Reviewerly/exHarmony huggingface dataset:
Emerging Trends in Revolution 2020

Indian writing in English has largely changed in its content, characterization and language. There is a significant shift from writing about serious and dark issues to narrating stories about common people. Writing about the day-to-day monotonous lifestyles seems to be the new ‘mantra’ in the Indian style of writing. One of the first authors to write about such type of stories is Chetan Bhagat. His writing has revolutionized and mirrored the postmodern literature. Revolution 2020: Love, Corruption, Ambition is a novel by Bhagat. Revolution 2020 happens in three stages in Chetan Bhagat’s novel and is listed as love, ambition and corruption. All these three stages are played by Gopal, Aarti and Raghav characters in the novel. The ambition of Gopal’s father is to make his son an engineer but Gopal’s passion is different. He meets a politician and opens a technical institute, initiating corruption. Raghav is from a middle - class family and cracks the IIT entrance test but his passion is to be a press reporter writing against corruption and how it has polluted the whole education system from top to bottom. Finally, the most beautiful girl friend of his, right from school days, aspiring to become an air hostess the daughter of the District Magistrate loves Raghav. This paper analyses how the emerging trends of revolution as love, ambition and corruption play a crucial role in the lives of Gopal, Aarti and Raghav .&#x0D;
Data report: X-ray fluorescence core scanning of IODP Site U1474 sediments, Natal Valley, Southwest Indian Ocean, Expedition 361

Analytical data and best values are presented for three new U.S. Geological Survey mafic rock reference samples: W-2, another portion of the diabase at Centreville, Va., which replaces W-1; DNC-1, a dolerite from North Carolina; and BIR-1, an Icelandic basalt.The supplies of each powdered sample are expected to last 30 years or more.Best values for an oxide or element in a sample were calculated by the sequential procedure of (1) calculating a homogeneous variance for sets of six determinations for a constituent, (2) calculating from this variance the standard deviation of the means of six determinations, and (3) using this standard deviation to Studentize the range of means to decide which means could have been derived from the same population.The chondrite-normalized abundances of rare-earth elements, calculated from best values for the three samples, show that the contents of light rare-earth elements are low in DNC-1 and are very low in BIR-1.Future data by isotope dilution mass spectrometry will result in smoother rare-earth element plots for the three samples.In addition to data for the light rare-earth elements, there are best values or magnitudes for other trace elements whose contents decrease with decreasing amounts of K20 in the samples.Data for other elements useful in geochemical studies are desirable, especially for samples DNC-1 and BIR-1.Determinations of several elements, including gold and the platinum group metals, were not reported.
Machine-learning-based Real-Time Multi-object Recognition Method for Urban Autonomous Driving

This study proposes a method for detecting 51 types of traffic signs, 3 types of traffic lights, and different types of vehicles and pedestrians, that encountered in driving situations, by using a monocular camera. Object detection on the road was performed using a Faster RCNN (Faster Region Convolutional Neural Network) model, and a SVM (Support Vector Machine) classifier was used to improve the recognition performance and reduce the instances of both false positives and false negatives. Herein, this algorithm was defined as the hybrid faster RCNN-SVM detector. Data were obtained and monitored in real time. On average, the accuracy of our algorithm was 5.8% higher than those of the existing learning models. Furthermore, false-positive and false-negative instances decreased by approximately 31.4% and 38.3% on average, respectively. The processing speed of the algorithm was improved by an average of approximately 24% using a median flow tracker and the hybrid faster RCNN-SVM detector in combination.
Psychometric Analysis of a Proposed Model to Determine Factors Influencing Selection of a Research Supervisor

