Reviewerly/exHarmony huggingface dataset:
Development of English Teaching Model Applying Artificial Intelligence through Maker Education

The purpose of this study is to demonstrate how EFL learners can overcome the limitations of traditional classes and practice communication through the learning activity model. As a research method, it was conducted from March to June 2019 to develop and derive strategies and guidelines through model development, validation, and application. After two validity tests, the model was applied to the experimental group, resulting in an increase of self-direction, engagement, problem-solving, and participation. Moreover the post results showed significant results in all fields, the usefulness of this model was confirmed. However, continuous follow-up research is needed, including the development of software that can easily apply AI related to English learning to classes, and the presentation of convergence activities with more systematic maker education in learning activities.
Voter Privacy and Big-Data Elections

Big data and analytics have changed politics, with serious implications for the protection of personal privacy and for democracy.Political parties now hold large amounts of personal information about the individuals from whom they seek political contributions and, at election time, votes.This voter data is used for a variety of purposes, including voter contact and turnout, fundraising, honing of political messaging, and microtargeted communications designed specifically to appeal to small subsets of voters.Yet both privacy laws and election laws in Canada have failed to keep up with these developments in political campaigning and are in need of reform to protect voter privacy.We provide an overview of big data campaign practices, analyze the gaps in Canadian federal privacy and election law that enable such practices, and offer recommendations to amend federal laws to address the threats to voter privacy posed by big data campaigns.
Membangun Keterampilan Berpikir Kritis Siswa Melalui Literasi Digital Berbasis STEM pada Masa Pandemik Covid 19

During the Covid 19 pandemic, learning allowed children to think critically and creatively through online learning based on STEM (Science, Technology, Engineering, Mathematics). STEM is developed integrated with the implementation of 2013 curriculum learning that leads to the development of 21st-century learning. STEM-based digital literacy is learning that has a syntax to build students' skills in solving problems and critical thinking skills (Critical Thinking Skills) so that they can actively build knowledge through digital literacy STEM-based. STEM as a learning approach as a bridge in the development of science to improve students' critical thinking skills (Critical Thinking Skill). This research is a qualitative research which is library research using books and other literature as the main object. The expected results by improving STEM-based online critical thinking skills for teachers are to provide changes in the pattern of learning activities and increase the content of new STEM-based science, especially during the Covid 19 pandemic, which can provide more space for teachers to explore student skills from afar with STAM-based digital literacy. The result of science education from STEM-based digital literacy that is expected for students is that students can learn and be skilled to improve critical thinking skills through STEM-based Digital Literacy, which in turn can be skilled in solving problems in real life.
Teaching EFL Students Using Selected Media: Offline Video Taken From YouTube

English is a foreign language that must be studied, there are four skills that students must master, namely writing, speaking, reading and pronunciation. The development of technology in the learning process of teachers is required to use media not only using books, markers, or other things that are not related to technology that will make teachers or students miss technological development. The current era of globalization is a lot of media or tools for teaching teachers, namely using smartphones, laptops, one of them is a website, application, social media such as Facebook, Instagram, Twitter, Line or other social media, and the last is YouTube. In this study we use YouTube offline video as a teaching media process. Using the media in the learning process and YouTube include new media or ways in the learning process and using YouTube definitely brings impact. Of course a positive impact for students or teachers in the learning process. This study will tell so much about the use of YouTube as a medium for teacher learning in learning English.
An Ensemble Machine Learning Approach to Understanding the Effect of a Global Pandemic on Twitter Users’ Attitudes

