Reviewerly/exHarmony huggingface dataset:
Text area detection in handwritten documents scanned for further processing

In this paper we present an approach to text area detection using binary images, Constrained Run Length Algorithm and other noise reduction methods of removing the artefacts. Text processing includes various activities, most of which are related to preparing input data for further operations in the best possible way, that will not hinder the OCR algorithms. This is especially the case when handwritten manuscripts are considered, and even more so with very old documents. We present our methodology for text area detection problem, which is capable of removing most of irrelevant objects, including elements such as page edges, stains, folds etc. At the same time the presented method can handle multi-column texts or varying line thickness. The generated mask can accurately mark the actual text area, so that the output image can be easily used in further text processing steps.
UNPUBLISHED LETTER N.M. KARAMZIN TО E.A. PROTASOVA (BORN BUNINA)

Currently, with the financial support of the RFBR, the project "Karamzin and his entourage" is being implemented for the first time - a consolidated collection of personal information about contemporaries with whom N.M. Karamzin communicated throughout his life. The creation of such a reference book is necessary to restore the real picture of N.M. Karamzin, to compile the chronicle of his life and work. The richest source of the necessary data is the epistolary N.M. Karamzin, which makes it possible to expand, clarify, and detail also information about those who directly communicated with the historiographer or corresponded with him. Many of the letters from Karamzin have not yet been published. The lines from the letter of N.M. Karamzin is an addition to the love story of V.A. Zhukovsky to Maria Andreevna Protasova. The study was carried out with the financial support of the Russian Foundation for Basic Research within the framework of the scientific project No. 19-012-00292 A “N.М. Karamzin and his entourage".
Yablo’s Paradox, the Liar, and Referential Contradictions from a Graph Theory Point of View

F -systems are useful digraphs to model sentences that predicate the falsity of other sentences. Paradoxes like the Liar and the one of Yablo can be analyzed with that tool to find graph-theoretic patterns. In this paper we studied this general model consisting of a set of sentences and the binary relation ‘... affirms the falsity of...’ among them. The possible existence of non-referential sentences was also considered. To model the sets of all the sentences that can jointly be valued as true we introduced the notion of conglomerate, the existence of which guarantees the absence of paradox. Conglomerates also enabled us to characterize referential contradictions, i.e., sentences that can only be false under a classical valuation due to the interactions with other sentences in the model. A Kripke-style fixed-point characterization of groundedness was offered, and complete (meaning that every sentence is deemed either true or false) and consistent (meaning that no sentence is deemed true and false) fixed points were put in correspondence with conglomerates. Furthermore, argumentation frameworks are special cases of F -systems. We showed the relation between local conglomerates and admissible sets of arguments and argued about the usefulness of the concept for the argumentation theory.
Soundscapes: Rhetorical Entwinements for Composing Sound in Four Dimensions

Ahern introduces rhetoric and composition scholar-teachers to the genre of soundscape. She proposes four entwinements of soundscape and rhetoric: the selection of sound sources entwined with doxa, the ordering of sounds in time entwined with dispositio, the construction of simultaneity and layers entwined with copia, and the spatialization of sound entwined with saphes. This chapter demonstrates that the soundscape can make concepts like process, collaboration, and rhetorical awareness clearly accessible for writing classes across the curriculum, at all levels of composition instruction.
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning

Due to the broad range of applications of reinforcement learning (RL), understanding the effects of adversarial attacks against RL model is essential for the safe applications of this model. Prior theoretical works on adversarial attacks against RL mainly focus on either observation poisoning attacks or environment poisoning attacks. In this paper, we introduce a new class of attacks named action poisoning attacks, where an adversary can change the action signal selected by the agent. Compared with existing attack models, the attacker's ability in the proposed action poisoning attack model is more restricted, which brings some design challenges. We study the action poisoning attack in both white-box and black-box settings. We introduce an adaptive attack scheme called LCB-H, which works for most RL agents in the black-box setting. We prove that the LCB-H attack can force any efficient RL agent, whose dynamic regret scales sublinearly with the total number of steps taken, to choose actions according to a policy selected by the attacker very frequently, with only sublinear cost. In addition, we apply LCB-H attack against a popular model-free RL algorithm: UCB-H. We show that, even in the black-box setting, by spending only logarithm cost, the proposed LCB-H attack scheme can force the UCB-H agent to choose actions according to the policy selected by the attacker very frequently.
Deep Echo State Networks for Functional Ambulation Categories Estimation