Aim/Purpose: This paper found some factors which influence research supervisees’ selection of their research supervisors. Background: Research on supervisor-supervisee relationship is mostly conducted when research students have already initiated their studies. Research on how a supervisor is selected before the research begins is researched less. How do supervisees select their supervisors? Which factors do they consider important? These questions were not clearly answered in the literature so far. Methodology: A scale was developed to measure factors which influence the selection of research supervisors. Using an online survey, data was collected from 315 research students in Malaysia between August and October 2018. Psychometric properties of the scale were assessed using exploratory factor analysis followed by confirmatory factor analysis. Construct reliability, convergent validity, and discriminant validity of the scale were assessed using composite reliability, maximal reliability, average variance extracted, and maximum shared variance. Contribution: How research supervisees select their supervisors is an understudied area. Most of the research on supervisor selection is done after the research journey has begun. This research focuses on the thought processes before supervisor selection. Findings: Demographics, expertise, and physical appearance emerge as important constructs that influence the thought process of a research supervisee. Each of these constructs is composed of several dimensions, each with its own weight and importance. Recommendations for Practitioners: Research supervision is an integral part of contemporary teaching profession. To develop this important dimension of an academic’s career, this research holds high significance. The emerging factors will help researcher supervisors enhance their profiles and become more visible. This has practical implications for higher education institutions as well. Recommendation for Researchers: Further studies in this area can explore these factors across different cultures, distinction between undergraduate and postgraduate students, public and private higher education institutions, and scholarship or self-funded students. Impact on Society: Attracting better and relevant research students will result in a better match between researcher’s capability and supervisor’s expertise leading to high impact research. Future Research: This research was done on only 315 respondents. More respondents from diverse population might influence the outcome.
Linguistic Inquiries of Unjust Sentence of Victim’s Rape: A Defense from Media

This article discusses the media's perspective related to incest rape case in Jambi written by the editorial board and posted in The Jakarta Post's opinion column.The aim of this paper is finding out the media's evaluation toward the case using an appraisal system.The analysis is done by classifying which utterance in the data that consists of several arguments and opinions.Later on, the researchers highlight and discuss expressions which represent the media's evaluation of the government, the family member of the victim, and the whole society and state relating to the case.The result of the analysis presents that the media put their negative evaluation toward the hearing decision and sentence addressed to the victim, especially negative evaluation of judgment and appreciation.Those negative evaluations are deemed as a form of defense from media instead of a neutral commentary.
The Current State of Social Commerce Fraud in Malaysia and the Mitigation Strategies

Previous research has acknowledged the widespread adoption of social networking (SNS) platforms for online shopping among social media users, known as social commerce.While the positive effects of shopping on social network platforms have received much attention in the literature, the negative outcomes of this type of commerce remain under-explored.Notably, the prevalence of cybercrime, specifically related to online purchasing fraud, has increased rapidly.To address this gap, this paper explores the online purchasing fraud cases related to shopping on the social network platforms, the demographic characteristics of the victims and the financial impact of the fraud to the victims.In addition, this paper also explores the strategies to mitigate the fraud cases.The data was gathered from social network users via an online survey.The findings show that almost seventy percent of the respondents were currently using social network platforms to shop, with Facebook being the most popular platform.For those who use social commerce, as many as twenty-four percent (24%) admitted to having been victims of purchasing fraud.The overwhelming majority of the victims were highly-educated middle-income earning married women, aged between 26-35.In terms of financial impact, the majority of the victims had lost nearly RM400.Few mitigation strategies were identified, including word of mouth, background check of sellers, reliable and transparent transaction process, and avoidance.
MEDIA BERBASIS ADOBE FLASH UNTUK PEMBELAJARAN KETERAMPILAN MENYIMAK DI PROGRAM STUDI PGSD

Learning media always develops the following technological developments. Likewise, the media in learning is listening to Indonesian. The development of learning media is important. Adobe Flash-based media developed to facilitate lecturers and students in learning Indonesian listening skills in the Elementary School Teacher Education Study Program. Media that are designed to be interesting may not only train cognitive knowledge but also practice practical listening skills. Adobe Flash-based media for listening are designed by displaying several menus, namely the home menu, RPS (Semester Learning Plan), materials, practices, games, music, profiles, and references, as well as close menus appearing in accordance with the cross (x) to be used using the application. The hope media developed was able to assist lecturers in delivering learning material, as well as helping students learn well in the classroom and outside the classroom.
Аn intelligent decision support system for cultural property identification

The article considers the problem of identifying cultural property, the development of methods and models for organizing and processing data and knowledge in an intelligent decision support system for cultural property identification. The complexity of the approach for solving the stated problem, prospects and ways of further research of this subject area are noted and identified.
Digital Color Image Encryption Using Cellular Automata and Chaotic Map

