Reviewerly/exHarmony huggingface dataset:
Development of the Accordion Chamber Compositions in Bulgaria in 80`s - 90`s of XX c. - Georgi Mitev`s Rore for their Approval in Music Performing Art and Pedagogical Practice

The report reflects the problems related to the development of accordion pedagogy in Bulgaria at the end of the XX century and the accordion chamber performing art. Reveals part of the pedagogical and creative versatility of Georgi Mitev / Assoc. Dr. at AMTII "Prof. A. Diamandiev" - Plovdiv /, as head of the "club of accordion chamber ensembles". Keywords: Accordion, Accordion Pedagogy, Arranging for Accordion Chamber Ensembles
충청북도 중학교의 명칭 특성

Despite the 110-year history of secondary education in Chungbuk province, systematic research on education in Chungbuk province was very insufficient. Analysis about characteristics of Chungbuk middle school started from there. The results of analysis about names of 128 middle schools are as follows, as of April 1st 2021. In word type analysis, ‘Sino-Korea’ word was overwhelming at 98.4%. In syllable analysis, mean number of syllable was 2.63. In name source analysis, school name related to administrative district name was outstanding with 67.2% and school name related to humanistic factor was followed by 18.0%. Since large number of middle schools named after township(Myeon) name were abolished, the proportion of school name related to administrative district name decreased considerably. The proportion of private middle schools has decreased from 25.6% in 1970 to 14.8% now. The proportion of girls' middle schools has decreased from 20.6% in 1981 to 13.3% now. There have been 15 cases of renaming of middle school since 1952, and the proportion of private and girls' middle schools from renaming schools was relatively high. There were 19 middle schools that were actually abolished after 1952, and most of them were public middle schools in rural areas.
A Study on the Pollution Status of Akata Lake Sediments, Katsina-Ala Benue State, Nigeria

Heavy metals accumulation in biological system are by inhalation of contaminated air, intake of contaminated food or drinking water has been considered to be an ecological menace to man and other organisms. This study was carried out to ascertain the pollution status of Cr, Pb, Zn, Cu and Cd in sediment of Akata Lake, Katsina- Ala Benue State, Nigeria. Sediment samples were collected, digested using 30% H2O2 followed by 0.5M HCl and the metal concentrations were determined with Varian AA240 Atomic absorption spectrophotometer equipped with Zeeman’s background correction (Varian, New Jersey, USA). The mean level of the heavy metals, Pb, Zn, Cu, Cd and Cr are 31.05, 2.72, 19.22 and 0.88 mg/kg while the concentration of Cr was below the detection limit of the instrument. The values obtained were compared with the established soil and sediment standard by World Health Organization (WHO). The contamination factors value for Zn, Cu and Cd are &lt;1 while that of Pb is &gt;1, hence it shows that the sediment is polluted by lead. The pollution load index (PLI) and Geo-accumulation index (Igeo) levels for Cr, Zn, Cu, Cd heavy metals in sample A - E were less than 1 except for Pb which is &gt;1, this show that, the sediment were polluted with Pb.
Quality Exploration and Feasibility of Mass Media News Text Presentation as a Learning Media of Indonesian in High School Students

News teaching materials in textbooks do not have news comprehension and readability is low and contains information on old events.Based on these findings the author is interested in studying them.The objectives to be achieved include three things.One knows the typography and graphic text of the mass media news used in textbooks as learning material?Two know the description of the strengths and weaknesses of the message content in the news text that is used as a learning medium?What do three knowing the description of the feasibility of using news texts in textbooks in learning?The method of data analysis uses descriptive qualitative methods.(a) The translational equivalent method is used for typographic and graphical analysis of news texts, (b) the translational and equivalent referential methods for analyzing the strengths and weaknesses of news texts, (c) Expert triangulation techniques for analyzing the feasibility of text descriptions.The object of the news text research is in the accompanying textbooks of Indonesian Language Subjects in SMA X, XI and XII.Each book is taken 3 samples so that 9 texts are collected.Lack of text does not contain the completeness of the news element.News texts have a low degree of disregard.The information presented in the form of information has long been happening.The results of research from 9 data there are 3 data that have a complete news element.The 6 data elements of information are incomplete.Besides being incomplete, there are 6 graphic texts or supporting images to support the text.For the readability of the text data 3 and 5 according to the data, what is needed by students, but approaching the invalid area.The data 1, 2, 4, 6, 7, 8, and 9 readabilities does not correspond to the level of use.The eligibility criteria for data texts 1, 2, 3, 4, 5, and 6 do not have the element of news novelty.
Target Languages (vs. Inductive Biases) for Learning to Act and Plan

