Reviewerly/exHarmony huggingface dataset:
Using Pick and Speak Board Game to Enhance Pupils’ Speaking Skill

English as a Second Language emerges as an essential language to acquire and impacts most of the education system. The scenario also requires our young learners to master the language to polish their interpersonal and communication skills to prepare them for a developing nation. This research aims to study whether using the Pick and Speak board game can help enhance speaking skills among primary school pupils. The research applied quasi-experimental research to obtain data. 67 Year 3 pupils from four suburban schools in Sarawak were identified as the research sample. The researchers grouped them into high achievers, intermediate and low achievers. Pupils were given questionnaires to get their feedback to support the data finding from the pre-test and post-test. With the aid of this board game intervention, pupils are expected to construct sentences using present continuous tense and describe their daily routines orally. The study indicated that using Pick and Speak board game impacts pupils' speaking performance, especially in vocabulary and pronunciation, followed by grammar and fluency aspects during the learning process. The researchers hoped that this board game innovation would benefit both the teachers and young learners as the alternative teaching aid to teach and develop their speaking skills with grammatical errors free. Most importantly, the Pick and Speak board game offers an effective way of improving teaching efficiency, encouraging the element of collaborative and competition in a non-threatening environment.
An Exploratory Study to Extract Analogies from Arabic Text

Word analogies is a relationship of the form “x is to y as w is to z”, it can hold over an arbitrary number of ordered pairs. Automatically extracting analogies from text is a task that is not well explored for Arabic. Thus, in this paper we investigate the use of Zero-Shot Learning with word embeddings as an automatic approach for Arabic analogies extraction. We applied this approach on three corpora: King Saud University Corpus of Classical Arabic (KSUCCA), Gigaword, and Wikipedia embeddings. The best obtained precision result was 0.667 with Gigaword corpus. We further analyzed the extracted analogies relations manually to understand their types.
On the Care and Feeding of Virtual Assistants: Automating Conversation Review with AI

With the rise of intelligent virtual assistants (IVAs), there is a necessary rise in human effort to identify conversations containing misunderstood user inputs. These conversations uncover error in natural language understanding and help prioritize improvements to the IVA. As human analysis is time consuming and expensive, prioritizing the conversations where misunderstanding has likely occurred reduces costs and speeds IVA improvement. In addition, less conversations reviewed by humans mean less user data are exposed, increasing privacy. We describe Trace AI, a scalable system for automated conversation review based on the detection of conversational features that can identify potential miscommunications. Trace AI provides IVA designers with suggested actions to correct understanding errors, prioritizes areas of language model repair, and can automate the review of conversations. We discuss the system design and report its performance at identifying errors in IVA understanding compared to that of human reviewers. Trace AI has been commercially deployed for over 4 years and is responsible for significant savings in human annotation costs as well as accelerating the refinement cycle of deployed enterprise IVAs.
Segmentation of Low Contrast Satellite and Medical Image s Based on Level Set Function with Harmony Search Optimization Algorithm

The main aim of digital image segmentation for portioned the image in to its constituents parts for getting information regarding features of image also used to get pathological details from medical images. The literature available from last two decades the important scheme for image segmentation is with Level Set technique, multilevel thresholding of gray scale on histogram of image is also a traditional method of image segmentation. In this paper low contrast images from medical and satellite images considered for image segmentation to extract features. This paper puts forward a novel image segmentation method via Level Set Function along with Bi-Histogram Equalization based on Harmony Search Algorithm(LSFBHEHS). The Selective Binary and Gaussian Filtering Regularised Level Set (SBGFRLS) is efficient novel region based Active Contour Model, it uses a novel region-based signed pressure force (SPF) function, it can adeptly halt the contours at blurred edges and weak edges. Other important advantage is internal and external boundaries can be distinguished by fixing the initial contour may be anyplace in the considered image. This method is resourceful but requires more time and inefficient for segmentation of low contrast images. This problem is rectified by applying bi-histogram equalization(BHE) image enhancement method prior to Level Set, it can be treated as pre-processing. In BHE technique of image enhancement, the image histogram is partitioned into two divisions based optimized gray level threshold , and equalize each part of histogram separately and combined later. To find the optimized threshold level to slice the histogram into two parts, Otsu’s multilevel thresholding method used to find threshold level, to find optimized thresholding level Harmony Search Algorithm(HSA) is implemented to maximize inert class variance as objective function. For evaluating the proposed method and SBGFRLS, the qualitative measured used like Dice similarity index, Measure of Enhancement(EME) and time required, for experimentation numerous low contrast satellite and medical images are considered, results clarified that the proposed method is more efficient for low contrast and inhomogeneous images.
Calibration Method for Line-Structured Light Three-Dimensional Measurements Based on a Single Circular Target

