Reviewerly/exHarmony huggingface dataset:
A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling

Structural equation modeling offers various estimation methods for estimating parameters. The most used method in covariance-based structural equation modeling (CB-SEM) is the maximum likelihood (ML) estimator. The ML estimator is typically used when fitting models with normally distributed data. The growth of partial least squares path modeling (PLS-PM), including consistent partial least squares (PLSc), has also been noticed by researchers in the SEM fields. The PLSc has elevated interest in the scholastic setting in measuring the performance of various estimation methods in structural equation modeling. The choice of estimation methods has substantial impact in yielding parameter estimates. There could be a trade-off among the estimation methods’ ability to deal with different types of data based on the model tested. Accordingly, this study aims to compare the performance of ML, generalized least squares (GLS), and scale-free least squares (SFLS) for CB-SEM as well as partial least squares (PLS) and consistent partial least squares (PLSc). Multivariate normal data were generated using Monte Carlo simulation with pre-determined population parameters and sample sizes using R Programming packages. To produce the estimated values, data analysis was performed using AMOS and SmartPLS for CB-SEM and PLS-SEM, respectively. The findings illustrate notable similarities between CB-SEM (ML) and PLS-SEM results when the true indicator loading is certainly high.
Survei Literatur: Deteksi Berita Palsu Menggunakan Pendekatan Deep Learning

Social media has become an inseparable part of everyday life in modern society to make it easier to interact and communicate with each other. The purpose of this study is to review and compare the deep learning methods implemented in the case of detecting fake news from several previous studies, and to get an overview of the corpus or dataset used by previous studies. This research is also to help researchers identify and map the use of deep learning algorithms in cases of detecting fake news. The research method is conducting a literature survey of 12 literatures obtained from the ScienceDirect and IEEE Xplore websites. The collection of literature that has been surveyed is selected based on the year published in 2021 with the topic of research on detection of fake news using a deep learning approach. The results of this study summarize that the strategy to detect fake news can be done with four approaches, based on the content, based on the writing style, based on the distribution pattern, and based on the credibility of the source. The results of this research also show that the Convolutional Neural Network algorithm is a favorite of researchers by appearing 6 times in the literature collection. The next favorite algorithm is Long Short Term Memory which appears in 5 literatures and Bidirectional LSTM which appears in 4 literatures.
IMPLEMENTATION OF HYPERPARAMETER OPTIMISATION AND OVER-SAMPLING IN DETECTING CYBERBULLYING USING MACHINE LEARNING APPROACH

Online social networks have become a necessity to everyone around the world. Particularly, online social networks have enabled us to connect to one another regardless of time, for as long as we have social media and social networking as platforms for broadcasting information and communicating, respectively. However, this evolution has resulted in people possibly committing various cybercrimes, such as cyberbullying. To address this issue, machine learning can be utilised to counter cyberbullying in online social networks. Thus, this study proposed a framework with a set of features consisting of word and character term frequency–inverse document frequency and word embedding by using Word2vec and six types of list terms: profane words, proper nouns, negation words, ‘allness’ term, diminisher words and intensifier words. These features were divided into four groups before being fed into the linear support vector classifier to train our model using ASKfm as data set in hyperparameter tuning and over-sampling environment. Results indicated that the proposed framework provided significant outcomes, in which the highest percentage of area under curve is 99.24% and F-measure is 97.38% as performed by our trained model.
Matching Metadata on Blockchain for Self-Sovereign Identity

Self-Sovereign Identity (SSI) is a privacy-preserving identity paradigm where users own and manage their digital identities. SSI is also referred to as blockchain identity, as it is commonly implemented using distributed ledger technologies. In this work, we describe the problem of schema matching on blockchain-based SSI implementations, systematically review the literature for tools that attack this problem, introduce a novel solution, and empirically compare it with the works reported from our literature review. Our solution uses pre-trained word vectors to find semantic similarities between user queries and schemas on the blockchain. Experimental evaluation shows that it outperforms existing solutions regarding queries that approach natural language.
Analisis Topik Modelling Terhadap Penggunaan Sosial Media Twitter oleh Pejabat Negara

