Reviewerly/exHarmony huggingface dataset:
Sesame Street to Mount Sinai: BERT-constrained character-level Moses models for multilingual lexical normalization

This paper describes the HEL-LJU submissions to the MultiLexNorm shared task on multilingual lexical normalization.Our system is based on a BERT token classification preprocessing step, where for each token the type of the necessary transformation is predicted (none, uppercase, lowercase, capitalize, modify), and a character-level statistical machine translation step where the text is translated from original to normalized given the BERT-predicted transformation constraints.For some languages, depending on the results on development data, the training data was extended by back-translating Open-Subtitles data.In the final ordering of the ten participating teams, the HEL-LJU team has taken the second place, scoring better than the previous state-of-the-art.
SCRATCH: La programación como detonante del pensamiento matemático

The use of new technologies as support in the teaching and learning processes is one of the challenges faced by teachers in basic education, the plans and programs of 2011 and 2017 for Basic Education in Mexico, emphasize the importance of considering various resources To enhance learning, go beyond the textbook and other printed materials. The purpose of this text is to present the results of a quantitative investigation that was conducted seeking to improve mathematical thinking skills, using a visual programming language called Scratch. The objective was to examine the impact of the program on the academic performance of fifth and sixth grade students of an Official Primary School in the city of San Luis Potosi (Mexico). The results show that there were improvements in the academic performance of the students in the intervened groups, above the control groups.
Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits

With heritage in nonlinear adaptive control (as proposed by Slotine) and physics-based control (as proposed by Lorenz), recently proposed methods referred to as deterministic artificial intelligence (D.A.I.) claim slight performance improvement over the parent methods. This brief communication firstly validates claims of slight improvement, but furthermore highlights a key feature: indications that improvements in observer implementations are the proper path for subsequent development in the field. The manuscript validates the recently published 97% performance improvement over classical methods using nonlinear adaptive methods, with an addition 0.23% performance improvement using D.A.I. compared to nonlinear adaptive control. Furthermore, the work also identifies strong correlation between system performance and observer performance, which is significant since D.A.I. eliminates controller tuning. Thus, observer improvement is recommended for future developments. The recently published 2-norm optimal learning scheme (of Smeresky) is recommended as the next step in the lineage of research in the discipline assuming augmentation with nonlinear state observers.
Application of Multidimensional FFT for the Extraction of Wave Vectors and Computer-Aided Visualization of LDV Measurements

In this contribution the FFT-aided visualisation of LDV scan data, as well as the extraction of various wave parameters are discussed. The robustness of several 2D phase-unwrapping algorithms to the measurement noise are compared. 2D phase unwrapping is used for extraction of local wave vectors' projections on the scan surface. In addition, few implementation details are discussed briefly. Finally, an interactive MATLAB graphical user interface is presented.
Implementasi Algoritma Fisher Yates Shuffle pada Aplikasi Belajar Huruf Hijaiyah

The form of coaching for early childhood is providing stimulation so that children have readiness to enter the next level of education. One of the basic educations provided by parents is religious education in the form of introduction to hijaiyah letters. Children interacting with smartphones more often than books, causing parents to have basic education such as reading and writing using books to be less attractive to children. From this problem, the writer wants to build a learning media application using smartphone media. This Hijaiyah Letter Learning application was created using the fisher yates shuffle algorithm, by randomizing the questions in this application. Based on the test results that the implementation of the Fisher Yates Shuffle algorithm for randomization of questions in the hijaiyah learning application is running well, there are no questions that appear repeatedly.
Quantum Proof Encryption Technology

Abstract: Nowadays we use text passwords to encrypt a file. This research paper proposes to use multimedia files like images videos, audio files and even applications as the password key to encrypt sensitive information. This algorithm can encrypt bulk data as well as single data sets. Keywords: steganography, multimedia file as key, Quantum computer, cryptography, Quantum computer proof encryption.
Continual Few-Shot Learning for Text Classification.

Natural Language Processing (NLP) is increasingly relying on general end-to-end systems that need to handle many different linguistic phenomena and nuances. For example, a Natural Language Inference (NLI) system has to recognize sentiment, handle numbers, perform coreference, etc. Our solutions to complex problems are still far from perfect, so it is important to create systems that can learn to correct mistakes quickly, incrementally, and with little training data. In this work, we propose a continual few-shot learning (CFL) task, in which a system is challenged with a difficult phenomenon and asked to learn to correct mistakes with only a few (10 to 15) training examples. To this end, we first create benchmarks based on previously annotated data: two NLI (ANLI and SNLI) and one sentiment analysis (IMDB) datasets. Next, we present various baselines from diverse paradigms (e.g., memory-aware synapses and Prototypical networks) and compare them on few-shot learning and continual few-shot learning setups. Our contributions are in creating a benchmark suite and evaluation protocol for continual few-shot learning on the text classification tasks, and making several interesting observations on the behavior of similarity-based methods. We hope that our work serves as a useful starting point for future work on this important topic.
Classification of Lung Cancer Using Nuclear Chromatin Characteristics

