Reviewerly/exHarmony huggingface dataset:
Public perception of the influence of social media platforms on indigenous languages in Bayelsa, Nigeria

Background: The 21st century has witnessed enormous changes in communication engagements. Time and space boundaries have become narrower than even before. Social media platforms have changed the scope and nature of communication significantly. Such social media platforms include: Facebook, WhatsApp, Twitter, among others. These platforms have changed communication dynamics in areas like politics, merchandising, and religion, among others. Although social media platforms come with their advantages, there are concerns that they may have negative impact on indigenous languages. These concerns, however, have not received significant attention in literature.Objective: This study intends to ascertain public perception of the influence of social media platforms on indigenous languages. The study adopts Survey research design to achieve the study objective.Methodology: The study was conducted in Bayelsa state. The researchers surveyed a sample of 384 respondents who were selected through a multi-stage sampling procedure. The questionnaire served as the instrument for data collection while results were presented in tables.Results: The result of the study showed that the public perceive social media as negatively impacting on indigenous languages. We found that 59% of the respondents were of the view that the use of social media as a means of communication has posed a serious challenge to the use of native languages to a large extent.   Unique contribution: The study has shown specific aspects of native languages that are negatively affected as a result of social media use. This information will be beneficial for designing and implementing programmes that are aimed at promoting native languages in Nigeria.Conclusion: The use of social media platforms like Facebook, Twitter, WhatsApp, among others pose a serious threat to native languages in Nigeria.
Operational Detection of Entanglement via Quantum Designs

From an operational point of view, we propose several new entanglement detection criteria using quantum designs. These criteria are constructed by considering the correlations defined with quantum designs. Counter-intuitively, the criteria with more settings are exactly equivalent to the corresponding ones with the minimal number of settings, namely the symmetric informationally complete positive operator-valued measures (SIC POVMs). Fundamentally, this observation highlights the potentially unique role played by SIC POVMs in quantum information processing. Experimentally, this provides the minimal number of settings that one should choose for detecting entanglement. Furthermore, we find that nonlinear criteria are not always better than linear ones for the task of entanglement detection.
Addressing the Vulnerability of NMT in Input Perturbations

Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations.As real input noise is difficult to predict during training, robustness is a big issue for system deployment.In this paper, we improve the robustness of NMT models by reducing the effect of noisy words through a Context-Enhanced Reconstruction (CER) approach.CER trains the model to resist noise in two steps: (1) perturbation step that breaks the naturalness of input sequence with madeup words; (2) reconstruction step that defends the noise propagation by generating better and more robust contextual representation.Experimental results on Chinese-English (ZH-EN) and French-English (FR-EN) translation tasks demonstrate robustness improvement on both news and social media text.Further finetuning experiments on social media text show our approach can converge at a higher position and provide a better adaptation.
Efficient Dark Channel Prior Based Blind Image De-blurring

Dark channel prior for blind image de-blurring has attained considerable attention in recent past.An interesting observation in blurring process is that the value of dark channel increases after averaging with adjacent high intensity pixels.L o regularization is proposed to curtail the value of dark channel.Half quadratic splitting method is used to solve the non-convex behavior of L o regularization.Furthermore, Discrete Wavelet Transform has been incorporated prior to de-blurring to increase the efficiency of algorithm.The most significant finding of this paper is a universal blind image de-blurring algorithm with reduced computational complexity.Experiments are performed and their results are comparable with state of the art de-blurring methods to evaluate the performance of algorithm.Experimental results also reveals that wavelet based dark channel prior image de-blurring is efficient for both uniform and nonuniform blur.
Robot Navigation Through QR and Text Detection

In the rapid advancement of today’s technological world, smarter technology is believed to solve problems. QR code detection and text recognition are one of the extensively using applications. So, retrieving information using these applications accurately is one of the important factors. In this paper, the accuracy rate of both the applications is briefly characterized in terms of time consumption for retrieving information by varying distance. The experimental set-up includes a camera fixed on the robot vehicle and navigates as per the given QR and text instructions. The accuracy rate is analysed with respect to the response time of the robot. Finally, the performance of both approaches has been briefly discussed.
IMPLEMENTASI ALGORITMA K-MEANS DALAM MENENTUKAN TINGKAT PENYEBARAN PANDEMI COVID-19 DI SUMATERA BARAT

