Reviewerly/exHarmony huggingface dataset:
Modelling the machines’ language with natural language processing and machine learning algorithms

Language is one of the most important elements of communication for people and all living things. A large number of studies have been conducted in the literature on the formation of languages and natural language processing processes. It has been observed that these studies contain differences in terms of analysis, approach and method. In the study, a model has been proposed for machines to create their own language as a contribution to the literature. With this point of view, it has been contributed to the idea that the efficiency of all processes will increase by communicating with each other in their own language like humans. With the aim of developing an approach for machines to produce their own languages, it is aimed to distinguish the sounds of living things by classifying them and to generate new sounds by using convolutional deep neural network method. In the study, the applied alphabet was decided by the system. In next step, Regenerative Artificial Neural Networks were used together with Mel Frequency Cepstral Coefficient and Dynamic Time Warp methodologies to create similar sounds and the living things were named with their respective sounds. Unlike similar studies, a visual data was applied which was converted from the audio files that was obtained from Kaggle open data repository "Audio Cats and Dogs" dataset. In addition, the model was supported with CNN networks to achieve effective performance.
Formal Identification of Argumentation Patterns in Scientific Texts

In this paper we present the methods for the automatic identification of argumentation patterns and the results of their application. These methods have been employed to analyze argumentation annotations of 25 scientific texts from two thematic areas. Under study were the expert annotations constructed manually with the help of web tools for visualizing argumentative statements and argumentation schemes, as well as for modelling the argumentation structure of a text as an oriented graph. Such graphs contain two node types: information nodes denoting statements and their connecting reasoning models (schemas) from Walton's compendium. The regularly employed reasoning models and their structural combinations (argumentation subgraphs) form argumentation patterns. Patterns containing more than one scheme are unmarked in initial graphs. As a result of processing argumentation annotations from the collection, we have constructed a joint spectrum of argumentation patterns with their absolute and text frequencies. The methods of frequent subgraph mining have been used to identify argumentation patterns containing three or more schemes. The subgraph matching has been performed through the use of the NetworkX package which implements the VF2 algorithm for subgraph isomorphism testing. We have analyzed the calculated frequencies to identify both the general principles behind the use of argumentation patterns typical of scientific texts, as well as the specific tendencies of their functioning within distinct thematic areas (linguistics and computer science). These general principles regulate the use of both separate schemes and their structural combinations. The latter appear in two configuration types: either as sequencies of argumentation schemes (up to 5 elements) or as tree structures (which contain up to 8 nodes). Specifically, we demonstrate that branching within a tree-form pattern typically presupposes the parallel use of identical argumentation schemes. Additionally, branching of argumentation patterns occurs more often in proximity to the main thesis of a text, than near the initial premises. Finally, the thematic area can condition not only the use of separate schemes, but also their structural combination within complex patterns. The results obtained are applicable to the evaluation of text similarity based on argumentation patterns employed in them, which, in its turn, can improve the clustering and classification of texts, evaluation of their persuasiveness, as well as the formal synthesis of argumentation.
Generation of Three-Mode Entanglement Based on Parametric Amplifiers Using Quantum Entanglement Swapping

Quantum entanglement swapping is one of the most promising quantum techniques to create or manipulate large-scale multi-mode entanglement between two distant quantum entangled systems. In this work, a scheme for the generation of three-mode entanglement based on parametric amplifiers using quantum entanglement swapping has been proposed. The newly generated three-mode entanglement is always present in the whole power gain region from parametric amplifiers and its dependence on transmission loss and feedforward gain is also investigated. In addition, the effects of power gain, transmission loss, and feedforward gain on the two-mode entanglement of the three pairs in the newly generated three-mode entanglement have also been analysed in detail. The results presented here may find some practical applications in quantum secure communication.
Methodology of higher education in the new reality: additive construction of the up-to-date product

