Reviewerly/exHarmony huggingface dataset:
Rank Prediction for Articles and Conference Papersusing Machine Learning Techniques

Searching for an optimal article which was given highest and best priority is quite harder based on requirements. Ranking is one of the best measure or a method to get the best rated and optimal article or a conference or a research paper through this huge Internet World. As Technology been increasing day by day Artificial Intelligence is the first step to get through any problem for a solution Machine learning is also an important aspect of Artificial Intelligence. Machine Learning is best known for classifying, categorizing and predicting. Rank prediction can be done through many different algorithm implementations in machine learning. But choosing the best is important for accurate results. This paper gives the most accurate results of algorithms that can be used for rank predictions for articles. To simplify and resolve this problem, solutions were given in many different ways but to achieve accuracy is necessary, in previous models this is given using supervised learning only. We proposed this research work with perfect results using both supervised and unsupervised learning. Neural Networks is the best algorithm in supervised learning for classifying and predicting within data. In unsupervised learning we used K-means clustering because of grouping the data. This work helps the user(s) for optimal search of an article and also gives a competitive spirit for author to get into the top, totally this is implemented using Machine Learning Techniques of Neural Networks, K-Means Algorithm which is a mixture of supervised and unsupervised learning for predicting ranks.
Semantic image retrieval using random forest-based AdaBoost learning

Efficient image retrieval from a large image repository is still a challenging task because of the semantic gap. In this paper, a stride is made towards reducing the semantic gap by proposing an efficient approach using relevance feedback and random forest-based AdaBoost learning. Initially, user feedback is used to move the query point more towards the relevant images. The user feedback is also used to train the random forest classifier, for learning the user retrieval intention. Then AdaBoost learning is used to identify the weak classifiers and to assign more weights to weak classifiers in the weighted majority voting scheme. AdaBoost learning is adopted to overcome the prediction variance of the random forest classifier. The experimental evaluation is performed on two different real world image databases and shows that the proposed approach is more efficient, as an average precision of 95% is achieved in six iterations of relevance feedback.
Security Assumptions for Ubiquitous Secure Smart Grid Infrastructure using 2 Way Peg Blockchain and Fuzzy Specifications

The smart grid cyber-physical system and advanced metering infrastructure is an immensely growing term in industrial research based on the electrical power grid system and has uncovered an efficient paradigm for the power generation, transmission, and for the expenditure management as well. These systems allow home area networks and advanced metering infrastructure to communicate bi-directionally. False data injection is a new and climacteric security threat to the power grid to tamper such important data. However, these communication channels that the energy service interfaces provide two-way communication according to the central control unit. Thus, for the security and robustness of the grid, it is critical to separate the false or infected data. The existing protocol on this problem is based on state estimation which is less secure and computationally expensive. In this paper, we propose a 2-way peg blockchain security system by providing a digital signature scheme between home area network device (Smart Meter) and AMI server to get authorization, providing an infected data detection algorithm based on fuzzy rule specification and 2WP based blockchain protocol and finally infected performance reputation updating algorithm by computing the probability distribution of uncouth level to detect the infected or compromised the. We get more efficiency and security by performing a detailed analysis.
Herramientas tecnológicas para el seguimiento del alumnado en la FP dual

The increasingly rapid changes in society also imply new ways of doing in the educational field. The different agents have, therefore, to adapt to these advances. Vocational training in dual modality, together with the relationship between students and society-environment-entities, means that the teacher must look for new training and monitoring models that go through significant moments of non-attendance. The objective of this contribution aims to show different tools used in dual FP for the higher degree cycle of Teaching and socio-sports animation. The following are proposed: Banc Integrat de dades –BID–, telephone, email, Whatsapp, Google Drive and Google Classroom –within the Google G suite for educational centers–.It is verified that the use of different tools allows a tutoring-monitoring of the students in an integral way and from different points of view, which allows a more individualized, real and functional attention. These tools are proposed for other training models, although they will have to be adapted to the characteristics of each teaching.
A Case Study of the FLIPPED LEARNING Based on Google Classroom -Focused on the Japanese Service Practical Conversation -

