Reviewerly/exHarmony huggingface dataset:
ADVANTAGES, CHALLENGES, AND SUCCESS FACTORS IN IMPLEMENTING INFORMATION TECHNOLOGY INFRASTRUCTURE LIBRARY

While Information Technology Infrastructure Library (ITIL) is best practice for Internet Technology Service Management (ITSM) and provides many advantages for an IT service provider, there can be many issues related to implementing this system.Many of these problems can be prevented when there is proper support.The purpose of this research paper is to provide a roadmap of best practices that smooth the adoption of ITIL.We first survey the advantages of ITIL adoption and implementation.We then provide a taxonomy of challenges that organizations often face when choosing to adopt ITIL as its ITSM framework.We determine critical success factors which, when implemented, will ameliorate those challenges.Lastly, we take a deeper look at the psychological components among stakeholders during ITIL adoption that lead to the identified challenges and provide insight into ways to overcome them.
Feature Selection for Phishing Detection with Machine Learning

In the last 10 years machine learning has been widely used to combat phishing attacks. The most common approach was to build a classification model that would be able to detect whether or not a given URL or website is a phishing attack. In order to effectively detect a phishing page with machine learning we must find an effective method to represent websites (both phish and benign) as features which can be fed into a machine learning model. One of the challenges faced by these approaches was to find a good set of features to represent the phishing and benign sites. Within the last 10 years hundreds of different features had been proposed and used to great success [6] [7] [9]. However, due to the curse of dimensionality, use of all available features will exponentially increase the sparsity of the dataset, lowering the odds of successful classification. In this work we extract 31 features that had been commonly used in the literature and perform an in depth feature ranking analysis in order to find the most effective features for phishing detection. Using both filter and wrapping methods we were able to find 23 effective features for phishing detection. The F1-score for all 31 features was 0.88 and time taken to train the multilevel perceptron model was 45.49 seconds and the size of the data used is 100k. Using these 23 features we were able to train a model that has 0.99 F1-score and which was comparable with all previous work while reducing the overall dimensionality of the data and time taken to train the model was 43.71 seconds.
Angle histogram of Hough transform as shape signature for visual object classification – (AHOC)

This work presents a new method for object classification using Hough transform (HT) and angle histogram as an object signature. Several methods are reported in the literature that exploit HT and other techniques as a pre-processing step to characterise objects to be used in detection, recognition, classification, etc. HT is a powerful technique to extract shape features from 2D objects; it has been used in many studies and implemented successfully in many applications. Our study is unique by post processing HT voting space using a binary threshold then computing an angle histogram of the resulting angle space as a shape signature of objects. Our image set consisted of 25 simple geometric shapes and six complex natural object classes of: trees, people, cars, airplanes, houses and horses. The method was trained and tested using 225 images from six different classes and found to be robust with a classification accuracy of 95.83%.
Essay Writing Performances at the Tertiary Level: The Case of Indonesian EFL Learners

Some methods and approaches have been put into practice by stakeholders, including both teachers and lecturers in the teaching and learning process of English, specifically writing. Nevertheless, writing English is still burdensome for some learners, particularly EFL learners. For this reason, the survey study was conducted to point out the mistakes made by the students in composing an essay. Eleven EFL learners, coming from non-Anglicized linguistic and cultural backgrounds at the Institut Ilmu Keislaman Zainul Hasan Genggong, Probolinggo, East Java, Indonesia were taken out as the sample of this study. The major instruments distributed were writing prompts and a semi-structured interview. Their writing results were scored with the scoring scale adopted from Jacobs (1981), cited in (Weigle, 2002, p. 116). From the five components in writing, consisting of Vocabulary, Content, Language Use related to the ideas in the text, Organization of the text, and Mechanics, the results demonstrate that Language use (Tense, Preposition, Run-ons, and Articles)and Mechanics (Spelling, Punctuation, and Capitalization) are dominant errors committed by the EFL Learners in writing.
Developing a New CNN Technique for Arabic Handwritten Digits Recognition

Convolutional Neural Networks (CNN) have many applications in object recognition such as character and digit recognition. Few researches are performed on Arabic handwritten digits recognition. In this research, we will develop a new algorithm to utilize the convolutional neural networks with sigmoid function (σ-CNN) to recognize Arabic handwritten digits recognition. The performance of this method provides minimum cost functions with maximum testing accuracy results in compared to other existing techniques.
Statistical Analysis Based Survey of Indian Renewable Energy Scenario

