Reviewerly/exHarmony huggingface dataset:
Peningkatan Minat Baca dan Literasi Digital melalui Pojok Baca Interaktif Ramah Anak

The Community Partnership Program aims to build an interactive and child-friendly reading corner at the Mukarramah Orphanage in Nan Sabaris District, Padang Pariaman Regency. The targets to be achieved in this program are: 1) Providing literacy facilities for children of orphanages in the form of child-friendly reading corners, 2) Increasing the reading interest of orphanage children by providing interesting and varied collections, 3) Providing search soft skills training digital information for children of the Mukarramah orphanage. The implementation of this activity begins with designing and building in such a way a corner of the Mukarramah Orphanage by attracting and paying attention to child-friendly elements. Procurement of furniture consisting of bookshelves that are easily accessible to children, procurement of bean bags so that children relax reading, procurement of interesting and interactive collections that suit the needs of children, and procurement of tablet computers with kids mode facilities that can be used online and offline as a means for children to learn and play, as well as providing soft skills training in the form of digital information tracking to teenagers in orphanages. For the evaluation of the achievement of objectives will begin by looking at the extent to which interactive and child-friendly reading corners are built to attract children to come and utilize collections, and measure the ability of adolescent children at the Mukarramah Orphanage to effectively track needed information.
USE OF CARTOGRAPHIC MATERIALS OF THE INTERNET WHEN COMPLETING THE “ELECTRONIC ARCHIVE OF MAPS OF SIBERIA AND THE FAR EAST”

The article describes Internet resources containing cartographic materials. Their condition, the ability to use when creating onElectronic Archive of maps of Siberia and the Far East from the 17th century to 1941. Using the comparison method, the authors conclude that electronic resources regarding maps can be divided into four groups. The first is web sites of libraries or archives, where copies of maps available for download, with good resolution. Also, on these sites there are bibliographic descriptions of these maps. The second group consists of cartographic sites that display maps for consumers, also with the right to download, providinj to links to this site (example site - “EtoMesto”). However, the authors of the sites or organizers initially declare only the quality of the maps, without a description of their history. More often map legend, time of creation and its scale are indicated. The third - thematic sites dedicated to any natural objects (the Baikal Nature Site), one of the sections of which are maps of different times with a fairly good resolution, but without bibliographic descriptions. The fourth group is the sites of administrations of constituent entities of the federation, municipalities. In the sections devoted to the history of administrative-territorial formation, maps with different quality of permission are placed. They are also without bibliographic description. But they can be useful in accumulating information about existing maps.
The Fox and Shepherd Problem

Reports on anti-trust issues that are likely to impact Facebook, Google/Alphabet, Apple, and Amazon.
Psychological Aspects of Musical Education

The paper reveals the role of psychology of musical education in teaching practice. The author tries to identify correlation of psychology of music and human psychology. The growing role of the anthropocentric approach to education is emphasized. Scientific originality of the study lies in the fact that the author examines correlation of music as an artistic matter and a source of musical meanings and a human being as a subject of cognition . The following conclusions are justified: psychology of musical education plays an essential role in teaching music, taking into account growing popularity of the anthropocentric approach to education.
On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer

Hybrid Autoregressive Transducer (HAT) is a recently proposed end-to-end acoustic model that extends the standard Recurrent Neural Network Transducer (RNN-T) for the purpose of the external language model (LM) fusion. In HAT, the blank probability and the label probability are estimated using two separate probability distributions, which provides a more accurate solution for internal LM score estimation, and thus works better when combining with an external LM. Previous work mainly focuses on HAT model training with the negative log-likelihood loss, while in this paper, we study the minimum word error rate (MWER) training of HAT -- a criterion that is closer to the evaluation metric for speech recognition, and has been successfully applied to other types of end-to-end models such as sequence-to-sequence (S2S) and RNN-T models. From experiments with around 30,000 hours of training data, we show that MWER training can improve the accuracy of HAT models, while at the same time, improving the robustness of the model against the decoding hyper-parameters such as length normalization and decoding beam during inference.
Countermeasures Optimization in Multiple Fault-Injection Context