In this work, we introduce a novel application for the automatic estimation of Functional Ambulatory Category (FAC) based on deep Echo State Networks (ESNs).FAC is a clinical scale for assessing the gait ability used in post-stroke rehabilitation and, in general, for disease monitoring.In this application, the estimation is performed automatically by analyzing signals gathered from wearable sensors (located on both tibiae, pelvis, trunk and head) during the execution of a walking test.This is performed by analysing the whole time-series through the DeepESN model without preprocessing.The experimental results show that the use of a deep recurrent neural network allows the model to exploit the richness contained in the whole raw temporal signal improving the performance w.r.t. the shallow recurrent model.Overall, our approach obtained 0.37 of mean absolute error with a maximum error of 0.78 resulting very accurate in the classification of the gait ability through the estimation of the FAC value.Considering the experimental results obtained, the proposed approach represents a good baseline for medical applications based on the automatic estimation of the FAC scale.* This research was
A bag of nodes primer on weightless graph classification

This paper proposes a weightless architecture for graph classification scenarios.This architecture is a three-headed arrangement composed of graph hand-picked features, a quantization method and a final classifier.Although multiple new strategies for graph classification have been proposed in recent years, it is still necessary to settle comparable studies with respect to weightless neural networks.The proposed architecture is evaluated along with other baseline classifiers and independent strategies, showing that weightless architectures are able to compete with other well-established methods such as graph kernels.
UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction.

Toxicity is pervasive in social media and poses a major threat to the health of online communities. The recent introduction of pre-trained language models, which have achieved state-of-the-art results in many NLP tasks, has transformed the way in which we approach natural language processing. However, the inherent nature of pre-training means that they are unlikely to capture task-specific statistical information or learn domain-specific knowledge. Additionally, most implementations of these models typically do not employ conditional random fields, a method for simultaneous token classification. We show that these modifications can improve model performance on the Toxic Spans Detection task at SemEval-2021 to achieve a score within 4 percentage points of the top performing team.
Robust Pedestrian Attribute Recognition Using Group Sparsity for Occlusion Videos

Occlusion processing is a key issue in pedestrian attribute recognition (PAR). Nevertheless, several existing video-based PAR methods have not yet considered occlusion handling in depth. In this paper, we formulate finding non-occluded frames as sparsity-based temporal attention of a crowded video. In this manner, a model is guided not to pay attention to the occluded frame. However, temporal sparsity cannot include a correlation between attributes when occlusion occurs. For example, "boots" and "shoe color" cannot be recognized when the foot is invisible. To solve the uncorrelated attention issue, we also propose a novel group sparsity-based temporal attention module. Group sparsity is applied across attention weights in correlated attributes. Thus, attention weights in a group are forced to pay attention to the same frames. Experimental results showed that the proposed method achieved a higher F1-score than the state-of-the-art methods on two video-based PAR datasets.
Generation of Bell and GHZ states from a hybrid qubit-photon-magnon system.

We propose a level-resolved protocol in a hybrid architecture for connecting a superconducting qubit and a magnon mode contained within a microwave cavity (resonator) to generate the local and global entangled states, enabling a wide range of applications in quantum communication, quantum metrology, and quantum information processing. Exploiting the high-degree of controllability in such a hybrid qubit-photon-magnon system, we derive effective Hamiltonians at the second- or the third-order resonant points by virtue of the strong counter-rotating interactions between the resonator and the qubit and between the resonator and the magnon. Consequently, we can efficiently generate the Bell states of the photon-magnon and the qubit-magnon subsystems and the Greenberger-Horne-Zeilinger state of the whole hybrid system. We also check the robustness of our protocol against the environmental noise by the Lindblad master equation. Our work makes this hybrid platform of high-degree of controllability a high-fidelity candidate for the realization of the maximally-entangled multiple states.
Sim2Air - Synthetic aerial dataset for UAV monitoring.

In this paper we propose a novel approach to generate a synthetic aerial dataset for application in UAV monitoring. We propose to accentuate shape-based object representation by applying texture randomization. A diverse dataset with photorealism in all parameters such as shape, pose, lighting, scale, viewpoint, etc. except for atypical textures is created in a 3D modelling software Blender. Our approach specifically targets two conditions in aerial images where texture of objects is difficult to detect, namely illumination changes and objects occupying only a small portion of the image. Experimental evaluation confirmed our approach by increasing the mAP value by 17 and 3.7 points on two test datasets of real images. In analysing domain similarity, we conclude that the more the generalisation capability is put to the test, the more obvious are the advantages of the shape-based representation.
An Analysis on Monitoring of Healthcare by Means of Internet of Things