Social media is experiencing rapid development until now. Social media makes it easy for humans to be able to connect with one another. One of the social media that is widely used is Twitter. The ease of use makes social media widely used among the public, including state officials. State officials use Twitter to convey policies, opinions and interact with the public. By conducting a topic analysis of tweets shared by state officials, we can find out the relevant topics discussed by state officials. We can find out the focus of attention of state officials through topic modeling. Latent Dirichlet Allocation (LDA) is a topic modeling method that finds certain patterns in documents and produces several different topics. Tweets from the @jokowi account are collected using a scraping technique. The results of the tweet collection are then preprocessed for further analysis using the Latent Dirichlet Allocation (LDA) method. The results of the analysis model are evaluated using perplexity calculations and coherence scores. The evaluation of the model resulted in a perplexity value of -8.069 and a coherence score of 0.375 for a total of 7. This shows that the model used is good for analyzing and finding topics in tweets of state officials.
Automatic Scoring of Spoken Language Based on Basic Deep Learning

The oral English test in domestic universities requires teachers to modify a large number of candidates’ oral recordings. This is the work of using time repeatedly. Using the CALL system to realize the automation of conversation recording can reduce the burden of teachers’ work. Therefore, it is of great practical significance to develop an automatic and accurate scoring system for oral English. With the development of artificial intelligence, deep learning technology has been gradually applied in various fields. Similarly, in the application of oral scoring, deep learning technology makes the implementation of such a system possible. Based on the deep learning technology, this paper proposes an automatic scoring algorithm for spoken language and implements a detailed design and evaluation system. The system consists of two modules. The pronunciation standard of spoken pronunciation and the content of spoken pronunciation are scored, and the sum of these two scores is the final score. Finally, this paper uses 650 oral English recordings from a college English test to train the artificial neural network. Experimental results show that if the training data set is small, the BP network model can obtain better comprehensive evaluation performance.
An efficient resource utilization technique for scheduling scientific workload in cloud computing environment

&lt;span lang="EN-US"&gt;Recently, number of data intensive workflow have been generated with growth of internet of things (IoT’s) technologies. Heterogeneous cloud framework has been emphasized by existing methodologies for executing these data-intensive workflows. Efficient resource scheduling plays a very important role provisioning workload execution on Heterogeneous cloud framework. Building tradeoff model in meeting energy constraint and workload task deadline requirement is challenging. Recently, number of multi-objective-based workload scheduling aimed at minimizing power budget and meeting task deadline constraint. However, these models induce significant overhead when demand and number of processing core increases. For addressing research problem here, the workload is modelled by considering each sub-task require dynamic memory, cache, accessible slots, execution time, and I/O access requirement. Thus, for utilizing resource more efficiently better cache resource management is needed. Here efficient resource utilization (ERU) model is presented. The ERU model is designed to utilize cache resource more efficiently and reduce last level cache failure and meeting workload task deadline prerequisite. The ERU model is very efficient when compared with standard resource management methodology in terms of reducing execution time, power consumption, and energy consumption for execution scientific workloads on heterogeneous cloud platform.&lt;/span&gt;
A PRELIMINARY REVIEW ON THE METALLOGENY OF SEDIMENT-HOSTED PB-ZN DEPOSITS IN BALOCHISTAN, PAKISTAN

Lead and Zinc deposits are very much important economic booster for the country all over the world. Economic geologists are engaged in the search of these economy booster minerals and rocks for three decades. Lead and zinc are profuse resources in the Lasbela-Khuzdar belt of Balochistan province of Pakistan, with reserves of about 50 million tons all over the country. In this paper, we have presented field observations of the Dudder mine area and summarised the work of earlier papers to provide the salient features of these ore mineralizations and deposits. The tectonic settings and important ore controls have been discussed based on field observations and previous work. The Pb-Zn dominantly occupied by exposures of rocks of the Ferozabad Group of Jurassic age in the Mor range, which is comprised of Lower-Middle-Upper Jurassic carbonates and deep-marine siliciclastics rock sequence. This group contains syngenetic and epigenetic Pb-Zn mineralization classified as a stratiform replacement, and vein-type fissure fillings observed at various places of Duddar, Gunga, and Surmai deposit areas. Generally, these deposits are hosted pyrites nuggets with fine-grained sphalerite matrix with galena in black shale, argillaceous limestone, and mudstone. We construct a Pb-Zn deposit predictive tectonic model that regards mineralization as the primary factor and the ore rock as secondary. The tectonics were more active when sedimentation of the Anjira Formation started in a disturbing third-order basin. The Hydrothermal solution comes into the basin along faults and gave rise to syngenetic mineralization of sulfides in the Anjira Formation, and epigenetic one in the underlying Spingwar and Loralai Formations. These deposits are considered as SEDEX deposits according to the distribution of Pb-Zn deposits, we concluded that a multi-period, multi-cycle orogenic environment is the most positive for lead-zinc deposit growth. With this, we analyze the association between tectonic evolution, geological mineralization, and Pb-Zn metallogenic epoch. The tectonic and mineralization mechanism models are expected to ease the detailed study on the geological and geochemical conditions of mineralization in the Ferozabad Group and economic assessment of the resources.
An enhanced support vector regression model for agile projects cost estimation

