Reviewerly/exHarmony huggingface dataset:
US Extremism on Telegram: Fueling Disinformation, Conspiracy Theories, and Accelerationism

Several alternative social media platforms have emerged in response to perceptions that mainstream platforms are censoring traditional conservative ideologies. However, many of these alternative social media platforms have evolved to be outlets for hate speech and violent extremism. This study examines hate-based channels on Telegram from a US perspective. While Telegram has often been studied in relation to ISIS, less is known about its usage by US extremist users and movements. The authors used OSINT and observational methods on a sample of 125 Telegram channels containing hate speech and violent extremist content from far-right and far-left perspectives. The authors hypothesized that there would be a greater and growing presence of far-right activity compared to farleft activity due to current migration trends away from mainstream social media by the far-right. The authors also sought to observe the presence of disinformation campaigns, conspiracy theories, and accelerationism on Telegram. This study had four major findings: (1) the findings supported the hypothesis that more channels were host to farright dialogues, yet there were several far-left channels present, (2) 64.8% of the channels grew in size over a oneweek period, (3) 47 of the 125 channels were connected to well-known violent extremist movements or hate groups, and (4) QAnon and the COVID-19 pandemic were the most prominent sources of disinformation and conspiracy theories on Telegram. The findings of this study highlight that alternative social media platforms are a growing environment for a range of hateful ideologies and are aiding the spread of disinformation campaigns. This study concludes with a discussion on future strategies to combat the influence of the Internet on radicalization outcomes. © 2021. All Rights Reserved.
Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data

Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science. This paper studies both statistical and computational problems of kernel smoothing for directional data. We generalize the classical mean shift algorithm to directional data, which allows us to identify local modes of the directional kernel density estimator (KDE). The statistical convergence rates of the directional KDE and its derivatives are derived, and the problem of mode estimation is examined. We also prove the ascending property of our directional mean shift algorithm and investigate a general problem of gradient ascent on the unit hypersphere. To demonstrate the applicability of our proposed algorithm, we evaluate it as a mode clustering method on both simulated and real-world datasets.
THE COMPREHENSION OF THE PHENOMENA “NETWORK EDUCATIONAL INTERACTION” AND “NETWORK PERSONALITY” IN THE PEDAGOGICAL CONSCIOUSNESS

The article introduce the results of a survey of members of the pedagogical community, scientists and practitioners of education, participants in the international scientific and practical conference “Network Educational Interaction in the Training of an Information Society Teacher” held on October 25–26, 2019 at the Far Eastern Federal University. The issues discussed are part of a study aimed at finding mechanisms to solve the problem of organizing the process of preparing teachers for educational interaction with a new character (network personality) in new sociocultural environment (environment of the information society). The purpose of the questionnaire was to comprehend the content of such categories as “educational interaction” and “network personality”. Processing the results of the questionnaires in the aspect of the dialogue culture made it possible to identify the semantic content of the category “network educational interaction”, an understanding of which is necessary for organizing the process of preparing pedagogues for dialogue with various individuals in complicated socio-cultural conditions. The survey was attended by 158 participants, not only domestic, but also representatives of global pedagogical science and practice. The Russian pedagogical community included participants of the Far Eastern region, the Central Federal District, the Ural Federal District, the Lugansk People’s Republic, etc., the foreign community – representatives of the People’s Republic of China, Finland, Holland, the Czech Republic, Australia, Malaysia, Vietnam, etc. The survey participants represented different areas of education; pre-school, general education, higher education with different pedagogical experience (from young – up to 5 years, to teachers with an experience of more than 25 years), occupying various positions (educators, teachers, lecturers, representatives of the administration). The results (reception of correlation of answers) made it possible to identify differences in the understanding of the categories “network educational interaction” and “network personality”. The nature of the differences and the semantic content of these categories are discussed in this article.
The DEformer: An Order-Agnostic Distribution Estimating Transformer

Order-agnostic autoregressive distribution estimation (OADE), i.e., autoregressive distribution estimation where features can occur in an arbitrary order, is a challenging problem in generative machine learning. Prior work on OADE has encoded feature identity (e.g., pixel location) by assigning each feature to a distinct fixed position in an input vector. As a result, architectures built for these inputs must strategically mask either input or model weights to learn various conditional distributions necessary for inferring full joint distribution of dataset in an order-agnostic way. In this paper, we propose an alternative approach for encoding feature identities, where each feature's identity is included alongside its value in input. This feature identity encoding strategy allows neural architectures designed for sequential data to be applied to OADE task without modification. As a proof of concept, we show that a Transformer trained on this input (which we refer to as the DEformer, i.e., distribution estimating Transformer) can effectively model binarized-MNIST, approaching average negative log-likelihood of fixed order autoregressive distribution estimating algorithms while still being entirely order-agnostic.
FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration.