Fault attacks consist in changing the program behavior by injecting faults at run-time, either at hardware or at software level. Their goal is to change the correct progress of the algorithm and hence, either to allow gaining some privilege access or to allow retrieving some secret information based on an analysis of the deviation of the corrupted behavior with respect to the original one. Countermeasures have been proposed to protect embedded systems by adding spatial, temporal or information redundancy at hardware or software level. First we define Countermeasures Check Point (CCP) and CCPs-based countermeasures as an important subclass of countermeasures. Then we propose a methodology to generate an optimal protection scheme for CCPs-based countermeasure. Finally we evaluate our work on a benchmark of code examples with respect to several Control Flow Integrity (CFI) oriented existing protection schemes.
A Proposed Methodology for Detecting Human Attacks on Text-based CAPTCHAs

A CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) is a simple test that is applied on websites to differentiate between human users and automated programs, which indulge in spamming and other fraudulent activities.A text-based CAPTCHA is the most popular security technique used by many websites on the internet, such as Microsoft, eBay, Google and so on.A textbased CAPTCHA human attack means hiring third-party humans in order to solve the CAPTCHA tests.Consequently, a CAPTCHA, by design, is unable to differentiate between a human-based attacker and a legitimate human user.This paper proposes a new methodology for detecting human attacks on text-based CAPTCHAs.
Multilingual BERT Post-Pretraining Alignment

We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved zero-shot cross-lingual transferability of the pretrained models. Using parallel data, our method aligns embeddings on the word level through the recently proposed Translation Language Modeling objective as well as on the sentence level via contrastive learning and random input shuffling. We also perform sentence-level code-switching with English when finetuning on downstream tasks. On XNLI, our best model (initialized from mBERT) improves over mBERT by 4.7% in the zero-shot setting and achieves comparable result to XLM for translate-train while using less than 18% of the same parallel data and 31% less model parameters. On MLQA, our model outperforms XLM-R_Base that has 57% more parameters than ours.
Multi-party quantum fingerprinting with weak coherent pulses: circuit design and protocol analysis

Quantum communication has been leading the way of many remarkable theoretical results and experimental tests in physics. In this context, quantum communication complexity (QCC) has recently drawn earnest research attention as a tool to optimize the amounts of transmitted qubits and energy that are required to implement distributed computational tasks. On this matter, we introduce a novel multi-user quantum fingerprinting protocol that is ready to be implemented with existing technology. Particularly, we extend to the multi-user framework a well-known two-user coherent-state fingerprinting scheme. This generalization is highly non-trivial for a twofold reason, as it requires not only to extend the set of protocol rules but also to specify a procedure for designing the optical devices intended for the generalized protocol. Much of the importance of our work arises from the fact that the obtained QCC figures of merit allow direct comparison with the best-known classical multi-user fingerprinting protocol, of significance in the field of computer technologies and networking. Furthermore, as one of the main contributions of the manuscript, we deduce innovative analytical upper bounds on the amount of transmitted quantum information that are even valid in the two-user protocol as a particular case. Ultimately, comparative results are provided to contrast different protocol implementation strategies and, importantly, to show that, under realistic circumstances, the multi-user protocol can achieve tasks that are impossible by using classical communication alone. Our work provides relevant contributions towards understanding the nature and the limitations of quantum fingerprinting and, on a broader scope, also the limitations and possibilities of quantum-communication networks embracing a node that is accessed by multiple users at the same time.
An Improved Deep Learning Algorithm for Risk Prediction of Corporate Internet Reporting

Corporate internet reporting (CIR) has such advantages as the strong timeliness, large amount, and wide coverage of financial information. However, the CIR, like any other online information, faces various risks. With the aid of the increasingly sophisticated artificial intelligence (AI) technology, this paper proposes an improved deep learning algorithm for the prediction of CIR risks, aiming to improve the accuracy of CIR risk prediction. After building a reasonable evaluation index system (EIS) for CIR risks, the data involved in risk rating and the prediction of risk transmission effect (RTE) were subject to structured feature extraction and time series construction. Next, a combinatory CIR risk prediction model was established by combining the autoregressive moving average (ARMA) model with long short-term memory (LSTM). The former is good at depicting linear series, and the latter excels in describing nonlinear series. Experimental results demonstrate the effectiveness of the ARMA-LSTM model. The research findings provide a good reference for applying AI technology in risk prediction of other areas.
Resolution Adaptative Network for Cryptanalysis of Asymmetric Cryptosystems

