Reviewerly/exHarmony huggingface dataset:
Identification of Fake Video Using Smart Contracts and SHA Algorithm

Deepfake is as a matter of fact a medium where one individual is supplanted by another who appears as though him. The profound bogus demonstration has been continuing for quite a long while. Profound phony uses incredible strategies, for example, AI and man-made consciousness to create and control visual and sound substance with high potential for the gadget. Profound misrepresentation relies upon the sort of impartial association called and the programmed encoder. These are essential for an encoder, which lessens a picture to a lower dimensional ideal and an ideal introduction picture. I examined various answers on various advances via web-based media stages like twitter and face book. From these examinations we are roused to extend this objective. In our proposed framework, we centre around identifying profound phony recordings utilizing blockchains, keen agreements, and secure hashing calculations. We utilize a few calculations to relieve the issue, for example, the SHA string
Using Distance Learning in the Process of Professional Training in the Context of the COVID-19 Pandemic

Due to the threat of the spread of coronavirus, in order to organize and conduct classes, as well as to ensure the continuity of the educational process during the quarantine period, educational institutions are recommended to conduct classes in the mode of remote support of the educational process. Distance learning is a form of education using computer and telecommunication technologies that provide interactive interaction between teachers and students at different stages of training, as well as independent work with information network materials. The article reveals the importance of using distance learning technologies at the present stage of development of information and communication technologies in the process of training future teachers. The place and role of distance learning technologies in the higher education system is determined. It is noted that with the development of the technical capabilities of the teaching methodology and technology, the means of the teacher's educational activity change. It became possible to use new organizational forms of the educational process, using information and communication technologies. The idea of ​​what a postmodern person should be like, what competencies should have, is changing.
Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis  Testing

Machine learning models are known to be susceptible to adversarial attacks which can cause misclassification by introducing small but well designed perturbations. In this paper, we consider a classical hypothesis testing problem in order to develop fundamental insight into defending against such adversarial perturbations. We interpret an adversarial perturbation as a nuisance parameter, and propose a defense based on applying the generalized likelihood ratio test (GLRT) to the resulting composite hypothesis testing problem, jointly estimating the class of interest and the adversarial perturbation. While the GLRT approach is applicable to general multi-class hypothesis testing, we first evaluate it for binary hypothesis testing in white Gaussian noise under $\ell_{\infty}$ norm-bounded adversarial perturbations, for which a known minimax defense optimizing for the worst-case attack provides a benchmark. We derive the worst-case attack for the GLRT defense, and show that its asymptotic performance (as the dimension of the data increases) approaches that of the minimax defense. For non-asymptotic regimes, we show via simulations that the GLRT defense is competitive with the minimax approach under the worst-case attack, while yielding a better robustness-accuracy tradeoff under weaker attacks. We also illustrate the GLRT approach for a multi-class hypothesis testing problem, for which a minimax strategy is not known, evaluating its performance under both noise-agnostic and noise-aware adversarial settings, by providing a method to find optimal noise-aware attacks, and heuristics to find noise-agnostic attacks that are close to optimal in the high SNR regime.
The construction for several few-weight linear codes and their  applications

In this paper, for any odd prime $p$ and an integer $m\ge 3$, several classes of linear codes with $t$-weight $(t=3,5,7)$ are obtained based on some defining sets, and then their complete weight enumerators are determined explicitly by employing Gauss sums and quadratic character sums. Especially, for $m = 3$, a class of MDS codes with parameters $[p,3,p-2]$ are obtained. Furthermore, some of these codes can be suitable for applications in secret sharing schemes and $s$-sum sets for any odd $s>1$.
SAC-GAN: Structure-Aware Image-to-Image Composition for Self-Driving

We present a compositional approach to image augmentation for self-driving applications. It is an end-to-end neural network that is trained to seamlessly compose an object (e.g., a vehicle or pedestrian) represented as a cropped patch from an object image, into a background scene image. As our approach emphasizes more on semantic and structural coherence of the composed images, rather than their pixel-level RGB accuracies, we tailor the input and output of our network with structure-aware features and design our network losses accordingly. Specifically, our network takes the semantic layout features from the input scene image, features encoded from the edges and silhouette in the input object patch, as well as a latent code as inputs, and generates a 2D spatial affine transform defining the translation and scaling of the object patch. The learned parameters are further fed into a differentiable spatial transformer network to transform the object patch into the target image, where our model is trained adversarially using an affine transform discriminator and a layout discriminator. We evaluate our network, coined SAC-GAN for structure-aware composition, on prominent self-driving datasets in terms of quality, composability, and generalizability of the composite images. Comparisons are made to state-of-the-art alternatives, confirming superiority of our method.
Traversing within the Gaussian Typical Set: Differentiable  Gaussianization Layers for Inverse Problems Augmented by Normalizing Flows

