Reviewerly/exHarmony huggingface dataset:
Cross-Platform Reputation Generation System Based on Aspect-Based Sentiment Analysis

The active growth of Internet-based applications such as social networks and e-commerce websites leads people to generate a tremendous amount of opinions and reviews about products and services. Thus, it becomes very crucial to automatically process them. Over the last ten years, many systems have been proposed to generate and visualize reputation by mining textual and numerical reviews. However, they have neglected the fact that online reviews could be posted by malicious users that intend to affect the reputation of the target product. Besides, these systems provide an overall reputation value toward the entity and disregard generating reputation scores toward each aspect of the product. Therefore, we developed a system that incorporates spam filtering, review popularity, review posting time, and aspect-based sentiment analysis to generate accurate and reliable reputation values. The proposed model computes numerical reputation values for an entity and its aspects based on opinions collected from various platforms. Our proposed system also offers an advanced visualization tool that displays detailed information about its output. Experiment results conducted on multiple datasets collected from various platforms (Twitter, Facebook, Amazon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\dots $ </tex-math></inline-formula> ) show the efficacy of the proposed system compared with state-of-the-art reputation generation systems.
Text Summarization using Deep Learning

In this century, Artificial Intelligence AI has gained lot of popularity because of the performance of the AI models with good accuracy scores. Natural Language Processing NLP which is a major subfield of AI deals with analysis of huge amounts of Natural Language data and processing it. Text Summarization is one of the major applications of NLP. The basic idea of Text Summarization is, when we have large news articles or reviews and we need a gist of news or reviews with in a short period of time then summarization will be useful. Text Summarization also finds its unique place in many applications like patent research, Help desk and customer support. There are numerous ways to build a Text Summarization Model but this paper will mainly focus on building a Text Summarization Model using seq2seq architecture and TensorFlow API.
Optimal probabilistic distillation of quantum coherence

We complete the task of optimal probabilistic coherence distillation protocol, whose aim is to transform a general state into a set of $n$-level maximally coherent states via strictly incoherent operations (SIO). Specifically, we present the necessary and sufficient condition for the transformation from a mixed state into a pure-state ensemble via SIO. With the aid of this condition and the simplex algorithm, we can accomplish the probabilistic distillation protocol by presenting an analytical expression of the maximal average distillable coherence of a general state and constructing the corresponding operation achieving this bound. Our protocol is a universal protocol since it can be applied to any coherence measure.
A Bibliometric Analysis of Accounting in the Blockchain Era

Research aims: This study aims to conduct a comprehensive accounting and blockchain analysis with a bibliometric study.Design/Methodology/Approach: The sample consisted of 67 documents published in the 2017-2021 period using the Scopus database with keywords: accounting and blockchain. This study utilized VOSviewer software to provide graphical analysis of bibliometric data and visualization of research results.Research findings: From the visualization, three main groups (colors) of nine clusters were generated. The red area consists of topics related to blockchain technology, ledger technology, and Bitcoin. The green cluster area includes keywords related to the application and the design science research methodology. Lastly, the blue area focuses on accountants and millennial accountants. From the network analysis results, it can be stated that accountants and millennial accountants accepted the presence of blockchain technology. They could integrate blockchain into the company's business model and make their work easier. From these findings, a research methodology emerged, especially in conducting blockchain research, namely the design science research approach.Theoretical contribution/Originality: With the new and hyped topic of blockchain in the accounting sphere, the bibliometric analysis would be able to review multiple studies efficiently and offer a systematic, transparent, and replicable literature review. Also, it guides researchers to the most influential works and maps areas of research with less subjective bias. Besides, it enables analysis more objectively and reliably and improves the quality of the review.
International mobility and publication activity of early-career-researchers: What do statistics, bibliometrics and scientists themselves say?