Corona viruses (CoV) are part of a family of viruses that cause illnesses ranging from the flu to more severe illnesses such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). This virus can be transmitted from person to person through small droplets from the nose or mouth when coughing, sneezing or speaking. Because the spread of this virus is very fast, it requires fast handling so that this virus does not spread, one of which is by implementing health protocols, namely maintaining distance, washing hands and using masks. All provinces in Indonesia have not been spared from this virus, including the province of West Sumatra. Classification of the spread of this virus is necessary in order to break the chain of its spread. One of the techniques used in this grouping is k-means, which uses several groups to assign multiple data to a partition system. The results of this study indicate that the regions in the first cluster have the highest rates of positive cases and patients who die, while the areas in the second and third clusters have the potential for the spread of Covid-19 which is also a concern of the government.
Comparing Large-unit and Bitwise Linear Approximations of SNOW 2.0 and SNOW 3G and Related Attacks

In this paper, we study and compare the byte-wise and bitwise linear approximations of SNOW 2.0 and SNOW 3G, and present a fast correlation attack on SNOW 3G by using our newly found bitwise linear approximations. On one side, we reconsider the relation between the large-unit linear approximation and the smallerunit/ bitwise ones derived from the large-unit one, showing that approximations on large-unit alphabets have advantages over all the smaller-unit/bitwise ones in linear attacks. But then on the other side, by comparing the byte-wise and bitwise linear approximations of SNOW 2.0 and SNOW 3G respectively, we have found many concrete examples of 8-bit linear approximations whose certain 1-dimensional/bitwise linear approximations have almost the same SEI (Squared Euclidean Imbalance) as that of the original 8-bit ones. That is, each of these byte-wise linear approximations is dominated by a single bitwise approximation, and thus the whole SEI is not essentially larger than the SEI of the dominating single bitwise approximation. Since correlation attacks can be more efficiently implemented using bitwise approximations rather than large-unit approximations, improvements over the large-unit linear approximation attacks are possible for SNOW 2.0 and SNOW 3G. For SNOW 3G, we make a careful search of the bitwise masks for the linear approximations of the FSM and obtain many mask tuples which yield high correlations. By using these bitwise linear approximations, we mount a fast correlation attack to recover the initial state of the LFSR with the time/memory/data/pre-computation complexities all upper bounded by 2174.16, improving slightly the previous best one which used an 8-bit (vectorized) linear approximation in a correlation attack with all the complexities upper bounded by 2176.56. Though not a significant improvement, our research results illustrate that we have an opportunity to achieve improvement over the large-unit attacks by using bitwise linear approximations in a linear approximation attack, and provide a newinsight on the relation between large-unit and bitwise linear approximations.
Practical lessons in technology: Characteristics of organization and conduct

The article describes the essence of the formation of creative abilities in students by increasing the activity of students in the organization and conduct of practical classes in school technology education.
taka at the FinSBD-3 task: Tables and Figures Extraction using Object Detection Techniques

FinSBD-3 is a shared task organized in the context of the 1st workshop on Financial Technology on the Web. The task focuses on extracting the entire structure of noisy PDF financial documents that include 1) sentences, lists, items, and organization of lists and items; 2) figures and tables; 3) headers and footers. This paper describes the approach that allows us to extract the figures and tables using their visual cues. We applied the object segmentation techniques in image processing to detect the location of figures and tables in the PDF files. A post-processing method is then executed in order to find exact content. The result shows the potential of this approach.
Telepathic Headache: Mitigating Cache Side-Channel Attacks on Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are the target of several side-channel attacks aiming at recovering their parameters and hyper-parameters. Attack vectors include monitoring of the cache, power consumption analysis and execution time measurements. These attacks often rely on the knowledge of a certain – large – set of hyper-parameters among which the victim model lies. The goal of the potential attacker is then to reduce that search space or even deduce the correct architecture. One such attack, Cache Telepathy by Yan et al., monitors access to a common matrix multiplication algorithm, GeMM (Generalized Matrix Multiply), in order to determine the victim model’s hyper-parameters. In this paper, we propose to change the order in which the computations are made and add randomness to the said computations in order to mitigate Cache Telepathy. The security analysis of our protection shows that the Cache Telepathy attack on a protected VGG-16 has an increased search space: from 16 to \(2^{22}\).
Comment on “TVOR: Finding Discrete Total Variation Outliers Among Histograms”

Recent paper “TVOR: Finding Discrete Total Variation Outliers Among Histograms” introduces the Total Variation Outlier Recognizer (TVOR) method for identification of outliers among a given set of histograms. After providing a theoretical discussion of the method and verifying its success on synthetic and population census data, it applies the TVOR model to histograms of ages of Holocaust victims produced using United States Holocaust Memorial Museum data. It purports to identify the list of victims of the Jasenovac concentration camp as potentially suspicious. In this comment paper, we show that the TVOR model and its assumptions are grossly inapplicable to the considered dataset. When applied to the considered data, the model is biased in assigning a higher outlier score to histograms of larger sizes, the set of data points is extremely sparse around the point of interest, the dataset has not been reviewed to remove obvious data processing errors, and, contrary to the model requirements, the distributions of the victims' ages naturally vary significantly across victim lists.
X-RAY DIFFRACTION TO DETERMINE THE MINERALOGY IN SOIL SAMPLES IN THE UK