Renewable energy is a solution for electricity generation for cleaner and green energy. The aim of this paper is to find the energy potential of India in terms of sources, per-capita energy consumption and the main potential consumers. Comparing consumption of fossil fuels and Renewable energy sources (RES) of India in 2019 and 2020 and finally to find whether there is any change in energy generation of two solar power plants in different geographical location of India with the help of independent t-test statistics. In this paper two statistical analysis are proposed. One is the statistical analysis of installed capacity, generation and consumption of fossil fuels and renewable energy in India. Other one is the statistical analysis of two solar power plants located at different geographical locations in India. From the statistical analysis it is found that, installed capacity of coal, RES and hydro is increased in 2020 as compared to 2019. Total demand in January 2020 is 2,77,140.33 MW whereas total installed capacity is 3,71,126 MW, this means that installed capacity is more but are not in running condition. From the statistical analysis of two independent solar power plants it is found that solar power plant-1 generates more energy but with high conversion loss hence poor efficiency.
Determinants of Industrial Development

Global evidence suggests that, for many countries, manufacturing typically has an inverted U-shaped relationship with development. But unlike the historical experience of most developed countries, for most developing countries the turning point of this relationship is occurring sooner in the development process, and at substantially lower levels of income. This is termed ‘premature deindustrialization’. The consequences of this may be particularly important if such countries can no longer rely on manufacturing-led development. Why are some countries more industrialized, or more deindustrialized, than other comparable countries? To explore these issues, this chapter uses panel-data econometric techniques to analyse the determinants of the share of manufacturing in GDP, across countries and across time. Domestic determinants include investment, government consumption, population size, human capital, democracy, and natural resource endowments. External determinants include trade openness, capital account liberalization, and exchange rate depreciation.
Sistem Pakar Menggunakan Metode Case Based Reasoning dalam Akurasi Penyakit Disebabkan oleh Bakteri Staphylococcus Aureus

Staphylococcus aureus is the most worrisome bacteria in the world of health because it is highly pathogenic and can cause serious infections in previously healthy individuals. Staphylococcus aureus has gram-positive cells, round (cocci) 0.7-0.9 µm in diameter, non-spore-forming, non-motile, facultative anoerobes, in colony shaped like a grape string. This bacterial infection in humans varies in severity, from minor skin infections (furunculosis and impertigo), urinary tract infections, respiratory infections, to eye infections. This causes many patients to need an expert (doctor) to consult about the symptoms that a patient appears or experiences and the risk factors felt by the patient. So they conducted this research using the Case Based Reasoning (CBR) method which consists of 4 steps, namely retrieve, reuse, retain and revise. By using the case-based reasoning method for a problem with the staphylococcus aureus bacterial disease, it is easier for a patient to find treatment and a solution. With an expert system using this method (case based reasoning), experts (doctors) can easily find out what diseases are suffered by staphylococcus. That way, patients with staphylococcus disease are prepared to deal with the disease they are suffering from.
I/O-Efficient Algorithms for Topological Sort and Related Problems

This paper presents I/O-efficient algorithms for topologically sorting a directed acyclic graph and for the more general problem identifying and topologically sorting the strongly connected components of a directed graph G = (V, E).Both algorithms are randomized and haveis the I/O cost of sorting an |E|-element array on a machine with size-B blocks and size-M cache/internal memory.These are the first algorithms for these problems that do not incur at least one I/O per vertex, and as such these are the first I/O-efficient algorithms for sparse graphs.By applying the technique of time-forward processing, these algorithms also imply I/O-efficient algorithms for most problems on directed acyclic graphs, such as shortest paths, as well as the single-source reachability problem on arbitrary directed graphs.
Performance of the Expanded Technology Acceptance Model in the Sub-Saharan African Context

The Technological Acceptance Model (TAM) and its extensions propounded by Fred Davis and other authors respectively, has broadly been applied on various technological novelties worldwide and has been very fruitful in predicting various technological acceptance behaviors in so many settings.On the basis that TAM may not hold true for some cultures including Sub-Sahara Africa, the expanded TAM for the Sub-Saharan African environment was developed.To ascertain expert opinion on it and assess its performance, this study examined the expert opinion and performance of the developed expanded TAM in the Sub-Saharan African context.Conducting telephone interview with six information systems and human computer interaction experts and administering questionnaire to 308 students in five public universities (using e-learning systems) in five Sub-Sahara Africa countries in the five Sub-Saharan Africa regions, data was collected and multiple regression analysis applied to compute variance (R2) of the independent and dependent constructs (PU, PEOU, PP, PB and BI respectively) in the developed expanded TAM model in the Sub-Saharan African context.Based on the validation of the model by the six experts, its constructs resulting variance were compared to the existing TAMs constructs variance.Results of the comparison showed that, the developed expanded TAM in the African context, outperformed the existing TAMs.Though this research was able to reach findings, it had a limitation of not being able to collect primary data on existing TAM models to compute their variance: future research in this area should work at overcoming this.
Adaptive multi-crossover evolutionary algorithm for real-world optimisation problems