The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA), mean absolute error (MAE), prediction at level p (Pred(p)), mean balanced relative error (MBRE), mean inverted balanced relative error (MIBRE), and logarithmic standard deviation (LSD). Throughout a dataset with 21 agile projects using the leave-one-out cross-validation (LOOCV) technique. The results obtained prove that the proposed model enhances the accuracy of the SVR-RBF model, and it outperforms the majority of existing models in the literature.
Systemic Decision Business Opportunities Model

In this paper, we introduce a universal systemic decision model for managerial decisions applying to business opportunities in quantum computing as an illustrative example. A systemic business decision model is formulated which orients on long term trajectorial high impact global human decisions. We give as a background an overview of business opportunities models and the state of the art of Quantum Computing (QC) as an emerging technology trend and extract from this a perspective towards future applications. Finally, we focus on Academic Spin-off Quantum Computing Business Opportunities: (1) How do they apply to QC from a systemic standpoint? (2) How can business opportunities be derived from this from the actual technology situation? (3) What does this mean for the ecology of start-ups, the industry and society as a whole, represented by all scales of societal decisions, as a snapshot? (4) How can this, as a whole, be coordinated and ordered with a systemic business intelligence tool?
Extractive text summarization for scientific journal articles using long short-term memory and gated recurrent units

Along with the increasing number of scientific publications, many scientific communities must read the entire text to get the essence of information from a journal article. This will be quite inconvenient if the scientific journal article is quite long and there are more than one journals. Motivated by this problem, encourages the need for a method of text summarization that can automatically, concisely, and accurately summarize a scientific article document. The purpose of this research is to create an extractive text summarization by doing feature engineering to extract the semantic information from the original text. Comparing the long short-term memory algorithm and gated recurrent units and were used to get the most relevant sentences to be served as a summary. The results showed that both algorithms yielded relatively similar accuracy results, with gated recurrent units at 98.40% and long short-term memory at 98.68%. The evaluation method with matrix recall-oriented understudy for gisting evaluation (ROUGE) is used to evaluate the summary results. The summary results produced by the LSTM model compared to the summary results using the latent semantic analysis (LSA) method were then obtained recall values at ROUGE-1, ROUGE-2, and ROUGE-L respectively were 76.25%, 59.49%, and 72.72%.
Security Analysis of Continuous-Variable Measurement-Device-Independent Quantum Key Distribution Systems in Complex Communication Environments

Continuous-variable measure-device-independent quantum key distribution (CV-MDI QKD) is proposed to remove all imperfections originating from detection. However, there are still some inevitable imperfections in a practical CV-MDI QKD system. For example, there is a fluctuating channel transmittance in the complex communication environments. Here we investigate the security of the system under the effects of the fluctuating channel transmittance, where the transmittance is regarded as a fixed value related to communication distance in theory. We first discuss the parameter estimation in fluctuating channel transmittance based on these establishing of channel models, which has an obvious deviation compared with the estimated parameters in the ideal case. Then, we show the evaluated results when the channel transmittance respectively obeys the two-point distribution and the uniform distribution. In particular, the two distributions can be easily realized under the manipulation of eavesdroppers. Finally, we analyze the secret key rate of the system when the channel transmittance obeys the above distributions. The simulation analysis indicates that a slight fluctuation of the channel transmittance may seriously reduce the performance of the system, especially in the extreme asymmetric case. Furthermore, the communication between Alice, Bob and Charlie may be immediately interrupted. Therefore, eavesdroppers can manipulate the channel transmittance to complete a denial-of-service attack in a practical CV-MDI QKD system. To resist this attack, the Gaussian post-selection method can be exploited to calibrate the parameter estimation to reduce the deterioration of performance of the system.
De Doctrina Christiana and Milton’s Canonical Works: Revisiting the Authorship Question