Background/Objectives: Studies on automatic diagnosis to assist pathologists have been accelerating due to artificial intelligence technology. We investigated whether two types of lung cancers, squamous cell carcinoma (Sca) and neuroendocrine tumor (NET), can be automatically classified with only the features of nuclear chromatin, similar to the method for diagnosing cancer by a pathologist.Methods/Statistical analysis: In order to classify lung cancer with only nuclear chromatin features, it is necessary to extract texture features of the nuclear chromatin area or train a deep learning model with only the nuclear chromatin area as input. We developed a segmentation algorithm that automatically extracts nuclear areas based on the k-means algorithm to automatically generate training and testing datasets for deep learning models. And using four types of deep learning models, we performed lung cancer classification using only nuclear chromatin features. Findings: The pathologist judges the presence and type of cancer by looking at the size and placement of the nuclei in the cell, and the texture characteristics. In this paper, a data set for a deep learning model was created by extracting only the nuclear region from a pathological image with a 40x magnification of WSI using an automatic segmentation algorithm. For four classification models, an average of 84% classification accuracy was obtained. Since only the nuclear region was used for training and testing, the deep learning model extracted and classified the size, shape, and texture features of the nuclei. Therefore, the classification of lung cancer performed in this study is similar to the method that the pathologist classifies cancer based on the features of nuclear chromatin. Since it showed 84% classification accuracy for the four types of models, it was proved that the automatic classification system using deep learning technology can be used to assist pathologists' diagnosis.Improvements/Applications: The accuracy of a cancer diagnosis system that applies only deep learning models has limitations. To improve diagnostic accuracy, we are going to study on a model that combines a deep learning model and machine learning technology that extracts and uses biomarker features for analyzing nuclei texture patterns.
On a class of bent, near-bent, and 2-plateaued functions over finite fields of odd characteristic

&lt;abstract&gt;&lt;p&gt;The main purpose of this paper is to study a class of the $ p $-ary functions $ f_{\lambda, u, v}(x) = Tr_1^k(\lambda x^{p^k+1})+Tr^n_1(ux)Tr_1^n(vx) $ for any odd prime $ p $ and $ n = 2k, \lambda\in GF(p^k)^*, u, v\in GF(p^n)^*. $ With the help of Fourier transforms, we are able to subdivide the class of all $ f_{\lambda, u, v} $ into sublcasses of bent, near-bent and 2-plateaued functions. It is shown that the choice of $ \lambda, u $ and $ v $, ensuring that $ f $ is bent, 2-plateaued or near-bent, is directly related to finding the subset $ A\subset GF(p)^3 $. The efficient method for defining the set $ A\subset GF(p)^3 $ is described in detail.&lt;/p&gt;&lt;/abstract&gt;
Sistem Pendukung Keputusan Penerimaan Bantuan Pupuk Kompos terhadap Kelompok Tani di Kecamatan Payung menggunakan Metode Visekriterijumska Kompromisno Rangijanje (VIKOR)

Compost fertilizer is organic fertilizer made by the process of decaying the remains of organic matter, both plant and animal waste, which is processed by fermentation first. At this time compost is one of the government programs to help the community Farmers Group. In the current farmer group program, the government is experiencing difficulties in determining the acceptance of compost fertilizer assistance, including the difficulty in determining who is really eligible to receive compost fertilizer assistance. To make it easier for the Umbrella sub-district to determine the receipt of compost fertilizer assistance in order to obtain precise and accurate results, a decision support system is needed to determine compost fertilizer assistance using the VIKOR method. The existence of a decision support system is able to provide convenience to the Umbrella sub-district in determining the correct and accurate receipt of compost fertilizer assistance.
Pembuatan Alat Penjernihan Air Dengan Filter Bertekanan Rendah Untuk Masyarakat Desa Laut Dendang Kecamatan Percut Sei Tuan Kabupaten Deliserdang