Purpose: The purpose of this study is to find out whether the flipped-learning class case applied to the foreign language subject <Japanese Service Practical Conversation> is a suitable teaching method, whether Google Classroom-based media classes are effective in achieving learning goals, and what specific factors affect student satisfaction. Method: The method of investigation was to analyze the study reflection diary and end-of-semester questionnaire posted in the class room for one semester with prior consent from the students(33 students) of the subject in the second semester of 2019. According to the analysis result, it was suitable as a teaching method and was very effective in achieving the learning goal, and the factors such as interaction, interest and motivation, media utilization efficacy, and learner satisfaction greatly influenced the evaluation of the class case. In the meantime, there have been only a few attempts to teach flipped learning and blended learning, which are a mix of on and off, in Japanese language courses in universities. Results: The case analysis by Google Classroom base and factor in Japanese subject was attempted for the first time, and it is expected that the learner-centered teaching method will be widely attempted by actively utilizing Corona 19 times and online digital contents and using Google Classroom-based mediums. Through this study, it is meaningful to provide basic data in predicting and implementing effective education in the future, and suggests that it is attempted by utilizing various digital contents. Conclusion: Flipped learning promoted the innovation of classroom instruction by converting the teaching-oriented teaching method into learner-centered participatory learning as a means of interaction of technology. This study is able to find out that it is an opportunity to build a new teaching culture such as improving the participation of learners in class, self-directed learning, mutual communication, and teaching ability in the language class(Japanese) using the Google class room-based medium.
An Evaluation of the Implementation of the Dual Language Programme (DLP) among Science and Mathematics Teachers

The Dual Language Programme (DLP) is a programme introduced under the foundation of Memartabatkan Bahasa Malaysia Memperkukuh Bahasa Inggeris (MBMMBI). The DLP was first implemented in 2016 at selected schools. As such, researchers had conducted a study to evaluate the implementation of the DLP for science and mathematics which has been used in secondary school based on the CIPP model from the aspect of context, input, process and product. The first objective of this research is to identify the level of context, input, process and product dimensions of the DLP. The second objective is to identify the relationship among the level of context, input, process and product dimensions during the implementation of the DLP with the teachers’ teaching experience. The third objective is to identify the advantages, weaknesses, challenges and suggestions by science and mathematics teachers in realising the DLP. This research had involved 210 teachers who taught science and mathematics in secondary school in Zone Keramat, Kuala Lumpur. This research was conducted using the survey form and interview questions as the research instruments. The data gathered from the research has been analysed using descriptive and inferential statistics by getting the value of the mean score and percentage and also the analysis of Pearson correlation. The data collected from interview were transcribed and analysed using Nvivo software. The result showed that the level of context, input and product dimensions were average while the level of process dimension was high. The research hypothesis was accepted where there was no relationship between context, process and product dimensions with the teachers’ teaching experience. From the interview, we found that the DLP has its advantages and also disadvantages, but the essential steps have been proposed in this research. In conclusion, this research has given a new contribution to science and mathematics teachers in Malaysia to evaluate and improve the implementation of the DLP as whole.
Cloud-based business model for SMEs sector in India - developed and validated cloud computing adoption factors through a study on Indian SMEs

This study is on adoption of cloud computing by small and medium enterprises (SMEs) in India. This paper, through preliminary survey, finds out different situations for adopting cloud computing services. It was found that SMEs can benefit the most if they adopt cloud services for computation intensive jobs like data mining, optimisation, risk modelling and simulation. The other advantages for SMEs are lowered investment in hardware, more efficient use of computing systems in existing data centres, easier scale-up of the applications and services and cost saving on technology infrastructure and faster software upgrades without much expense. The results can be used by SMEs as decision point for adopting cloud services.
CCEODSP-Cloud Computing Environment Observation for Data Security and Privacy

Today’s world, the cloud computing concepts has developed rapidly in both the private and public area, it is mainly provide the basic services and resources that are open to all kinds of end users on a particular platform through internet on a rented basis have the ability to extend up or down their service necessities or needs. This cloud computing model has numerous benefits including elasticity, scalability, efficiency, flexibility, etc. activities of a business organization. It bids a ground-breaking business concept for organizations to accept information technology enabled services without advance investment. Cloud computing model enables convenient, on-request network accessibility to a shared pool of IT computing resources like servers, services, storage networks, and applications. In this computing model can be speedily provisioned and released with negligible management exertion or service provider interaction. Quick variation of cloud computing had resulted in increasing strictness of privacy and security concerns as well as permissible challenges. In this learning includes various cloud computing issues that weakens of security and privacy of users data, as well as presents on threat that impacts data residing in the cloud and also added with this, a secure cloud computing open issues are noted in paper
Legal Challenges in the New Digital Age

