Reviewerly/exHarmony huggingface dataset:
Performing opinion mining and analytical study for cashless transactions

Cashless economy and digital payments are one of the hot topics of research that are gaining enormous popularity day by day.It is primarily due to their ability to combat issues of corruption, back money, illegal transactions and counterfeit money.With the increase in number of options of digital payments, people are inclining themselves towards ease of payment.This paper focuses on two aspects: firstly, it provides an analytical study performed in the field of cashless transactions and digital payments.Secondly, it finds out the opinion of the people about cashless transactions using a hybrid approach of type 2 fuzzy logic and hesitant fuzzy sets for polarity assignment.A sentiment analysis on 'cashless economy' has been carried out using data obtained from Twitter in the form of tweets.
Examination of Kernel Based Noise Classifier with Cross Resolution Dataset and with Untrained Class

Determining the effect of untrained classes in the kernel based noise classifier is the prime object of the paper. It further includes, studying the effect of studied classifier over different datasets. Distinct nine Kernel functions has been associated with conventional supervised Noise Classifier. Landsat8 and Formosat2 along with Resourcesat-1 data have been opted for the performance evaluation. Decrease in classification accuracy has been found, in presence of untrained classes. A subtle consistency has been in classification accuracy in case of cross resolution data sets, thus, showing the robustness of the algorithm.
Blockchain and Smart Contracts for Know Your Customer (KYC)

Distributed Ledger Technology (DLT) and Blockchain Technology (BCT) are options for introducing smart and transitive energy trading via a Know Your Customer or Know Your Client (KYC) system using an Ethereum Blockchain. The most significant context in which BCT can create transformative change is in the verification of other parties in an energy trading system. Currently the KYC trade mostly relies on internal controls to send and receive KYC information across the globe, resulting in slower and error prone data management and governance. BCT can solve this by providing immutable and verifiable data sources. Implementing smart contract addressing, the issue of storing critical data necessary at different stages of KYC and making it verifiable by all stakeholders is of critical importance. The literature review looks at the current research in BCT, programming languages used in BCT, Game Theory, Zero-Knowledge Proofs and energy trading in context to KYC. Having a foundational concept mathematically by creating game theoretic models and a simple computer programming simulation is the first step in the task of creating a true BCT platform that can be used in energy trading for KYC. Simulating the scenarios of n-player games is important to the advancement of this platform. The key result of these games and interaction between traders will help change the trading systems regionally and internationally. This can deliver new ways of working and brings visibility and control to each energy trade. The researcher analyses the strength of the system through the decisions of the traders using infinite games. New trading strategies and BCT resources with different parameters gain a larger market presence in international trade with these proofs, codifications and simulations. A gap exists in the literature in BCT in the creation of game theory algorithms as well as using simulation tools and software and using these methods to create forecasted energy trading scenarios with the implementation of KYC. Looking at an infinite game mathematically and presenting a simple codified computer simulation using Ethereum via Solidity involving a supplier, operator and regulator will give new implications in game theory and conflict-resolution scenarios within context to energy trading and KYC via BCT.
An effective and robust approach of Image Cryptography using Phase Congruence Correlative method and Arnold Cat Map

In this paper, an effective and robust method of image cryptography scheme is proposed using Phase Congruence Correlative (PCC) method and Arnold Cat Map (ACM).The secret image or a secret watermark is scrambled using ACM method.This scrambled secret image is then embedded into a host image using Adaptive α-β Blending method.The place of embedding in the host image is effectively obtained using PCC method.The embedded image is subjected to various attacks and the secret image is retrieved from the distorted embedded image.This retrieved secret image is compared with the original secret image and various parameters like Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Absolute Difference (AD), Mean Absolute Error (MAE), Maximum Difference (MD) are obtained.This proposed method proves to provide better performance metrics over the past methods.
Assisting Rural Womens for using Basic Electronic Gadgets for Safety Purposes

The Indian Self-Help Group (SHG) movement has been working in the exact direction in recent years to empower women and eradicate poverty in rural and urban areas. Women in India have a strong confidence in the campaign and are responsible for improving their livelihoods. The present study is to evaluate the quality of SHGs chosen for the investigation from the district of Kanchipuram and the district of Chennai. The study's broad goal is to analyze the SHGs operating system for saving mobilization, credit delivery to the needy, repayment of loans, and building SGH members ' opinion on increased decision-making power. All primary and secondary data are obtained and demographic information analyzes age, family structure, and number of dependents in the household. The study emphasizes the role of SHGs for their integration in empowering women, social cohesion, and socio-economic improvement of the poor.
Predictive System for Medical Emergency and Admissions Control

