three cases, we are able to prove that the PSNR value of the final image is increased after fusion with an optimal carrier image.
Human activity recognition from histogram of spatiotemporal depth features

The recent evolution in sensor-based depth information has been developed a sounding scope to work for human activity recognition using depth image sequences. Activity recognition is having a key importance due to its advantages in several domain like surveillance systems at airport, patient monitoring system, care of elderly people, etc. This work proposed the objective and evaluations to recognise daily life human activities by spatiotemporal depth information. For this purpose, Kinect sensor is used to collect the data pertaining to a single activity performed by multiple person at a time. The spatiotemporal depth features are computed for activity recognition and support vector machine is used in classification phase. We have nine class of human actions in the database for RGB-D human activity recognition. This dataset is reconfigured from Cornell human activity and Berkeley multimodal human action databases. For multiple human action recognition, 91.38% accuracy is achieved on the synthetic dataset.
Design of interactive English reading teaching system based on hybrid communication network

In order to overcome the problem of long response time and poor interaction in traditional English reading teaching system, an interactive English reading teaching system based on hybrid communication network is proposed. The hardware part of the system is composed of seven modules, among which the course management module mainly adopts the combination of file system and multimedia attribute database to manage the uploaded data of teachers. Students use the hyperlink location field to mark the connection location of the media, can see more colourful learning pages, to achieve the interactive design of the teaching system. In the software part, the learning evaluation model is constructed by covering method or error method to realise the comprehensive evaluation of students' learning state. Simulation results show that this method can comprehensively understand and evaluate students' learning status and knowledge points, shorten the response time of the system, and has great advantages.
BGICC conference Abstract Book 2019

Restricted accessAbstractFirst published online March 6, 2019BGICC conference Abstract Book 2019Volume 105, Issue 1_supplhttps://doi.org/10.1177/0300891619826184
Measuring uncertainties: a theoretical approach

When our aim is to draw the possible developments of future events, we are faced with a practical obstacle. Indeed, we cannot have any empirical experience of the future. Have we, therefore, to be inferred that forecasting, exploring future or, better: exploring futures, or anticipating futures have not to be considered activities of a scientific kind? Answer to such a difficult question requires a multidisciplinary approach, where statistical models, methodology of social science and of course statistics and sociology as a whole - are enhanced in their ability to express the change - and sometimes the risk that the change itself implies. A great help in understanding complexity, and trends, comes from a method for multi-way data, based on the joint application of a factorial analysis and regression over time, called dynamic factor analysis (DFA).
Improved and Efficient Dynamic Load Balancing Algorithm in Cloud Based Distributed System

The basic idea of building Distributed Systems (DS) is to replenish systematic way to access disseminated resources in a decisive way. As It ensures remote access to the resources, the vital part in distributed systems is to allow proper allocation of the resources. Efficient allocation of resources amongst the various Computing Nodes (CN) can happen with the help of various scheduling policies. The various scheduling policies that are being used for efficient load balancing are Round Robin, Throttled, equally spread Load Balancing Algorithms. These are the traditional algorithms which help in performing load balance amongst CN. In this research paper an Improved and Efficient Dynamic Load Balancing Algorithm is proposed which fairs comparatively well compared with the traditional existing approaches which come along with the Cloud Simulator. The simulation of the same is made using Cloud Simulator whose efficiency in terms of response time and cost is proved to be more than 5% when compared to the traditional approaches.
Cost Optimization Algorithm for Data Center Management in Cloud

One of the main challenge in cloud is Cost Optimization. In cloud computing, the cloud providers usually calls CSP offer provisioning plans for the cloud consumers in the following two ways one is reservation plan(also referred as long term plan) and the other is on-demand plan(also referred as short term plan). In general, cost of computing asset provisioned by reservation plan is cheaper when compared to cost obtained by the on-demand plan, With the reservation plan, the consumer can reduce the total asset provisioning cost since cloud consumer has to pay to provider in advance. However, the best advance reservation of assets is difficult to be accomplish due to uncertainty of cloud consumer’s future demand and providers’ resource prices. To address this problem, an OCRP algorithm is proposed by formulating a SPI model. The OCRP algorithm can provision computing assets for being used in multiple provisioning stages as well as a long-term plan. The demand and price uncertainty is considered in OCRP. In this project, different approaches to obtain the solution of the OCRP algorithm are considered including DEF, SAA and Benders decomposition. Numerical the studies are extensively performed in which the outcomes clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of asset provisioning in cloud computing environments.