Evolutionary algorithms (EAs) have been extensively used since their invention. EAs are considered as a powerful tool to solve numerous optimisation problems in various fields. Their search mechanisms have been actively developed to improve their search efficiency toward global optima solutions. This study aims to investigate the effects of using different types of recombination (crossover) schemes. It introduces an adaptive version of EA called adaptive multi-crossover evolutionary algorithm (AMCEA). The proposed AMCEA offers multiple forms of heuristic crossover operators based on genetic algorithm (GA) and harmony search algorithm (HSA). The proposed technique improves the search attitude by allowing the effective utilisation of exploration and exploitation strategies during the evolution process. The quality of the proposed AMCEA is evaluated on six real-world numerical optimisation problems (IEEE-CEC2011), and results are compared with those obtained with five variants of GA and HSA. Results demonstrate the superiority of the AMCEA over previously improved algorithms in terms of solution quality; it achieves the lowest mean results and lowest best results in 75% and 66% of the total experiment cases, respectively.
The scientometric profile of Boreskov Institute of Catalysis

This paper presents the results of a scientometric analysis of the publications of Boreskov Institute of Catalysis, the Siberian Branch of the Russian Academy of Sciences, over a 60-year period (1958–2017). The current research used the SciAct information system, and the WoS, Scopus, CAPlus and RSCI databases. The main types of catalysts and catalytic processes studied at the Institute are specified, together with the number and citation dynamics of publications, grants supporting the research by domestic and foreign funds, types and languages used in publications, the locations of publications and where they were issued, their authors and affiliations, each publication’s mission and remit, as well as their main bibliometric indicators.
Classification of Pruning Methodologies for Model Development using Data Mining Techniques

Knowledge discovery process deals with two essential data mining techniques, association and classification. Classification produces a set of large number of associative classification rules for a given observation. Pruning removes unnecessary class association rules without losing classification accuracy. These processes are very significant but at the same time very challenging. The experimental results and limitations of existing class association rules mining techniques have shown that there is a requirement to consider more pruning parameters so that the size of classifier can be further optimized. Here through this paper we are presenting a survey various strategies for class association rule pruning and study their effects that enables us to extract efficient compact and high confidence class association rule set and we have also proposed a pruning methodology
Sentiment Analysis on E-commerce Product using Machine Learning and Combination of TF-IDF and Backward Elimination

E-commerce is a website or mobile application platform that help people to buy products. Before purchasing the product, customer will decide to buy it or not by reading the review from previous buyer. There is a problem that there are a lot of review so it will take a long time for customer to read it all. This research will be using sentiment analysis method to classify the review data. Sentiment analysis or opinion mining is a machine learning approach to classify and analyse texts or documents about human’s sentiments, emotions, and opinions. In this research, sentiment analysis was used to classify product reviews from e-commerce websites into positive or negative classes. The results could be processed further and be used to summarize customers' opinions about a certain product without reading every single review. The goal of this research is to optimize classification performance by using feature selection technique. Terms Frequency-Inverse Document Frequency (TF-IDF) feature extraction, Backward Elimination feature selection, and five different classifiers (Naïve Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree, Random Forest) were used in analysing the sentiment of the reviews. In this research, the dataset used are Indonesian language and classified into two classes(positive and negative). The best accuracy is achieved by using TF-IDF, Backward Elimination and Support Vector Machine (SVM) with a score of 85.97%, which increases by 7.91% if compared to the process without feature selection. Based on the results, Backward Elimination feature selection succeeded in improving all performance for all classifiers used in this research.
DEVELOPING INQUIRY LEARNING MODEL-BASED ENGLISH INSTRUCTIONAL MATERIALS FOR THE SEVENTH GRADE OF JUNIOR HIGH SCHOOL