An emergency department (ED), provides specialized facility in emergency medicine they are the life saving one for many people in emergency cases. Some main issues in ED’s are overcrowding, quality of care for the patients etc., here overcrowding is still a big issue in most of the hospitals. This paper implements some advanced methods to improve patient flow and prevent overcrowding. The ED overcrowding can be reduced by predicting the future of patient’s admission using Machine Learning and making the resources available beforehand. This technique help us to learn, analyze and predicting the future results. In this work we implement Predictive Gradient Boosted Machines (PGBM) which produces a prediction model in the form decision trees. The data set which we use in this paper has several factors like hospital admissions, including site of the hospital, patient’s age, mode of arrival, previous admission in past month and past year. Decision Trees creates a training model which predicts the data by understanding decision rules that are observed from training data. The accuracy of the system is achieved by implementing Deep Neural Network (DNN) which uses efficient mathematical model to process data in complex ways.
Preface of “The Ninth Symposium on Advanced Computation and Information in Natural and Applied Sciences”

First Page
ACM SIGAI activity report

We are happy to present the annual activity report of the ACM Special Interest Group on AI (ACM SIGAI), covering the period from July 2018 to June 2019. The scope of ACM SIGAI consists of the study of intelligence and its realization in computer systems (see also its web-site at sigai.acm.org). This includes areas such as autonomous agents, cognitive modeling, computer vision, constraint programming, human language technologies, intelligent user interfaces, knowledge discovery, knowledge representation and reasoning, machine learning, planning and search, problem solving and robotics. Members come from academia, industry and government agencies worldwide. ACM SIGAI recently added two new ACM SIGAI chapters, namely one professional chapter in Laguna Nigel (USA) and one student chapter at the SRM Institute of Science &amp; Technology in Chennai. ACM SIGAI also added two new officers this year to be able to serve its membership better, namely Iolanda Leite from the Royal Institute of Technology (Sweden) as second newsletter editor-in-chief and Marion Neumann from Washington University in St. Louis (USA) as diversity officer, thus increasing diversity in the ACM SIGAI leadership committee by increasing both the number of international officers and the number of female officers and also furthering the internationalization of the ACM SIGAI newsletter. Marion will be covering diversity also as part of the ACM SIGAI newsletter. ACM SIGAI started officers meetings at major AI conferences already in 2018 and continued the new practice in 2019 (so far at the AAAI Conference), in addition to all-officers teleconferences and a monthly ACM SIGAI leadership teleconference.
Motormouth

The Motormouth project suggests that a methodology which incorporates the findings of Zajonc and Smalley can be used to mediate the novelty factor in sonic recontextualization, increasing the likelihood of liking, arousal potential and attitudinal enhancement, and, hopefully therefore, musical enjoyment. Amongst the affordances of the 'tapeless studios' was the ability to extract grooves and breaks from existing recorded material for recontextualization into new compositions, the practice going on to form a cornerstone of hip hop, and feeding many other forms of pop production, once subsequent devices became more affordable. An explanation of how discordant noise can become musically acceptable lies in the research of Deutch et al. namely, that repetition affords musicality. The arrangements were such that they largely resembled more traditional pre-mix recordings. The organization of sequenced tracks into, e.g., 'kick,' 'snare,' 'hate,' 'bass,' 'synth,' facilitated a familiar approach to mixing.
Sparse Representation Based Multi Object Tracking using GPU

This work proposes a sparse based representation for tracking multi object for the longer sequence of video frame. Object of interest are first identified and then represented with set of low dimensional feature covariance matrix. These feature of different object are kept in a dictionary. In order to classify the object, sparse based Orthogonal matching pursuit(OMP) algorithm is used. Furthermore, towards reducing the computational overhead, proposed model is implemented on a graphical processing unit enhanced with the multi threaded resource for parallelization of the task. Experimental results shows that proposed method out perform as compared with the state of art in identifying the objects.
Enhancing skill prediction through generalising Bayesian knowledge tracing

Learning Analytics (LA) have been widely investigated and applied to understand and optimise the learning process and environment. Among a number of LA tools, Bayesian Knowledge Tracing (BKT) was developed aiming at predicting the probability that a skill has been successfully acquired by a learner. While current development has proved BKT to be sufficiently accurate in prediction and useful, the state-of-the-art BKT methods suffer from a number of shortcomings such as the incapability to predict multiple skills learnt by a student. In this paper, we extend the ordinary BKT model to predict unlimited number of skills learned by a learner based on a non-parametric Dirichlet Process (DP). Another characteristic of our approach is that it can easily incorporate prior knowledge to our model resulting in a more accurate prediction. The extended model is more generic and able to handle border applications. We have developed two efficient approximate inference methods based on Gibbs sampling and variational methods.
Wayside Train Monitoring Systems