Generative networks such as normalizing flows can serve as a learning-based prior to augment inverse problems to achieve high-quality results. However, the latent space vector may not remain a typical sample from the desired high-dimensional standard Gaussian distribution when traversing the latent space during an inversion. As a result, it can be challenging to attain a high-fidelity solution, particularly in the presence of noise and inaccurate physics-based models. To address this issue, we propose to re-parameterize and Gaussianize the latent vector using novel differentiable data-dependent layers wherein custom operators are defined by solving optimization problems. These proposed layers enforce an inversion to find a feasible solution within a Gaussian typical set of the latent space. We tested and validated our technique on an image deblurring task and eikonal tomography -- a PDE-constrained inverse problem and achieved high-fidelity results.
Noise-Tolerant Object Detection and Ranging Using Quantum Correlations

Imaging, detection and ranging of objects in the presence of significant background noise is a fundamental challenge in optical sensing. Overcoming the limitations imposed in conventional methods, quantum light sources show higher resistance against noise in a time-correlation-based quantum illumination. Here, we introduce the advantage of using not only time correlations but also polarization correlations in photon pairs in the detection of an object that is embedded in a noisy background. In this direction, a time- and polarization-correlated photon pair source using the spontaneous parametric down-conversion process is exploited. We found that the joint measurement of correlated pairs allows distinguishing the signal from the noise photons and that leads to an improved signal-to-noise ratio. Our comparative study revealed that using polarization correlations in addition to time correlations provides improved noise rejection. Furthermore, we show that polarization correlation allows undoing the detector limitation where high background often leads to detector saturation.
CSG0: Continual Urban Scene Generation with Zero Forgetting

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, i.e., with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach significantly outperforms the brute-force one by a large margin.
Teaching Writing Skill on Bahasa Indonesia Through Synchronous Computer Mediated Communication (SCMC): A Case of Borneo University Student on non-Language Department Learner

This paper aims to reveal the effectiveness of within the teaching Bahasa Indonesia writing skills.This analysis used a quasi-experimental design.The population consisted of non-linguistic students from the Borneo Tarakaan University, two classes were randomly selected as a sample, agrotechnology class A1 was used as the experimental group, while agrotechnology class A2 became the control group using the conventional approach.The data were analyzed by descriptive and inferential statistics using the computer program SPSS 20 for Windows and the results were as follows: (1) There were significant differences in writing performance among students taught with the Synchronous Computer Mediated Communication (SCMC) and conventional approach (2), the use of Synchronous Computer Mediated Communication (SCMC) was more effective than the conventional approach in teaching writing skills.
Quantifying n-photon Indistinguishability with an Integrated Multi-Port Interferometer

We introduce a novel method to directly characterize the indistinguishability of a state of multiple photons using a multiport interferometer. We demonstrate this method using four photons from a quantum dot single-photon source.
Romeo Castellucci – an apologist for postdramatic theater

The purpose of the article. To analyze the early performances directed by R. Castellucci, to explore the main features of his directing method, and on the basis of the analysis to identify the characteristics of post-dramatic theater in his work. The methodology applies a textual method for the elaboration of the conceptual apparatus of post-dramatic theater. The apply a hermeneutic method and the method of stylistic analysis allow to study the performances that represent the main artistic trends in the directing of R. Castellucci of the late ХХ century. The scientific novelty lies in understanding the creative pursuits of R. Castellucci, in the aesthetics of post-dramatic theater. For the first time, his performances are analyzed and their affiliation to the post-dramatic theater is classified based on the conceptual and categorical apparatus formed by scientists. Conclusions. R. Castellucci's early directorial works are characterized by performativity, imagery, visuality, distancing from text-centrism, which dominates in dramatic theater and rethinking the meaning of the word as a vestige of dramatic performance. These features of the director's method give the right to define him as an exponent of ideas of post-dramatic theater.&#x0D; Keywords: Romeo Castellucci, post-dramatic theater, performance, visuality.
Best Practices for Community Policing and Crime Prevention in Nigeria