Recent breakthroughs in AI have shown the remarkable power of deep learning and deep reinforcement learning. These developments, however, have been tied to specific tasks, and progress in out-of-distribution generalization has been limited. While it is assumed that these limitations can be overcome by incorporating suitable inductive biases, the notion of inductive biases itself is often left vague and does not provide meaningful guidance. In the paper, I articulate a different learning approach where representations do not emerge from biases in a neural architecture but are learned over a given target language with a known semantics. The basic ideas are implicit in mainstream AI where representations have been encoded in languages ranging from fragments of first-order logic to probabilistic structural causal models. The challenge is to learn from data the representations that have traditionally been crafted by hand. Generalization is then a result of the semantics of the language. The goals of this paper are to make these ideas explicit, to place them in a broader context where the design of the target language is crucial, and to illustrate them in the context of learning to act and plan. For this, after a general discussion, I consider learning representations of actions, general policies, and subgoals ("intrinsic rewards"). In these cases, learning is formulated as a combinatorial problem but nothing prevents the use of deep learning techniques instead. Indeed, learning representations over languages with a known semantics provides an account of what is to be learned, while learning representations with neural nets provides a complementary account of how representations can be learned. The challenge and the opportunity is to bring the two together.
PENGEMBANGAN MODEL PERKULIAHAN DARING DALAM MENINGKATKAN BERPIKIR HOTS MELALUI PEMAHAMAN ISI BACAAN MAHASISWA PRODI BAHASA INDONESIA IKIP SILIWANGI TAHUN 2020

A special model is needed to stimulate higher order thinking in students. Given a problem to be able to reach a high level of understanding, High Order Thinking Skill (HOTS) is also needed so that in the future students can be more productive and creative. This study aims as a development effort in improving reading skills through the HOTS mode. The research method used is quantitative or experimental research. The design used was a quasi-experimental research design. Then the subjects studied were the IKIP Siliwangi students for the academic year 2020-2021. The number of second-level students was 48 people. The score in the first cycle is 2.55 or has an achievement level of 51% which is in the qualification which is almost enough to be sufficient. The score in the second cycle is 3.41 or has an achievement level of 68.2% which is more than good enough qualification. Thus, there was a growth of 17.2% from cycle I. Meanwhile, the score for the third cycle or the last cycle was 3.80.
Influence of Personal Experience on Readership of Local Contents in National Newspapers Among Residents of Select Communities in Anambra North

This study is a response to over concentration of media studies on media effect. Premised on the negligence of what personal experiences and social ties can do to media consumers. The study examined whether personal experiences stimulate the need for information among respondents; if personal experience of issues informs readership of local news in national newspapers; determine whether community attachment encourages exposure to local news in national newspapers and establish whether people who are more involved in a community read local contents in national newspaper more than those who are less involved. The study applied survey and FGD research methods using questionnaire and interview guide to gather data from 400 respondents purposively selected from a total of 1,554,095 residents of the study area. The community attachment and sense of community theories were used to provide theoretical background for the study. Findings revealed that personal experiences of an issue in a community involvement of the resident encourage the desire for media interpretation of the issues already known. Drawing from available literatures and field data from the study, the researcher equally found that the more closely tied and committed one is to one‟s community, the more one desires to know about that community. The two null hypotheses tested and rejected provided more statistical support to the research findings obtained from the study.
Recognizing Face Image Based on Gabor and DCT Feature Extraction using SVM

This paper has proposed a hybrid approach by combining the Gabor filter and a Discrete Cosine Transform.Face recognition systems can use in authentication, human-computer interaction, surveillance, and lots of applications.These application users have demanded that a higher accuracy, more efficient, low-cost, and low-calculation time for the facial recognition system.The research issue in image recognition is to maximize recognition accuracy by improving the pre-processing of face-set images, developing the method of extraction of faces, as well as using the most effective face classifier.Feature extraction is an important step that can influence the accuracy of the recognition system.The advantage of the Gabor filter is that it may calculate a large number of features by projection in various directions and sizes.The problem with the Gabor filter is that it has a high dimension and high redundancy and that can be minimized by certain filtering and sampling techniques.In the proposed process, the Gabor features have been filtered by the sampling filtration and the Discrete Cosine Transform extracts the low-frequency features of the Gabor filter sampled.Then assign the obtained optimum features to the support vector machine (SVM) classifier.This algorithm aims to improve accuracy with fewer features for different expressions.The ORL (Olivetti Research Lab) face dataset has used for the experiment.The accuracy of the facial recognition method is 96 percent (verified by using 5-fold crossvalidation).
Deep Content Guidance Network for Arbitrary Style Transfer

