Reviewerly/exHarmony huggingface dataset:
Beyond True and False: Fake News and the Digital Epistemic Divide

The massive fact-checking, flagging, and content removal campaigns run by major digital platforms during the 2020 elections and the Covid-19 pandemic did some good. However, they failed to prevent substantial portions of the population from believing that the election was stolen or that vaccinations are dangerous. In this Article, we argue that the reason for the ineffectiveness of truth-based solutions—such as fact-checking— is that they do not reach the heart of the problem. Both scholars and policymakers share the implicit or explicit belief that the rise of digital fake news is harmful mainly because it spreads false information, which lays a rotten groundwork for both individual decisions and collective policy making. While acknowledging the importance of accurate information, we argue that the main problem with fake news is not that it is false. Instead, what is distinctly threatening about digital misinformation is its ability to circumvent and undermine common knowledge-producing institutions including the sciences, courts, medical and other professions, and the media. The fundamental challenge is the fragmentation of our societies into separate epistemic communities. This shakes the factual common ground on which we stand. What does fact-checking matter if twenty percent of the population thinks that the fact-checkers are chronic liars? We call this new reality the Digital Epistemic Divide. Epistemic fragmentation of society is both more fundamental and more dangerous than the harms of false information as such. It is more fundamental because once a society is epistemically fragmented, the lack of trust in common epistemic authorities will inevitably proliferate disagreement over factual beliefs. It is more dangerous because it can exacerbate political polarization. It is one thing to believe that the other side of a political issue holds wrong values and preferences; it is quite another to believe that they are either constantly lying or deeply manipulated. To bridge the digital epistemic divide, we must go beyond truth-based solutions and implement policies to reconstitute societal trust in common epistemic authorities.
Cause-Effect Relation in Language Use Among English Department Students Towards Their Lecturers on WhatsApp Chat

This study aims to reveal a cause-effect relationship in language use among students towards their lecturers in the English department UNESA on WhatsApp as one of the online communication platforms.This study's objectives are to seek the language style used by the students in contacting their lecturers through WhatsApp chat, their lecturers' responses, and how it affects their relations as learners and educators.A documentation technique was used in collecting chat proofs between students and lecturers to reveal which language styles were used.In discovering lecturers' opinions towards students' language styles, an online questionnaire with open and close-ended questions was distributed to all lecturers in the English department UNESA.This study adopted a qualitative approach by applying descriptive analysis.The results show that the students and lecturers use the tu and vous language style to express politeness and solidarity.However, in some cases, students used informal language unacceptable to some lecturers.As impolite as those are, the analysis shows that students' informal language style has little effect on their student-lecturers relationship.This study may help other researchers later within the same topic by exploring more depth-analysis among different social environments to see if the results show the same outcome.
The Influence of Arrangements, Maintenance of Archives, and Employee’s Competency on the Quality of Services

Service is the key to success in various service activities.A service is said to be successful if the archive team member can provide information in the archive and provide the library quickly and precisely.The success or failure of a service is highly dependent on the implementation element or team member who carries out the service activity.The problem in this study is whether an arrangement, archive maintenance, and team member competence affect the quality of service at the State University of Surabaya.This study aims to determine the effect of arrangement, archive, and maintenance team member employees affect competence on the quality of service at the State University of Surabaya.The population of this research is employees of the University of Surabaya.This team member search is research with pop, so the sample of this study is the population with the number of team member respondents.There are four team members studied in this study, namely archive arrangement (X1), archive team member competence (X2), archive maintenance (X3), and service quality (Y).The results of this study indicate that Archive Arrangement (X1), Employee Competence (X2), and Maintenance (X3) have a significant influence on the dependent variable of Service Quality.
Integration of DICTs in Education: The Educational Demand Faced with the Profiles of Immigrants and Digital Natives

Proposes here in this work that the implementation and use of TDIC”, necessarily, need to be absorbed by teachers and that for this to happen it is important to have an adequate understanding of their role as a mediator of knowledge and not a supplier; that there is a need for understanding that the saboteur positions that many teachers have is due to their paradigmatic positions and that it is important to understand this dysfunctional maladaptive condition through a cognitive restructuring so that their role as a mediator of knowledge is understood. That done, these digital immigrant teachers must be inserted in intensive training policy to know the tools of TDIC”s and be able to adapt their new role in the mediation between information and their native digital students. These, in turn, need to be “educated” about the use of digital information and communication technologies not only as an instrument of chatting or games, without a mediating application of knowledge.
Secret Sharing Scheme Based on the Differential Manifold

