Reviewerly/exHarmony huggingface dataset:
Top Management Support Functions in Higher Education Management Accounting Information Systems

The function of the management accounting information system is very important in universities, as it provides input to leaders for accurate decision making. Higher education leaders must listen more and provide good work management and require an accounting system to survive amid the heavy workload of lecturers that are not balanced with balanced wages. Accounting information systems can help universities expand their reach to remote locations, acquire new forms and workflows, and possibly change teaching patterns virtually. The management accounting information system is a collection (integration) of sub-systems/components, both physical and non-physical, which are interconnected and harmoniously cooperate to process transaction data related to financial matters into financial information. The purpose of this study was to determine the effect of top management support on the function of the accounting system. The unit of analysis in this research is the related sub-unit within the management of higher education institutions in South Sumatra-Indonesia. The results showed that top management support greatly influenced the function of the management accounting information system. The results of the study found that the life cycle of accounting information system development supports top management which includes planning, design, and implementation (top management support is conceptualized as the involvement and participation of top-level management of the organization in information technology/accounting information system activities on lecturer work reports, lecturer salaries and managers.
USING SENTIMENT ANALYSIS TO MEASURE EMOTIONAL TOXICITY OF SOCIAL MEDIA DATA DURING THE COVID PANDEMIC

The current health pandemic has altered the world's emotional state via imposed social distancing restrictions, quarantines, illness, anxiety, depression, death, layoffs, and organization closures.The pandemic has prompted a societal change which accelerated the digital transformation of communication and social media usage for customers and organizations.This study's purpose endeavors to conduct a qualitative analysis of the subjective nature and changes in toxicity of social media posts, before and during COVID-19.The research methodology uses Microsoft Azure Learning and Microsoft Excel Add-In to conduct a sentiment analysis of one company's social media posts and compare end-users' emotional state and toxicity of comments, before and during the pandemic.The results may assist companies with additional tools to understand the subjective nature of social media posts and improve business decision making.Educators and researchers may apply this technique to other business functions and challenges.
METODE 2M2PR BERBASIS QUANTUM LEARNING DAN PERMAINAN TRADISIONAL DALAM PEMBELAJARAN BAHASA INDONESIA DI SEKOLAH DASAR

This article aims to describe how the application of the 2M2PR (Motivation, Multimedia, Game, Strengthening, Celebration) Method Based on Quantum Learning and Traditional Games in Indonesian language learning in elementary schools. This research is included in library research. The result obtained is that the integration of traditional games is an innovative effort in the language learning process related to national culture. Many methods can be developed in learning such as the 2M2PR method based on quantum learning and traditional games. Traditional games can be integrated with language learning such as snake and ladder games and crank games and many more. In this case, the teacher must have a creative attitude in selecting and modifying traditional games so that they are following the material to be taught. It is hoped that the 2M2PR method and the integration of traditional games in Indonesian language learning can increase attractiveness in language learning and make it easier for students to convey and understand the subject matter. In addition, integrating traditional games, can foster a sense of love for culture and introduce national culture to students from an early age.Â Â
Интернет-коммуникационные факторы ценностных ориентаций российской молодежи

The article deals with the actual topic of the impact of Internet communications on the formation of value orientations of modern youth. The research focuses on the disclosure of groups of factors related to the topic of network influence on the components of value orientation. The article presents the results of the author's research conducted at the universities of Moscow.
Various Research Opportunities in High Utility Itemset Mining

Pattern mining is a technique, which discovers interesting, hidden, unpredicted and useful patterns of data from the database. Most of the research work in pattern mining has been focused on the traditional way of Frequent Itemset Mining (FIM) and Association Rule Mining (ARM) for pattern-discovery. Patterns in frequent itemset mining are based on the occurrence frequency of items. Although frequent pattern mining is useful, the assumption that ‘frequent patterns are interesting,’ doesn’t hold for numerous applications. High Utility Itemset Mining (UIM) overcomes this limitation of frequent itemset mining. The aim of HUIM is to find the patterns based on a utility function where the utility can be measured in terms of revenue, profit, weight, frequency, interestingness or time spent on some webpage, etc. Mining patterns with high utility can be seen as a generalization of FIM where the transaction database is the input and every item is having a utility factor representing its importance and might have non-binary quantities in the transactions. This paper surveys various recent advances and research opportunities in the field of high utility itemset mining.
About this title - Aspects of the Life and Works of Archibald Geikie

