Reviewerly/exHarmony huggingface dataset:
Digital Art Using Machine Learning

Digital art creation uses digital art that includes multiple colors support for several applications. Digital art implies making out of semantic characters in a could not capacity by hand or finger advancements. Digital arts contrast from customary scribble, the last consists of object-up-object-down development, when the past needs a determined plan of creating occurrence. We label digital art affirmation issues in a few accomplice papers. For some type of exceptions, the data which have been drawn through hand signals were processed or trained in a easy way. We label digital art on two levels: development characters and multicolored characters. Detached digital art characters could be seen like development movements regardless of the way that with extended intricacy and variance. In order to avoid overlapping of letters, we created different types of characters and stored in a virtual space for exhibiting the data. A covered Markov model is employed for digital art exhibiting and affirmation. We display the event data along estimations past a two-dimensional bearing might be helpfully particular for digital art affirmation. We display the development data along estimations past a two-dimensional bearing could be helpfully particular for digital art affirmation. Now, look into the general ampleness of various component estimations of optical and inertial after signs and resulting the plausible affirmation execution accordingly. The proposed structure attains a characters screw up pace of 0.8% for characters-based affirmation and 1.9% for different color-based affirmation.
A Blockchain-based Mobility as a Service concept design with the adoption of Autonomous Vehicles: An exploratory study of the most elemental systems, requirements and possible configurations

Autonomous vehicles have the potential to introduce numerous changes, opportunities, and innovations in mobility services, environment, data, value networks, and economy, among others. These automated vehicles are expected to impulse a disruptive change in society, especially within transport technology economics, travel behavior, and network performance. This introduces new challenges and opportunities for the automotive industry and society. Mobility as a Service could take a significant role in the utilization of autonomous vehicles, especially with the current downwards tendency of car ownership in Europe and North America. The scientific literature and consultancy firms reinforce this by providing analysis and predicting a decline of car ownership in the global markets. Nevertheless, there is a need for trust and more secure and tamper-resilient processes within autonomous vehicles and Mobility as a Service ecosystems. Because of this, the idea of this research is to explore what a Blockchain-based Mobility-as-a-Service concept design operated by Autonomous Vehicles would look like, elemental systems, requirements, and possible configurations. This research is conducted from a complex socio-technical point of view that considers a Design Science Research methodology. By doing so, a set of requirements are developed to create a Blockchain architecture, business processes and a reference architecture that will serve as a proof of concept of how a Blockchain-based Mobility-as-a-Service concept design operated by Autonomous Vehicles would look like. Later the artefacts and the process realized in this research are evaluated through a semi-structured interview process. By doing so, some corrections were included in the final design but in general positive impression were given by the experts. This research could be useful to have a first approach on the requirements, BC architecture, business processes and reference architecture of a Blockchain-based Mobility-as-a-Service concept design operated by Autonomous Vehicles. In this regard, Mobility as a Service operators could find this research helpful to start creating or implementing such a system.
The Use of Speech Acts in Donald Trump Tweets

This study explores the use of speech acts in Computer Mediated Communication (CMC), particularly on Social Networks Sites (SNS). The present study expands on previous studies by Dauterman, Schrock ...
AN IMPROVED MULTI-LABELED LSTM TOXIC COMMENT CLASSIFICATION

The origin of text classification was far back to the early '60s. Text classification classified text into different predefined classifications. One of the techniques used for text classification long short term memory, which is an artificial recurrent neural network architecture. Today, all around the world people are expressing themselves with their opinions and also discuss among others via the media. In such a setup, it is quite observable that discussions may arise due to differences in opinion. These discussions might take a dirty side and which may further result in combats over the social media platforms and may lead to offensive language termed as toxic comments. To identify online hate speech, a large number of scientific studies have been devoted to using Natural Language Processing in combination with Machine Learning and Deep Learning methods. Among the challenges of toxic comment, classifiers are the Out-of-vocabulary words problem, which is the occurrence of words that are not present in the training data. Long-Range Dependencies are also a challenge to toxic comment classification. Which is a situation whereby the toxicity of comments often depends on expressions made in the early parts of the comment. This is especially problematic for longer comments. Another challenge is the low accuracy of comment classification techniques. Epoch was used in improving the accuracy of long short term memory. Epoch tends to improve the accuracy of the classifier since it positively affects the speed and quality of the learning process. We have an improvement of 0.4068 in precision, 0.2871 in a recall, 0.2293 in F1, and 0.4291 inaccuracy.
Organizational culture and social climate in Kazakhstani higher education institutions during the COVID-19 crisis: KazNU case study