Clean water is water that is fit for consumption. Clean water is not only clear, odorless, and tasteless, but also must meet health requirements. These health requirements, among others, do not contain toxic chemicals or bacterial germs that can interfere with health. Dirty water contains solid or liquid substances resulting from waste disposal such as garbage, carcasses, used washing water, household waste and others. Polluted water will have an impact on human health. One of the waters that has been polluted with heavy metals is in the sea village of Percut Sei Tuan District, Deli Serdang Regency. For this reason, it is necessary to treat well water in the village of Laut Dendang. There are various simple ways that can be used to get clean water, and the most common way is to make a water filter and perhaps the most appropriate is to make a water purifier or a simple water filter. It is undeniable, that simple water filtration cannot eliminate completely as water with conditions, because processing dirty water into clean water must be done carefully so that the germs in the water are completely gone. Based on the description above, socialization and manufacture of water purification equipment with low pressure filters were undertaken for the people of Laut Village, Dendang village 6 (XI), village 8 (XIII), and village 9 (IX) Percut Sei Tuan District, Deli Serdang Regency. Several other things become obstacles in making water purification equipment with low pressure filters for the field community. For this reason, an innovation is needed to manufacture water purification equipment with low pressure filters for the community and overcome obstacles or solutions for making water purification equipment with low pressure filters. The Harapan University Service Team Medanundertaken socialization activities and direct field practice on the manufacture of water purification equipment with low pressure filters for the people of Laut Village, Dendang village 6 (XI), village 8 (XIII), and village 9 (IX) Percut Sei Tuan District, Deli Regency Serdang.
Fuzzy Cognitive Maps for Impact Assessment in Psychological Research: Case Study of Psychological Well-being

Assessing the impact of main factors on a given factor is important in various fields of psychological research.There are some methodological and statistical methods to tackle that problem.Recently, other methods and algorithms are being developed for this purpose.One of the methods is Fuzzy Cognitive Maps, which are recurrent neural network for modelling and simulations.The main advantage of this method is that it provides flexibility to model complex systems using causal relationships, thus allowing for what-if simulations.The key purpose of this paper is to introduce and apply this method in the Psychology field.More explicitly, this paper discusses this model in depth and applies it to a psychological well-being case study.
Don’t Go Far Off: An Empirical Study on Neural Poetry Translation

Despite constant improvements in machine translation quality, automatic poetry translation remains a challenging problem due to the lack of open-sourced parallel poetic corpora, and to the intrinsic complexities involved in preserving the semantics, style and figurative nature of poetry. We present an empirical investigation for poetry translation along several dimensions: 1) size and style of training data (poetic vs. non-poetic), including a zero-shot setup; 2) bilingual vs. multilingual learning; and 3) language-family-specific models vs. mixed-language-family models. To accomplish this, we contribute a parallel dataset of poetry translations for several language pairs. Our results show that multilingual fine-tuning on poetic text significantly outperforms multilingual fine-tuning on non-poetic text that is 35X larger in size, both in terms of automatic metrics (BLEU, BERTScore, COMET) and human evaluation metrics such as faithfulness (meaning and poetic style). Moreover, multilingual fine-tuning on poetic data outperforms bilingual fine-tuning on poetic data.
Query interface schema extracting from deep web using ontology

There are abundant information on the network, such as weather information, book information and so on. Now the Web information has far exceeded human imagination. However, most deep web information resources are not available through simple click, and often need query interface. We use domain ontology to construct the interface schema extraction method, the approach extracts interface schema from the perspective of visual units and the internal codes. There are many data extraction methods. Among them, CAW (Context-Aware Wrapper) is representative and outstanding. Thus, we choose it as our state-of-art baseline algorithm. The extraction effect of this method is better than CAW in the experiment. Finally, the effectiveness of this method is verified.
DEVELOPING STUDENT`S WRITING ACHIEVEMENT BY USING REFLECTION EXERCISES AT TENTH GRADE OF SMA PGRI 20 SIBORONGBORONG IN ACADEMIC YEAR 2018/2019

This study was aimed to investigate the  developing student`s writing achievement by using reflection exercises. the objective this study is  to find out the developing of reflection exercises on student`s writing ability of the SMA PGRI 20 Siborongborong.The writer use some theories those support this paper they  are Schon (1987), kolb (1989), J.A ( 2001), Fade (2005 ), George and Julia (1980), Arikunto (2006). The experimental design was applied on this study.The population of at Tenth grade  students of SMA PGRI 20 Siborongborong,exactly was 5 classes with totally 200 students and the sample that is chosen randomly are 2 classes (60 students) or 33%. As the  sample  divided into 2 groups, The first group was experimental group, it was taught by using reflection exercises, while the second group was control group, it is taught without reflection exercises and the taught by using topic development Writing test. The instrument for collecting data. It is descriptive  writing. The data  was analyzed by using t-test to prove the hypothesis data reability of the test is high 0,61. And the result of the research show that t­­­­-observed is show is 3,14 while t­­­­­­­­­­­­-table is 1,990 at the level of significance 0,05 with the degree freedom is 58. Itmeans the value of t-observed is higher than the value of t-table, so the proposed Hypothesis (Ha) is accepted while Ho is rejected, therefor it is concluded that reflection exercises significantly effect writing ability.
In the Company of Robots

