Reviewerly/exHarmony huggingface dataset:
Enhancement of Data Security using Circular Queue Based Encryption Algorithm

Data security is a progressing provocation for designers and Hackers. To battle different attacks by hackers, there is a need for increasingly solid security advancements. In this proposed system, a low complexity circular queue data structure security algorithm can be created. Numerous complicating variable components are used to enhance the quality of this algorithm and make recuperation of original message for hackers, becomes progressively troublesome. These tunable components are the size of the circular queue, the start of the picked keyword letter and the various portrayals of a number are in the Fibonacci manner. All letters ought to be changed over into ASCII binary configuration so as to be utilized by security algorithm in the sensible and shift operations. The outcomes demonstrate that our proposed security algorithm has half low complexity than analyzed multiple circular queues algorithm (MCQA). Fibonacci manner and variable number of difficult factors in this algorithm give adaptability in changing the security of the algorithm as per the conditions. Circular queue is an data structure can be utilized in the data security to make figured message progressively hard to disentangle. For example, a calculation that utilizes the moving and supplanting tasks of bi-section bi-push for round line to expand security. An irregular number was utilized in this algorithm to control the moving between the line and section, in the end this lead to expand the unpredictability of plaintext decrypting.
Motivational aspects of learning activities among engineering specialties’ students

Т. С. Пилишвили, И. А
Remote and Collaborative Virtual Reality Experiments via Social VR Platforms

Virtual reality (VR) researchers struggle to conduct remote studies. Previous work has focused on working around limitations imposed by traditional crowdsourcing methods. However, the potential for leveraging social VR platforms for HCI evaluations is largely unexplored. These platforms have large VR-ready user populations, distributed synchronous virtual environments, and support for user generated content. We demonstrate how social VR platforms can be used to practically and ethically produce valid research results by replicating two studies using one such platform (VRChat): a quantitative study on Fitts’ Law and a qualitative study on tabletop collaboration. Our replication studies exhibited analogous results to the originals, indicating the research validity of this approach. Moreover, we easily recruited experienced VR users with their own hardware for synchronous, remote, and collaborative participation. We further provide lessons learned for future researchers experimenting using social VR platforms. This paper and all supplemental materials are available at osf.io/c2amz.
Axiological characteristics of digitalized education

Due to the global trend of digitalization, which influences all spheres of human activity, it is necessary to train highly qualified teachers who not only know how to use modern technologies, but also are capable of creating future technologies in everyday professional activities. Thus, it is necessary to develop professional skills (Hard skills) and personal qualities (Soft skills), which include cognitive, communicative, creative skills, as well as cooperation, emotional intelligence, decision-making skills, managerial skills, etc. The leading role in this process belongs to education, which should both adapt future educators to the needs of the system and modify modern digital technologies that follow integration of cognitive practices. This process should be based on humanistic ideals, universal and national values implemented into every new component in the general education system. This article draws attention to the necessity of a purposeful formation of value beliefs and a national code in the process of digital transformation of the education system, of identifying and presenting the risks of this process, as well as means to solve possible problems.
Unknown Location Determination using TDOA Computation with Convergence Points

Real time location/unknown target position is important in Electronic Warfare (EW) system. To find the location the hyperbolic multilateration method is used. Many algorithms are available to solve nonlinear hyperbolic equations. The techniques which are mostly used for solving and determining non-linear measurements are the Taylor Series method and Ezzat’s approach. Taylor series approach computes the position fix in an iterative fashion where as Ezzat’s solution gives a direct solution. In this paper to solve the non-linear measurements which are in the hyperbolic form we used two types of techniques. These two techniques are implemented on different receiver/ sensors distributions ex. square, triangle etc. In this paper we explores the optimal value for different receiver combinations and also we compares the convergence issues, relative performance for all combinations and in three dimensions. Finally we determined the standard deviation for every case and compared it for better optimal solution.
Agile Project Management: Feasible Methodology in Construction Industry

The business characteristics in the construction environment has changed considerably over the previous 2 years because of rapid changes in info and construction technology, changes in market conditions, improved consumer needs (i.e. fast response, lower fees, greater customization etc.), item proliferation with uncertain and shorter life cycles, intensified off shoring and outsourcing methods, and improved competition from local to worldwide arena. This paper research on AM is actually centered on improving the systematic methodologies to study. The Scrum method designed for software development sector has to be defined to comply with construction business first. Artifacts, events, and the roles of scrum framework shall be connected to the stakeholders, group meetings, and documents applying in the construction business. The developed model then supported to the conduct the case study. The study was conducted to the donor funded task that is sealed the time and project budget with its top limit.
An Effective Knowledge-Based Pre-processing System with Emojis and Emoticons Handling on Twitter and Google+