In this paper, the concepts of topological space and differential manifold are introduced, and it is proved that the surface determined by functioning the intersection of the tangent plane and the hypernormal of the differential manifold to construct the shared master key of participants, an intuitive, secure and complete ( ) , t n -threshold secret sharing scheme is designed.The paper is proved to be safe, and the probability of successful attack of attackers is only 1 1 t p -.When the prime number p is sufficiently large, the probability is almost 0. The results show that this scheme has the characteristics of single-parameter representation of the master key in the geometric method, and is more practical and easy to implement than the Blakley threshold secret sharing scheme.
An Error Analysis on Tense and Aspect Shifts in Students’ Chinese-English Translation

Translation of scientific texts has been regarded as having the least freedom and variety in rendition; however, inconsistencies occur when translators work with two language systems that differ in reference to time, such as between Chinese and English. Tense is one of the core components of English grammar, but such information is not apparent in Chinese because the Chinese language tends to express time with lexical items in context instead of adding morphological changes to the verbs. The present study collected Chinese-English translated texts from graduate students on topics in the popular science domain. Analyses were conducted by comparing the tense information in the source text and the tense rendered in the target text on a sentence-by-sentence basis. The results showed that while the source text in Chinese stayed in the present tense, the students tended to switch from the present tense to the future or past tense during translation. In particular, the Chinese word “hui” has a habitual meaning that usually describes facts instead of presenting a future tense in scientific texts, but the students tended to interpret it as a future tense word, thus producing inappropriate tense shifts during translation. The pedagogical implication of such findings on inconsistent tenses between the source and target texts suggests that in the teaching of translation, analysis of the tense structure of the Chinese text and the consistency of tense information rendered in English should be emphasized.
Based on the Linear Regression Method, the Influence of Research and Development Investment of Listed Enterprises in Hebei Province on the Performance of Enterprises

According to the influencing factors of corporate financial performance, an enterprise performance evaluation index system composed of profitability, development ability and debt paying ability is constructed, and the comprehensive performance evaluation results of enterprises are obtained by the factor analysis method.Then, the explanatory variables, explained variables and control variables were determined, panel regression model was constructed, and the specific effect and degree of influence of R&D input on performance of listed enterprises in Hebei Province was obtained.Finally, in view of the main problems of the influence of R&D input on performance, the corresponding countermeasures and suggestions are putting forward.
Data Analysis of Effective Frontier of The Shanghai and HongKong Stock Market in Different Periods

To demonstrate the risk and yield changes of the Shanghai and Hong Kong stock market in different periods, effective frontier computing model of the stock market was founded by comparing the historical data of more than 20 composite indices of the Shanghai-Hong Kong stock market, each four stock indices were selected as the research object, the two effective frontier curves were drawn, and the effective frontier curves of the Shanghai-Hong Kong stock market were compared with the change of the expected rate of return in 2010-2019.The results showed that with the increase of investment risk, when the investor's willingness to invest in a risky portfolio gradually increased, the expected return rate in the Shanghai stock market was higher than that of the Hong Kong stock market, the investment value was higher in general.The effective frontiers comparison of the two stock markets in 2010-2013 and 2016-2019 showed that the investment risk of the Shanghai-Hong Kong Stock Exchange decreased after the opening of Shanghai-Hong Kong Stock Connect.
Asset Lending Information System at Faculty of Languages and Arts

The Faculty of Language and Arts (FBS) is a part of the State University of Surabaya, which is a government-owned institution that provides higher education services for the society.In providing educational services, of course, adequate facilities are needed.Information about these facilities should be easy to find and easily accessible to those in need.The Faculty of Languages and Arts Unesa has created a website-based Information System to be able to borrow educational facilities.However, the website is considered unfavorable for various reasons, including the display that is less responsive and the system and programming that is not updated.The development of information system really needs to be done to support the educational process which began to be carried out directly on campus after previously only being done online.Analysis of the problems in this study using the PIECES method (Performance, Information, Economic, Control, Efficiency, Service).Meanwhile, the development of this information system uses PHP, MySQL, and AdminLTE templates.While the development method using Agile Development Method.The most important result of this research is the assessment of the old information system that is not good enough so that a new, better and responsive new information system design is needed.
Offline Arabic Handwritten Text Recognition for Unsegmented Words Using Convolutional Recurrent Neural Network