Most existing monocular 3D pose estimation approaches only focus on a single body part, neglecting the fact that the essential nuance of human motion is conveyed through a concert of subtle movements of face, hands, and body. In this paper, we present FrankMocap, a fast and accurate whole-body 3D pose estimation system that can produce 3D face, hands, and body simultaneously from in-the-wild monocular images. The core idea of FrankMocap is its modular design: We first run 3D pose regression methods for face, hands, and body independently, followed by composing the regression outputs via an integration module. The separate regression modules allow us to take full advantage of their state-of-the-art performances without compromising the original accuracy and reliability in practice. We develop three different integration modules that trade off between latency and accuracy. All of them are capable of providing simple yet effective solutions to unify the separate outputs into seamless whole-body pose estimation results. We quantitatively and qualitatively demonstrate that our modularized system outperforms both the optimization-based and end-to-end methods of estimating whole-body pose.
Central Bank Issued Digital Currencies

Digital payments are essential to the functioning of a modern economy, yet pose challenges to the prudential supervision of the financial system. At the same time, the use of physical cash persists in most countries due to its ease of use and the perceived or actual advantages of anonymity. A digital currency is seen as a possible solution to the oversight problem and a suitable instrument for modernising trade and exchange through the elimination of physical cash and its attendant production, storage and transport costs as well as its unsuitability for online payments. In this paper, we provide a non-technical overview of the advantages a central bank issued digital currency (CBDC), mechanisms through which such a currency can be implemented and how it might interact with the conventional banking system. The discussion includes but is not limited to distributed ledgers and we briefly consider the examples of three pilot CBDC projects.
Radar working mode recognition based on hierarchical feature representation and clustering

The classification of intercepted radar signals has gained considerable attention in the field of electronic reconnaissance. Currently, the multi-function radar (MFR) is capable of transmitting complex and agile signals with different working modes, and the classification of radar waveforms using the traditional methods does not provide satisfactory results. Therefore, it is urgent to develop a new intelligent algorithm to recognize the working mode of MFR. In particular, this paper designs a novel feature extraction method to obtain the sequential relationship of the input signals. At the same time, a hierarchy of signal features is established to represent the signals layer by layer, and then the working mode of the emitter is determined effectively by unsupervised clustering method. The effectiveness and robustness of the proposed method is experimentally verified, through simulating the real complex electromagnetic environment and generating the signal samples of MFR.
Europe’s Readiness for the AI Takeover: Some Salient Points and Comments from the EC’s White Paper on AI

AI is fast causing a global revolution in almost every area of human endeavor. In a bid to ensure that the existing laws are able to properly regulate AI, the European Commission drafted a white paper highlighting Europe’s policy direction to AI regulation. This commentary identifies salient points from the whitepaper with the author’s comments being made where necessary.
Cost and routing of continuous-variable quantum networks

The authors give insight into the amount of entanglement that can be achieved from Gaussian graph states as well as the amount of squeezing required to prepare them. They find that these quantities are highly dependent on the structure of the quantum network.
Hybrid encryption using playfair and RSA cryptosystems

The security is demanded to transport important data over the network. In this work a hybrid encryption depending on playfair cipher and RSA algorithm is proposed to give more confidentially and get rid of the defects of the two methods. The first cipher text is obtained by playfair cipher by using (812) expanded key matrix that is filled by decimal ASCII characters of Alphabets, numbers and common characters of ASCII table while the final cipher text is obtained by RSA cryptosystem by applying it for blocks of two characters. The proposed hybrid encryption avoids to take large numbers in RSA algorithm and performs complex operations that need a long time to do. The proposed hybrid cipher is tight security that is difficult to analyze and detect from hackers and intruders.
An Overview of Spoof Detection in ASV Systems