This research aimed at developing instructional English materials for the students of the seventh grade especially in basic competencies 3.4 and 4.4 at MTs Madani Pao-pao based on 2013 curriculum supported by using inquiry learning model and using the concept of HOTS. The research design is Research and Development (R&amp;D )with the combination of ADDIE model. The procedures included analyzing materials needed by spreading questionnaire to the students and analyzing the basic competencies, designing a blueprint of materials and the instructional materials, and validating the material by the expert. There were 146 students as the respondents, teachers, and experts. The instruments used in this research were the documents, questionnaires, and validation checklist. There were three systematic aspects : materials Systematic Organization, Systematic English Teaching and Systematic Content of English. Therefore, the result indicated that based on experts judgment, the developed basic competency 3.4 and 4.4 were applicable to be taught at the seventh grade students of Junior High School.
Design of Intelligent Educational Administration System Based on Big Data Technology

The main task of educational administration is educational administration. Nowadays, many schools have thousands or even thousands of teachers, leading to the increase of a large amount of data such as various systems and databases operated by the school, such as management of school conditions and management performance analysis. How to use these data to transform existing management data into useful knowledge, improve school management decision-making, and improve school management level and quality is an urgent problem for schools to solve. This paper studies the intelligent educational administration management system based on big data technology. After understanding the relevant theories of big data technology and intelligent educational administration management system based on literature data, the intelligent educational administration management system based on big data technology is designed, and the designed system is tested, and the test results show that when the number of users in the system is 40, the average response time of the system is 0.31s, and the average response time of student score query is 0.63s, which basically meets the design requirements of the system.
Representation of Indian Migrants on Google Doodle: Evidence from India

This paper investigated the peoples of Indian origin who are migrated or settled in different countries and contributing immensely in various aspects of life throughout the world. Their contributions have been recognized by different mediums of communication. One of the most popular and widely accessed medium Google has played a very important role in this regard. The well-known animated feature ‘Doodle’ of Google has been a recurring phenomenon on the front page of the search engine since 1998. It has celebrated the contribution of famous individuals and cultural events at global platform. The present study deals with the portrayal of Indian migrants by Google Doodles and analyses the news coverage of Doodles by digital news media. The findings of the paper show that the doodles represent the contribution of Indian migrants through its animated feature ‘Doodle’ and got a good amount of press coverage globally. Since 1998 to 2018 total 46 Indians and 16 People of Indian origin got homage by Google. Through the analysis of the news portals we have concluded that BBC.com is the only news portal that has never published any story related to Doodle but the news portal of Indian Express has given the maximum coverage for Google doodle. Thus, Google doodle has become a vital source of information and honorary tool for referencing contributions of individuals in the present society. It is also an established fact that the global reach of doodle is a significant aspect for the growing importance of doodles. Some of the doodles like Amrita Shergil, Chandrashekhar Subhramanyan, M.F Hussain, Hargovind Khurana, Abdul Sattar Edhi, Fazlur Rehman have reached around 8 to 28 countries Google’s home page. The present study has surprised to find that Doodles which are shown in India’s Google home page or visa versa, were not displayed in neighbouring countries.
System for Identifying and Correcting Invalid Words in the Devanagari Script for Text to Speech Engine

The Text To Speech (TTS) system takes text as an input and generates speech as an output. If input text is incorrect then overall quality of speech output may degrade. The main aim of the proposed system is to provide correct input text to the TTS. The system takes Unicode word as an input, identifies invalid word and corrects it by inserting, deleting or updating characters of the word. In this system, the State Machine is used to identify and correct invalid word in the Devanagari script which in turn is based on rules. Rules are developed for converting character to input symbol. Actions and States are identified for State Machine. Finally, the state transition table is developed for validation and correction of word. Using this system, incorrect words of the Devanagari script can be corrected to valid words (word contains all the valid Devanagari syllables) based on Devanagari script grammar. Since, all Devanagari characters are not present in Hindi language; this system will correct these nonHindi characters to Hindi
Object Detection

Automatically detecting objects in an image either static or derived from a constant video capturing source has numerous applications as you can imagine. Here are just a very few of them:
Unsupervised SIFT features-to-Image Translation using CycleGAN

The generation of video content from a small set of data representing the features of objects has very promising application prospects. This is particularly important in the context of the work of the MPEG Video Coding for Machine group, where various efforts are being undertaken related to efficient image coding for machines and humans. The representation of feature points well understood by machines in a video form, which is easy to understand by humans, is an important current challenge. This paper presents results on the ability to generate images from a set of SIFT feature points without descriptors using the generative adversarial network CycleGAN. The impact of the SIFT keypoint representation method on the learning quality of the network is presented. The results and a subjective evaluation of the generated images are presented.