The present competitive world of transport particularly the rail industry is driven by automation and centralization. New ways are being devised each day by the operators and managers to improve efficiency, operational safety, and risk control. Big Data and its multiple applications play a significant role in developing ways of analyzing and evaluating the rail data gathered and using it to enhance the transport industry. Wayside train Monitoring System is a field that is slowly gaining popularity through the different methods it provides to handle the big Data of the transport industry. It can measure the operational performance of rolling stock and infrastructure assets as well as the direct surroundings. The chapter addresses the problem of overall safety and optimum cost of railways transportation. Consequently, the chapter aims to resolve the following issues: How can the rail industry leverage the enormous amount of data available? How can industry players benefit from the data and use it to understand the real needs of travelers?
A Machine Learning based Preventing the Occurrence of Cyber Bullying Messages on OSN

The process of threaten or harassment of any user with the help of posting wrong/abused or vulgar messages using the social media in the internet is known as Cyber bullying .These messages may sometime contain a text posted by a teen, or preteen or a child who want to threaten or harassed or embarrassed other child by posting the messages. So in this project, we mainly try to propose another depiction learning strategy to handle this issue known as SEMdae. Here the semantic augmentation comprises of predefined words that contain noise or abused meaning which is posted into the database by the admin and these words are classified based on the five categories that are available in the literature like “HATE, VULGAR, OFFENSIVE, SEX, and VOILENCE”.
Preface

The 2021 International Conference on Computer Engineering and Innovative Application of VR (ICCEIA-VR 2021) was planned to be held on June 11-13, 2021 in Guangzhou, China. Due to the impact of COVID-19, many communities from all over the world were under strict health measures and strict travel restrictions, and participants of 2021 International Conference on Computer Engineering and Innovative Application of VR (ICCEIA-VR 2021) also meet with the difficulty of travel restrictions. Considering the situation that most of the authors would like to publish their articles and make academic communications as scheduled, ICCEIA-VR 2021 was held on-line instead of postponing the conference. It was a challenge, not only to the organizers, but also to the participants who were delivering their speeches using videoconference tools, under different time zones. The conference was hosted by Technical Committee on Simulation Education Technology, China Association for Educational Technology and co-organized by Guangzhou Institute of Technology, Western Sydney University and AEIC Academic Information Exchange Center. In order to promote the development of teaching method, scientific research and academic activities for universities and scientific research institutes around the world through experience exchange in computer network, VR education innovation application, VR teaching, data communication, network technology, scientific research, etc. The conference also provided an international platform for sharing experiences, expanding professional networks, face-to-face exchange of new ideas and presenting research results. List of Committee member is available in this pdf.
Job Hunting

This chapter will cover the strategies on how to find a SOC analyst job including common job titles, what job boards to use, resume tips, networking with other professionals, and common interview questions.
SOME ASPECTS OF QUALITY ASSESSMENT OF SCIENTIFIC AND TECHNICAL TRANSLATION

The article presents an analysis of the appraisal component in the meaning of the -ish lexeme that recently has been conventionalized in English.It was found that the use of the linguistic unit under study indicates the work of a complex cognitive mechanism.Based on the comments of native speakers the data were analyzed.A whole range of shades of meanings was identified with which the ish operator corrects the semantics of the statement.
A Computational Modeling for Knowledge Binding of the Unstructured Web Data

The focus of this manuscript is laid towards extracting insightful data embedded into web-based information which is crucial for various academic and commercialized application requirements. The study thereby introduces a robust computational modeling by means of computing knowledge from collaborative web-based unstructured information. For this purpose, this design is simplified with Fuzzy based matching algorithm and also with a set of procedures which reduces the computational effort to a significant extent. The numerical theoretical analysis shows that the effectiveness of the formulated model. It also shows that the formulated concept outperforms the baseline modeling by almost 50% when computational performance is concerned.
Pupils' attitude to school geography and motivational factors of its study