The purpose of this study is to analyze the current characteristics of organizational culture and climate in Kazakhstani higher educational institutions during the COVID-19 crisis Materials for the study were collected from interviews and online discussions published on the website of Al-Farabi Kazakh National University (KazNU) In addition, results from the social monitoring systems of the university's educational activities as well as an official survey have been used The study offers details of how Kazakhstani universities dealt with the crisis by presenting KazNU case study The paper presents the responsible actions developed at the university as well as the problems faced by students and teachers One important lesson to be learnt is that educational organization needs to be more comprehensive, caring for appropriate technical equipment, helping develop skills for staff and students, and include vulnerable groups of the population © 2020 Lippincott Williams and Wilkins All rights reserved
Research on Discourse Parsing: from the Dependency View

Discourse parsing aims to comprehensively acquire the logical structure of the whole text which may be helpful to some downstream applications such as summarization, reading comprehension, QA and so on. One important issue behind discourse parsing is the representation of discourse structure. Up to now, many discourse structures have been proposed, and the correponding parsing methods are designed, promoting the development of discourse research. In this paper, we mainly introduce our recent discourse research and its preliminary application from the dependency view.
Is Bitcoin a Substitute for Traditional Commodity Money? A Safe Haven for Africa’s Forex Markets during the COVID-19 Outbreak

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Depth Self-optimized Learning Toward Data Science

We propose a two-stage model called Depth Self-Optimized Learning (DSOL), which aims to realize ANN depth self-configuration, self-optimization as well as ANN training without manual intervention. In the first stage of DSOL, it will configure ANN of specific depths according to a specific dataset. In the second stage, DSOL will continuously optimize ANN based on Reinforcement Learning (RL). Finally, the optimal depth is returned to the first stage of DSOL for training, so that DSOL can configure the appropriate ANN depth and perform more reasonable optimization when processing similar datasets again. In the experiment, we ran DSOL on the Iris and Boston housing datasets, and the results showed that DSOL performed well. We have uploaded the experiment records and code to our Github ( https://github.com/workharduwillwin/Depth-Self-Optimized-Learning-Toward-Data-Science ).
Investigating Effects of Online Learning Management Tools on English Language Learning Among Undergraduate Students During COVID-19, at Shaheed Benazir Bhutto University, Shaheed Benazir Abad

The aim of this study is to investigate an 'effects of Online Learning Management Tools on English Language learning among undergraduate students at Shaheed Benazir Bhutto University.This study investigates the experiences and perceptions of teachers and the effects of online LMS on English language learners about online learning management tools.A mix-method and explanatory design was selected to achieve the objective of this study.This study applied mix method approach to collect data.The data was gathered in a descriptive form with respect to the scope and nature of the issue.The quantitative data used Qualtrics, and then the data was analyzed with SPSS statistical software package.In a qualitative phase, the semi-structured interviews were conducted from the participants through google meet application and data was analyzed through thematic analysis.This study concludes that, an idea of LMS is important in these day which actually reduces the distance communication problem among the specific participants.Different contents can be added such as video, audio, text documents and special types of class announcement.There are certain technical as well as student's personal reasons that create gap in online learning; such as network problems which most often disconnect the teachers and students to completely convey and understand the lecture.
RETRACTED ARTICLE: Temporal and non-temporal contextual saliency analysis for generalized wide-area (GWA) search within UAV video

Unmanned aerial vehicles (UAVs) can be used to great effect for wide-area searches such as search and rescue operations. UAV enables search and rescue teams to cover large areas more efficiently and in less time. However, using UAV for this purpose involves the creation of large amounts of data, typically in video format, which must be analyzed before any potential findings can be uncovered and actions taken. This is a slow and expensive process, which can result in significant delays to the response time after a target is seen by the UAV. To solve this problem, a deep model using a visual saliency approach to automatically analyze and detect anomalies in UAV video is proposed. Temporal contextual saliency model is based on the state of the art in visual saliency detection using deep convolutional neural networks and considers local and scene context, with novel additions in utilizing temporal information through a convolutional LSTM layer and modifications to the base model. Additionally, the impact of temporal versus non-temporal reasoning for this task is evaluated. This model achieves improved results on a benchmark dataset with the addition of temporal reasoning showing significantly improved results compared to the state of the art in saliency detection.
Students’ Writing Assessment Analysis on Narrative Text (A Study Case of Food Technology Department, UNIMUS)