Arbitrary style transfer refers to generate a new image based on any set of existing images. Meanwhile, the generated image retains the content structure of one and the style pattern of another. In terms of content retention and style transfer, the recent arbitrary style transfer algorithms normally perform well in one, but it is difficult to find a trade-off between the two. In this paper, we propose the Deep Content Guidance Network (DCGN) which is stacked by content guidance (CG) layers. And each CG layer involves one position self-attention (pSA) module, one channel self-attention (cSA) module and one content guidance attention (cGA) module. Specially, the pSA module extracts more effective content information on the spatial layout of content images and the cSA module makes the style representation of style images in the channel dimension richer. And in the non-local view, the cGA module utilizes content information to guide the distribution of style features, which obtains a more detailed style expression. Moreover, we introduce a new permutation loss to generalize feature expression, so as to obtain abundant feature expressions while maintaining content structure. Qualitative and quantitative experiments verify that our approach can transform into better stylized images than the state-of-the-art methods.
Values and Emotions in Personality System of Adolescents and Youths

This article addresses the problem of identifying relationship between the emotional manifestations of adolescents and young people and dominant values in their personality system in order to establish priorities in the acquisition of the components of the value orientation at the subjects. The age of adolescence is the period of social and emotional development, cognitive and emotional explosion, and psychic and value system formation. Personality formation takes place in the social environment through interrelation with parents, friends, and teachers in different life situations. Adolescents may experience positive and negative emotions of varying intensity. Emotional stability develops gradually through experiences, socialization, cognitive progress, self-knowledge and self-affirmation which are already being formed and are specific to young people. It is important to self-recognize and self-analyse by the subject of his/her own specific emotions, to determine the causes of their occurrence, to know how to regulate the negative ones. The role of emotions is enormous for the human being. The affective sphere is one of fundamental elements for: the fixation of externalized manifestations through the behavioural display of suitable emotions, shaping of attitudes, the development of beliefs and, therefore, values. These components are organized hierarchically, forming the content of value orientation or values orientation. The experimental study carried out with adolescent and young subjects consisted in determining the specificity of emotions and dominant values in adolescence and youth ages, highlighting the dynamics of emotional and value changes, and establishing the relationship between the studied variables. The experiment results provide us with the current information regarding dynamics of the relationship of emotions and values, which, therefore, allows to elaborate new ways of emotions knowing and regulating during adolescence age including youth one. These strategies can be applicable in educational institutions, ensuring by them well-being for all education actors. Well-being means not only feeling well inside, but also to be in well- being created conditions in the environment around us, favouring the wellbeing of all subjects.
AN SEMI SUPERVISED ANOMALY DETECTION IN CLOUD COMPUTING BASED ON CSI-HAC CLUSTERING AND IRLS-cGAN DETECTION TECHNIQUE

Identifying cyber-attacks in cloud infrastructures is essential for protecting the cloud environment from cyberattacks.It is difficult to detect cyber-attacks in cloud infrastructures due to the complex and distributed nature of cloud infrastructures.In addition, various attacks that happen dynamically or randomly, online attacks, adversarial attacks, and data leakages increase the difficulty and complexity of cyber-attack detection.The work has proposed highly secured semi supervised anomaly detection in a cloud environment using the IRLS-cGAN detection technique.To enhance the capability of the proposed framework, the work has explored Data preprocessing, data extraction, data selection, and finally, attack classification.The approach arrests the probability of attack caused due to unnecessary data by providing a proper structuration with the help of data preprocessing that includes redundant data removal, handling categorical features, data scaling, and handling imbalanced data.In order to bring an optimal relation between the features and the outcome, the approach has extracted the informative data using GRA and selected the relevant data using the MRSO technique.Thereafter, the behavior of various environments based on clouds, VM, networking, and attacks are grouped into a cluster using the CSI-HAC technique, which is then trained to IRLS-cGAN for detecting the attacks.The results obtained demonstrate that the proposed cloud-based anomaly detection model is superior in comparison to the other stateof-the-art models (used for network anomaly detection) in terms of accuracy, detection rate, false-positive rate, and falsenegative rate.
Deep Cross of Intra and Inter Modalities for Visual Question Answering