Community engagement lies at the core of policing. Therefore, through collaborative efforts, community and police members address crime and disorder using approaches determined to be best practices for their communities. Today, community policing involves a new philosophy of police operations and management; hosting an accountability system involving the entire organisation. Nevertheless, to be effective in preventing crime, Nigerian police must place renewed emphasis on community engagement and practices. Based on this premise, this paper examines the best practices for community policing and crime prevention in Nigeria. The paper began with an introduction which x-rayed community policing as evolving to meet the new expectations that emerge in the 21st Century. The paper further looked into the philosophical principles of community policing, crime prevention, community policing best practices for Nigeria, organisational changes for implementing community policing best practices for Nigeria, and the expected benefits of community policing to Nigerians. The paper concluded that while the ways of implementing community policing may differ in practice in accordance with certain conditions in the communities, it is imperative to know that the basic philosophical principles of community policing should always be adhered to, if the best practices for community policing is to be achieved in Nigeria.
A nyelvi adatközlő életrajzának jelentőségéről

Future workVe′rdd has relations other than translation relations to deal with.These include: "Etymological relation" for marking associations between cognates; "Compound relation" for associating compound words with the elements; "Derivation relation" for associating derived words with their source stems and derivational affixes.These could, in the future, be predicted automatically by incorporating methods such as the ones by Alnajjar et al. ( 2017), Hämäläinen -Rueter (2019) and Alnajjar (2021), test materials could be found in Keresztes (2013).Etymological relations between words of the Mordvin idioms may be readily augmented in Ve′rdd at the present by entering documentation metadata from sources such as (Keresztes 2011: 107-118), and even more extensive alignments can be found in the Mordwinisches Wörterbuch (1990Wörterbuch ( , 1992Wörterbuch ( , 1994Wörterbuch ( , 1996)), both at the lemma level and the etymology level (cf.Rueter 2020).Future work will involve the enhancement of parallel corpora development for Erzya and Moksha, both literary and vernacular in collaboration with other centers of Mordvin studies.The lexical resources we have created and described here could be used in future in research on morphological derivations, dialect areas and lexical distributions.Furthermore, this approach can be useful for other endangered languages as well.
Model Architecture of CNN for Recognition the Pandava Mask

This research was conducted to observe the use of architectural model Convolutional Neural Networks (CNN) LeNEt, which was suitable to use for Pandava mask objects. The Data processing in the research was 200 data for each class or similar with 1000 trial data. Architectural model CNN LeNET used input layer 32x32, 64x64, 128x128, 224x224 and 256x256. The trial result with the input layer 32x32 succeeded, showing a faster time compared to the other layer. The result of accuracy value and validation was not under fitted or overfit. However, when the activation of the second dense process as changed from the relu to sigmoid, the result was better in sigmoid, in the tem of time, and the possibility of overfitting was less. The research result had a mean accuracy value of 0.96.
Analysis of the Platform E-Learning Utilization on DSS05 Domain Using the COBIT 5 Framework at Private Universities in Riau Archipelago

Most of the private universities in Kepulauan Riau have used the E-Learning system.The increasing use of E-learning has been felt since the COVID 19 Pandemic.Until now there has never been a measurement of the use of E-Learning in Private Universities in Kepulauan Riau using the COBIT 5 Framework.This research was conducted to determine the level of capability of the use of E-Learning in Private Universities in Kepulauan Riau.The focus of this research is on the DSS05 process (Managing Service Security).The results of this study indicate that the level of capability in the DSS05 process reaches level 3, namely the Established Process.The improvement strategy given is the need for improvement in periodic testing of E-Learning and the availability of standard documents related to procedures for regulating the receipt, use, transfer, and disposal of sensitive documents in E-Learning.
Factors Influencing Vietnamese Teenagers’ Intention to Use Mobile Devices for English Language Learning