The convergence of computer science and electronics domain has resulted in the Internet of Things, which is perhaps the most striking innovative outcome (IOT). The Internet of Things (IoT) is a innovative technology with the aim of rapid evolvement by means of numerous new advancements in clinical and wellness areas. Over the last few years, the impact of IOT in medical services has significantly increased. Internet of Things (IoT) based Health-related wearable devices are posing new challenges by incorporating creative innovation and assets. In/out understanding’s wellbeing status can be observed intermittently and routinely using wellness wearable gadgets.The Internet of Things (IoT) will profoundly help patient investigation and testing methods due to tiny internet protocol based sensors in a remote location that can be mounted on the patient’s body. Currently there are devices with seven billion in value, with expertise predicting the number will rise to ten billion by 2020 and 22 hundred million by 2025. This paper aims to examine and comprehend the utilization of Internet of Things in redesigned healthcare services, as well as how outstanding medical care can be obtained with ease. To summarise, the discussion has been projected on IoT capabilities on remote and detection frameworks and on the relevances utilized on effective medical care applications.
Віртуальна і доповнена реальності в творчості українських мистців

The purpose of the publication is to analyze the virtual and augmented reality in the work of Ukrainian artists of the last thirty years. The study digital visual art space is the analytical methods to determine internal trends and prospects of contemporary Ukrainian art. The main methodological approach is the cultural-semiotic analysis of the forms of manifestation of visualization of modern art culture. The application of a systematic approach allowed to study semiotic systems that contribute to the comprehensive disclosure of the problem of semantics of modern forms of visualization. The peculiarities of the use of technologies for the support and demonstration of works of art are revealed, the emphasis is on the emergence of new digital trends in contemporary art, thanks to which visual works function in their own media environment. Virtual and digital realities in the fine arts replace the artist's canvas, and the ironer allows him to immerse himself in other unreal worlds of the author's subconscious. Thus, from a simple fascination with technology, the modern generation of artists quickly moved to quality and content requirements, conceptuality and communication between the author, viewer and work of art, which requires artists to understand modern technology, traditional techniques of painting. The scientific novelty of the work is to identify the main characteristics of the development of modern visual arts with the help of virtual space in the Ukrainian cultural environment. The practical significance of the obtained results is that they can be used as a factual and theoretical basis for the creation of relevant sections in scientific and scientific-methodical publications on the history of contemporary Ukrainian art.
THE ROLE OF THE FAMILY IN THE SOCIALIZATION OF CHILDREN WITH DISADILITIES

This article is devoted to the problem of family education of a child with disabilities. The article reveals the main difficulties that such families may face. It also describes the features characteristic of families raising children with special needs.
Pemetaan Daerah Rawan Longsor Menggunakan Machine Learning di Kecamatan Muara Tami, Kota Jayapura, Papua

One of the high landslide vulnerability areas in Indonesia is located in Muara Tami District, Jayapura City. The main factors triggering the landslide are steep slopes and high elevations. However, there are still several other factors that also influence the occurrence of landslides, such as vegetation, land cover, and curvature. Landslides that occur can cause damages to all parties, both material and non-material. Therefore, it is necessary to map landslide-prone areas as a non-structural mitigation planning. This planning is useful for identifying areas that are relatively safer from landslides so that the fatalities incurred can be minimized. In this mapping, several parameters are used that are thought to trigger landslides, then they are calculated by machine learning using the random forest method. Based on the parameters used, the eastern and northwestern areas have high slope and elevation values, high curvature contrast values, and dry land forest cover. These results indicate the high potential for landslide vulnerability in both parts of the study area. Meanwhile, areas with low potential for landslide vulnerability have a curvature of 0, a relatively low slope and elevation. The model accuracy value obtained by the random forest method is 0.9. This value is categorized as good enough because it shows that the parameters used are good enough in mapping landslide vulnerability in the study area. These results are also supported by the high sensitivity and specificity values ​​based on the ROC curve. Areas with high potential for landslide vulnerability are Mosso Village and the border between Skow Mabo, Skow Yambe, Koya Tengah, Koya Timur, and Holtekam. Meanwhile, areas with low landslide potential were Skow Sae Village, the central part of East Koya, the northern part of West Koya, and the southern part of Holtekam.
AN INTERVIEW ABOUT USING ONLINE GAMES IN VOCABULARY ACQUISITION AMONG THE SECOND YEAR STUDENTS OF UNIVERSITY COLLEGE SABAH FOUNDATION