The article presents the results of a research project on the international mobility of the young Russian scientists. This study is focused on the impact of education or work experience abroad on their future scientific careers, namely their publication activity. The project attempts to consider academic mobility not from the traditional point of view of “brain drain”, but from the perspective of “brain circulation” which sees mobility as a mechanism for the transfer of knowledge and a valuable source of innovation as well as a necessary element of training and development of human resources in science. The participation of the young Russian researchers in international mobility was shown with the help of data from several nationally representative sociological surveys. The original feature of this project consists is combining two different methodological approaches: both objective and subjective assessments were brought together in order to evaluate the impact of international mobility on the future publication activity of young researchers. The case study of one large Russian university was examined: a unique database combining both biographical data (open information from CV and publication activity indicators (data from Scopus)) of employees of this university was collected. In addition, the in-depth interviews were conducted to complement the analysis. According to the study, the involvement of young Russian scientists in international academic mobility over the past years has been low in comparison with other countries participating in the Organization for Economic Co-operation and Development (OECD). A positive relationship between the international mobility and scientific productivity of Russian scientists was revealed. As was shown, young researchers involved in international mobility not only publish more scientific article on average, but their papers are in fact published in higher ranking journals and are cited more often. Based on the materials of in-depth interviews, the specific mechanisms pushing a young researchers to upgrade their publication activity after or during their academic mobility were identified.
The Acceptance of E-Learning Among ESL Primary School Students During Covid-19

The past years have seen a strong focus in Malaysia on the increase of infusion of Information Communication Technology (ICT) in educational institutions to stimulate innovations and strengthen global economic competitiveness. The integration of ICT has transformed the Malaysian education system by reshaping the landscape of education, from teacher-centred to learner-centred education. However, the COVID-19 crisis has changed the landscape of the education system drastically. The educators have turned their focus on online learning as an alternative means to replace the face-to-face classroom. Therefore, this paper focuses on the investigation on the perception towards e-learning among ESL primary school students. This study also determined the level of acceptance towards e-learning. A set of questionnaires was distributed via Google Form to 100 upper primary students in Sibu, Sarawak. The results revealed that the perception towards e-learning was positive, and the level of e-learning acceptance among ESL primary school students was high due to its features of e-learning, such as flexibility, user-friendliness, and the students’ attitude towards using as well as the intention to use in the future. It was recommended that teachers should utilize student-friendly digital tools to deliver their teaching and learning materials so that the students, especially at the primary level find it easy and enjoy learning English via e-learning.
Volatility spillovers among G7, E7 stock markets and cryptocurrencies

Purpose The existence of long memory and persistent volatility characteristics of cryptocurrencies justifies the investigation of return and volatility/shock spillovers between traditional financial market asset classes and cryptocurrencies. The purpose of this paper is to investigate the dynamic relationship between the cryptocurrencies, namely Bitcoin and Ethereum, and stock market indices of G7 and E7 countries to analyze the return and volatility spillover patterns among these markets by means of multivariate (MGARCH) approach. Design/methodology/approach Applying the newly developed VAR-GARCH-in mean framework with the BEKK representation, the empirical results reveal that there exists an evidence of mean and volatility spillover effects among Bitcoin and Ethereum as the proxies for the cryptocurrencies, and stock markets reviewed. Findings Interestingly, the direction of the return and volatility spillover effects is unidirectional in most E7 countries, but bidirectional relationship was found in most G7 countries. This can be explained as the presence of a strong return and volatility interaction among G7 stock markets and crypto market. Originality/value Overall, the results of this study are of particular interest for portfolio management since it provides insights for financial market participants to make better portfolio allocation decisions. It is also increasingly important to understand the volatility transmission mechanism across these markets to provide policymakers and regulatory bodies with guidance to eliminate the negative impact of cryptocurrency's volatility on the stability of financial markets.
PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way.However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost.For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios.To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces.With a combination of the extracted models and user-defined scenarios, PerfSim can then simulate the performance behavior of all services over a given period and provide an approximation for system KPIs, such as requests' average response time.Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster.We achieved ∼81-99% simulation accuracy in approximating the average response time of incoming requests and ∼16-1200 times speed-up factor for the simulation.
DDKA-QKDN: Dynamic On-Demand Key Allocation Scheme for Quantum Internet of Things Secured by QKD Network

In the era of the interconnection of all things, the security of the Internet of Things (IoT) has become a new challenge. The theoretical basis of unconditional security can be guaranteed by using quantum keys, which can form a QKD network-based security protection system of quantum Internet of Things (Q-IoT). However, due to the low generation rate of the quantum keys, the lack of a reasonable key allocation scheme can reduce the overall service quality. Therefore, this paper proposes a dynamic on-demand key allocation scheme, named DDKA-QKDN, to better meet the requirements of lightweight in the application scenario of Q-IoT and make efficient use of quantum key resources. Taking the two processes of the quantum key pool (QKP) key allocation and the QKP key supplement into account, the scheme dynamically allocates quantum keys and supplements the QKP on demand, which quantitatively weighs the quantum key quantity and security requirements of key requests in proportion. The simulation results show that the system efficiency and the ability of QKP to provide key request services are significantly improved by this scheme.
Key-Dependent Feedback Configuration Matrix of Primitive <i>σ</i>–LFSR and Resistance to Some Known Plaintext Attacks

