Reviewerly/exHarmony huggingface dataset:
THE INFLUENCE OF VOLUNTEER ACTIVITIES ON THE SOCIALIZATION OF CHILDREN FROM ORPHANAGES BY MEANS OF ART

Purpose of Study: The study is aimed at substantiating theoretically and methodologically the influence of volunteer activities on the socialization of children from orphanages using art and socio-cultural rehabilitation.&#x0D; Methodology:The study is divided into three stages, during which different methods of analysis and information collection are used. At the first stage, a preliminary analysis of secondary data from open sources is carried out. At the second stage, an analysis of the primary information received from the orphanage staff from semi-structured interviews and questionnaires developed by the authors of the study is carried out. At the third stage, conversations with children, as well as the observation during their activities and questionnaires for both the children and the volunteers, are carried out and projective methods are used.&#x0D; Main Findings: The study has revealed a significant role and influence that volunteers and their artistic and creative activities with children have on the process of socialization of children through art. Several practical recommendations have also been developed for organizing the selection of volunteers to work in orphanages and to organize, accompany and support volunteer activities there.&#x0D; Applications of this study: The results of the study can be used in pedagogical activities in the process of organization of leisure and extracurricular activities as additional education, both in orphanages for children with special needs and in educational institutions during the implementation of inclusive education. Besides, the results of the study can be used in the process of organization of psychological assistance and/or corrective work with children with special needs to help them socialize successfully through art.&#x0D; The novelty of the study: The theoretical and methodological substantiation of the positive influence of volunteer activities on the socialization of children from orphanages by means of art and socio-cultural rehabilitation. It was carried out with the help of questionnaires and surveys developed by the authors of the study, analysis and assessment of the degree of volunteers’ influence on the process of socialization of children with special needs, with identifying the main requirements for volunteers, practical recommendations for psychological support and support of volunteers and their work with children with special needs in orphanages.
Structured financial producs trading and it's implementation perspectives in emerging economies

The main objective of the research is to examine key aspects of the structured trade financing instruments and to analyze prerequisites for its existence. Hereby, the main focus is concentrated around developing economies. The research was carried out based on research methods such as comparative analysis, empirical approaches and logical generalization. Also, for the research purposes the current market situation, infrastructure, opinions and recommendations of international organizations were analyzed, and an optimal model and market conditions were generated. As a result of research an importance of the derivative financial instruments, organization of spot markets and warehouse receipt systems were investigated. The main factor limiting the study is a variable statistical need and mathematical modeling based on the economic specifications of each economy. As each market will need more country-oriented research and solution rather than general model that suits all. The practical significance of the research is to ensure synergies between financial and commodity markets and to develop a sustainable risk management tool, enhance access to capital by introducing an alternative financial instrument and market environment for local producers, including small and medium size entrepreneurs.
CNN Application in Detection of Privileged Documents in Legal Document Review

Protecting privileged communications and data from disclosure is paramount for legal teams. Legal advice, such as attorney-client communications or litigation strategy are typically exempt from disclosure in litigations or regulatory events and are vital to the attorney-client relationship. To protect this information from disclosure, companies and outside counsel often review vast amounts of documents to determine those that contain privileged material. This process is extremely costly and time consuming. As data volumes increase, legal counsel normally employs methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information. Keyword searching is relied upon as a method to target privileged information and reduce document review populations. Keyword searches are effective at casting a wide net but often return overly inclusive results - most of which do not contain privileged information. To overcome the weaknesses of keyword searching, legal teams increasingly are using machine learning techniques to target privileged information. In these studies, classic text classification techniques are applied to build classification models to identify privileged documents. In this paper, the authors propose a different method by applying machine learning / convolutional neural network techniques (CNN) to identify privileged documents. Our proposed method combines keyword searching with CNN. For each keyword term, a CNN model is created using the context of the occurrences of the keyword. In addition, a method was proposed to select reliable privileged (positive) training keyword occurrences from labeled positive training documents. Extensive experiments were conducted, and the results show that the proposed methods can significantly reduce false positives while still capturing most of the true positives.
Asertividad comunicacional en los procesos académicos en época de pandemia en las Instituciones de Educación Superior