The purpose of this study is to investigate the vocabulary acquisition among English as a second language (ESL) Learners. The study is based on grounded theory research designs. Grounded theory allows the study to generate a far-reaching comprehensible theory founded on behaviourism, cognitivism, and constructivism on the central phenomenon (using online games in vocabulary learning); from the results and findings (Creswell, 2012). A systematic design is applied which highlights the use of data analysis procedures of open, axial, and selective coding, as well as the development of logic paradigm or visual picture(s) from the generated theory (Creswell, 2012). The interview protocol consists of three parts: personal information, advantages and disadvantages factors, as well as possible factors that support or inhibit vocabulary acquisition. Based on the findings, there are basically eight (8) descriptor codes, and thirty (30) in-vivo codes. All of these codes are emerged from the inductive coding method. The Emerging possible categories from the first sample: The possible categories that could be derived from the pattern of codes are: a. Personal preferences &amp; knowledge on computer or online games, b. Habitual playtime on playing computer or online games, c. Online games &amp; learning English vocabulary, and d. Factors that could hinder learning English vocabulary.
AN INTUITIONISTIC FUZZY APPROACH FOR COVID-19 SURVEY ANALYTICS

In 2020 there were numerous studies on the impact of COVID-19 on the human body. In this paperwe target to analyze the results from a sociologic survey over a very big group of responders whoseanswers comprise a big data set. We suggest an approach for analysis based on a simple acquisitionof intuitionistic fuzzy sets where intuitionistic fuzzy degrees are assigned on the dimensions health,socio-economic status, social and family relations in the context of COVID-19 for the purposes of dataanalysis and visualization. The intuitionistic fuzzy sets of assessments are based on the classicalAtanasov’s intuitionistic fuzzy set theory and the subsequent analyses use an approach forcomparison between such sets based on Intuitionistic Fuzzy norms and distances. We apply thesuggested approach on a real big dataset, obtained from the Survey of Health, Ageing and Retirementin Europe (SHARE) Wave 8. COVID-19 Survey 1. Release version: 0.0.1. beta. SHARE-ERIC. Dataset. DOI: 10.6103/SHARE.w8cabeta.001 (Borsch-Supan, A. 2020). As a result, EU countries areclassified into several groups that segment the database in terms of health, socio-economic status,social and family relationships in the context of COVID-19. The methodology we offer is broad enoughto be extended to other research or disciplines.
Distance educational technologies as a facilitator of students’ cognitive flexibility: risks and prospects

The article is devoted to the analysis of training methods in higher educational institutions, the comparison of the main characteristics of the educational process in institutions of full-time and distance learning, their disadvantages in modern conditions are identified, the main advantages of distance learning, compared to the ways of providing and processing information, the interaction of teachers and students of different forms of education, the application of methods of facilitation and development of cognitive flexibility in modern distance education are considered. The directions of distance learning influence on the process of formation of students' creativity and freedom of opinions, ability to search for solutions, and aspiration to self-education are described. The relationship between the methods of facilitation and formation of cognitive flexibility in students' education is revealed. The main directions of self-improvement and peculiar characteristics of teachers who can perform the functions of a facilitator of the educational process are described. The influence of the teacher-facilitator and distance education methods directly as a facilitator of the process of formation of cognitive flexibility of students is considered.
When is gray-box modeling advantageous for virtual flow metering?

Integration of physics and machine learning in virtual flow metering applications is known as gray-box modeling. The combination is believed to enhance multiphase flow rate predictions. However, the superiority of gray-box models is yet to be demonstrated in the literature. This article examines scenarios where a gray-box model is expected to outperform physics-based and data-driven models. The experiments are conducted with synthetic data where properties of the underlying data generating process are controlled. The results show that a gray-box model yields increased prediction accuracy over a physics-based model in the presence of process-model mismatch, and improvements over a data-driven model when the amount of available data is small. On the other hand, gray-box and data-driven models are similarly influenced by noisy measurements. Lastly, the results indicate that a gray-box approach may be advantageous in nonstationary process conditions. Unfortunately, model selection prior to training is challenging, and overhead on gray-box model development and testing is unavoidable.
METHODOLOGY OF SOCIAL AND PEDAGOGICAL COUNSELING IN THE CONTEXT OF LIFELONG EDUCATION

Deepening socio-economic development, competition in the labor market, capital, financial resources to the fore put forward the requirements of employers to improve the personnel professionalism, the quality of professional training of graduates of educational institutions.The need for constant advanced training and professional specialists training is becoming quite justified.The concept of "education for life", which has traditionally developed over many decades, has been replaced by the concept of acquiring new knowledge, developing skills, and mastering professional skills throughout life.The negative impact of scientifically unreliable information flow on student youth, characteristic of the post-industrial era, initiates the need for the formation of competencies in selection, analysis, practical use of verified information, generation of innovative ideas to find competent solutions to emerging problems.