Visual Question Answering (VQA) has recently attained interest in the deep learning community.The main challenge that exists in VQA is to understand the sense of each modality and how to fuse these features.In this paper, DXMN (Deep Cross Modality Network) is introduced which takes into consideration not only the inter-modality fusion but also the intra-modality fusion.The main idea behind this architecture is to take the positioning of each feature into account and then recognize the relationship between multi-modal features as well as establishing a relationship among themselves in order to learn them in a better way.The architecture is pretrained on question answering datasets like, VQA v2.0, GQA, and Visual Genome which is later fine-tuned to achieve state-of-the-art performance.DXMN achieves an accuracy of 68.65 in test-standard and 68.43 in test-dev of VQA v2.0 dataset.
Formation of meaning-making motivation of undergraduate students in the process of foreign language education

Introduction. In the modern educational context of knowledge society, the strategies of intellectualization of education are put forward as priorities. Fostering of personal intellectual culture is an actual purpose of the professional education of future teachers. The important condition of the process of intellectualization of foreign language education in higher school is the stipulation of the development of speech-thinking activity. The motivational factor of fostering of personal intellectual culture in undergraduate pedagogical students is connected with the actualization of the motives of communication, social interaction, self-realization, self-regulation, as well as the formation of meaning-making motivation for performing speech-thinking activity. The purpose of this study is to identify the pedagogical conditions for formation of meaning-making motivation in the process of learning a foreign language to ensure students’ speech-thinking activity as the means of fostering of personal intellectual culture. Materials and methods. The methodological basis of the research are the principles of the concepts of intellectualization of education and fostering of personal intellectual culture, motivation, speech-thinking activity and systemic, activity-based, communicative-cognitive, polysubjective, personality-developing, personality-oriented approaches. The research methods: modeling of the process of fostering of personal intellectual culture in undergraduate pedagogical students by means of foreign-language education; the analysis of educational technologies for the formation of cognitive, social and meaning-making motives. Research results. The potential efficiency of applying the educational technologies and methods of the symbolic-modeling type of activity (the method of role-playing games, the method of scenarios, art technologies, the technology of cognitive travel, etc.) as the practical tools for implementing the conceptual-theoretical foundations of the motivational aspects of the strategy of intellectualization of foreign language education has been analyzed and revealed. Conclusion. Meaning-making motivation is related to professional and creative self-educational activities, self-actualization of the personal potential and the demonstration of the cognitive independence of future teachers.
Analysis of the Relation Among Global Stocks Using Fuzzy/Linguistic IF-THEN Rules

In this paper, using a fuzzy mathematical model, we analyze multiple stocks and produce a description for their interrelation.Our analysis is based on theories of F-transform and fuzzy natural logic.The suggested method consists of two main steps; first, we smooth out time series using F-transform.Afterward, we evaluate their similarities in different lags and provide a linguistic description of their interrelations.A python package is developed to automatize the suggested method fully.The method is tested on 26 global stocks from five different regions.
Space-Time Loop Tiling for Dynamic Programming Codes

We present a new space-time loop tiling approach and demonstrate its application for the generation of parallel tiled code of enhanced locality for three dynamic programming algorithms. The technique envisages that, for each loop nest statement, sub-spaces are first generated so that the intersection of them results in space tiles. Space tiles can be enumerated in lexicographical order or in parallel by using the wave-front technique. Then, within each space tile, time slices are formed, which are enumerated in lexicographical order. Target tiles are represented with multiple time slices within each space tile. We explain the basic idea of space-time loop tiling and then illustrate it by means of an example. Then, we present a formal algorithm and prove its correctness. The algorithm is implemented in the publicly available TRACO compiler. Experimental results demonstrate that parallel codes generated by means of the presented approach outperform closely related manually generated ones or those generated by using affine transformations. The main advantage of code generated by means of the presented approach is its enhanced locality due to splitting each larger space tile into multiple smaller tiles represented with time slices.
SEARCHING FOR THE FORGOTTEN: EXAMINING THE ONLINE PRESENCE OF VICTIMS OF POLICE KILLINGS

This paper examines the potential role of social media in enhancing the understanding and perception of victims of police killings and the data collection surrounding these incidents. Through a series of content analysis and social media mining exercises, the authors observe the emergence of three distinct types of social media content offered on victims of police killings: persistence of the deceased’s activity across social media, sensational commentary on videos and blog postings, and memorials on Facebook, Twitter, and Tumblr. As part of a larger investigation of the availability and accessibility of official police homicide data, this paper aims to present social media data as a potentially powerful source of information to supplement quantitative reports. This process may be especially useful for the most affected communities, particularly BIPOC communities.