Communicational assertiveness in academic processes allows higher education institutions to respond in a pertinent way with social responsibility in changing environments and scenarios with academic and scientific projects and links with society that contribute to improving the quality of life of the population. The objective of the research was to determine the incidence of assertive communication in academic processes in times of pandemic, specifically it was sought to analyze, identify and establish, techniques, learning climate and its applicability in virtual environments, through assertive communication channels articulate essential activities. The methodology used was quantitative, non-experimental descriptive design, it consisted of applying Likert scale surveys to a sample of 285 teachers, 371 students, 236 administrative personnel and an assertiveness test applied to academic coordinators; Among its main results there is evidence of a fragile communicational assertiveness between teachers and students in online classrooms, with special attention to elements such as the use of language, handling of expressions, thoughts, feelings and emotions. It is concluded that Universities should continue to promote with holistic humanistic pedagogy, the same one that has given the transforming and non-dominating meaning to education, consequently it is necessary to promote training programs for the development of teaching skills in virtual learning through the use of assertive techniques for teaching, with emphasis on the generation of two-way teacher-student dialogues with critical thinking in the search for the truth.
노랫말을 통해 보는 한국 현대사회의 상호종교적 소통구조: 번역과 의식(儀式)1)

This study investigates an interreligious aspect of Korean modernism based on the examplary vocal text of the some selected composers ‘inside’ Korea and the composers ‘outside’ Korea who were (are) active in Germany. The religion includes in this study cultural symbols that is bound to the collective memory of a society. I selected the song texts in which the religious symbols are shown relatively well in both area.In the vocal works of Geon-Yong Lee, the Song of Field (1994) which based on the text of Gang Baek Lee assumes ‘God’ in the sense of the Korean own religion Cheondo, and the Cantata for the Heenyeon-Jubilee (1991) which based on the text of Jeong Hee Goh assumes the Christian ‘God’. These have different religious contexts but they share a social conciousness. In particular, the cantata for the Cantata for the Heenyeon-Jubilee shows the thought of Korean liberation theology which is important for social engagement. On the other hand, the vocal text of Ak-Jang (樂章 2000) of Man-Bang Lee based on his own poem sings the ‘Eol’(soul) of the Confucian ancestors, and his Chan, Prajῆā-pra-mitā-sutra, Song (讚, 摩訶般若波羅蜜多心經, 頌2004) arranged a new song based on the Prajῆā-pra-mitā-sutra (般若心經) which is sung in Buddhist ritual.The vocal text of Isang Yun which most of all based on Taoism, Buddhism, and Christianity is specially characterised through its interaction with the lyric poems of Nelly Sachs, a female Jewish poet in exile. And The wise man (1977) which mixes the Bible’s Ecclesiastes and Lao-tzu’s Tao Te King implicates the interreligious dialogue between the composer and German scriptwriter in a Christian environment. The emotion of silence in the Goethe’s lyric Parable, on which is based the vocal text of Younghi Pagh-Paan’s Bi-Yu (1999) overlaps with the characteristics of Eastern religious thought. In addition, the several works with the subject ‘Nim’ - a Korean own noun (which means a religious object or homeland or nostalgic person etc.) are connected organically. Since the year 2006 this topic is also connected to the subject ‘light’.The vocal works of the above-mentioned composers except for the text with the mother tongue belong to the category of translational reproduction. They hybridizise culture and religion. As long as composers ‘outside’ their homeland remember their mother tongue and culture, their vocal texts are compatible with the vocal texts of the composer ‘inside’ each other. This interaction of the both-area which materializes traditional religious symbols can be regarded as an aspect of an interreligious ‘ritual’.
Modelling Medieval Vagueness.

The project An Agile Approach Towards Computational Modeling of Historiographical Uncertainty is building a taxonomy of historiographical uncertainty. We are focusing on early medieval texts as our case studies, because they are characterised by a high degree of “high stakes” uncertainty and a varied historiography characterised by a vivid debate. The additional factor of the manuscript text-transmission ensues that also the material aspect of the textual study will be covered in our attempt to build an adaptable taxonomy of historiographical uncertainty. Computational humanities need a robust methodological platform, that can be applied to a wide variety of projects. Uncertainty in general and geographical uncertainty in particular stand as the crucial aspects of this platform. We investigate a methodology of visualising geographical locales in historical texts and their historiographies that explicitly models uncertainty in.
A Methodology for Encoding Regulatory Rules.