The purpose of the article is to diagnose the attitude of pupils to geography classes at general educational institutions and identify the main motivating factors of its learning, depending on the pupils’ age and place of residence. Main material. The diagnosis of motives for studying geography was preceded by a clarification of the attitude of pupils to this subject. Pupils were asked to name their favourite school subjects and the results of the survey were used to calculate the rating of disciplines. For sixth-graders in rural and urban schools, geography ranked first, for seventh– and eighth-graders, and for rural ninth- and tenth-graders, it ranked second. For city ninth- and tenth-graders, geography took third place in the ranking of school subjects. The article presents the rating of school geography among other subjects on the basis of a questionnaire survey conducted in 2018/2019 academic year among 1163 respondents, who are the pupils of 6-11 grades at general educational institutions in Lviv region. The research analyzes positive and negative motivational factors of learning geography, depending on age and place of pupils’ residence. The results are interpreted in the context of the popular theory of self-determination developed by E. Deci and R. Ryan. Conclusions and further research. The research, conducted among the pupils studying geography at school, in a particular region of Ukraine, showed the highest rating of this subject among other school disciplines and the positive attitude towards it (more than 75% of the pupils who participated in the study). The degree of interest in geography is affected by the age of the pupils and the type of area in which the pupils live: the highest rank of favourable attitude to the subject was shown by rural pupils and pupils who are studying natural courses of the discipline (grades 6–7). The leading factors of a positive attitude to geography depend on the content of this subject and modern forms of studying it, which ultimately contributes to the formation of internal motivation for cognition. The results of an empirical study can be used to develop psychological, pedagogical and methodological models for teaching geography in different classes of general educational institutions.
Filtering Concept for Web Learning

The fast development of the WWW presents uncommon difficulties for broadly useful crawlers and web crawlers. The Former strategy used to slither site pages was FOCUS (Forum Crawler Under Supervision).This undertaking presents an aggregate conduct learning calculation for web creeping. The aggregate conduct learning calculation creep the site pages dependent on specific watchword. Discriminative learning removes just the related URL of the specific catchphrase dependent on separating. The objective of this venture is to creep applicable gathering content from the web with negligible overhead. The undesirable URL is expelled from the website pages and the site page slithering is decreased by utilizing the aggregate conduct learning. The site pages must be removed dependent on certain learning procedures and can be utilized to gather the undesirable URL'S.
The Determinants of Deposit Growth in Case of Commercial Banks in Ethiopia

Deposits are the foundation upon which Banks prosper and grow.And deposit is engine to financial institution operations due to the fact maximum companies in rising markets depend closely on financial institution loans as a supply of financing.So, this study investigates bank-specific variables and macro-economic determinant of commercial banks deposit growth in Ethiopia.Macro-economic factors consider under this study were age dependency ratio, unemployment rate, population growth, broad money supply and Inflation.While bank specific variables included were branch expansion and bank size.Eight commercial banks have been the subject for the study by incorporating a 10 years' data ranging from the period 2010 to 2019.A quantitative research approach and explanatory design were adopted in carrying out this research.The study had employed fixed effect panel data estimation techniques to analyse the influence of bank specific and macroeconomic factors on deposit growth using Eviews 10.The results of regression analysis showed that branch expansion, bank size, broad money supply and inflation have a positive and significant effect on deposit growth of commercial banks.On the other hand, Age dependency ratio and population growth have negative and statistically significant effect on deposit growth.Finally, the study recommends there should be stable inflation, a healthy money supply and population growth and branch expansion.
Deep Features based Multilingual Text Detection and Recognition in Video Frames

One of the major causes for achieving poor text detection results in video frames are complex background, illumination and de-blurring of the frames and it is also important challenges for the researchers to overcome such problems. Therefore, motivated from this kind of observation from recent survey, we propose a text detection method based on Deep Neural Networks known as TextBoxes which is capable of detecting text in video frames with improved performance when compared to state-of-the-art techniques. In parallel this we also propose a Text candidate detection for video frames and scene images by extracting words based on Automatic Window Detection by making use of Discrete Wavelet Transform (DWT) with the sliding window for extracting high frequency sub bands for each sliding window. K-means clustering technique has been used to obtain the text components and to decrease the background complexity and noise. Six-layer convolutional neural network model has been designed to recognize the text in multilingual images. Experiments for text detection are done on our own multilingual South Indian database, ICDAR-2015 Videos, YVT videos, SVT, and MSRA Scene datasets and demonstrated in terms of Recall, Precision and F-measure and for recognition ICDAR-2015 Videos, ICDAR 2011 and SVT scene images.
An Efficient Design for Multiple Data Stores Cloud Applications

New data management prerequisites have been released by massive data development and cloud computing. Numerous applications really need to interact on the basis of the types they really need to control with a lot of heterogeneous data stores: standard data types, documents, social network graph data, simple key-value data, etc. A unifying data model is attracting considerable for developers due to flexible communication with heterogeneous relations and NoSQL data stores. The OPEN-PaaS-DataBase API (ODBAPI), which is a well-accepted REST API, allows developers to code their implementation software in the target data storage system separately. Secondly, we propose virtual data stores that interact with the integrated data store wrapped in ODBAPI. It was shown that this prototype responsible for probable solution technologically advanced capable to introduce OpenPaaS circumstances.