The asymmetric optical cryptosystems have been widely concerned due to their prominent characteristics of high security and robustness against attacks. In this paper, a resolution adaptative network is proposed to attack the asymmetric optical cryptosystems, which solves the issue of requiring a lot of experiments to change the network structure and parameters for effectively attacking to ciphertexts with different resolutions. The proposed network of fixing the network structure and parameters can be trained by inputting plaintext-ciphertext pairs with different resolutions, and then the trained network model can generate the retrieved plaintexts with corresponding resolutions. Numerical simulation and analysis show that the classical asymmetric optical cryptosystems can be attacked by the proposed network successfully. Moreover, the excellent resolution adaptability of the proposed network is verified by training and testing the images with different resolutions. Furthermore, the generalization ability and robustness of the proposed network is verified. In general, the issue of the input resolution adaptability of the cryptanalysis network is addressed firstly to our best knowledge.
Collocation Method for Multiplicative Noise Removal Model

Image denoising is a fundamental problem in both image processing and computer vision with numerous applications. It can be formulated as an inverse problem. Variational methods are commonly used to solve noise removal problems. The Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with multiplicative noise into a more general technique for inverse problems such as denoising, deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. Multiplicative noise removal based on TV regularization has been widely researched in image science. In multiplicative noise problems, original image is multiplied by a noise rather than added to the original image. This article proposes a novel meshless collocation technique for the solution of a model having multiplicative noise. This technique includes TV and local collocation along with Multiquadric Radial Basis Function (MQ-RBF) for the solution of associated Euler-Lagrange equation for restoring multiplicative noise from digital images. Numerical examples demonstrate that the proposed algorithm is able to preserve small image details while the noise in the homogeneous regions is removed sufficiently. As a consequence, our method yields better denoised results than those of the current state of the art methods with respect to the Peak-Signal to Noise Ratio (PSNR) values.
Digital competence as arequirement for development of innovative educational environment at university

The study examines innovative educational technologies for their potential impact on and use in teaching, learning, and creative inquiry within the higher education environment. The analysis of the problem connected with creating (transforming) the educational environment in the context of digitalization showed that this multifaceted task requires further study. The aim of the work is to review and assess the implementation of digital technologies in the university educational environment. Theurgency of using digital technologies in the teaching-learning process has been substantiated; the importance of the digital competence formation for all educational process participants has been shown; the significance of filling the curricular with online products has been emphasized; the need to combine traditional educational methods with innovative technologies has been confirmed by the example of foreign language teaching. A systematic approach, desk research and expert assessment methods were used. The new ways of forming digital competence based on the use of open online resources are revealed; original ways of convergence of traditional and innovative educational environment are presented. The authors attempted to develop a methodological approach towards connecting learning inside/outside the classrooms through the digital cloud and estimate the impact of the digital revolution on the learners and teachers’ profile. The research results can be used by teachers perceiving digital competence as a new tool for converting learning environment into a comfort zone.
Hate Speech on Social Media: Content Moderation in Context

For all practical purposes, the decision of social media companies to prohibit hate speech on their platforms means that the longstanding debate in the United States about whether to limit hate speech in the public square has been resolved in favor of greater regulation. Nonetheless, revisiting these debates provides several insights essential for developing more empirically-based and narrowly tailored policies regarding online hate.First, a central issue in the hate speech debate is the extent to which hate speech contributes to violence. Those in favor of more robust regulation claim a connection to violence, while others dismiss these arguments as too tenuous to support regulation. The data generated by social media, however, now allow researchers to begin to empirically test whether there are visible, measurable harms resulting from hate speech. These data can assist in developing evidence-based policies to address the most significant harms of hate speech, while avoiding overbroad regulation that is inconsistent with international standards.Second, reexamining the U.S. debate about hate speech also reveals the serious missteps of social media policies that prohibit hate speech without regard to context. The policies that social media companies have developed attempt to define hate speech solely with respect to the content of the message. As the early advocates of limits on hate speech made clear, the meaning, force, and consequences of speech acts are deeply contextual, and it is impossible to understand the harms of hate speech without reference to local political realities and the power asymmetries between social groups. Regulation that is abstracted from this context will inevitably be overbroad.This Article revisits these hate speech debates and considers how they map onto the platform law of content moderation, where emerging evidence indicates a correlation between hate speech online, virulent nationalism, and violence against minorities and activists. It then concludes by developing specific recommendations to bring greater consideration of context into the policies and procedures of social media content moderation.
Formalization of Requirements for Correct Systems