Since the discovery of De Doctrina Christiana almost 150 years after John Milton’s death, the Latin manuscript has commonly been attributed to the English writer—but not without controversy. For many scholars, the most recent phase of the debate seemed to end with the 2007 publication of Milton and the Manuscript of De Doctrina Christiana, which used stylometry to argue confidently for Milton’s authorship. This article is presented in dissent. Prompted by disjunctures in style and substance between the treatise and Milton’s canonical works, we revisit the authorship question. Using the complete text from the manuscript, a broader selection of candidates, and newer stylometric methods, we show some limitations of the earlier approach. Finally, drawing upon a neglected tradition of scholarship, we suggest that Jeremias Felbinger is a more plausible candidate for authorship, and we evaluate his candidacy through multiple stylometric tests.
Comparative analysis of deficits in formation of pedagogical values at different stages of professionalization

The article is devoted to the analysis of the deficient development of pedagogical values of representatives of the pedagogical community who are at different levels of professionalization: students of pedagogical classes and pedagogical colleges, students and teachers of pedagogical universities, and the administration of educational institutions. The study used a questionnaire and a modified version of the Must-test. Values that have the last places in the ranking of all participants in the educational process have been identified. It was found out that values from all the identified groups fall into the deficit area: values — relationships, values — qualities, values — knowledge, values focused on the professional activities of the teacher, the educational environment and the child. For all categories of study participants, the most insignificant values turned out to be in the field of ensuring the effective development and education of students. Common values and values specific to different levels of professionalization were identified: for schoolchildren and students, information about the comfort and safety of the educational environment and a tolerant attitude to individual characteristics, as well as knowledge of the ways of targeting, planning and organizing their own independent activities turned out not to be important; teachers have the dominance of declared values over real ones, the ability to combine demanding and respect for the student, to create success situations for the student. Secondary school teachers do not consider it important to be able to motivate students and tolerate their originality. Higher school teachers do not place emphasis to modern types of professional activities (project, research and reflexive) and do not consider it important to involve students into them. Administrative staff of educational institutions pay little attention to cooperation with all participants in the educational process and actually deny the role of feedback in its organization
Foreign Direct Investment and Economic Growth Nexus: Empirical Insight from Guinea

Although the predominant theoretical literature presumed foreign capital inflows to carry broad benefits to recipient nations, this assumption has been recently questioned in Guinea since the resulting GDP growth has weakly impacted social welfare. This empirical study offers a better insight of the extent to which foreign direct investment (FDI) influences economic growth in the Guinean context over the period 1990-2017. Fiscally, the per GDP FDI net inflows and GDP growth rate are respectively employed as FDI estimate and economic advancement indicator. The findings established in this study included: first, foreign direct investment in the long run positively affects economic advancement in Guinea at 1% significance level. This outcome suggests that 1% raise in FDI per GDP leads to 0.45 increase in GDP growth. Moreover, this finding is in line with the main literature related to foreign investment induced effects in Africa and other developing regions. Second, the short run coefficients suggest the same story exposed by the long run investigation. FDI per GDP [L1], [L2] positively and significantly influences economic growth in the Guinean context. Even though there is strong evidence that FDI positively affects growth in Guinea, government should consider the form of foreign investments to be promoted in the country. The study recommends that resource seeking type without transformation must be discouraged for the benefit of market or efficiency seeking investment. By doing so, Guinean economy will experience healthier and welfare enhancing growth.
Identifying Incorrect Patches in Program Repair Based on Meaning of Source Code

Automatic Program Repair (APR) techniques have shown the potential of reducing debugging costs while improving software quality by generating patches for fixing bugs automatically.However, they often generate many overfitting patches which pass only a specific test-suite but do not fix the bugs correctly.This paper proposes MIPI, a novel approach to reducing the number of overfitting patches generated in the APR.We leverage recent advances in deep learning to exploit the similarity between the patched method's name (which often encloses the developer's intention about the code) and the semantic meaning of the method's body (which represents the actual implemented behavior) for identifying and removing overfitting patches generated by APR tools.Experiments with a large dataset of patches for QuixBugs and Defects4J programs show the promise of our approach.Specifically, in a total of 1,191 patches generated by 23 existing APR tools, MIPI successfully filters out 254 (32%) of the total 797 overfitting patches with a precision of 90% while preserving 93% of the correct patches.MIPI is more precise and less damaging to the APR than existing heuristic patch assessment techniques, achieving a higher recall than automated testing-based techniques that do not have access to the test oracle.In addition, MIPI is highly complementary to existing automated patch assessment techniques.