Legal Challenges in the New Digital Age addresses a wide range of legal issues related to emerging technologies. These technologies pose prominent legal challenges, in particular, how to wedge new phenomena into old frameworks; whether we can and should delegate responsibilities to technologies and how to cope with newly created powers of manipulation. Edited by Ana Mercedes Lopez Rodriguez, Michael D. Green and Maria Lubomira Kubica, the book’s sixteen chapters are written by highly qualified international practitioners and academics from different jurisdictions. Familiarity with the intricacies of emerging technologies is essential for judges, practitioners, legal staff, business people and scholars. This book’s combination of highly thought-provoking topics and in-depth analysis will prove indispensable to all interested parties.
Deep LSTM for Emoji Twitter Sentiment Exploration via Distributed Embeddings

Social media’s sentimental data is the most vital digital marketing platform that can help us to reveal the real world events including qualitative insights to understand people’s visibility about brands, politics, emotional status, and so on. With today’s interrelated world, a public relations disaster can be initiated with one post or a tweet. Conventional sentimental analysis is the process of defining whether the shared post on social media is neutral, positive or negative and has been focused by the Dealers, Administrations to understand public feelings of their products and corporation. However, extensive usage of emoji in social media has attracted an increasing interest. In this proposed framework, we suggest a novel scheme for Twitter sentiment method on emojis by considering pre-trained word and emoji embeddings. We first train our model to learn word, emoji embeddings under positive and negative tweets; later a classifier passes them through a neural network combining LSTM to achieve better performance. Our tests show that the proposed model operational for extracting sentiment-aware emojis and outperforms the state-of-the-art simulations.
Pengembangan Aplikasi Listening Test Berbasis Android

Listening is one of the skills that must be possessed by learners who learn a language. Listening becomes very important skill because it supports other skills that is speaking. The skills must always be trained to improve. Listening courses are currently done by manual, which students answer on a piece of paper and lecturers check the test result manually. The research used Research and Development method. The output of research is an application that is still in the form of prototype. With this application, lecturers more easily in organizing listening test questions and facilitate scoring test results. From the results of the validation of media experts and material experts, 68.75% were obtained. From these results, the application still needs revisions. From the results of testing to students, the results obtained 92% and can be categorized into valid criteria. Research can be continued to test its use while on learning process so that it can be used as teaching material that is suitable for use in the listening courses in the following semester.
DiVincenzo Criteria

The DiVincenzo criteria, named after the physicist, David DiVincenzo, is a list of criteria that every quantum computer should satisfy [, ].
ANALISIS ALGORITMA K-MEANS DALAM PENGELOMPOKAN PERKARA PERCERAIAN BERDASARKAN KELURAHAN DI KOTA JAMBI

Getting married and living happily ever after is the hope of both brides when carrying out the wedding. During the household, no one expects a disputelet alone an end to divorce. A number of areas in jambi city based on the Annual Report of jambi Religious Court from 2017, 2018 and 2019 are knownthat there is an increase in divorce rates reviewed from the number of cases received by the Jambi Religious Court which each year is always increasing.Therefore, it is necessary to know which areas are experiencing an increase in divorce cases by using one of the methods contained in the data mining,namely the k-means clustering algorithm. From the case, the authors conducted a study to group divorce cases by village in jambi city by applyingquantitative research methods with a sample of 62 data with the aim of knowing the divorce rate in jambi city in order to provide a solution that is ableto map the villages that conduct divorce. The results of this study showed that the highest divorce rate in jambi city for high clusters (C2) as many as11 villages, for medium clusters (C1) as many as 20 Villages and for low clusters (C0) as many as 30 Villages then the results obtained from data testingon Rapidminer with k-means algorithm obtained results that matched displaying three classes of classification results.Keywords: K-Means, Clustering, Divorce , Marriage, Rapidminer
Augmented Reality for Veterinary self-learning during the pandemic: a holistic study protocol for a remote, randomised, cross-over study

The objective of the study is to compare a smartphone AR self-learning application with a non-AR smartphone app teaching tool to see which one will more effectively support veterinary students' selflearning.To achieve this, participants will install a mobile application and use it as a self-learning support tool.Feedback will be obtained via a System Usability Scale (SUS) survey in the app at the end.But beyond this common questionnaire, the study will compare the training efficiency and training effectiveness between the AR mode and non-AR mode, exploring the impact on student's learning motivation and knowledge acquisition.This will be achieved by leveraging the remote nature of the experiment by recording the time taken within the application and future results of the participants who are Veterinary medicine undergraduate and postgraduate students from a university in Ireland and a veterinary medical university in UK.
Energy-Time Entanglement Based Dispersive Optics Quantum Key Distribution over Optical Fibers of 20 km