Social networks, nowadays, are an imperative source for users to express their opinions freely on diverse topics including various brands and services. Presence of millions of users results in the generation of gigantic volume of data. The generated data are actually a treasure trove for business organizations to understand public sentiments towards their brand and services. This data is dynamic and highly unstructured due to varying writing patterns such as use of slangs, misspelled words, emojis and so on. The unstructured data makes pre-processing an underlying and challenging step in sentiment analysis. Therefore, authors have thoroughly explored a series of pre-processing steps on two social networks and observed that the sequence order of pre-processing steps plays an important role in improving overall pre-processing results. Hence, an improved ordered sequence of pre-processing steps has been proposed. It has also been observed that the presence of emojis in the text act as a pivot in determining users’ sentiments. Therefore, a detailed handling of emojis has also been included in the proposed pre-processing steps. New dictionaries have been compiled to provide a language to the emotional contents carried by emojis and emoticons. Few existing dictionaries have also been extended to make them more comprehensive for lookup task. Additional pre-procesing steps for handling multiword usernames and hashtags have also been incorporated in the proposed work. Further, experiments have been carried out to compare the proposed system with the existing ones. Results show that the proposed system outsmart the existing approaches mainly due to implementation of pre-processing steps in an ordered sequence and handling of emojis.
TurkiS: A Turkish Sentiment Analyzer Using Domain-specific Automatic Labelled Dataset

A preliminary task of sentiment analysis aims to detect polarities of a text either positive or negative.To increase the overall performance of the polarity detection for supervised learning methods, it requires properly labeled training texts.Also, the quality of labeled texts is critical for correct polarity detections.In this study, we provide a training and test dataset generator for Turkish sentiment analysis in which supervised learning methods can be trained without any human labor.To achieve these requirements, we extract comments and polarity scores from a popular e-commerce website for electronic devices in Turkey.Also, we employ a lexicon-based polarity detection to filter automatically labeled comments which exploit a translated version of a well-known sentiment lexicon into Turkish.We compare well-known supervised learning methods trained by both unfiltered and filtered versions of this dataset.The experimental setup is conducted by the generated evaluation dataset for the e-commerce domain.The test set contains 30% randomly selected from the generated dataset.Experimental results show that all supervised learning methods including SVM with linear kernel model, Multinomial Naive Bayes, and Logistic Regression perform 8.2 percent better in the filtered version of the dataset than the unfiltered version.Moreover, the logistic regression gets the highest score when it uses count vectorizer as a feature extraction mechanism.
An Efficient Romanization of Gurmukhi Punjabi Proper Nouns for Pattern Matching

A Romanization system is used to convert some text of a source script to the Roman script through word by word mapping. The phonological characteristics of the source word are not lost. Only writing script is changed, without any changes in the spoken language. This paper presents a rule based approach for Romanization of Gurmukhi script proper nouns. The aim is to develop a lightweight Romanization system, which may produce multiple possible results for the same input word. The algorithm uses a list of Gurmukhi script characters along with their equivalent character combinations in Roman script. Direct mapping of Gurmukhi script characters to their equivalent Roman script character combinations does not produce efficient results, so some rules are applied to get the correct mappings. The rules are basically to place or remove the letter ‘a’ in between the mapped consonants. Three different sets of rules are applied to get three different Romanized outputs. All these outputs are acceptable for information extraction using pattern matching. In Gurmukhi, some words are written differently than these are pronounced. To handle such words, these words or part of these words are stored in a database table. Along with these words their Romanized form is also stored in second column. The table is used to directly pick the Romanization from the table and use it for Romanization of these words. The result of this Romanization system is a set of possible words that can be generated from the source script word. It enables an application to pattern match those output words with some text or database to get the required information.
Rendimiento académico y patrones de uso del campus virtual: Un estudio de caso controlado

Having records of the massive use of digital tools allows observing additional aspects of the learning process, from the Learning Analytics approach. This study, as a controlled case, analyzes the uses and describes the types of students, according to access to digital resources and temporality, which differ according to the course, gender or presence. It is carried out in cetheris paribus conditions, for different courses, first and fourth, with the same teacher, subject, teaching methodology and period. The results show that first-year students have a more temporally polarized behavior and a greater digital gap with respect to the average, while fourth-year students are more committed to the program, with women being more digitally active, pointing to a positive relationship between grades and digital activity, more consolidated in fourth grade. The cluster analysis confirms the differences in the grouping of students in the different courses, conditional on digital use and performance.
THE EFFECT OF THE USE OF PORTABLE READING TOOLS ON IMPROVING THE ABILITY TO READ AWARENESS WRITINGS OF LOW VISION STUDENTS