Sir Archibald Geikie (1835–1924) was one of the most distinguished and influential geologists of the late nineteenth and early twentieth centuries. He was Director-General of the Geological Survey of Great Britain, President of the Geological Society of London, President of the British Association, Trustee of the British Museum and President of the Royal Society. He was also an accomplished writer, a masterful lecturer and a talented artist who published over 200 scientific papers, books and articles. The papers in this volume examine aspects of Geikie's life and works, including his family history, his personal and professional relationships, his art, and his contributions as a field geologist and administrator. Together, they provide a deeper understanding of his life, his career and his contribution to the development of Geology as a scientific discipline. Much of the research is based on primary sources, including previously unpublished manuscripts, donated in part by members of the family to the Haslemere Educational Museum, UK.
FEEDBACK TYPOLOGY FOR FORMATIVE ASSESSMENT PURPOSES

The article is devoted to the study of the types and characteristics of feedback as a component of formative assessment. The research problem is associated with a variety of approaches to the classification of feedback types and the presence of a significant number of typologies which causes difficulties in developing effective strategies for its implementation for the purposes of formative assessment. An analysis of the feedback classifications proposed by researchers in the context of the educational process made it possible to distinguish the following approaches: a general approach, considered feedback as a component of the educational process; a special approach focused on the development of feedback types in relation to a specific academic subject; instrumental approach, differentiating types of feedback depending on the means used for its implementation – both technical and non-technical. The considered classifications of feedback are focused on the allocation of one classification basis and, therefore, allow us to consider feedback in the only selected aspect. To determine the effectiveness of feedback strategies, it seems insufficient to use one parameter and, accordingly, make separately taken typologies. The aim of the study is to identify types of feedback based on the analysis of the components of the assessment situation and being based on them to create a multidimensional classification of feedback types. Feedback characteristics were determined due to the influence of the subject, object, scope and basis of the assessment, as well as the assessment tool. We came to the conclusion that feedback characteristics during the implementation of formative assessment can be referred to different levels of information use obtained during the assessment. Feedback, therefore, may include information on the current situation, the planned learning outcomes and effective techniques for achieving educational goals.
Securing Personal Health Records using Advanced Multi-Factor Authentication in Cloud Computing

Cloud computing is a novel prototype to provide services via the internet. Most of the fields like banking, industries, educational institutions, and healthcare are storing their applications, data and accessing it through cloud because of its versatility and affordable price. Even though cloud has a many special feature, data security and privacy are the most important issues in a cloud background. Especially in healthcare sector, according to recent report numerous amount of healthcare data breached by unknown websites and hackers, and much healthcare information breaches are occurring around the world for different purposes and still it is vulnerable on account of storing and accessing data through third party cloud servers. Due to many attacks on personal health records data security and privacy in cloud environment, it is a primary task to concentrate on solving this issue because such health information are really sensitive, and playing vital role in decision-making of patients health which wrong decision may spoil patient’s health, life along with health institution’s reputation. To ensure security and privacy, data encryption and authentication are the key technologies which are a technique to secure data and create proof identities to obtain access of data in the system. Conventional password authentication does not provide sufficient security for information to the new means of attacks. So, this paper introduces a framework for data encryption using standard RSA and Hash function and advanced multi factor authentication technique to cloud data access which authenticates the user based on different factors such as contextual, signcryption and iris bio-metric features. This prototype to cloud computing is implemented using open source technology. The proposed advanced system minimizes intermediate data access due to the complexity of key access and strong authentication. The performance of the system has been evaluated using experimental results such as encryption and decryption time, authentication accuracy, execution time.
Orthogonal Grey Wolf Optimization Algorithm for Task Scheduling in Cloud Environment