In current years, voice based application are used broadly in varied applications for speaker recognition. By and by, there is a wide work in the investigation of parodying and against mocking for Automatic Speaker Verification (ASV) framework. The current advancement within the ASV system ends up interest to secure these voice biometric systems for existent world applications. This paper provides the literature of spoofing detection, novel acoustic feature representations, deep learning, end-to-end systems, etc. Moreover, it conjointly summaries previous studies of spoofing attacks with stress on SS, VC, and replay alongside recent efforts to develop countermeasures for spoof speech detection and speech sound disorder tasks.
Shape-Based Floor Plan Retrieval Using Parse Tree Matching

Existing floor plan retrieval methods match residential floor plans based on room function and adjacency, ignoring the shape of the interior rooms. Inspired by shape grammars, we incorporate interior layout into the similarity metric using a tree structure that represents both the layout hierarchy and room shapes of the floor plan. We create parse trees from floor plans and evaluate their similarity using an appropriately defined tree edit distance. We evaluate the method on a public dataset of 11,250 vector graphic representations of Japanese homes. A user study shows that our method retrieves layouts preferential to those obtained using the exterior outline or room adjacency matrix.
Prediction of Global Solar Irradiation by Using Fuzzy Logic

Forecasting is defined as the process of predicting the value of a variable or Variables in the future time, the fuzzy logic model was used to forecast solar irradiation using two input data (temperature and cloud cover) for four Iraqi stations. It was chosen because of the model's accuracy in prediction. There are several uncertainties in load forecasting, such as temperature, rainfall, wind speed, air pressure, and solar radiation, that cannot be precisely defined numerically, as a result, the most practical technique to employ under such situations is the fuzzy logic solution. Iraq's position is made up of two key points: a latitude and a longitude, latitude (38-29) N Longitude (38-49) E. This coordinate knows us that Iraq has located overhead the equator, Iraq located in the world's northern hemisphere, Because Iraq located above of equator so Iraq is considered to have abundant solar radiation rates from its north to its south when compared to European countries, which have made great strides in the field of investing in solar energy technologies, data are taken from ECMWF in (noon) was daily data for two years (2018-2019). The method that uses fuzzy logic was accuracy according to result and error percentage. Fuzzy logic has industrial and practical applications. It has the power to monitor computers and consumer goods. It does not have exact logic, but appropriate reasoning aids in the management of engineering complexity. So, when getting result note that the high elevation area receives more amount of surface solar radiation than the flat area that illustrate the topography play important role in these measurements.
Interaction-GCN: A Graph Convolutional Network Based Framework for Social Interaction Recognition in Egocentric Videos

In this paper we propose a new framework to categorize social interactions in egocentric videos, we named InteractionGCN. Our method extracts patterns of relational and non-relational cues at the frame level and uses them to build a relational graph from which the interactional context at the frame level is estimated via a Graph Convolutional Network (GCN) based approach. Then it propagates this context over time, together with first-person motion information, through a Gated Recurrent Unit architecture. Ablation studies and experimental evaluation on two publicly available datasets validate the proposed approach and establish state of the art results. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Features and trained model available at https://github.com/simonefelicioni/InteractionGCN
Image and audio steganography based on indirect LSB

Hiding the presence of data during communication has become a pressing concern in this overly digitalized world as a consequence of illegitimate access. These concerns have led to cryptography and steganography techniques as methods for securing data. This paper presents a modified information hiding technique based on an indirect least significant bit. Instead of saving each bit of the secret message in the least significant bit (LSB) of the cover media, each bit of the secret message is compared to a mask bit in the cover media. The result is saved in the cover media’s LSB. In this paper, two steganography schemas are designed in which the cover media are image and audio, while the secret message is a text file. A simple encryption technique is used to transform the secret message into an unreadable format before the hiding process begins. The experimental results indicate that the proposed algorithm achieves promising performance
THE APPLICATION OF INTERACTIVE TEACHING TECHNOLOGIES IN A MATHEMATICS LESSON IN PRIMARY SCHOOL