&#x0D; &#x0D; &#x0D; It is thought that the COVID-19 outbreak has significantly fuelled racism and discrimination, especially towards Asian individuals[10]. In order to test this hypothesis, in this paper, we build upon existing work in order to classify racist tweets before and after COVID-19 was declared a global pandemic. To overcome the difficult linguistic and unbalanced nature of the classification task, we combine an ensemble of machine learning techniques such as a Linear Support Vector Classifiers, Logistic Regression models, and Deep Neural Networks. We fill the gap in existing literature by (1) using a combined Machine Learning approach to understand the effect of COVID-19 on Twitter users’ attitudes and by (2) improving on the performance of automatic racism detectors. Here we show that there has not been a sharp increase in racism towards Asian people on Twitter and that users that posted racist Tweets before the pandemic are prone to post an approximately equal amount during the outbreak. Previous research on racism and other virus outbreaks suggests that racism towards communities associated with the region of the origin of the virus is not exclusively attributed to the outbreak but rather it is a continued symptom of deep-rooted biases towards minorities[13]. Our research supports these previous findings. We conclude that the COVID-19 outbreak is an additional outlet to discriminate against Asian people, instead of it being the main cause.&#x0D; &#x0D; &#x0D;
The Influence of a Profile in a Professional Social Network on the Development of a Specialist's Career

A successful career requires improving professional skills and developing business relationships. A personal profile in professional social networks and its proficient management can have a significant impact on career development. The article is devoted to investigating the impact of a specialists profile in professional social networks on the career development. The study analyses the statistical data on the use of social networks for recruiting. The relationship between a profile in professional social networks and the development of a specialist's career was investigated by conducting a sociological survey of networks users from various fields of activities. Most of the surveyed respondents (61 %) answered positively to the question about the benefits of professional social networks for career advancement, another part of the respondents (19 %) is not sure, but tends to answer positively. Only 11 % of the respondents are inclined to give a negative answer to this question and 1 % answered negatively, pointing out the uselessness of professional networks in their careers. Thus, the study confirmed the need to apply the profile for the development of a specialist's career. The advantages of a profile in professional networks are highlighted, allowing the development of effective professional communications: users of social professional networks, actively participating in forum discussions, publishing papers on the site, can attract attention of potential employers and develop their reputation. The authors propose to use a profile in a professional social network more widely in order to develop a specialist's career.
Blockchain-enabled Charging Scheduling for Unmanned Vehicles in Smart Cities

Recently, mobile crowd sensing (MCS) has been widely used in smart cities with the help of unmanned aerial vehicles (UAVs) and autonomous cars. To ensure long-term, long-distance sensing tasks of UAVs, mobile unmanned charging stations, called “ Carriers ”, are scheduled to reach preset charging locations in a city to provide charging services. However, existing methods not only have difficulty in serving a large number of UAVs, but also face a big challenge of potential security vulnerabilities in the charging transactions. To address the above issues, this paper explores the blockchain technology to design an auction-based framework for scheduling charging services for UAVs while improving the security of charging transactions. In particular, considering the revenues of Carriers and the utilities of UAVs, we develop a new auction mechanism based on the emerging deep reinforcement learning technique to improve the auction performance. Experimental results demonstrate that our method can enhance charging transactions’ security and optimize the system performance.
Decision Optimization: Internet Data Assistance for Students during Learning from Home

The teaching and learning process during the Covid-19 pandemic made it difficult for students to provide internet data packages for access online learning media from home. An internet assistance program policy is provided to support student online learning. In order for the distribution of internet data to students is objective and proportion, for that reason, measuring the amount of data usage and then used in decision making. The method used in measuring data usage is the Drive-test method for incoming and outgoing data bandwidth. As for the analysis of data assistance decision making using the optimization method and the Rank Order Cendroid (ROC) weighting method for each criterion. Measurement results of internet data usage obtained an average value of 407MB-1.0GB per hour with 40 participants in the group. The results of the decision making analysis method for internet data assistance obtained the highest optimization value obtained by 0.40117 with a weighted ROD value of 0.4667. The research study on internet data usage and optimization of decision-making methods is a case study in the Computing classification field: networks and applied computing. Both are implemented in management optimization internet data assistance to students.
Technoeconomic Performance Analysis of Solar Tracking Methods for Roof-Type Solar Power Plants and Electric Vehicle Charging Stations