his research was aimed to analyze students’ writing assessment on EFL learners’ writing product (narrative text). The subject of this research was students from second semester of Food Technology Department, University of Muhammadiyah Semarang (academic year 2018/2019) with total 56 students. This research used descriptive qualitative method and focused on students’ writing product to be analyzed. The research showed that students’ lexical error had influenced to students’ writing composition through assessing based on writing assessment rubric by Brown (2007) on part of grammar (51.3%) as the lowest percentage score from learners’ writing product, then organization (61.2%), vocabulary (67.5%), content (67.9%), and mechanic (82.6%). Based on those percentages, it concluded that lexical error numbers affected to students’ writing composition in part of grammar, organization and vocabulary. Keywords: Students’ writing product, lexical error analysis, rubric of writing assessment
Special Issue on the International Conference on Algorithmic Aspects in Information and Management 2019 (AAIM'19)

We consider the problem of topology recognition in wireless (radio) networks modeled as undirected graphs. Topology recognition is a fundamental task in which every node of the network has to output a map of the underlying graph i.e., an isomorphic copy of it, and situate itself in this map. In wireless networks, nodes communicate in synchronous rounds. In each round a node can either transmit a message to all its neighbors, or stay silent and listen. At the receiving end, a node v hears a message from a neighbor w in a given round, if v listens in this round, and if w is its only neighbor that transmits in this round. Nodes have labels which are (not necessarily different) binary strings. Each node knows its label and can use it when executing the algorithm. The length of a labeling scheme is the largest length of a label. We concentrate on wireless networks modeled by trees, and we investigate two problems.•What is the shortest labeling scheme that permits topology recognition in all wireless tree networks of diameter D and maximum degree Δ?•What is the fastest topology recognition algorithm working for all wireless tree networks of diameter D and maximum degree Δ, using such a short labeling scheme? We are interested in deterministic topology recognition algorithms. For the first problem, we show that the minimum length of a labeling scheme allowing topology recognition in all trees of maximum degree Δ≥3 is Θ(log⁡log⁡Δ). For such short labeling schemes, used by an algorithm working for the class of trees of diameter D≥4 and maximum degree Δ≥3, we show almost matching bounds on the time of topology recognition: an upper bound O(DΔ), and a lower bound Ω(DΔϵ), for any constant ϵ<1.Our upper bounds are proven by constructing a topology recognition algorithm using a labeling scheme of length O(log⁡log⁡Δ) and using time O(DΔ). Our lower bounds are proven by constructing a class of trees for which any topology recognition algorithm must use a labeling scheme of length at least Ω(log⁡log⁡Δ), and a class of trees for which any topology recognition algorithm using a labeling scheme of length O(log⁡log⁡Δ) must use time at least Ω(DΔϵ), on some tree of this class.
A Universal Dependencies Conversion Pipeline for a Penn-format Constituency Treebank

The topic of this paper is a rule-based pipeline for converting constituency treebanks based on the Penn Treebank format to Universal Dependencies (UD). We describe an Icelandic constituency treebank, its annotation scheme and the UD scheme. The conversion is discussed, the methods used to deliver a fully automated UD corpus and complications involved. To show its applicability to corpora in different languages, we extend the pipeline and convert a Faroese constituency treebank to a UD corpus. The result is an open-source conversion tool, published under an Apache 2.0 license, applicable to a Penn-style treebank for conversion to a UD corpus, along with the two new UD corpora.
Who's a Good Boy? Reinforcing Canine Behavior using Machine Learning in Real-Time

In this paper we outline the development methodology for an automatic dog treat dispenser which combines machine learning and embedded hardware to identify and reward dog behaviors in real-time. Using machine learning techniques for training an image classification model we identify three behaviors of our canine companions: sit, stand, and lie down with up to 92% test accuracy and 39 frames per second. We evaluate a variety of neural network architectures, interpretability methods, model quantization and optimization techniques to develop a model specifically for an NVIDIA Jetson Nano. We detect the aforementioned behaviors in real-time and reinforce positive actions by making inference on the Jetson Nano and transmitting a signal to a servo motor to release rewards from a treat delivery apparatus.
Constructions of pseudorandom binary lattices using cyclotomic classes in finite fields

Abstract In 2006, Hubert, Mauduit and Sárközy extended the notion of binary sequences to n -dimensional binary lattices and introduced the measures of pseudorandomness of binary lattices. In 2011, Gyarmati, Mauduit and Sárközy extended the notions of family complexity, collision and avalanche effect from binary sequences to binary lattices. In this paper, we construct pseudorandom binary lattices by using cyclotomic classes in finite fields and study the pseudorandom measure of order k , family complexity, collision and avalanche effect. Results indicate that such binary lattices are “good,” and their families possess a nice structure in terms of family complexity, collision and avalanche effect.
Analysis of the Impact of Foreign Aid on Poverty Reduction in Nigeria, 1981-2017