Today, with the expansion of multimedia communications, computer networks, and the distribution of information on the Internet, maintaining the security of information exchanged through insecure channels has become an important and essential issue in data communication. One way to protect the information in passive defense is to encrypt data so that people can communicate securely on a secure channel while maintaining their privacy and data authenticity. Because color image data has certain features compared to traditional data such as text and binary data, special algorithms are needed to encrypt digital images to maintain the efficiency, security, and speed of encryption. The present study provides a way to encrypt digital images using reversible cell automation and chaotic mapping. The basis for encrypting the proposed method is the use of the concepts of Shannon's confusion and diffusion technique, which takes place in two main stages. In the first step, the plain image is received as input, then it is permuted using the 3D chaotic map by using suitable key. In the second step, the cipher image from the previous step are extracted to 24 one-bit plates image and XOR by suitable 2D reversible cell automata. The proposed method will be compared with several cryptographic methods and has good outperform results.
GSIC: A New Interpretable System for Knowledge Exploration and Classification

Machine learning and data mining techniques have been developed rapidly in recent times. In tasks such as classification, machine learning techniques have been shown to equal to and even surpass human performance. However, high performance models are usually complex, opaque and have low interpretability thus making it difficult to explain the underlying behaviors of those models that lead to the final outcomes. In many domains such as medicine and healthcare, interpretability is one of the most important factors when considering the adoption of those models. In this paper, we propose a two-stage binary classification system applicable for healthcare (or general) data that benefits from a high level of interpretability and can at the same time achieve the results comparable to commonly used classification techniques. The motivation behind the proposed system is the lack of effective classification methods for handling data generated by various distributions (such as healthcare or banking data) that can harmonize both performance and interpretability perspectives. In this work, we tackle the problem by applying divide and conquer strategy on a new disentangled representation of the underlying data. The merit of our system is evaluated by a classification experiment with a wide range of real data and popular transparent and black-box models. Furthermore, a use case in data of sepsis patients staying in the ICU (Intensive Care Unit) is depicted to prove the interpretability of the proposed model.
A Synergy of Ontologyand Knowledge Classification for Decision Support Systems

Many problems within the software industry today emanate from the obliviousness that system development for decision support belongs to a category of problems that are classified complex or "wicked" in type.Therefore, procedural paradigms that address this phenomenon constitute areas of much exploration.Distinct with conventional problems, complex problems in information systems are problems that lack clarity in both their aims and solutions; lack definitive constructs and deficient of inherent logic that distinctively indicate when desired goals are achieved.This paper provides a discourse on the complex problems in Insurance Categorization.Ontology and knowledge classification in Insurance systems fall under this class of problems as insurance policies commonly describe problems exigent to unravel due to incomplete, contradictory and obstinately changing requirements.Because people exist in very unpredictable cultures with extensive categories of risk trajectories, it is commonly expected that individuals at some point in life are going to be challenged with situations which will hamper their privileges.Today, insurance businesses evolve either adversely or optimistically contingent upon the kinds of solutions they proffer for complex problems which may range from competition, management and pecuniary situation.This paper decomposes key functionalities of complex insurance processes to model knowledge-based ontology adopting standard Enterprise Architecture archetypes that impress upon the intricacies of insurance information systems management.The relationships between classifications, perspectives and knowledge are examined to promote the organization of homogenous entities intended for grouping procedures for decision support systems.The strengths and constraints of imperative categorization approaches are described in terms of their ability to ruminate, discover, and apply contemporary expositions through modelling so as to harmonize business goals with application infrastructures.
Online Blind Equalization Algorithm for Satellite Channel Based on Echo State Network

Two online blind equalization algorithms based on Echo State Network (ESN) in this paper are proposed for the nonlinear satellite channel. These two algorithms take advantage of the good nonlinear approximation of ESN to bring the High-Order Statistics (HOS) of the transmitted signal into the ESN, and constructing cost function of blind equalization by combining Constant Modulus Algorithm (CMA) and Multi- Modulus Algorithm (MMA). Then, the Recursive Least Squares (RLS) algorithm is used to iteratively optimize the network output weights, and the online blind equalization of the constant modulus signals and the multimodulus signals over the channel of Volterra satellite are realized. Experiments show that the proposed algorithms can effectively reduce the distortion of the transmitted signal by the nonlinear channel. Compared with the traditional Volterra filtering method, they have faster convergence speed and lower mean square error. © 2019, Science Press. All right reserved.
The DEVORA Borehole Database: Status Update and Manual

The DEVORA Borehole Database:Status Update and Manual.Report 1. 2011.03Tracy Howe, Jan Lindsay, Nick Mortimer, Graham Leonard
Applying Feature-Oriented Software Development in SaaS Systems: Real Experience, Measurements, and Findings