Single circular targets are widely used as calibration objects during line-structured light three-dimensional (3D) measurements because they are versatile and easy to manufacture. This paper proposes a new calibration method for line-structured light 3D measurements based on a single circular target. First, the target is placed in several positions and illuminated by a light beam emitted from a laser projector. A camera captures the resulting images and extracts an elliptic fitting profile of the target and the laser stripe. Second, an elliptical cone equation defined by the elliptic fitting profile and optical center of the camera is established based on the projective geometry. By combining the obtained elliptical cone and the known diameter of the circular target, two possible positions and orientations of the circular target are determined and two groups of 3D intersection points between the light plane and the circular target are identified. Finally, the correct group of 3D intersection points is filtered and the light plane is progressively fitted. The accuracy and effectiveness of the proposed method are verified both theoretically and experimentally. The obtained results indicate that a calibration accuracy of 0.05 mm can be achieved for an 80 mm × 80 mm planar target.
Directed Acyclic Graph-based Distributed Ledgers – An Evolutionary Perspective

Blockchain platforms like Bitcoin and Ethereum have introduced a distributed and decentralized cryptocurrency system with no third-party intermediation required. These peer to peer network systems allows Internet users to directly transact with each other. However due to the heavy emphasis on decentralization, scalability has taken a back seat. It has also become a key issue in the wider adoption of these technologies. The change to the underlying data organizing structure to Direct Acyclic Graphs (DAG) of the distributed ledger, has significantly increased transaction scalability. In this paper, we analyse some of the Distributed Ledger Technologies that use DAGs and have shown marked improved in transaction performance without weakening security.
Framework to analyze the use of colors in informational maps related to Covid-19 data

This paper presents a study about using colors in maps that present information related to the Covid-19 pandemic. A sample was collected from two different governmental websites of the state of Rio Grande do Sul, Brazil. An analysis framework was developed based on the sample and drew from a literature review about the use of colors and maps in data visualization. The analysis showed that one of the examples collected presents an inconsistent use of colors, which could prompt difficulties in visualizing the data; the other example uses color more consistently, potentially being more effective in communicating information. In addition, the study showed potential for continuity and expansion due to the relevance of information design applied to health information.
"A Smack of Irrelevance" in Inconsistent Mathematics?

&#x0D; &#x0D; &#x0D; Recently, some proponents and practitioners of inconsistent mathe- matics have argued that the subject requires a conditional with ir- relevant features, i.e. where antecedent and consequent in a valid conditional do not behave as expected in relevance logics —by shar- ing propositional variables, for example. Here we argue that more fine-grained notions of content and content-sharing are needed to ex- amine the language of (inconsistent) arithmetic and set theory, and that the conditionals needed in inconsistent mathematics are not as irrelevant as it is suggested in the current literature. &#x0D; &#x0D; &#x0D;
Education of Patriotism of Younger Teenagers by Means of Gamificatio

The article reveals the relevance of patriotic education, substantiates the emergence of new modernized, rethought forms and methods of work in the context of patriotic education. The scientific understanding of the education of patriotism on the basis of the model of humanistic-oriented developmental education through the subject-subject and socio-pedagogical strategy is revealed. Possibilities for realization of these strategies through using means of gamification as space for creation of effective conditions of education of the citizen-patriot are substantiated. Gamification is characterized as one of the key trends in education in the XXI century. The purpose of the article is to reveal the potential of gamification in the process of educating the patriotism of young adolescents, to identify the benefits and possible risks, to expand teachers' understanding of the possibilities and resource potential of gamification in education. The advantages of gamification as a means of patriotic education are described, the optimality of using this tool in work with younger adolescents is revealed, the peculiarities of gamification introduction in this age period are revealed, the task of gamification in the context of patriotic education is revealed. The novelty of the study is to introduce forms of patriotic education by means of gamification into practice of general secondary education and out-of-school education. The functions of gamification aimed at the development and formation of personality are described. It is emphasized on the fact that the use of elements of gamification in no way implies the replacement of education or upbringing, but is another methodological tool for working with younger adolescents in the context of patriotic education. The risks that may arise in the process of introducing elements of gamification are outlined, aspects that are important to consider when gamifying the process of educating the patriotism of young adolescents to avoid them are revealed. The conclusions confirmed that in patriotic education of younger teenagers this methodological toolkit has a number of advantages and helps to make the educational process more effective.
A MODEL FOR EVALUATING AND ELIMINATING THE PROBLEMS OF “SHOСK” TRANSITION TO DISTANCE LEARNING IN A MODERN UNIVERSITY