Abstract in the soil samples collected for this study, the mineral composition present was identified using x-ray diffraction (XRD). The method used to identify the minerals was an automated search/match. From the results obtained from this method, all the peaks related to silica in the samples correspond to the silica in the database, while the calcite did not appear in the automated search. Therefore, a manual comparison was made by superimposing the reference XRD pattern over the measured XDR pattern. Silica, calcite and hematite were identified as present in all the soil samples
Nonclassical states of light in a nonlinear Michelson interferometer

Nonlinear quantum metrology schemes can lead to faster than Heisenberg limited scalings for the measurement uncertainty. We study a Michelson interferometer embedded in a Kerr medium [Luis and Rivas, Phys. Rev. A 92, 022104 (2015)] that leads to non-linear, intensity dependent phase shifts corresponding to relative changes in the lengths of its two arms. The quantum Cramer-Rao bound on the minimum achievable measurement uncertainties is worked out and the requirements, in practice, to saturate the bound are investigated. The choice of input state of light into the interferometer and the read out strategy at the output end are discussed. The ideal, non-classical states of light that must be used to saturate the bound are found to be highly susceptible to photon loss noise. We identify optimal states at each noise level that are both resilient to noise and capable of giving the enhanced sensitivities and discuss practical implementations of the interferometry scheme using such states.
FOSTERING EXTERNAL AND INTERNAL SUSTAINABILITY WITHIN THE ENGLISH FOR ACADEMIC PURPOSES COURSE IN THE COVID-19 PANDEMIC

The paper aims to explore sustainability from both the external and internal perspectives underpinning the implementation of the empirical study within an English for Academic Purposes course at a Master level in the COVID-19 pandemic. The research is based on the methodology of the development of the system of the external and internal perspectives. The exploratory type of the case study was employed. The interpretive paradigm was used in the research. The observation was conducted on the 23rd August 2020. The sample was composed of the 10 international students of the Master programme “Information and Electrical Engineering” at Hochschule Wismar, Germany. The theoretical research defines the external and internal sustainability. The empirical finding is that the COVID-19 pandemic has influenced the organizational aspect of the English for Academic Purposes course (timetable, classroom arrangements, group work organization). Another finding is that the implemented English for Academic Purposes course is partially sustainable and requires its dynamic equilibrium and localized robustness to be adjusted. Implications for higher education aimed at increasing both the external and internal sustainability in education imply the implementation of the combination of evaluation, namely external evaluation, mutual evaluation, and self-evaluation, jointly carried out by all the process participants.
Higher Order Statistics Based Blind Steg analysis using Deep Learning

Universal isteganalysis of grey level JPEG images is addressed by modelling the neighbourhood relationship of the image coefficients using the higher order statistical method developed by two-step Markov Transition Probability Matrix (TPM). The implementation of TPM together with the neighbouring pixel relationship provides a better and comparable detection results. The detection accuracy is evaluated on the stego image database using eXtreme Gradient Boosting (XGBoost) with Principal Component Analysis (PCA) on nsF5 and JUNIWARD hiding techniques. Execution time is also compared for all the classifiers. The images are taken from Green spun library and Google website- eXtreme Gradient Boosting.
Investors’ Judgment and Decisions after a Cybersecurity Breach: Understanding the Value Relevance of Cybersecurity Risk Management Assurance

This study investigates how voluntary cybersecurity risk management (CyRM) assurance affects non-professional investors’ judgments and decisions. The study also examines how the value relevance of CyRM assurance is altered when having such assurance is expected/unexpected. Employing an experimental approach, we find that after a cyber-breach occurs, companies previously engaging in voluntary CyRM assurance receive more favorable investor assessments of management credibility and, in turn, higher stock valuations. We also find that investors’ assessments of management credibility and stock valuations are more extreme for companies that engage (do not engage) in CyRM assurance in industries where such assurance is not (is) the norm. This study begins to address the question of whether there is a demand for CyRM assurance offered by audit firms, particularly given lingering concerns in research and practice as to the viability of IT-related assurance services. Our research reinforces the profession’s position that management and boards need to recognize that cyber risk will differ by industry and that investors will react to violations of implicit industry standards for cyber risk management. The results also demonstrate the value to management credibility of having prior CyRM assurance after a cyber-breach; the reputation and damage control is important for both management and the company.
End-to-End Verifiable Electronic Voting System Using Delegated Proof of Stake On Blockchain