Distributing software as a service (SaaS) has become a major trend for web-based systems. However, this software distribution model poses many challenges. One of them is feature variability, that is, some features of the system may be required by some users, but not by all of them. In addition, variability is more complex than just including or excluding a feature, since different types of relationships may exist between features. The implementation of this variability, and the parametrization and configuration of the system can be complex in this context, so the development process of a SaaS system must adequately address variability management. In this paper we present an experience applying feature oriented software development (FOSD) in the context of SaaS web-based systems development. We present a real experience in the development of a web-based system for managing home care services for dependent people. The article describes the problem of variability management in this domain, and the feature model of the system. Finally, we present an empirical evaluation of the feature model of the system based on data obtained from its real deployment after two years of use. The empirical evaluation was based on state-of-the-art measures for variability management, and revealed relevant insights for software development in this context.
THE PRACTICES AND CHALLENGES OF IMPLEMENTING PRE-PRIMARY SCHOOL EDUCATION IN CHENCHA ZURIYA WOREDA, IN GAMO ZONE.

The main objective of this study was to investigate the practices and challenges of pre–primary school enrolment on implementation of pre-primary school education in Chencha Zuriya Woreda. The subjects of the study were including 20 parents, 73 teachers, 16 clusters of the schools. The participants were selected based on descriptive survey method of simple random sampling and Availability Sampling Technique used. The data collected through questionnaires , interview and observations . The instruments were pilot tested to ten teachers which were not used in the final study. Mixed methods were used to conduct the study. The study employed descriptive survey research design. Descriptive statistics involving frequency, mean, standard deviation and percentages were used to analyze data. The information was presented using tables, bar graphs and pie charts to facilitate comparisons b/n variables. The major findings of the study were lack of training of teachers, lack of good relationship of parents with teachers, lack of awareness of parents in preparing their children for school, lack of prepared text books based on the curriculum. The study concludes that most of the pre-primary teachers have lacked the training, majority of parents have shortage of good relationship with teachers, and teaching learning materials were inadequate in most pre-primary schools. This study recommends that role of parents must be increased, all teachers at level must be trained, and the number of toilets in pre-primary school be increased because young children are more frequent in toileting than older children.
Information and Media Literacy and "Cloud" Technologies in Training of Higher Education Applicants: The Sustainable Development Paradigm

The authors developed a structural model of formation of information and media literacy of higher education applicants and the use of "cloud" technologies in the educational process of sustainable development.The researchers analyse the levels of formation of such literacy of higher education applicants in the process of sustainable development (cognitive and informative, constructive and search, creative and productive levels).The authors noted the effectiveness of the designed model.Positive dynamics of the development of indicators of the mobility under research are demonstrated on the basis of comparative analysis of the generalized obtained data before and after the experiment.The article presents the methodology of interaction of information and media literacy and "cloud" technologies in the educational process.Summarizing the results of the experimental research using statistical and comparative methods of analysis, made it possible to trace the dynamics of quantitative and qualitative changes in the basic structural components, in particular in the indicators of the interaction of information and media literacy and "cloud" technologies in the educational process of sustainable development of higher education applicants.The results of the research indicate the significant increase in all indicators.
Practical Searchable Symmetric Encryption Supporting Conjunctive Queries Without Keyword Pair Result Pattern Leakage

The research of searchable symmetric encryption (SSE) has been focused on the trade-off between security, performance, and functionality. Recently proposed Hidden Cross-Tags (HXT) protocol improves the Oblivious Cross-Tags (OXT) by avoiding the leakage of `Keyword Pair Result Pattern' (KPRP), at the cost of increasing the storage size by two orders of magnitude. In this paper, we reconsider the principle of designing SSE protocols to prevent KPRP leakage. At first, we introduce a new primitive called subset membership check (SMC), where a set is encrypted such that its subset membership can be checked only through a protocol between Sender and Tester. The holistic security of SMC requires that nothing is revealed other than the membership of a subset after each execution of the protocol. We propose a hash-based SMC implementation with efficient computation, communication, and storage. Secondly, based on the hash-based SMC, we present a practical SSE protocol that supports conjunctive queries without KPRP leakage. Our SSE protocol, called `Practical Hidden Cross-Tags' (PHXT), maintains the same storage size as OXT while preserving the same privacy and functionality as HXT. Compared with HXT, our PHXT reduces the storage size and the communication overhead by 91.29%, 64.29%, respectively, without degrading the computational efficiency.