In building integrated photovoltaic (BIPV) solar energy projects, cost effectiveness, durability, and long-term reliability are among the criteria that should be taken into consideration as well as the gain in electricity generation efficiency. Also, in a study, it is stated that a dual-axis solar tracking system occupies approximately 100% more space than a single-axis system and 160% more than a fixed-angle system. It has been observed that most of the studies that are mounted on the building and include a tracking system are small-scale experimental studies. The aim of this article is to present a systematic analysis with a low investment cost, a low operating cost, and high reliability, in a real application especially for roof applications in buildings. Three buildings in the same location and with the same roof area were selected. Photovoltaic power plants with 23.68 kW power were installed; these panels had three types: fixed-angle, manually controlled, and single-axis solar tracking systems. The energy generation system is connected to the network with a double-sided meter, and there is a double-sided energy flow. The energy produced is used to meet the energy needs of the vehicle charging station and common areas of the buildings. Although the single-axis tracking system is 27.85% more efficient than other energy generation methods, the manually adjusted method has proven to have the shortest amortization time. The study also presents shading, which is a serious problem in large-scale roof projects, and the area covered by the module per unit watt produced.
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval

In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA) from source to target domain.While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs.This significantly reduces the gap between the target domain and synthetic data distribution, and reduces model overfitting to the source domain.We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains.We find that back-training vastly outperforms selftraining by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6% top-20 retrieval accuracy across both domains.We further propose consistency filters to remove low-quality synthetic data before training.We also release a new domain-adaptation dataset-MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.
Multi-Cascade Perceptual Human Posture Recognition Enhancement Network

The current researches trend to adopt a low-resolution hot spot map to restore the original high-resolution representation to save computing cost, resulting in unsatisfactory detection performance, especially in human body recognition with a highly complex background. Aiming at this problem, we proposed a model of parallel connection of multiple sub-networks with different resolution levels on a high-resolution main network. It can maintain the network structure of a high-resolution hot spot map in the whole operation process. By using this structure in the human key point vector field network, the accuracy of human posture recognition has been improved with high-speed operation. To validate the proposed model’s effectiveness, two common benchmark data sets of COCO key point data set and MPII human posture data set are used for evaluation. Experimental results show that our network achieves the accuracy of 72.3% AP and 92.2% AP in the two data sets, respectively, which is 3%-4% higher than those of the existing mainstream researches. In our test, only the accuracy of backbone’s SimpleBaseline with ResNet-152 is close to ours, yet our network realized a much lower computing cost.
Medchain-Medical Record Securance Using Blockchain

MedChain uses blockchain technology to create a network of blocks for storing user-focused electronic health records while conserving only one true version of the user’s data. MedChain permits healthcare professionals access to the user's data if the user deems it. MedChain then archives collaborations with this data in an ascertainable, transparent, and protected way on MedChain’s distributed ledger. The project shows how blockchain technology is used and how MedChain is utilizing it to address specific issues to make healthcare better for users. MedChain can be a platform for other applications to build on that can complement and improve the user experience. Users will be able to clout their medical data to power a surplus of applications and services. Our mission is to place the patient at the center of the digital transformation of healthcare so that their data will always be secure and under supervision.
Country-level Arabic Dialect Identification using RNNs with and without Linguistic Features

This work investigates the value of augmenting recurrent neural networks with feature engineering for the Second Nuanced Arabic Dialect Identification (NADI) Subtask 1.2: Country-level DA identification. We compare the performance of a simple word-level LSTM using pretrained embeddings with one enhanced using feature embeddings for engineered linguistic features. Our results show that the addition of explicit features to the LSTM is detrimental to performance. We attribute this performance loss to the bivalency of some linguistic items in some text, ubiquity of topics, and participant mobility.
Analysis of the integration of ICT in Second Language Learning in Prescholl in Colombia

&lt;p&gt;The introduction of Information and Communications Technologies (ICT) in the educational sphere is becoming more and more essential due to its potential as a pedagogical tool. This study aims to evaluate some factors that facilitate and hinder the pedagogical use of ICT in the context of learning English as a second language (L2) at preschool education levels. Through the methodology of Design Thinking, it is possible to structure the characteristics that a playful interface should have in order to support this educational process and, especially, to satisfy the needs of an environment that intends to make the most of the technological resources it has. To this end, this methodology takes into account the experience of direct users (children), indirect users (teachers) and the socio-economic dimension of educational centres, headed by managers. In this sense, two &lt;em&gt;ad hoc&lt;/em&gt; questionnaires were completed by the school executives and teachers, respectively, in a specific educational context of Barranquilla, Colombia. The questionnaires were administered to 25 schools. Data analysis showed the importance of ICT in improving the teaching and assessment process. At the same time, it shows the levels of frustration of some teachers who use technologies with low levels of training or with self-generated content that limit the use of ICT in an appropriate way. These results will outline a number of characteristics that should be considered when implementing actions to improve the teaching of English in the classroom using digital tools.&lt;/p&gt;
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company’s Reputation