This paper presents an analytical approach for offline Arabic Handwritten Text Recognition (HTR), based on Convolutional Recurrent Neural Network (CRNN). The suggested method is a three-part end-to-end trainable deep learning system that includes feature extraction, label prediction, and transcription part. The first part is performed by Convolutional Neural Network (CNN) layers, where sequential features are extracted. In the label prediction part, the extracted features are used to generate new sequential contextual features by feeding them to recurrent layers. This set of features for Arabic texts is then used to predict label distributions with fully connected layers. In the third part of the system, the transcription part, the predicted label distributions are translated into actual label sequences, using the Connectionist Temporal Classification (CTC) method. The experiments are carried out and reported on the publicly available IFN/ENIT database. The results of the proposed system are encouraging, and the recognition rates are comparable to those of numerous other systems in the literature.
Enhanced Grey Wolf Optimizer for Data Clustering

Data clustering is an unsupervised learning method used to extract knowledge from data, it is an NP-Hard (nondeterministic polynomial time) problem; there is no known deterministic technique that can find the optimal solution with an appropriate time complexity. Metaheuristics are powerful tools used to find good solutions (near to the best one) in a feasible time. The objective of this work is to improve the quality of one of recent metaheuristic clustering-based algorithms, which is grey wolf optimizer metaheuristic (GWO) by proposing an enhanced version of GWO called Enhanced Grey Wolf Algorithm-based Clustering (EGWAC), GWO is applied to find the best cluster centers. The optimization is essentially done in the updation of wolves position. The assessment of the results is measured by three measures; precision, recall and G-measure. The enhanced version of GWO algorithm for data clustering showed the impressive effect of the optimizations.
Information Security Framework Adoption for South African Small and Medium Enterprise

The small and medium enterprises (SMEs) sector is the backbone of the economy, a big employment creator, poverty reducer and the main engine for economic growth. SMEs rely heavily on technology to conduct their business but are constraint by lack of information security knowledge to protect themselves against cyber-attacks that may render their business an easy prey. This paper investigates factors that may influence SMEs in South Africa in the adoption of a security framework. These factors were applied in the development of an information security adoption framework for SMEs. The study collected qualitative data by means of interviews. ISO/IEC 27002 information security framework formed the theoretical basis for this study. Thematic analysis was employed for analysis of the collected data. The results indicate that close to 90% of SMEs do not have security policies or a risk management strategy. The study identified new, important themes critical in the development of an information security adoption framework for SMEs: ISM Best Practices and technical security architecture. The study contributes a conceptual framework that illustrates how the identified themes relate to concepts essential to information security adoption among SMEs.
Some Notes on Arcsine Exponentiated-X Family

In 2020, He, Ahmad, Afify and Goual proposed a new family based on classical Arcsine distribution and exponentiated family named Arcsine Exponentiated-X family. The main purpose of this work is to continue studying the intrinsic properties of these families, namely to study the “saturation”—d to the horizontal asymptote in the Hausdorff sense. We obtain precious estimates for the value of the Hausdorff distance that can be used as an additional criterion in practice. We consider Hausdorff approximation of two submodels from the proposed family with baseline distribution—some extensions of the classical Weibull distribution. In this study, we also define a new “adaptive ASE-W model with polynomial variable transfer”. We develop several simple dynamic programming modules implemented within the programming environment CAS Wolfram Mathematica. All new results are illustrated with suitable numerical experiments with real cumulative data.
Quantifying the Bias of Non-linear Equating and Score Transformations

This paper shows that using non-linear functions for equating and score transformations leads to consequences that are not commensurable with classical test theory (CTT). More specifically, a well-known theorem from calculus shows that the expected value of a non-linearly transformed variable does not equal the transformed expected value of this variable. Translated to CTT this implies that the transformed observed test score does not have an unbiased expectation, i.e., is different from the transformed true score. In order to quantify the bias, second-order Taylor expansions are used in this work to show that non-linear equating and scale transformations do not only lead to variability of SEMs but also to predictable bias in the expected values of the transformed observed scores. In line with Lord’s finding that is often described as “Equating is either unnecessary or impossible,” this bias due to non-linear equating vanishes either for perfectly reliable tests, or if the equating function is indeed linear, i.e., the tests are congeneric.
Offline Text-Independent Writer Identification Using Local Black Pattern Histograms