This paper aims to introduce a scalable blockchain based system for balloting. The system can maintain privacy of the individual voter, remaining publicly accountable, and tamper proof. Blockchain offers a distributed ledger of votes across the voting nodes such that no duplicate votes are allowed. We use Consensus algorithm to facilitate fast propagation of blocks of votes. Increased transaction speeds are achieved by delegating certain nodes as block producers, relying on them for delegated proof of stake. For increased accountability, we include the voter’s biometric data in the transaction. The pilot implementation showcased significant improvement with respect to currently followed balloting process in India. Response time of the proposed system is evaluated on different load levels on network, experimental results are promising, and we recommend the system for the large-scale implementation.
SUPPLY CHAIN OPERATION REFERENCE ANALYSIS OF LOCAL VEGETABLE E-COMMERCE

This study aims to determine the chain of sales of vegetables in Teluk Jambe, Karawang Regency. In addition, it can cut off the chain of sales from farmers to consumers. While the scope of this study only discusses the vegetables only. This research type is descriptive qualitative and done with the concept of qualitative descriptive. Descriptive qualitative is the concept of research with the process of obtaining data that is potluck. Analyze the supply chain of vegetables commercial with the methods of Supply Chain Operation Reference (SCOR). The results of the study showed the presence of Sis-Log In Apps could be cut chain sales. So from the results of this study provide a positive impact for farmers. In conclusion, Sis-Log In Apps is designed to cut the chain of sale of commodity vegetables only. With the results as a liaison and direct intermediaries between farmers and consumers. Keywords: Agriculture, e-commerce, SCM, SCOR, Sis-Log In Apps
Research on Translation Style in Machine Learning Based on Linguistic Quantitative Characteristics Perception

Research on the metrological characteristics of linguistic quantitative characteristics (LQCs) based on corpus and metrological linguistic methods has gained wide attention in artificial and online machine translations.Although a support vector machine (SVM) is one of the most widely used machine learning (ML) algorithms in the field of text analysis, its application in the study of translation style is rare.This study compares the translation styles of Pride and Prejudice with ML using different linguistic measurement features.Firstly, the language measurement features of three translations are obtained with the information gain algorithm.Specifically, the corpus can be achieved through human-machine interaction (HCI), i.e., computers can look, hear, touch, smell, taste, and speak using sensors such as cameras and mathematical algorithms.Then a text classifier, i.e., an SVM, is constructed on the basis of these features to automatically classify the translated texts of the three translations.Finally, the validity of the classifier is verified by the tenfold cross-validation method.It is proved that the SVM algorithm has high classification accuracy and a strong predictive function, which is helpful for judging or predicting the translation or translator's style.Compared with the traditional method, this classification method based on an SVM saves time and effort, the process can be repeated, and the result is accurate and reliable.
Business Strategy analysis of Cybersecurity Incidents

Abstract In the current social and economic processes, information and communication services play a decisive role, changing several entities’ operations. The growing dependence that has developed over the last two decades made the security needs introduced political will, which has resulted in an iterative evolution of the regulatory environment. Hence, the legal framework requires that several entities develop protection that includes controls enhancing both preventive and reactive in a risk-proportionate manner under the business value to be protected. Nevertheless, due to the nature of cybersecurity, the development of such capabilities is not the task of a single organisation but all entities involved in cyberspace, including, e.g., individuals, non-profit and for-profit organisations, public sector actors. Therefore, each involved entity should design protection capabilities in a risk-proportionate manner, which requires strategic approaches and tools and requires organisations to learn from security incidents. This paper reviews the essential formal security strategy formulation tools, applying in the Facebook’s case based on publicly available information. The analysis aims to confirm the importance of management’s attitude and support for tackling cybersecurity’s challenges.
Blockchain Technology for Reinforcing Sustainability Practices Across Complex Multi-tier Supply Networks

Complex supply networks feature many inter-connected and geographically distributed partners. Many of these partners are beyond the direct realm of the company, making the management of sustainability a difficult task. Inevitably, this research aims to explore how blockchain technology can facilitate sustainability initiatives across multi-tier supply networks. The findings suggest that blockchain can enhance the visibility of suppliers’ sustainability practices. Managers will have increased transparency and traceability of their multi-tier supply networks, which will eventually reduce the cost of monitoring a large number of geographically distributed suppliers. Furthermore, information can be shared with different stakeholders to increase credibility and legitimacy.