One of the major purposes of the demand for foreign aid is to alleviate poverty. What is not clear, however, is whether the substantial foreign aid inflow to Nigeria in recent times has yielded the desired goal of poverty reduction. This work examined the impact of foreign aid on poverty reduction in Nigeria for the period 1981 to 2017. The objectives are to determine the impact of foreign aid on poverty reduction in Nigeria and to ascertain the direction of causality relationship between foreign aid and poverty reduction in Nigeria over the period of the study. Annual time series secondary data on: Poverty Reduction, Foreign Aid, Foreign Remittances, Inflation and Trade Openness, sourced from Central Bank of Nigeria (CBN) Statistical Bulletin 2017 online edition and the World Bank Development Indicator (WDI), were the variables used. Poverty Reduction is the dependent variable; others are the independent variables in the model. The study employed standard econometric method, involving theoretical (apriori), statistical (first-order) and econometric (second order)criteria. Autoregressive Distributed Lag (ARDL) Model and Pairwise Granger Causality Test were used to investigate the long run linkage between foreign aid and poverty reduction, the short run dynamics, and the direction of causality relationship between poverty reduction and foreign aid in Nigeria respectively. The ARDL bounds test result revealed that long run correlation exists among variables in the model. The result of ARDL Error Correction Model indicated that foreign aid had statistically significant impact on poverty reduction in Nigeria over the studied period. However, the result of Pairwise Granger Causality test revealed that there is no directional causality relationship between poverty reduction and foreign aid in Nigeria over the period studied. The study recommends that foreign aid flow should be directed more to the poor and the middle class in Nigeria by the government.
Actual situation of managing collective construction of positive pedagogy in high schools in Tan Phu District, Ho Chi Minh City

Study the current situation of active management of collective training in high schools in Tan Phu District, Ho Chi Minh City today to have a basis for proposing management measures to improve work efficiency. The management of positive collective construction at high schools in Tan Phu District, Ho Chi Minh City has developed relatively well, contributing to improving the quality of education and meeting increasing requirements of society. Pedagogical collectives are stable in number and structure, have a sense of discipline, have a healthy psychological atmosphere, have a sense of self-study to improve their qualifications, well obey the legislations. . However, the management of pedagogical collective development to become an active pedagogic group is not an easy task, requiring a lot of time and effort. Because some individuals in the pedagogical collectives are still not active, the internal conflicts continue to exist. The set of core values and values of the collective have not had a strong impact on the awareness and behaviors of all people.
STRING theorem from one dimension to high dimension

STRING is an important concept in geometry and physics, and is closely related to the distribution of energy system. This paper finds a geometric phenomenon that some properties of two-dimension STRING also exists in multiple-dimension STRING and proposes a STRING theorem from a dimension to high dimension. On this basis, an algorithm is designed to demonstrate the theorem and verify it. The concept of quantum energy density function is established to reveal the energy density conversion rule, and change trend between low-dimensional space and high-dimensional space in the framework of string theory.
Improving the students writing ability in Descriptive Text Throught scrapbook as the Media at MTs Al-Munawwarah Binjai

The objective of the study is to investigate the improvement of thestudents achievement in writing descriptive text by using Scrapbook as the mediain seventh grade students at MTs. Al-Munawwarah Binjai. This research usedClassroom Action Research method. The subjects of this research were students inVII grade that consist of 26 students. The instruments to collecting the data wasessay test. The result showed there were the improvement of students‘ writingability especially in descriptive test. The mean from the pre-test was 57,2 or only15,4% who success did the test. The mean from post-test I was 72,8 or 50%students who success did the test. The mean from post-test II was 81 withpercentage 92,3% students who success did the test. In conclusion, scrapbook asthe media can improve students‘ writing ability, especially in descriptive text.
NJU's submission to the WMT20 QE Shared Task.

This paper describes our system of the sentence-level and word-level Quality Estimation Shared Task of WMT20. Our system is based on the QE Brain, and we simply enhance it by injecting noise at the target side. And to obtain the deep bi-directional information, we use a masked language model at the target side instead of two single directional decoders. Meanwhile, we try to use the extra QE data from the WMT17 and WMT19 to improve our system’s performance. Finally, we ensemble the features or the results from different models to get our best results. Our system finished fifth in the end at sentence-level on both EN-ZH and EN-DE language pairs.