© 2020 CEUR-WS. All rights reserved. This paper introduces a methodology for the encoding of rules into a semantic logical format to facilitate the automated reasoning process. We demonstrate how to identify, capture, combine, and thus formulate all the components from rules into a computationally-oriented formalism. The need for the methodology is motivated by the desire for automated reasoning of automated vehicle information regarding traffic rules. We use Defeasible Deontic Logic as a formal foundation of our methodology. The overtaking traffic rule is our use-case to illustrate the usefulness of our methodology. Through this use-case, it is seen that the logical semantic representation of the traffic rules seems conceivable to support automated reasoning. This paper includes the source materials, the use cases, proposed methodology, and the example of encoding.
Complex Networks of Functions

Functions correspond to one of the key concepts in mathematics and science, allowing the representation and modeling of several types of signals and systems. The present work develops an approach for characterizing the coverage and interrelationship between discrete signals that can be fitted by a set of reference functions, allowing the definition of transition networks between the considered discrete signals. While the adjacency between discrete signals is defined in terms of respective Euclidean distances, the property of being adjustable by the reference functions provides an additional constraint leading to a surprisingly diversity of transition networks topologies. First, we motivate the possibility to define transitions between parametric continuous functions, a concept that is subsequently extended to discrete functions and signals. Given that the set of all possible discrete signals in a bound region corresponds to a finite number of cases, it becomes feasible to verify the adherence of each of these signals with respect to a reference set of functions. Then, by taking into account also the Euclidean proximity between those discrete signals found to be adjustable, it becomes possible to obtain a respective transition network that can be not only used to study the properties and interrelationships of the involved discrete signals as underlain by the reference functions, but which also provide an interesting complex network theoretical model on itself, presenting a surprising diversity of topological features, including modular organization coexisting with more uniform portions, tails and handles, as well as hubs. Examples of the proposed concepts and methodologies are provided respectively with respect to three case examples involving power, sinusoidal and polynomial functions.
High-Rate Quantum Private Information Retrieval with Weakly Self-Dual Star Product Codes

In the classical private information retrieval (PIR) setup, a user wants to retrieve a file from a database or a distributed storage system (DSS) without revealing the file identity to the servers holding the data. In the quantum PIR (QPIR) setting, a user privately retrieves a classical file by receiving quantum information from the servers. The QPIR problem has been treated by Song et al. in the case of replicated servers, both with and without collusion. QPIR over $[n,k]$ maximum distance separable (MDS) coded servers was recently considered by Allaix et al., but the collusion was essentially restricted to $t=n-k$ servers in the sense that a smaller $t$ would not improve the retrieval rate. In this paper, the QPIR setting is extended to allow for retrieval with high rate for any number of colluding servers $t$ with $1 \leq t \leq n-k$. Similarly to the previous cases, the rates achieved are better than those known or conjectured in the classical counterparts, as well as those of the previously proposed coded and colluding QPIR schemes. This is enabled by considering the stabilizer formalism and weakly self-dual generalized Reed--Solomon (GRS) star product codes.
Optimizing the Use of Library Resources Through Collaborative Partnership: A Case Study of AIIMS Patna

Information and Communication Technology has not just helped to change the publishing industry from print to electronic format, but has also posed some smaller challenges to the users in adapting to the newer formats of publishing. To overcome these smaller challenges, to enhance the use of e-resources and to promote e-resources among the user community, responsibility has somewhere shifted to library professionals, which they are entirely shouldering, especially in academic and research libraries. Library professionals are working in close coordination with the different stakeholders like authors, publishers, promoters, marketers, aggregators, e-content developers, and other agencies to facilitate the widespread use and promotion of e-resources among end-users. Library professionals are working as partners in perpetuating the change witnessed in the information world by working in tandem with the information creators and disseminators, ensuring effective handling and exploitation of digital content subscribed in their institutional libraries. The study is supplemented by a case study of AIIMS Patna, reflecting how organizing training and workshops have a considerable impact on the usage of e-resources subscribed by an institutional library.
Statistical Mechanics of Neural Processing of Object Manifolds

Invariant object recognition is one of the most fundamental cognitive tasks performed by the brain. In the neural state space, different objects with stimulus variabilities are represented as different manifolds. In this geometrical perspective, object recognition becomes the problem of linearly separating different object manifolds. In feedforward visual hierarchy, it has been suggested that the object manifold representations are reformatted across the layers, to become more linearly separable. Thus, a complete theory of perception requires characterizing the ability of linear readout networks to classify object manifolds from variable neural responses. A theory of the perceptron of isolated points was pioneered by E. Gardner who formulated it as a statistical mechanics problem and analyzed it using replica theory. In this thesis, we generalize Gardner's analysis and establish a theory of linear classification of manifolds synthesizing statistical and geometric properties of high dimensional signals. [..] Next, we generalize our theory further to linear classification of general perceptual manifolds, such as point clouds. We identify that the capacity of a manifold is determined that effective radius, R_M, and effective dimension, D_M. Finally, we show extensions relevant for applications to real data, incorporating correlated manifolds, heterogenous manifold geometries, sparse labels and nonlinear classifications. Then, we demonstrate how object-based manifolds transform in standard deep networks. This thesis lays the groundwork for a computational theory of neuronal processing of objects, providing quantitative measures for linear separability of object manifolds. We hope this theory will provide new insights into the computational principles underlying processing of sensory representations in biological and artificial neural networks.
Translation of English Complex/Compound Sentences into Indian Sign Language