T he current epidemic situation in Russia and the world caused by coronavirus infection requires prompt solutions to eliminate the consequences of the pandemic in almost all sectors of economic activity. The isolation of the population from mutual contacts was one of the effective solutions to the problem. This arose the need for the educational institutions to promptly switch to a remote mode of education. Those involved in the distance learning process faced a wide range of different problems, which defines the topicality of developing the efficient measures aimed to overcome the challenges of the distance learning process. The subject of the work is the analysis of the processes arising from the sudden transition of the educational environment of the university to the distance learning format, the development of a rating model designed to grade the problems which are important for the students and which are determined by the transition to the distance learning format, and the development of directions for solving the established problems. The aim of the study is to identify the problems that have arisen from the transition to a distance learning format among the students and to develop an organizational and managerial tool to fix these problems. The study relies on the information taken from a secondary data analysis performed by the Russian Public Opinion Research Center (VCIOM) and the results of their own research by the students specializing in Economics at the Institute of Economics and Management in Penza State University. The research methods included SPSS based comparative and frequency analysis. The scientific novelty of the work is as follows: 1) a rating evaluation of the problems arising from the transfer of the educational process of the university to a distance learning format in the context of a "shock" transition associated with force majeure caused by coronavirus infection has been developed; 2) University students’ satisfaction with distance learning and students’ readiness to apply the technological distance learning tools and possibilities of Moodle based electronic educational resources in the distance learning format have been identified. Statistical processing of the research results with nonparametric criteria contributes into the reliability of the findings and the recommendation. The practical significance of the research lies in the developed concept of the organizational and managerial mechanism for overcoming the problems arising from the transition to the university’s distance learning environment. This mechanism refers to the social and informational areas, reduces the time of transition to the distance learning format, and accounts for the interests and readiness of the teaching staff and students. Further research is seen to be connected with monitoring the validity of the revealed problems in their dynamics with a one semester lag.
PresB-Net: parametric binarized neural network with learnable activations and shuffled grouped convolution

In this study, we present a novel performance-enhancing binarized neural network model called PresB-Net: Parametric Binarized Neural Network. A binarized neural network (BNN) model can achieve fast output computation with low hardware costs by using binarized weights and features. However, performance degradation is the most critical problem in BNN models. Our PresB-Net combines several state-of-the-art BNN structures including the learnable activation with additional trainable parameters and shuffled grouped convolution. Notably, we propose a new normalization approach, which reduces the imbalance between the shuffled groups occurring in shuffled grouped convolutions. Besides, the proposed normalization approach helps gradient convergence so that the unstableness of the learning can be amortized when applying the learnable activation. Our novel BNN model enhances the classification performance compared with other existing BNN models. Notably, the proposed PresB-Net-18 achieves 73.84% Top-1 inference accuracy for the CIFAR-100 dataset, outperforming other existing counterparts.
Geochemistry and Petrogenesis of Dioritic-Gabbroic Pegmatites in the Bulfat Complex, Qala Diza, Northeastern Iraq