Introduction. A systematic understanding of the need and possibility of the transition of the process of cognition to a holistic display of the world in its actual reality is an urgent task of modern higher education. In a rapidly changing professional environment, it is especially important to justify and consciously choose the direction of the search for information and the creation of knowledge on its basis in the process of constructing an up-to-date product. The purpose of the study: theoretical justification and study of the advantages of parametric representation of interactions and relationships in the virtual design system in preparation for cognitive activity based on the transformation and translation of relevant information between the real environment and its personal representation in the knowledge of a specialist in the process of designing a demanded product with certain properties and value. Methodology and research methods. The activity approach was adopted as a fundamental one, the study of which is aimed at identifying the features of implementation in the process of learning the principles of the universal law of symmetry, which made it possible to formulate the conditions for the development of the ability to present information in proportion to the center of symmetry “activity”, establish a connection of any information with a certain direction of activity and correlate it with a change in the properties of the constructed product. In the course of the study, the study, we used the methods of analysis of the basic concepts of the study, cause-effect analysis of the phenomena under study and relationships in the systems, parametric modeling. Results. The article presents a new model of cognition, a parametric model of constructing an up-to-date product that provides virtual systemic impacts of the subject on the object (activity), forecasting and evaluating the results of these impacts (information on changes in the parameters of the object state depending on the direction of the subject's activity), and creating new valuable knowledge (what should be done to obtain an actual product). The expediency of implementing a new model of cognition based on additive technologies with a universal multi-event representation of the cognition process is substantiated. It is proved that the construction of an up-to-date product and valuable knowledge is provided by an additive synthesis of relevant information as a result of a superposition of events in personally chosen direction of activity. The process of additive construction of the up-to-date product, valuable knowledge and reflective evaluation is presented.
Open Research Data: Experimenting Towards a Publishing Infrastructure

In this editorial, we describe the work that has been undertaken by the ESTS editorial collective (EC) over the last two years towards establishing a publishing infrastructure for open research data. A broad movement in the scholarly community is pushing towards data sharing or “open data,” particularly in the natural sciences and medicine. Recognizing that there are compelling reasons why scholars are wary of data sharing and careful to protect their work, our EC has pursued experiments towards establishing a publishing infrastructure. The goal is to better understand the possible benefits for the STS community from data sharing and the role that a scholarly-run journal like ESTS could play in realizing such opportunities. The sharing of data could serve as an archive of work in/for STS; offer greater recognition of diverse contributions to scholarly research beyond individual author(s); enable reuse of data for new insights and pedagogical opportunities; and engender new forms of scholarly community in the field.
Fake COVID-19 videos detector based on frames and audio watermarking

With the innovation and development of advanced video editing technology and the widespread use of video information and services in our society, it is increasingly necessary to maintain the reliability of video information. As a result, sensitive video contents in various fields such as surveillance, medical, and others should be secured against attempts to alter them because malicious modifications could impact decisions based on these videos. In this paper, we present a fake video detector based on combining audio and frames watermarking. Furthermore experimental results demonstrate good robustness and integrity verification results for COVID-19 video news.
Training Neural Networks by Time-Fractional Gradient Descent

Motivated by the weighted averaging method for training neural networks, we study the time-fractional gradient descent (TFGD) method based on the time-fractional gradient flow and explore the influence of memory dependence on neural network training. The TFGD algorithm in this paper is studied via theoretical derivations and neural network training experiments. Compared with the common gradient descent (GD) algorithm, the optimization effect of the time-fractional gradient descent algorithm is significant when the value of fractional α is close to 1, under the condition of appropriate learning rate η. The comparison is extended to experiments on the MNIST dataset with various learning rates. It is verified that the TFGD has potential advantages when the fractional α nears 0.95∼0.99. This suggests that the memory dependence can improve training performance of neural networks.
FTS4VMC: A front-end tool for static analysis and family-based model checking of FTSs with VMC

FTS4VMC is a publicly available front-end tool for the static analysis and family-based model checking of a Featured Transition System (FTS). It can detect ambiguities in an FTS, disambiguate an ambiguous FTS, transform an FTS into a Modal Transition System (MTS), and interact with the VMC model checker for family-based verification.
The role of diversity and ensemble learning in credit card fraud detection

The number of daily credit card transactions is inexorably growing: the e-commerce market expansion and the recent constraints for the Covid-19 pandemic have significantly increased the use of electronic payments. The ability to precisely detect fraudulent transactions is increasingly important, and machine learning models are now a key component of the detection process. Standard machine learning techniques are widely employed, but inadequate for the evolving nature of customers behavior entailing continuous changes in the underlying data distribution. his problem is often tackled by discarding past knowledge, despite its potential relevance in the case of recurrent concepts. Appropriate exploitation of historical knowledge is necessary: we propose a learning strategy that relies on diversity-based ensemble learning and allows to preserve past concepts and reuse them for a faster adaptation to changes. In our experiments, we adopt several state-of-the-art diversity measures and we perform comparisons with various other learning approaches. We assess the effectiveness of our proposed learning strategy on extracts of two real datasets from two European countries, containing more than 30 M and 50 M transactions, provided by our industrial partner, Worldline, a leading company in the field.
11. Interactional spaces in stationary, mobile, video-mediated and virtual encounters