Not all topics are equally “flammable” in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.
Multi-attention-based soft partition network for vehicle re-identification

Abstract Vehicle re-identification helps in distinguishing between images of the same and other vehicles. It is a challenging process because of significant intra-instance differences between identical vehicles from different views and subtle inter-instance differences between similar vehicles. To solve this issue, researchers have extracted view-aware or part-specific features via spatial attention mechanisms, which usually result in noisy attention maps or otherwise require expensive additional annotation for metadata, such as key points, to improve the quality. Meanwhile, based on the researchers’ insights, various handcrafted multi-attention architectures for specific viewpoints or vehicle parts have been proposed. However, this approach does not guarantee that the number and nature of attention branches will be optimal for real-world re-identification tasks. To address these problems, we proposed a new vehicle re-identification network based on a multiple soft attention mechanism for capturing various discriminative regions from different viewpoints more efficiently. Furthermore, this model can significantly reduce the noise in spatial attention maps by devising a new method for creating an attention map for insignificant regions and then excluding it from generating the final result. We also combined a channel-wise attention mechanism with a spatial attention mechanism for the efficient selection of important semantic attributes for vehicle re-identification. Our experiments showed that our proposed model achieved a state-of-the-art performance among the attention-based methods without metadata and was comparable to the approaches using metadata for the VehicleID and VERI-Wild datasets.
What is the message of the robot medium? Considering media ecology and mobilities in critical robotics research

This article makes the case for including frameworks of media ecology and mobilities research in the shaping of critical robotics research for a human-centered and holistic lens onto robot technologies. The two meta-disciplines, which align in their attention to relational processes of communication and movement, provide useful tools for critically exploring emerging human–robot dimensions and dynamics. Media ecology approaches human-made technologies as media that can shape the way we think, feel, and act. Relatedly, mobilities research highlights various kinds of influential movement and stillness of people, things, and ideas. The emerging field of critical robotics research can benefit from such attention to the ways of thinking, feeling, and moving robotic forms and environments encourage and discourage. Drawing on various studies into robotics, I illustrate those conceptual alignments of media ecology, mobilities, and critical robotics research and point to the value of this interdisciplinary approach to robots as media and robotics as socio-cultural environments.
Bootstrapping Relation Extractors using Syntactic Search by Examples

The advent of neural-networks in NLP brought with it substantial improvements in supervised relation extraction.However, obtaining a sufficient quantity of training data remains a key challenge.In this work we propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts.We take advantage of search engines over syntactic-graphs (Such as Shlain et al. ( 2020)) which expose a friendly by-example syntax.We use these to obtain positive examples by searching for sentences that are syntactically similar to user input examples.We apply this technique to relations from TACRED and Do-cRED and show that the resulting models are competitive with models trained on manually annotated data and on data obtained from distant supervision.The models also outperform models trained using NLG data augmentation techniques.Extending the search-based approach with the NLG method further improves the results.
Automated Web Design And Code Generation Using Deep Learning

Excited by ground-breaking progress in automatic code generation, machine translation, and computer vision, further simplify web design workflow by making it easier and productive. A Model architecture is proposed for the generation of static web templates from hand-drawn images. The model pipeline uses the word-embedding technique succeeded by long short-term memory (LSTM) for code snippet prediction. Also, canny edge detection algorithm fitted with VGG19 convolutional neural net (CNN) and attention-based LSTM for web template generation. Extracted features are concatenated, and a terminal LSTM with a SoftMax function is called for final prediction. The proposed model is validated with a benchmark based on the BLUE score, and performance improvement is compared with the existing image generation algorithms.