The pegmatite dikes and associated plutonic rocks stand as a part of the igneous complexes associated with the Bulfat complex, located in the Zagros Suture Zone (ZSZ), NE Iraq.The Bulfat complex is a part of the ophiolite-bearing terranes that are allochthonous sheets.The complex represents the upper allochthon of the Albian-Cenomenian.The study area is located within Bulfat complex of ZSZ, specifically in the northwestern part of this zone and within the second unit of the Penjween-Walash Subzone.The rock samples were collected from pegmatite dike which is located to the northeast of the Darishmana village, the thickness of dike is about 5 m.Electron microprobe analyses (EMPA) of plagioclase in 8 spots of dioritic pegmatites ranges between oligoclase (An18.00-An28.23)and andesine (An32.53-An33.62).Bulk whole-rock chemical analysis of fourteen samples using ICP-MS analysis reveals alkalinity Index (AI) of pegmatites to be metaluminous (A/NK > 2).Generally, the silica content in these pegmatites is from 46.70 wt.% to 52.67 wt.%.The relatively flat pattern of REEs is characterized by the slight enrichment of LREEs compared to HREEs indicating the common ancestry of the studied pegmatites.Also, the enrichment of these pegmatites in LILEs (Sr, Pb, Rb) and depletion in HFSEs especially (Nb, Ta, Y) indicate the environment of the island arcs.Moreover, the low ratios of (Rb/Sr)N and (Ba/Sr)N refer to that these pegmatites are derived from a basic origin.Tectonic discriminate diagrams show that the tectonic environment of studied pegmatites is I-type, which is the oceanic island arcs environment of sub alkaline rocks.The pegmatites of the present study have a genesis relationship with intrusions close to them in the study area; these intrusions are gabbros of Wadi Rashid that represent the environment of E-MORB.Moreover, the gabbros of Wadi Rashid and studied pegmatites are part of ophiolite-bearing terranes, they are found within upper allochthon thrust sheet.The current study of pegmatites reflects the oceanic island arcs environment, this indicates the existence of double island arcs, the first adjacent to the Arabian shelf, and the second close to the middle of paleo-ridge.Numerous evidences support that the gabbros of Wadi Rashid being as the likely parent to the studied pegmatites such as geochemistry, tectonogenesis, and the close spatial distribution of the pegmatites to the gabbros of Wadi Rashid.Moreover, the
Region under Discussion for visual dialog

Visual Dialog is assumed to require the dialog history to generate correct responses during a dialog.However, it is not clear from previous work how dialog history is needed for visual dialog.In this paper we define what it means for visual questions to require dialog history and we propose a methodology for identifying them.We release a subset of the Guesswhat?! questions for which their dialog history completely changes their responses.We propose a novel interpretable representation that visually grounds dialog history: the Region under Discussion.It constrains the image's spatial features according to a semantic representation of the history inspired by the information structure notion of Question under Discussion.We evaluate the architecture on task-specific multimodal models and the visual transformer model LXMERT and show that there is still room for improvement. QuestionHR CMO +RuD 1. is it human?no no no 2. is it food?
Economic Growth, Economic Development, and Poverty: A Bibliometric Analysis

: The pertinacious problem of poverty in the world questions whether the existing mechanisms are effective in the poverty reduction process. Exploring this field has intensified since 1989, and this research provides an overview of the theoretical and empirical research that addresses economic development and poverty. Bibliometric analysis is performed, which includes names of the most common authors, journals, and countries of publications, as well as the frequency of words and expressions in the existing literature. Bibliometric units are researched from the Scopus database using keywords "economic development", "economic growth" and "poverty". The main contribution of this analysis is the review of existing literature addressing the key issues and identifying theoretical and empirical research in the field of economic development and poverty. This is the first study of its kind where scientific attention is being paid to economic growth, economic development, and poverty by the most influential journals and most prominent authors in economics.
Profile Multiple Intelligence Siswa SMA Negeri 5 Kerinci: Linguistic, Interpersonal, Intrapersonal

Students' ignorance of the potential they have, especially the intelligence they have, is an obstacle for students to be able to improve their achievements in school. For that, it needs to be revealed multiple intelligence profiles, especially linguistic, interpersonal, and intrapersonal intelligence of students. This research is a type of quantitative research with a descriptive approach. The population in this study was a student of State Junior High School 5 Sungai Penuh that amounted to 145 people and the study sample was taken with the formula Slovin with random sampling cluster sampling technique so that the number of samples in this study amounted to 107 people. This research instrument uses the Multiple Intelligence Scale that researchers develop themselves, and then the data that has been collected is tabulated and analyzed with descriptive statistics to calculate respondents' scores using ideal scores. The results of this study revealed that students' multiple intelligence profiles related to linguistic intelligence were in the medium category, then the student's interpersonal intelligence was in a low category, and the student's intrapersonal intelligence was in the medium category
Knowledge Base Completion Meets Transfer Learning

The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases.In this work, we introduce the first approach for transfer of knowledge from one collection of facts to another without the need for entity or relation matching.The method works for both canonicalized knowledge bases and uncanonicalized or open knowledge bases, i.e., knowledge bases where more than one copy of a real-world entity or relation may exist.Such knowledge bases are a natural output of automated information extraction tools that extract structured data from unstructured text.Our main contribution is a method that can make use of a large-scale pre-training on facts, collected from unstructured text, to improve predictions on structured data from a specific domain.The introduced method is the most impactful on small datasets such as ReVerb20K, where we obtained 6% absolute increase of mean reciprocal rank and 65% relative decrease of mean rank over the previously best method, despite not relying on large pre-trained models like BERT.