Cloud Computing (CC) is being popularly used by small organizations and startups as a business model in distributed computing environment. In cloud computing, it provides three architectures SaaS, the user tasks are organized and executed with suitable resources PaaS and IaaS to deliver the different types of services to the customer. In cloud, the time complexity of task execution is a common problem. In this paper, an efficient Task Scheduling (TS) technique is proposed namely Orthogonal Grey Wolf Optimization (O-GWO) algorithm. The O-GWO algorithm, schedules the tasks on VMs with minimum execution time. Also, it increases the convergence speed and reduces the Degree of Imbalance (DI) of total task schedule across VMs. An experimental analysis shows that, proposed O-GWO algorithm performance is measured with respect to efficient evaluation metrics such as makespan and Degree of Imbalance. The makespan of proposed O-GWO algorithm is achieved to the extent of 38.20 seconds of enhancement compared to the existing Hybrid Particle Swarm Optimization with Simulated Annealing (HPSO-SA).
Development of Self Assisted Voice Module for Visually Impaired

The visually impaired persons face challenge in recognizing the people around them and also reading the text content present in the documents.In order to overcome these issues, there is a need of assisted devices.This paper proposes a portable system which is able to recognize known faces and apprehends text.The proposed system has both Facial Recognition and Optical Character Recognition (OCR).Facial Recognition is carried out with the use of Local Binary Pattern Histogram (LBPH) face recognizer which makes use of HaarCascader to stumble on the faces.Initially the device identifies presence of person within its range and then applies the LBPH operator to recognize the face.In order, to access the statistics of textual content visually impaired person needs vision.OCR is used where text extraction is performed from scanned images.Tesseract OCR engine is used here.Extracted text is converted into speech using Google Text to Speech (gTTS) method which helps visually understand the text.This portable system is implemented using Raspberry pi.Upon getting into the digital camera view formerly stored faces are identified and acknowledges to the visually impaired through voice.The system can recognize face and characters with the highest accuracy.
The Recommender Systems Model for Smart Cities

Recommender systems were introduced in the early 1990s. They did not get too much attention and were limited to a narrow domain implemented by only a few companies until the outburst of E-commerce. As online shopping became popular, the recommender system started becoming an integral part of an organization marketing strategy and since then they have completely evolved a lot. This give an opportunity to start with a recommendation System project by collecting information from news of users to provide a best recommendation. The cities become smatter so, this paper review different methods of implementing Recommender systems models for smart cities along with their drawbacks and possible improvements.
A Hybrid Effort Estimation Technique for Agile Software Development (HEETAD)

Software development becomes a complex process when the software grows in size or complexity making it difficult to estimate usage of resources or development costs. Software effort estimation is that part of development which helps in assessing resource prior to development. An estimate is a quantified evaluation of the effort necessary to carry out a given development task and most often expressed in terms of durations. Effort estimation is done with an intent to aggregate individual estimates and obtain the overall duration, effort or cost of a software project. The workforce is measured as effort and the total time required is defined for a task in effort estimations which is usually expressed in units (Man-day, Man-month, and Man-year). Most other factors like cost or total time required to developed software are dependent on these estimations. Further, Algorithms used for estimating software developments efforts, may also be imprecise. Thus, Effort estimations plays an important part of software development in planning and monitoring projects. Agile methodology is relatively a new set of practices in software development. Agile estimations are based on many factors. Improperly recorded information from Agile methods can result in erratic estimations thus creating an impending need for precise effort estimations. It is difficult to find a single technique which can suit all conditions. Hence, this paper attempts to estimate agile development efforts by using a hybrid technique based on function points and user stories. Results of the proposed technique demonstrate that the arrived effort estimations based on user stories are efficient.
RANCANG BANGUN APLIKASI MEDIA PEMBELAJARAN MENGENAL NAMA HEWAN DALAM DUA BAHASA BERBASIS ANDROID MENGGUNAKAN THUNKABLE

With the rapid development of Android-based smartphone technology devices, many learning media applications for multimedia-based children have been developed, the aim of which is to increase children's interest and motivation to learn in order to better receive the learning material provided. In this study, an Android-based animal recognition application design was carried out using two languages namely Indonesian and English. The design process is done using UML modeling, while the process of making an application is done using a tool that is Thunkable. With Thunkable the process of making applications becomes easier, faster and efficient. To ensure that every functional application runs properly in accordance with expectations, Black-Box testing is performed. The results of this study are an Android application that uses a variety of multimedia resources such as images, audio (sound) and video, as well as having an attractive and easy to use interface so that it can help early childhood children learn the material presented in this application.
Extraction of Association Rule Mining using Apriori algorithm with Wolf Search Optimisation in R Programming