Improving the quality of a system begins by their requirements elicitation: the challenge is to bridge the gap between the requirements of the client and their formal specification defined by the scientist. A first step consists on understanding and rewriting the existing requirements. Along the development process, we introduce formal terms in the requirements coming the formal specification and make explicit the interactions between them by a glossary. The trace of the requirements and their corresponding specification is managed and serves to simplify the activities of validation and verification. The validation is studied since the understanding of the first requirements and all along the development of their formal specification. The verification may detect imperfections like incoherences and ambiguities in both the formal specification and their corresponding requirements.
Why-Not Questions &amp; Explanations for Collaborative Filtering

Throughout our digital lives, we are getting recommendations for about almost everything we do, buy or consume. However, it is often the case that recommenders cannot locate the best data items to suggest. To deal with this shortcoming, they provide explanations for the reasons specific items are suggested. In this work, we focus on explanations for items that do not appear in the recommendations they way we expect them to, expressed in why-not questions, to aid the system engineer improve the recommender. That is, instead of offering explanations on every item proposed by the system, we allow the developer give feedback about items that were not proposed. We consider here the most traditional category of recommenders, i.e., the collaborative filtering one, and propose ways for providing explanations for why-not questions. We provide a detailed taxonomy of why-not questions on recommenders, and model-specific explanations based on the inherent parameters of the recommender. Finally, we propose an algorithm for producing explanations for the proposed why-not questions.
DPD-InfoGAN: Differentially Private Distributed InfoGAN

Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN has no control over the kinds of images it generates. The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced. Due to the high model complexity of InfoGAN, the generative distribution tends to be concentrated around the training data points. This is a critical problem as the models may inadvertently expose the sensitive and private information present in the dataset. To address this problem, we propose a differentially private version of InfoGAN (DP-InfoGAN). We also extend our framework to a distributed setting (DPD-InfoGAN) to allow clients to learn different attributes present in other clients' datasets in a privacy-preserving manner. In our experiments, we show that both DP-InfoGAN and DPD-InfoGAN can synthesize high-quality images with flexible control over image attributes while preserving privacy.
Using Linear Regression Analysis and Defense in Depth to Protect Networks during the Global Corona Pandemic

The purpose of this research was to determine whether the Linear Regression Analysis can be effectively applied to the prioritization of defense-in-depth security tools and procedures to reduce cyber threats during the Global Corona Virus Pandemic.The way this was determined or methods used in this study consisted of scanning 20 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals for a list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce.The methods further involved using the Likert Scale Model to create an ordinal ranking of the measures and threats.The defense in depth tools and procedures were then compared to see whether the Likert scale and Linear Regression Analysis could be effectively applied to prioritize and combine the measures to reduce pandemic related cyber threats.The results of this research reject the H 0 null hypothesis that Linear Regression Analysis does not affect the relationship between the prioritization and combining of defense in depth tools and procedures (independent variables) and pandemic related cyber threats (dependent variables).
Compromised feature normalization method for deep neural network based speech recognition*

Feature normalization is a method to reduce the effect of environmental mismatch between the training and test conditions through the normalization of statistical characteristics of acoustic feature parameters.It demonstrates excellent performance improvement in the traditional Gaussian mixture model-hidden Markov model (GMM-HMM)-based speech recognition system.However, in a deep neural network (DNN)-based speech recognition system, minimizing the effects of environmental mismatch does not necessarily lead to the best performance improvement.In this paper, we attribute the cause of this phenomenon to information loss due to excessive feature normalization.We investigate whether there is a feature normalization method that maximizes the speech recognition performance by properly reducing the impact of environmental mismatch, while preserving useful information for training acoustic models.To this end, we introduce the mean and exponentiated variance normalization (MEVN), which is a compromise between the mean normalization (MN) and the mean and variance normalization (MVN), and compare the performance of DNN-based speech recognition system in noisy and reverberant environments according to the degree of variance normalization.Experimental results reveal that a slight performance improvement is obtained with the MEVN over the MN and the MVN, depending on the degree of variance normalization.