Low vision learners experience obstacles in reading the writing of caution, this happens also to one of the students who are in SLBN A pajajaran Bandung. With this condition, research creates a plan that aims to improve the ability to read it. In this thesis, researchers tested reading aids to determine the effect of using portable reading lights on improving the reading ability of low vision learners. Research using quantitative approach with Single Subject Research (SSR) method. The design used is A-B design. The subject of the study was a visually impaired student in grade 11 at SLBN A Pajajaran Bandung. Data collection by conducting tests to learners using advanced reading instruments. The data obtained is analyzed through descriptive statistics and displayed in the form of graphs and tables. The results showed a positive influence on improving the ability to read the writing after being given a portable reading light. This can be seen with a stable value at the time of intervention, as well as an increase in the middle value (mean) that is when the baseline phase has a middle value of 27.5% ¬and in the intervention phase has a middle value of 81.25%. The influence can also be seen from the percentage of overlap. The percentage of overlap between the baseline condition and the intervention phase is 0, which means that the smaller the percentage of overlap, the better the effect of the intervention. Based on the data obtained, the researchers concluded that portable reading lamps can be used for low vision learners who have difficulty reading in low light conditions. Thus, researchers recommend to the school and teachers to provide portable reading light aids to low vision learners to support in learning activities, especially reading writings.
STEGANALYSIS TECHNIQUES AND COMPARISON OF AVAILABLE SOFTWARES

The technological advancements of the present time bring along the necessity to maintain the security of the data which are available on the digital platform.Encryption and steganography techniques include the solution techniques used to ensure this security.The robustness of these techniques are also tested by analysis techniques.In this study, the steganalysis methods which are being used are explained.The results obtained are indicated in the charts.
DATA-DRIVEN JOURNALISM BASED ON BIG DATA ANALYTICS: A MODEL DEVELOPMENT FROM INDONESIA’S EXPERIENCE

This study aims to analyze the understanding and practice of data-driven journalism in Indonesia using big data analytics in media institutions such as data infrastructure availability, data structuring, data deepening, enrichment, presenting in the media, resource development, and management, and develop the model of data-driven journalism. The qualitative approach is used by in-depth interviews with 45 participants from media leaders, journalists, vloggers, and audiences. Besides, contents analyses are conducted on 48 samples of various media series along one year. The results showed that big data analytics was conducted for data-driven journalism step by step at national and local media in Indonesia. However, the knowledge and understanding of big data analytics and data-driven journalism in the newsroom and research supporters are still not adequate for both policy and technology. This study suggests the model development of the data-driven journalism process from Indonesia's experience and has managerial implications on empowering data-driven journalism in Indonesia.
Determinants of Cryptocurrency Price Movements

Cryptocurrency is a recent and popular topic that attracts the interest of investors and fund managers.Beyond the market discipline, researchers question the interaction between cryptocurrecies and macroeconomic variables.This study we focus on how the changes in gold and oil prices effect the daily price movements of different cryptocurrencies.The daily database includes prices of the cryptocurrencies of Bitcoin, Tether, Ethereum Litecon and EOS for the period between August 1, 2017 and April 3, 2019.Initially the stationarity of the series is tested by Ng and Perron (2001) method.The existence of the cointegration relationship between the series is tested by Johansen (1988) technique.The presence of causality relationships between the series is investigated with the Dolado and Lütkepohl (1996) causality test.The paper explains the details of the empirical findings.
Neural Network Autoregressive For Predicting Daily Gold Price

Gold is a precious metal that functions as a gem and also an investment. Gold investment is the reason for many people because it is practical, not easily damaged, easy cashed, not taxable, and other purposes. Based on this, many people choose gold as an investment. The problem for people who will invest in gold is related to uncertain gold price predictions so that the accuracy of forecasting methods are needed. The purpose of this paper is to forecast accurately daily gold prices using the Neural Network Autoregressive (NNAR) method. Training Data to find out the value of accuracy in the NNAR method uses secondary data obtained from Yahoo Finance in the form of daily gold prices. Test results on the NNAR method produce a better and more accurate level using the NNAR (25,13) model with a MAPE value of 0.370707, a MASE of 0.5851083, and an RMSE of 6.939331. The conclusion of the results of this paper is the daily price of gold is influenced by the daily price of gold a day ago to 24 periods ago with the NNAR (25,13) model.
A Personalized Machine-Learning-enabled Method for Efficient Research in Ethnopharmacology. The case of Southern Balkans and Coastal zone of Asia Minor