In this paper, we propose and evaluate a method for generating key-dependent feedback configurations (KDFC) for <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>-LFSRs. <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>-LFSRs with such configurations can be applied to any stream cipher that uses a word-based LFSR. Here, a configuration generation algorithm uses the secret key(K) and the Initialization Vector (IV) to generate a new feedback configuration after the initialization round. It replaces the older known feedback configuration. The keystream is generated from this new feedback configuration and the FSM part. We have mathematically analysed the feedback configurations generated by this method. As a test case, we have applied this method on SNOW 2.0 and have studied its impact on resistance to algebraic attacks. Besides, as a consequence of resisting algebraic attacks, SNOW 2.0 can also withstand some other attacks like Distinguishing Attack, Fast Correlation Attack, Guess and Determining Attack and Cache Timing Attack. Further, we have also tested the generated keystream for randomness and have briefly described its implementation and the challenges involved in the same.
Credit Card Fraud Detection using Imbalance Resampling Method with Feature Selection

Many fraud transactions exist in the online world that affects various financial institutions but Credit Card Fraud transaction is the most occurring problem in the world. Credit Card fraud is the situation in which fraudsters misuse credit cards for illegal purposes. Hence, detection of fraudulent transactions is essen-tial. Several researchers have worked on detecting fraud transactions and also provide solutions whose surveys are given in this paper. This study makes a major contribution to research on the detection of Credit Card fraud transactions through Machine Learning Algorithms suchas Decision Tree and Naive Bayes. The data have been selected from Kag-gle and categorize into training (80%) and testing (20%) data. The whole experiment was performed on the Jupyter Notebook tool for which the Anaconda Navigator has been installed. The Heatmap is used for visualization and colorfully represents the data. The main aim of this work is to balance the dataset with Near-Miss Under-sampling Method. The information gain method is applied for feature selection. The best algorithm founded in this paper is Decision Tree with 97% accuracy as compared to Naïve Bayes with 90%. The results are achieved based on Accuracy, Recall, Precision, and F1-score. We have also shown the ROC Curve and Precision-Recall Curve of the algorithm in this paper.
A Survey of Knowledge Based Question Answering with Deep Learning

The purpose of automated question answering is to let the machine understand natural language questions and give accurate answers in the form of natural language. This technology requires the machine to store a large amount of background knowledge. In recent years, the rapid development of knowledge graph has made the knowledge based question answering (KBQA) more and more popular. Traditional styles of KBQA methods mainly include semantic parsing, information extraction and vector modeling. With the development of deep learning, KBQA with deep learning has gradually become the mainstream method. This paper introduces the application of deep learning in KBQA mainly from the following aspects: the development history of KBQA, KBQA methods using deep learning, common datasets used in KBQA, the comparison of various methods and the future trend.
Evolution of C iv Absorbers. II. Where Does C iv Live?

We use the observed cumulative statistics of CIV absorbers and dark matter halos to infer the distribution of CIV-absorbing gas relative to galaxies at redshifts $0\!\leq\!z\!\leq\!5$. We compare the cosmic incidence $dN/dX$ of CIV absorber populations and galaxy halos, finding that massive $L \geq L_{\star}$ halos alone cannot account for all the observed $W_r \geq 0.05$~{\AA} absorbers. However, the $dN/dX$ of lower mass halos exceeds that of $W_r \geq 0.05$~{\AA} absorbers. We also estimate the characteristic gas radius of absorbing structures required for the observed CIV $dN/dX$, assuming each absorber is associated with a single galaxy halo. The $W_r \geq 0.3$~{\AA} and $W_r \geq 0.6$~{\AA} CIV gas radii are $\sim30-70\%$ ($\sim20-40\%$) of the virial radius of $L_{\star}$ ($0.1L_{\star}$) galaxies, and the $W_r \geq 0.05$~{\AA} gas radius is $\sim100-150\%$($\sim60-100\%$) of the virial radius of $L_{\star}$ ($0.1L_{\star}$) galaxies. For stronger absorbers, the gas radius relative to virial radius rises across Cosmic Noon and falls afterwards, while for weaker absorbers, the relative gas radius declines across Cosmic Noon and then dramatically rises at $z\!<\!1$. A strong luminosity-dependence of gas radius implies highly extended CIV envelopes around massive galaxies before Cosmic Noon, while a luminosity-independent gas radius implies highly extended envelopes around dwarf galaxies after Cosmic Noon. From available absorber-galaxy and CIV evolution data, we favor a scenario in which low-mass galaxies enrich the volume around massive galaxies at early epochs and propose that the outer halo gas ($>0.5R_v$) was produced primarily in ancient satellite dwarf galaxy outflows, while the inner halo gas ($<0.5R_v$) originated from the central galaxy and persists as recycled accreting gas.
College Enquiry Chatbot