An energy-time entanglement based dispersive-optics quantum key distribution is demonstrated experimentally over 20 km optical fibers, in which photon pairs are generated by spontaneous four wave mixing in a silicon waveguide.
Phishing

Email or malicious websites are used to collect user login credentials and are provided to the attackers in the phishing attack which is also considered to be the type of social engineering attack. Mid January 2017, many Gmail users all over the world experienced Gmail Phishing attack. The attackers used extraordinarily intelligent mechanism containing a duplicate but looking very real Gmail sign in page.
Design and sample size decisions

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.
A Quantitative and Qualitative Reasoning of Various Visual Cryptographic Schemes to Uphold Secrecy

The secret sharing scheme plays a vital role in cryptography which allows to transmit the secret digital information (image, video, audio, handwritten notes etc.) over a communication channel. The Encoding is done in special way using Visual cryptographic schemes and decoding is carried out by the human sensory system without complexity. The secrets are encrypted into shares in these schemes, stacking these shares with minimal loss will disclose the secrets. These shares are presented in n number of transparencies. This paper provides an overview of various VC schemes of single secret and multi secret of grayscale and color secret images. Also, comparative analysis study on these schemes are presented, based on pel expansion, contrast, accuracy, capacity, security, type of share, number of shares, image format.
THE LEACHING BEHAVIOUR OF HNO_3 SOLUTION IN ISHIAGU ORE, EBONYI STATE NIGERIA

The leaching behavior of HNO_3 solution was studied. The sourced sample was pulverized and the chemical characteristic determined. The results of the chemical analysis of the ore based on AAS revealed that the ore contained 31.75% Pb, 0.93% Zn, 0.525% Cu, 0.165%Mn, 0.0702% Mg, 0.0202% Ca, 9.23% Fe and 0.0056% Ni. The mineralogical analysis by X-ray diffraction technique revealed that the ore contained galena and other associated minerals. The results showed that the leaching of lead from galena concentrates increases with HNO_3 concentration, temperature, stirring speed, solid liquid ratio and a decrease in particle size increased the leaching rate positively. The study, also showed that complete dissolution of lead was achieved at 70oC, while other parameters were fixed. It was found that mixed kinetic is the controlling mechanism for this study. The E_a was determined to be 37.57kJ/mol. The reaction order and Arrhenius constant were calculated to be 0.179 and 〖79 X S〗^(-1),respectively.
Original image tracing with image relational graph for near-duplicate image elimination

This paper proposes a novel method for near-duplicate image elimination, by tracing the original image of each near-duplicate image cluster. For this purpose, image clustering based on the combination of global feature and local feature is firstly achieved in a coarse-to-fine way. To accurately eliminate redundant images of each cluster, image relational graph is constructed to reflect the contextual relationship between images, and PageRank algorithm is adopted to analyse this contextual relationship. Then the original image will be correctly traced with the highest rank, while other redundant near-duplicate images in the cluster will be eliminated. Experiments show that our method achieves better performance in both image clustering and redundancy elimination, compared with the state-of-the-art methods.
An Exploration of a Website Design Process and Results of a Usability Study

A transition of content management systems provided Kornhauser Health Sciences Library with an opportunity to redesign the library’s website to be more user-friendly, efficient, and visually appealing. A multistage approach was taken: (1) informal interviews of various stakeholders from the library, (2) a redesign using information gained from those stakeholders, and (3) a retrospective comparative usability study. This study was conducted with the goal to inform library staff whether the redesigned website improved accuracy and efficiency of information retrieval through the completion of timed tasks. In addition, user satisfaction was measured through guided interview questions.
Efficient Region of Interest Extraction Methods for Multiple Medical Images

Medical images do contain important and unimportant spatial regions. Compression methods which are capable of reconstructing the image with high quality are required to compress the medical images. For these images, only a portion of it is useful for diagnosis hence a region based coding techniques are significant for compressing and transmission. Extracting a significant region is of great demand since a slighter mistake may leads to wrong diagnosis. This paper is focused on investigating multiple image processing algorithms for medical images. All the images may not contain the same region of interest, so different approaches are supposed to apply for different images. In this three types of medical images were considered like magnetic resonance (MR) brain images, computer tomography (CT) abdomen images and X-ray lung images. In this paper three automatic region of interest extraction algorithms were proposed for different types of images.