In any focused social interaction, people come together, move, and position their bodies with respect to each other, and maintain and change such formations while they interact. Establishing and sustaining such formations makes it possible for them to see and hear others, to show and share objects, and to orient to same features in the environment. Forming copresence and a shared space is core and a precondition to any social interaction. Since the influential work by Adam Kendon (F-formations) and Erving Goffman (participation frameworks, focused encounters, withs) an accumulating body of research has explored - in different interactional settings - the pragmatics of how humans organize themselves spatially for interacting with each other. More recently, Lorenza Mondada (2009) has introduced the term “interactional space” to refer to the dynamic ways in which people not only initiate and establish copresent formations but also continuously (re)organize them with respect to each other, the unfolding activity and material environment. In this chapter, we offer an overview of pragmatics research on spatial arrangements in interaction. We illustrate how people organize their copresence in order to interact with each other in stable, mobile, video-mediated (i. e., distributed) and virtual settings. We explore “interactional space” as a visual phenomenon and thereby focus on situations where participants can (at least partly) see each other
PERANCANGAN PROGRAM APLIKASI TES BUTA WARNA BERBASIS ANDROID

Color blindness test is one way to find out whether a person has color blindness or not, is usually done with the Ishihara method. This Ishihara method is a method where when running a test with this method, the patient is usually faced with a book containing a circle pattern (disk) with many points of various colors and sizes in it. The circle pattern is usually 14, 24 and 38 dishes. Tests like this are usually done in health agencies, one of which is the Cahaya Insani Clinic in Garut, West Java. The process of implementing a color-blind test is still done manually where people who want to do a color-blind test must come directly to the clinic, making people who did color-blind tests had to be willing to wait a long time to be able to do a color-blind test. In addition to these problems, the lack of knowledge and understanding of the community regarding color blindness, makes people less aware of the importance of color-blinding tests early on. Therefore, a media is needed that can do color blindness tests quickly and easily, coupled with information about color blindness that can provide insight to the community. Mobile based application Android is one of the right media to make it easy for people to obtain information. Starting from this, then made an Android-based color blindness test application to make it easier for people to do color blindness tests anytime and anywhere, and can used as a medium of information about color blindness.
Tell Me Why - A Systematic Literature Review on Outlier Explanation for Tabular Data

Outlier explanation approaches are used to support analysts in investigating outliers, especially those detected by methods which are not intuitively interpretable such as deep learning or ensemble approaches. There have been several studies on outlier explanation and visualization in the last years. Nonetheless, there has been no overarching survey of existing outlier explanation approaches. We investigate what kind of explanation approaches exist for tabular data and how they can be characterized. For this, we perform a literature review of 418 papers uncovering 26 existing studies which we analyze and categorize according to: approach, model dependence, supported data, output and evaluation. Based on our categorization, we identify trends within outlier explanation. This includes the limited number of underlying approaches and the lack of a common evaluation method. We are also able to highlight avenues for further research such as utilizing different datasets for evaluation or exploring visualization approaches.
Research on Tibetan Part-of-Speech Tagging Based on Transformer

Aiming at the key problems of ambiguity such as polysemy and unregistered words in the Tibetan part-of-speech tagging task, and the currently cited recurrent neural network cannot model long Tibetan texts. This paper proposes Tibetan part-of-speech tagging for a hybrid model of ELMo and Transformer_Encoder. Solved the problem of polysemy and unregistered words in Tibetan that were not solved by static word vectors. ELMo is a bidirectional pre-training model that can extract Tibetan word features in both positive and negative directions. The pre-trained word embeddings are combined with the Transformer’s self-attention mechanism to extract the semantic features of Tibetan sentences to predict the part-of-speech tags of each word. This method perfects the Tibetan part-of-speech tagging task and achieves about 97% accuracy on the dataset used in this paper.
Integrated Language and Knowledge Resources for a Bulgarian-Centric Knowledge Graph

This paper reports on the integration of language and knowledge resources within CLaDA-BG infrastructure. The idea is to encode linguistic knowledge on all levels of language starting from text, grammatical annotation and lemmatization to semantic and conceptual annotation. Our goal is to support conceptual annotation of various research objects (mainly texts). One of the main applications will be the management of a Bulgaria-centric Knowledge Graph.
Machine Learning and Neural Networks Tools to Address Noisy Data Issues