This chapter lays out a transactional technique by which any existing natural or legal person can create an autonomous organization – specifically, an LLC with zero members controlled, as a matter of internal governance, only by software. The technique works under existing law and can be used with software without regard for the software’s “intelligence.”
Object DGCNN: 3D Object Detection using Dynamic Graphs

3D object detection often involves complicated training and testing pipelines, which require substantial domain knowledge about individual datasets. Inspired by recent non-maximum suppression-free 2D object detection models, we propose a 3D object detection architecture on point clouds. Our method models 3D object detection as message passing on a dynamic graph, generalizing the DGCNN framework to predict a set of objects. In our construction, we remove the necessity of post-processing via object confidence aggregation or non-maximum suppression. To facilitate object detection from sparse point clouds, we also propose a set-to-set distillation approach customized to 3D detection. This approach aligns the outputs of the teacher model and the student model in a permutation-invariant fashion, significantly simplifying knowledge distillation for the 3D detection task. Our method achieves state-of-the-art performance on autonomous driving benchmarks. We also provide abundant analysis of the detection model and distillation framework.
Official Development Assistance andPoverty Alleviation in Nigeria (1981-2017)

The aim of this study is to examine the long run equilibrium relationship between official development assistant and poverty alleviation in Nigeria over the period of 1981 to 2017 which past studies have failed to explore. Consequently, the study utilized data from UNCTAD, World Bank database, CBN Statistical Bulletin and Cointegration, DOLS and Granger Causality approach was used to address the objective of this study. However, the major findings in this study are summarized as follows. Firstly, there is a significant negative relationship between official development assistance and poverty level in Nigeria. However, FDI which also constitutes a strategic part of foreign capital in Nigeria does not contribute to poverty alleviation in Nigeria. Furthermore, official development assistance and poverty level in Nigeria have a bidirectional feedback. Due to the findings that emerged from this study, the following recommendations are made for the policy makers that whenever alleviation of poverty is the target of the policy makers in the country, the Nigerian government should be committed to the provision of a sound environment and good governance that can facilitate further inflows of official development assistance from the developed countries, especially G 7 countries. Also, the policy makers in Nigeria should ensure that ODA should be tailored towards projects and programs that have trickle down effects on the masses in the country.
AI Predictive Digital Analytics: A Model Computing Basis

This paper presents novel model computing techniques with model diagrams for the tableaux with the applications to predictive modeling, forecasting, planning, and predictive analytics support systems. Newer surface vs. deep digitization analytics is previewed. A novel sequent visual tableaux analytics computation system with a specific mathematical basis is developed. Applications for designing forecasting analytics are examined. Morph Gentzen logic from the authors’ publications since 1997 is the basis applied to forecasting analytics. Surface vs. deep digitization predictive analytics is presented with specific mathematical AI analytics techniques including nonmonotonic predictive model diagrams. Newer mathematical foundations are developed for the computing science that can forward visual agent computing predictive analytics, with applications to interactive predictive analytics. This includes Beth Tableaux’s descriptive characterizations the author developed around 2000, for Morph Gentzen sequent, to obtain Morph Gentzen model-theoretic sequent compactness to realize Tableau models. Applications to explaining interactive visual analytics are presented with specific examples from the enterprise modeling industry. Data filtering with keyed functions are novel techniques for a visual surface to deep digitization, while rapid content processing and content delivery are alternate surface digitization functionality realized. Newer examples from industry goals for the innovations areas, for example, interactive analytics or digitization for rapid business automation or enterprise cloud or web interface processing are examined. A primary distinctive feature for what is presented in this 150paper is that the predictive analytics are, for the most part independent from the historical data threads.
Use of E – Resources by Post Graduate College Students of Science of Dhamatari Distric, Chhattisgarh: A Study

This study attempt to know the use of eresources by post graduate college students of science of dhamatari district, Chhattisgarh.The main purpose of the study is to know the use of e-resources by the PG Students.The study was based on survey method.The study found that the most of the respondents use the internet daily with 1 -2 hours.Where majority 278(78.97%) of the respondents use e-books, 343 (94.44%) respondents use e-resources for their study, highest 75 (21.31%)respondents spent 2 -3 hours on the e-resources.and the maximum 229 (65.06%) respondents faced the slow downloading problem when they use electronic resources, and highest 260 (73.86%) respondents are highly satisfied with e-resources, 69 (19.60%) are satisfied, 13 (3.69%) are not satisfied and 10(2.84%) respondents fairly satisfied with using e-resources.