The problem of authenticating a writer from his/her writing samples has been the most important and prevalent one subject of active research in the field of handwriting biometrics for the last decade. In this paper, we have focused mainly on the forensic document analysis, more precisely, the offline automatic writer identification in a truly text-independent mode. A new and simple potential textural descriptor has been analyzed for characterizing the handwriting style of the writers, so as to be used to describe the intra and inter-writer variability by calculating the similarity measurements. In order to extract the textural properties from a scanned handwritten sample, an effective statistical texture descriptor is computed from binary connected-components: Local Black Pattern (LBLP). Classification is performed using k-Nearest Neighbors (k-NN) and the Chi-Square ( $$\chi ^2$$ ) distance in a Holdout strategy. The experimental results obtained on two well-known databases show that the proposed scheme achieves a very satisfactory performance and thus reflecting that our approach is still competitive against the state-of-the-art.
Learning More with Less Data: Reaching the Power-Law Limits in Steganalysis Using Larger Batch Sizes

Since the new trend of deep learning in steganalysis, practitioners tend to apply state-of-the-art deep learning techniques, good practices, and more generally grope to get the best performances possible. One major way is to increase datasets sizes, as it is likely to follow a power-law decrease of the testing error. However, once datasets get bigger and bigger, training becomes very costly and time-consuming. In this paper, we explore new perspectives of the batch size variation. We show that it can significantly improve models performances by impacting its complexity and helping learn faster by transforming the dataset error power-law curves, enabling models to train in shorter times, while also learning more features on relatively small datasets, without resorting to millions of images.
High-Speed Low Area 2D FIR Filter Using Vedic Multiplier

In this paper, a novel two-dimensional (2D) finite impulse response (FIR) filter is proposed using Vedic multiplier architecture. Several multipliers, like Vedic, array, Booth, and Wallace tree, are employed in the construction of filters to reduce filter area and power consumption. The VM lowers the partial products (PP) in multiplication among various multipliers. As a result, multiplication happens more quickly. The VM uses a sutra known as “Urdhva Tiryakbhyam,” which is based on ancient mathematics. Two techniques to maximize speed, area, and power are suggested in this research. The first technique makes use of a VM predictor block and the second method is based on reusable block for the VM. The proposed novelty design is coded by Verilog HDL and synthesized using Xilinx 14.7 tool. The proposed design results are compared with existing designs. The number of slices required for 2D FIR using VM is less when compared with FIR with normal multiplier.
MG-Net: Multiple Person and Gadget Detection for Online Exam Proctoring System

Recently, the education system has shifted from traditional classroom teaching to online or blended mode. Main challenge to education system is exam proctoring. One of the important limiting factors in the field of education is the inability to proctor online examinations. Manual proctoring through webcam is expensive and labor intensive. In this paper, we proposed MG-Net which automatically detects multiple persons and gadgets in the frame and alerts the proctor. The convolution operations were performed along with residual blocks to improvise the performance if the model over existing systems. Our model is trained using COCO dataset. Our model is tested on the customized dataset and performs well with 92.8 accuracy.
CS-FA Nature Inspired Algorithm-Based Robust Video Watermarking

This paper illustrates a hybrid algorithm that is amalgamation of two bio-inspired algorithms such as Cuckoo search and Firefly optimization algorithms are incorporated to find optimize scaling factor intended for the watermark insertion. This combination is considered as one of the utmost usages in the recent past. The foremost benefit is obtained by combining several features of both the algorithms. SSIM and BER are used to evaluate fitness function in this optimization technique. Contemplating above opportunities, a novel video watermarking method is proposed by using CS-FA in DWT, SVD domain. Besides, the watermark security is also improvised using secret sharing method. Experimental tests reveal that the video watermarking approach suggested has a reasonable imperceptibility and an improved robustness against attacks.
A Privacy-Oriented Neural Collaborative Filtering-Based Framework for Recommender System

Recommender system (RS) is growing rapidly and participating in deep learning to provide high quality of recommendations. RS is used to select the best choice for user what they might like. In recent years, it is obvious that various RSs have been proposed for a little while. This field is buzzing with innovation to be sure. Deep RS is a model which is trained for recommendations based on interaction with a neural network. The properties which make neural architecture effective are end-to-end differentiable and provide learning biases to input data type. We propose a framework named as privacy preserve neural collaborative filtering (NCF) by using differential privacy (DP). In this paper, we use general matrix factorization and multi-layer perception network which is apply on real-world datasets for experimentation. We also compare of our framework with other models that are based on matrix factorization and implicit feedback. To measure the performance evaluation, we use parameters such as: MAP, NDGC, precision, and recall. For measuring the DP performance, optimal Renyi differential privacy (RPD) order is considered which shows the effectiveness of the proposed framework.