The article deals with the investigation of the main interactive and innovative teaching methods and techniques. It is characterized the most commonly used interactiveteaching methods in primary school in the article. The aim of the article is to reveal the peculiarities of the interactive teaching technologies and methods application, and to prove the relevance of their use in mathematics lessons. On the basis of theoretical analysis of scientific studies and research concerning the problem of interactive teaching, it is defined that the application of interactive technologies at primary school lessons provides conditions for successful cooperation and productive dialogue between teacher and students. Thus, it is possible to implement the basic principles of personality-oriented teaching. Step-by-step introduction of interactive teaching technologies should be combined with traditional teaching methods and besides interactive teaching can be introduced in 5 steps: motivation of the class activity; announcement of a lesson topic and the expected results; providing necessary and relevant information; an interactive exercise or task – the centralelement of the lesson; summarizing lesson results and their evaluation, reflection. These steps are described in detail in terms of the interactive mathematics lessons. It is explained that organization of teaching using interactive methods and techniques may be difficult for a teacher, but it has a wide range of benefits. The application of innovative and interactive technologies in mathematics lessons gives the opportunities to students to form their character and personality, broaden the horizons, develop logicalthinking and coherent speech, individual abilities. Interactive and innovative exercises make positive impact on the development of mathematical abilities, stimulate students’ desire to study mathematics.
Robotics/Artificial Intelligence in Hospitality Industry: Is It a Bequest or Blight to Human Race?

“Robots” what is it??? A mechanized product which can be electrified or charged differently with the help of virtual energy that can be utilized as a living organism .With the development of technology and beneficial things to mankind this is another aspect where mankind has gone through. From the day when man landed at moon the development of technology has evolved and gone through different phases from inventions such as telephones to android mobile phone which can work as an super mechanized computers as well as a variety of inventions which can make the work of mankind easier and less time consuming. Among all these one of the most important invention to mankind was the “ROBOT”.This paper aims to deal with the introduction of robot in hospitality and also intervenes the use of artificial intelligence in the industry. This paper aims to compare the emergence of virtual or artificial intelligence in the context of the intangible sources provided by mankind in the said industry.
Exploring a Loan Translation and Its Consequences in an Oral Bilingual Corpus

Abstract This work applies computational tools that have been used to model loanwords in newspaper corpora to an analysis of a loan translation in an oral bilingual corpus. The explicit goal of the contribution is to argue that a specific collocation found in a corpus of Spanish spoken in Texas, agarrar+ NP (e.g., agarrar ayuda ), is a loan translation that is calqued on English get + np support verb constructions (e.g., get help ). We base our argument on the frequency and the linguistic distribution of the nonconventional usage within and between corpora and on the factors that favor its use. Our findings show that the overall frequency of agarrar is the same in Spanish in Texas as it is in the benchmark monolingual corpus of Mexican Spanish but that it is used differently in the two varieties, a difference that has grammatical, as well as semantic, ramifications.
Let Them Choose What They Want: A Multi-Task CNN Architecture Leveraging Mid-Level Deep Representations for Face Attribute Classification

Face Attributes Classification (FAC) is an important task in computer vision, aiming to predict the facial attributes of a given image. However, the value of mid-level feature information and the correlation between face attributes are always ignored by deep learning-based FAC methods. In order to solve these problems, we propose a novel and effective Multi-task CNN architecture. Instead of predicting all 40 attributes together, an attribute grouping strategy is proposed to divide the 40 attributes into 8 task groups correlatively. Meanwhile, through the Fusion Layer, mid-level deep representations are fused into the original feature representations to jointly predict the face attributes. Furthermore, the Task-unique Attention Modules can help learn more task-specific feature representations, obtaining higher FAC accuracy. Extensive experiments on the CelebA dataset demonstrate that our method outperforms state-of-the-art FAC methods.
HMA-Depth: A New Monocular Depth Estimation Model Using Hierarchical Multi-Scale Attention

Monocular depth estimation is an essential technique for tasks like 3D reconstruction. Although many works have emerged in recent years, they can be improved by better utilizing the multi-scale information of the input images, which is proved to be one of the keys in generating high-quality depth estimations. In this paper, we propose a new monocular depth estimation method named HMA-Depth, in which we follow the encoder-decoder scheme and combine several techniques such as skip connections and the atrous spatial pyramid pooling. To obtain more precise local information from the image while keeping a good understanding of the global context, a hierarchical multi-scale attention module is adopted and its outputs are combined to generate the final output that is with both good details and good overall accuracy. Experimental results on two commonly-used datasets prove that HMA-Depth can outperform the existing approaches. Code is available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/saranew/HMADepth.