Abstract: In the earlier days, students had to visit the college to enquire about details like courses, fee structure, admission process and other information's about the college, which is a tiresome and long process . This is where we thought of using an intelligent bot delivering the information. College Enquiry Chatbot is a simple python web application which aims to provide the information regarding college asked by the user. Keywords: Chatbot, Python, ChatterBot, Django
Structure-Augmented Keyphrase Generation

This paper studies the keyphrase generation (KG) task for scenarios where structure plays an important role.For example, a scientific publication consists of a short title and a long body, where the title can be used for de-emphasizing unimportant details in the body.Similarly, for short social media posts (e.g., tweets), scarce context can be augmented from titles, though often missing.Our contribution is generating/augmenting structure then encoding these information, using existing keyphrases of other documents, complementing missing/incomplete titles.Specifically, we first extend the given document with related but absent keyphrases from existing keyphrases, to augment missing contexts (generating structure), and then, build a graph of keyphrases and the given document, to obtain structure-aware representation of the augmented text (encoding structure).Our empirical results validate that our proposed structure augmentation and structure-aware encoding can improve KG for both scenarios, outperforming the state-of-the-art 1 .
An Ontology-Based Approach to Enhance Explicit Aspect Extraction in Standard Arabic Reviews

Currently, one of the most important and difficult areas of research is Arabic Sentiment analysis.Aspect extraction is the most important task in aspect-based sentiment analysis.In this work, we propose an aspect-based sentiment analysis approach for Arabic language.The basic idea of the proposed approach is to use the description logics to identify explicit aspects as well as their polarities.This approach was evaluated using the publicly available human annotated Arabic dataset (HAAD) based on Semantic Evaluation 2014 workshop (SemEval-2014) along with its baseline experiments conducted by HAAD providers.Experimental results showed that the proposed approach significantly improves the performance compared with baseline approach.The F-measure was improved by 19% and 55% for the aspect terms extraction and aspect term polarity tasks respectively.
Structural Social Capital Studies in Management and Organization Literature: A Bibliometric Network Study

Purpose: Many empirical and review studies have been performed in the management and organization literature from the structural social capital perspective. Since these studies focus on different topics periodically, we aimed to make a bibliometric analysis of the structural social capital literature that allows a regular, systematic, and quantitative evaluation of published articles in management and organization science to identify the underlying structure in this research area and to identify the main research themes of the scholarship. Methodology: The bibliographic matching technique was used for checking international databases to explore the main concepts and themes of the management and organization literature and to identify main themes in the research area. Therefore, this article offers a bibliometric analysis of the literature that allows for a regular, systematic, and quantitative evaluation of published articles to identify their underlying structure and to identify the main research themes in the scholarship. Research data were analyzed using the text mining method, which is a sub-branch of the data mining method and one of the most critical ways to analyze and process unstructured data. Findings: Structural social capital studies were examined in management and organization science databases in three periods forming four years. Although there are no completely different emerging themes, we noticed periodic differences and change in priority rankings. Moreover, we found that the subjects repeat every period with few new concepts. Implications: This research shows the main themes in the management and organization literature, where structural social capital elements are examined together, and covers all areas of management, organization and business science. Considering the main topics of the three periods of this research, there are similarities, namely the words that rank among the top in each period are “innovation,” “entrepreneurship,” “knowledge management,” “performance,” “leadership,” “technology,” and “human resources” and these themes are the most prominent. Originality/Value: This research presents an overview of all past studies that focus on the structural social capital present in social interactions. Since structural social capital influences management and organization literature, it is considered a valuable study and a guide for future research.