Association rules mining (ARM) is a standout amongst the most essential Data Mining Systems. Find attribute patterns as a binding rule in a data set. The discovery of these suggestion rules would result in a mutual method. Firstly, regular elements are produced and therefore the association rules are extracted. In the literature, different algorithms inspired by nature have been proposed as BCO, ACO, PSO, etc. to find interesting association rules. This article presents the performance of the ARM hybrid approach with the optimization of wolf research based on two different fitness functions. The goal is to discover the best promising rules in the data set, avoiding optimal local solutions. The implementation is done in numerical data that require data discretization as a preliminary phase and therefore the application of ARM with optimization to generate the best rules.
Volcanism and geochemistry of soil and vegetation cover of Kamchatka. Communication 1. Geochemical features of volcanic surface ashes

Common geochemical features of volcanic near-surface ashes of Kamchatka (ashes located under the surface organogenic soil horizons) are their depletion by most chemical elements in comparison with average indicators for volcanic rocks of corresponding composition and relative enrichment of a limited range of elements, mainly typomorphic for main rocks (P, Mg V, Cr, Cu, Zn, Zr, Sb). Against the background of small variations in the contents of chemical elements in the ashes of different areas of the peninsula, the range of their priority elements is determined by their petrochemical composition. The active activity of the Northern group of volcanoes of Kamchatka causes a relatively high content of mobile forms of elements in the near-surface ashes of the Northern province in comparison with the ashes of the southern part of the peninsula.
OpenVDB

This course will cover the compact volume data structure and various tools available in the open source library OpenVDB. Since its release in 2012 it has set an industry standard and has been used for visual effects in over 150 feature movies. Last year this open source project was the first to be adopted by the new Academy Software Foundation (ASWF) and the Linux Foundation. This means that the project is now changing governance and more importantly has open up for numerous external contributions. We will describe this new governance, specifically what it means for would-be contributors, and of course what has already been added to the project since last time this course was offered at SIGGRAPH in 2017.
Autoregressive Modeling with Error Percentage Spread based Triangular Fuzzy Number

Data collected by various methods are often prone to uncertainty of measurement which may affect the information conveyed by the quantitative result. This causes the developed predicted model to be less accurate because of the uncertainty contained in the input data used. Hence, preparing the data by means of handling inherent uncertainties is necessary to avoid the developed prediction model to be less accurate. In this paper, the standard autoregressive model is extended to the case where inherent uncertainty exist in the time series data input is handled by triangular fuzzy number. A systematic strategy to construct a symmetry triangular fuzzy number based on percentage error method to build the autoregressive model is presented. Three different spreads of 1%, 3% and 5% are evaluated under percentage error method. This method is applied to forecast the exchange rate of Association of South East Asian Nation (ASEAN) based on time series data. The enhancement made in data preparation of building fuzzy triangles in this study affirms that the proposed method can produce a better accuracy in predicting as compared to the standard auto regressive model. Importantly, the difficulties to build a triangular fuzzy number to treat the fuzziness which is contained in data is addressed. From the result, we could rank the best percentage error spread which gives higher accuracy among 1%, 3% and 5% model.
Deep Learning Feature Extraction using Pre-Trained Alex Net Model for Indian Sign Language Recognition

Indian sign language is communicating language among deaf and dumb people of India. Hand gestures are broadly used as communication gestures among various forms of gesture. The real time classification of different signs is a challenging task due to the variation in shape and position of hands as well as due to the variation in the background which varies from person to person. There seems to be no availability of datasets resembling to Indian signs which poses a problem to the researcher. To address this problem, we design our own dataset which is formed by incorporating 1000 signs for the sign digits from 1 to 10 from 100 different people with varying backgrounds conditions by changing colour, and light illumination situations. The dataset comprises of the signs from left handed as well as right handed people. Feature extraction methodologies are studied and applied to recognition of Sign language. This paper focuses on deep learning CNN (convolution neural network) approach with pretrained model Alexnet for calculation of feature vector. Multiple SVM (Support Vector Machine) is applied to classify Indian sign language in real time surroundings. This paper also shows the comparative analysis between Deep learning feature extraction method with histogram of gradient, bag of feature and Speed up robust feature extraction method. The experimental results shown that Deep learning feature extraction using pretrained Alexnet model give accuracy of around 85% and above for the recognition of signed digit with the use of 60% training set and 40% testing set.