Teenagers worldwide have been considered as 'digital natives' who are grown up under the influence of modern technologies, including mobile devices; however, their intention to use the devices for language learning is still under research in the literature.This study was conducted in an attempt to explore factors that influenced Vietnamese teenagers' intention to learn English through mobile devices and how they (the devices) could enhance learners' language skills.Using the extended technology acceptance model (UTAUT) as the theoretical framework, the study analyzed data from 617 high school and university students in north, central and south Vietnam.The study results revealed that attitude towards use was the most critical factor affecting teenagers' intention, but facilitating condition and effort expectancy did not correlate with the intention.Learners' gender and prior online learning experience also had an impact on the intention.Mobile devices were more beneficial to learning, listening, reading, and speaking than writing.The study findings offer practical implications for teachers and designers of mobile applications to foster mobile English language learning for teenagers in Vietnam.
University Library Service Model Innovation in the Digital Reading Era

Under the background of Internet, university libraries should make full use of new media technology, try their best to meet readers' personalized reading needs, and actively innovate library service model. By analyzing the current service mode of university libraries, this paper summarizes the innovative measures of university libraries in the digital reading era, which can effectively promote the integrated development of traditional reading and new digital reading.
A Visual Decision-Support System using Fingerprint Matrices applied to Cyclical Spatio-Temporal Data from Motorsports

Visualizing cyclical spatio-temporal data is an important part of understanding how and why objects move in the context of motorsports, which is critical feedback for drivers to improve their performance.Current methods have problems such as occlusion and loss of context which significantly limit our ability to see and understand vehicle data.Here we demonstrate how the fingerprint matrix method (which is normally used in lexical analysis) can be applied in vehicle motion analysis to overcome these two problems.Compared to traditional methods using traction circle scatterplot displays of acceleration force data from a race car, our prototype design allows decision makers to see individual datapoints in a more concise display.We show that informative but previously-hidden anomalies and patterns become more easily recognized in the data.Our design generalizes to other cyclical spatio-temporal visualization problems involving transportation, medicine, and the natural world.
Exploring Deep Fusion Ensembling for Automatic Visual Interestingness Prediction

In the context of the ever growing quantity of multimedia content from social, news and educational platforms, generating meaningful recommendations and ratings now requires a more advanced understanding of their impact on the user, such as their subjective perception. One of the important subjective concepts explored by researchers is visual interestingness. While several definitions of this concept are given in the current literature, in a broader sense, this property attempts to measure the ability of audio-visual data to capture and keep the viewer’s attention for longer periods of time. While many computer vision and machine learning methods have been tested for predicting media interestingness, overall, due to the heavily subjective nature of interestingness, the precision of the results is relatively low. In this chapter, we investigate several methods that address this problem from a different angle. We first review the literature on interestingness prediction and present an overview of the traditional fusion mechanisms, such as statistical fusion, weighted approaches, boosting, random forests or randomized trees. Further, we explore the possibility of employing a stronger, novel deep learning-based, system fusion for enhancing the performance. We investigate several types of deep networks for creating the fusion systems, including dense, attention, convolutional and cross-space-fusion networks, while also proposing some input decoration methods that help these networks achieve optimal performance. We present the results, as well as an analysis of the correlation between network structure and overall system performance. Experimental validation is carried out on a publicly available data set and on the systems benchmarked during the 2017 MediaEval Predicting Media Interestingness task.
The Influence of Social Attitude on Cognitive Competence and Practices in Learning Governance Courses on Family Welfare Education Students

The aims of this study were 1) to determine the effect of social attitudes on cognitive competence in the culinary arts course for family welfare education students, 2) to determine the effect of social attitudes on practical competence in the culinary arts course to family welfare education students, 3) to determine the interaction of social attitudes.on cognitive competence and practical competence in the culinary arts course for family welfare education students.The method used to collect data is a questionnaire method, knowledge test, and observation with instruments in the form of a closed statement questionnaire to collect data on social attitudes, multiple choice knowledge test to obtain data on cognitive competence in the form of student understanding in the culinary arts course, and an observation rubric.to obtain data on the practical competence of students in the culinary arts course.The results showed that 1) different competencies were not influenced by different social attitudes in the Dishes course, 2) the level of student social attitudes did not affect students' cognitive competence and practice, meaning that students from various levels of social attitudes, both high and medium social attitudes.or low have relatively balanced cognitive and practical competencies in the culinary arts course, 3) there is no significant interaction between students' social attitudes towards competence in the culinary arts course.Students who have high, medium and low social attitudes have relatively the same cognitive and practical competencies.