.This paper outlines the concept for translation of English text to Indian Sign Language using real-domain synthetic animations. The translation framework consists of a processing module that parses the English input sentence to phrase structure grammar representation on which Indian sign language grammar rules are applied to reorder English phrase terms. The input English sentences was given by the user are parsed through the converter module which in turn exchange the complex and compound English sentences to their simplify versions by means of complex to simple and compound to simple English grammar rules. Elimination module removes unacceptable terms from the reordered sentence. Lemmatization is done in order to translate the words into the root form since the Indian sign language does not use the word inflections. All the words in the sentence are then tested into a lexicon containing the English word and its HamNoSys notation and their synonym replaces the terms that are not in the lexicon. Sentence words are substituted with their HamNoSys counter code. In the event that the word is not in the lexicon, HamNoSys code is taken for each word alphabet. The HamNoSys data is translated to the SiGML tags, and these SiGML tags are sent to animation module which translates the SiGML code to the synthetic animation utilizing avatar.
AVALIAÇÃO E VIABILIDADE DE IMPRESSORAS 3D DE BAIXO CUSTO PARA REALIZAÇÃO DE ATIVIDADES ACADÊMICAS

This work was carried out
Model-free classification of multivariate time-series based on epsilon-complexity theory.

This work is devoted to a new model-free approach to a problem of binary classification of multivariate time-series. The approach is based on the original theory of epsilon-complexity which allows almost every mapping that satisfies Hoelder condition, be characterized by a pair of real numbers –complexity coefficients. Thus we can form a feature space in which a classification problem can be formulated and solved. We provide an example of classification of real EEG signals.
Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery

Deep graph embedding is an important approach for community discovery. Deep graph neural network with self-supervised mechanism can obtain the low-dimensional embedding vectors of nodes from unlabeled and unstructured graph data. The high-order information of graph can provide more abundant structure information for the representation learning of nodes. However, most self-supervised graph neural networks only use adjacency matrix as the input topology information of graph and cannot obtain too high-order information since the number of layers of graph neural network is fairly limited. If there are too many layers, the phenomenon of over smoothing will appear. Therefore how to obtain and fuse high-order information of graph by a shallow graph neural network is an important problem. In this paper, a deep graph embedding algorithm with self-supervised mechanism for community discovery is proposed. The proposed algorithm uses self-supervised mechanism and different high-order information of graph to train multiple deep graph convolution neural networks. The outputs of multiple graph convolution neural networks are fused to extract the representations of nodes which include the attribute and structure information of a graph. In addition, data augmentation and negative sampling are introduced into the training process to facilitate the improvement of embedding result. The proposed algorithm and the comparison algorithms are conducted on the five experimental data sets. The experimental results show that the proposed algorithm outperforms the comparison algorithms on the most experimental data sets. The experimental results demonstrate that the proposed algorithm is an effective algorithm for community discovery.
Hubungan Motivasi Belajar dengan Prestasi Belajar Matematika Siswa Kelas V SD Negeri Daerah Binaan 1 Sumbang Banyumas

This researched is aimed to discover the relationship between learning motivation and learning achievement of mathematics subject for 5 graders in Fostered Public Primary School 1 sub-district Sumbang, Banyumas. The methods used for this research was corelation with quantity approach. The population was 387 students of grade V from 11 public primary schools, while 197 students were used for sampling. The samples were collected using probability sampling. The technique used for data collection were a non-test technique in form of learning motivation questionnaire and a test technique in form of essay questions. The result of this research shows that there is a relationship between learning motivation and learning achievement with rhitungrtabel = 0,3490,1398 with significance value 0,0000,05, so it is said as significant and applicable to the population. Regression test result shows that Y` = a + bX with the value of a = 7,861, which means if learning motivation = 0, so learning motivation = 7,861. The coeficient of b = 0,611, which means if learning motivation is raised up to 1 denomination, so learning motivation will be raised up to 0,611 denomination as well. These datas show that if the value of X is raised, the value of Y will be raised as well for both of them has positive and same direction of relationship.
LibGuides: The Fourth Industrial Revolution: The Internet of Things

Professor Klaus Schwab is convinced that we are at ‘the beginning of a revolution that is fundamentally changing the way we live, work and relate to one another.’ He calls this revolution the Fourth Industrial Revolution.