In this paper, we present tools for addressing noisy keyword issues in digital libraries. Two tasks, language detection and misspelling detection and correction, are addressed using both machine learning and deep learning techniques. To train and validate the models, different datasets were used/created/scraped. Encouraging preliminary results are presented and discussed.
PFN-PT

This article presents PFN-PT, a robust system for the automatic semantic annotation of Portuguese, consisting of a new, parsing-oriented framenet and a rule-based frame- and role-tagger. The framenet provides almost 13,000 valency frames covering 7,300 verb lemmas with 10,700 senses. Frame and role tagging is achieved by iterated matching of syntactic structures and semantic noun types with slot-filler conditions in the framenet. We discuss design principles and present frame and role statistics. In an evaluation run on news data, the system achieved an overall F-score of 92.2% for frame senses.
Augmented Reality and Its Use as a Helping Tool in Education

This work suggests the use of Augmented Reality in education to provide an additional tool to help students’ understanding of studied content through visual stimulation, while capturing their attention and motivating them to complete tasks proposed. This assistance can be especially important for online laboratories—whether remote, virtual or hybrid—since activities in these environments are often performed with no tutors present—as they can usually be done at any time and place. Due to the COVID-19 pandemic, it was not possible to apply studied theories inside classrooms; therefore, a form was structured on Google Forms and shared with 30 educators from different areas and levels of education, seeking to hear their perceptions about the use of technology in their classes. Then there was an analysis and selection of seven AR applications for both Android and iOS operating systems, which were compared to check which one seems to have greater efficiency when being handled by educators. It was concluded that the Augmania™ software would be the best option for these professionals to create AR projects and activities to be performed in their classrooms, since it presents a simple and intuitive interface, while also offering different possibilities to augment information.
Person re-identification based on multi-scale global feature and weight-driven part feature

Person re-identification (ReID) is a crucial task in identifying pedestrians of interest across multiple surveillance camera views. ReID methods in recent years have shown that using global features or part features of the pedestrian is extremely effective, but many models do not have further design models to make more reasonable use of global and part features. A new model is proposed to use global features more rationally and extract more fine-grained part features. Specifically, our model captures global features by using a multi-scale attention global feature extraction module, and we design a new context-based adaptive part feature extraction module to consider continuity between different body parts of pedestrians. In addition, we have added additional enhancement modules to the model to enhance its performance. Experiments show that our model achieves competitive results on the Market1501, Dukemtmc-ReID, and MSMT17 datasets. The ablation experiments demonstrate the effectiveness of each module of our model. The code of our model is available at: https://github.com/davidtqw/Person-Re-Identification.
Data Augmentation for Low-Resource Quechua ASR Improvement

Automatic Speech Recognition (ASR) is a key element in new services that helps users to interact with an automated system.Deep learning methods have made it possible to deploy systems with word error rates below 5% for ASR of English.However, the use of these methods is only available for languages with hundreds or thousands of hours of audio and their corresponding transcriptions.For the so-called low-resource languages to speed up the availability of resources that can improve the performance of their ASR systems, methods of creating new resources on the basis of existing ones are being investigated.In this paper we describe our data augmentation approach to improve the results of ASR models for low-resource and agglutinative languages.We carry out experiments developing an ASR for Quechua using the wav2letter++ model.We reduced WER by 8.73% through our approach to the base model.The resulting ASR model obtained 22.75% WER and was trained with 99 hours of original resources and 99 hours of synthetic data obtained with a combination of text augmentation and synthetic speech generation.
NET-FLEET

Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. Also, with appropriate algorithmic designs, one could achieve the desirable linear speedup for convergence effect in FL. However, most existing works on FL are limited to systems with i.i.d. data and centralized parameter servers and results on decentralized FL with heterogeneous datasets remains limited. Moreover, whether or not the linear speedup for convergence is achievable under fully decentralized FL with data heterogeneity remains an open question. In this paper, we address these challenges by proposing a new algorithm, called NET-FLEET, for fully decentralized FL systems with data heterogeneity. The key idea of our algorithm is to enhance the local update scheme in FL (originally intended for communication efficiency) by incorporating a recursive gradient correction technique to handle heterogeneous datasets. We show that, under appropriate parameter settings, the proposed NET-FLEET algorithm achieves a linear speedup for convergence. We further conduct extensive numerical experiments to evaluate the performance of the proposed NET-FLEET algorithm and verify our theoretical findings.