An ethnopharmacology expert faces several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, the different quality of language use across sources, present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how the expert can be supported effectively through intelligent tools for the ethnopharmacological research in the Southern Balkans and Coastal zone of Asia Minor. Our work follows an “Expert-Apprentice” paradigm in a crawling process, where the apprentice is a Machine Learning (ML) algorithm, utilizing a combination of Active Learning (AL) and Reinforcement Learning (RL), and the Expert is the human researcher. ML-powered research improved 3.1 times the effectiveness and 5.14 times the efficiency of the domain expert, fetching a total number of 420 relevant ethnopharmacological documents in only 7 hours versus an estimated 36-hour human-expert effort. Therefore, utilizing Artificial Intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.
The Story of "I" Tracking: Psychological Implications of Self-Referential Language Use

We review extant research on the psychological implications of the use of first-person singular pronouns (i.e., “I-talk”). A common intuition is that I-talk is associated with an overly positive, highly agentic, and inflated view of the self—including arrogance, self-centeredness, and grandiose narcissism. Initial (small-sample) research provided evidence that frequent I-talk was associated with grandiose narcissism. More recent (large-sample) research, however, has found that the correlation is near zero. Frequent I-talk is, however, positively correlated with depressive symptoms, in particular, and negative emotionality (i.e., neuroticism), more broadly. Frequent I-talk is also positively related to the neurotic variety of narcissism called vulnerable narcissism. In addition, frequent I-talk has a positive association with sociodemographic characteristics such as (lower) status, (younger) age, and (female) gender; I-talk has a conditional association with truth-telling and authenticity—a correlation that appears to hinge on context. This review summarizes the literature on I-talk, provides some speculations about the emergent psychological meanings of I-talk, and provides a guide for future research.
A Novel Hybrid Adaptive Filter to Improve Video Keyframe Clustering to Support Event Resolution in Cricket Videos

Automated summary generation of sports videos poses many challenges of detecting exciting events of a game. In our research, we focus on the table of content-based video summarization for cricket videos to facilitate efficient indexing of cricket events. Initially, we have identified event boundaries accurately to detect the event start and end points. To distinguish the different types of events, we need to analyze the sequence of the camera focus area. By observing the characteristics, we have categorized the camera focus area into several categories. To overcome the challenge of low accuracy we have introduced a novel algorithm for adaptive filtering for the comparison of hue histogram. The results prove that this algorithm is sufficient for accurate image clustering and this may be used in other sports event clustering as well.
Registration and Fusion of CT and MRI Images

Image processing is growing day by day and it has an important role in medical imaging field. Medical imaging provides a better diagnosis of various diseases which will help in an early and exact detection of the disease, Thus help in curing faster and more accurately. In this paper, trying for an accurate registration and after that fusion of Computed Tomography (CT) and Magnetic Resource Imaging(MRI) images. This will help to get the tissues, blood vessels and bone structures combined in a single image rather than two different images.
Design of Offline and Online Writer Inference Technique

Writer inference systems tend to identify and verify the authorship of the handwritten documents. Each writer will have his own style of writing that uniquely identifies the writer. Hence authorship identification finds its application in forensic document analysis. It is also considered as one of the biometric features of a person, so helps in security to uniquely identify a person. Recognition of writers online has its application in detecting the identity thefts. That is compromising one’s social media account and sending messages to others as if he were an authentic sender. By discriminating the writing characteristics of the original and intruder, the masquerader can be identified. In this survey various works contributing to feature extraction and prediction of writers are discussed.
Trust Management Techniques, Models and Attacks in Cloud

Cloud computing is a technology that promises resilient and flexible computing resources to the growing business. Along with many benefits of Cloud computing, there are various complex problems such as security and privacy of data that exists on Cloud. Trust is one of the key obstacles in the adoption of Cloud by the growing market due to absence of any reliable and efficient trust evaluation mechanism. It is difficult task for Cloud consumers to choose trustworthy provider among various service providers who provide similar type of services. There is a need to have - proper techniques/methods to establish trust; proper trust based models to evaluate the trust; and awareness of various possible attacks to know the robustness of the models. Hence this paper discusses existing state of the art trust management techniques, models and attacks, to help research community interested in designing trust based schemes in Cloud environment.