Reviewerly/exHarmony huggingface dataset:
A Method to Estimation of Global Solar Radiation with Meteorological Parameters under Cloudless Sky Condition using Artificial Neural Network

In this work, variation of global radiation hourly basis and model developed to estimate the radiation under cloudless sky condition were purposed at Lopburi province (14.83°N, 100.62°E). Data of global radiation and meteorological parameters from 2012 to 2017 were investigated. For the variation of global radiation, the maximum and minimum of radiation are 3.46 MJ/m2 in April and 2.40 MJ/m2 in December, and there are some meteorological parameters influencing on the radiation. Therefore, a model for estimation of hourly global solar radiation under cloudless sky condition at this site was proposed based on the artificial neural network (ANN). This ANN has one input layer, two hidden layers and one output layer. The input layer consists of some meteorological parameters that are solar zenith angle, visibility, air temperature, relative humidity, wind speed and air pressure, and the output layer is global solar radiation under clear sky condition. The ANN was trained using the input and output data collected at Lopburi meteorological station during the year: 2012-2014. It was then validated against an independent data at the site for the period of three years (2015-2017). The validation result indicates that the estimated solar radiation under clear sky condition obtained from ANN are in good agreement with that from the measurement, with root mean square difference (RMSD) of 8.52% and mean bias difference (MBD) of 1.22%. Therefore, the model can be applied for estimation of global radiation under cloudless sky condition at other meteorological stations with similar climate. The estimated solar radiation data are useful for management in solar power plant, solar thermal energy system and also for the studies in the atmospheric field. Keywords: solar radiation, meteorological parameter, artificial neural network
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment

The cross-lingual language models are typically pretrained with masked language modeling on multilingual text or parallel sentences. In this paper, we introduce denoising word alignment as a new cross-lingual pre-training task. Specifically, the model first self-labels word alignments for parallel sentences. Then we randomly mask tokens in a bitext pair. Given a masked token, the model uses a pointer network to predict the aligned token in the other language. We alternately perform the above two steps in an expectation-maximization manner. Experimental results show that our method improves cross-lingual transferability on various datasets, especially on the token-level tasks, such as question answering, and structured prediction. Moreover, the model can serve as a pretrained word aligner, which achieves reasonably low error rates on the alignment benchmarks. The code and pretrained parameters are available at https://github.com/CZWin32768/XLM-Align.
THEORETICAL FUNDAMENTALS OF FORMATION OF PROFESSIONAL COMPETENCIES OF STUDENTS OF NON-LANGUAGE FACULTIES IN THE PROCESS OF INDEPENDENT WORK DURING EDUCATION

Modernization of education and introduction of new state educational standards determine the formation of independent work of students as a leading form of organization of the educational process. In this regard, the problem of organizing independent work and its methodological support becomes especially important. The purpose of this study is to determine the pedagogical conditions for the organization of independent work of students in a foreign language in non-language faculties. The subject of the study was chosen independent work with a foreign language as a means of language training for students of non-language faculties. In this work the array of psychological and pedagogical literature was analyzed and the role of independent work as an effective means of professional training of students was determined. Also, the author of this study identified the pedagogical conditions for the organization of independent work in the process of professional training of students and identified the types of effective independent work and their implementation in the process of language training of students.
A Description of The Motivation 3 In 1 Training Participants of Safety Pin Stitching at Balai Diklat Industri Padang

This research was motivated by the low motivation of the participants in participating in the 3 in 1 embroidery pin training at the Padang Industrial Education and Training Center. The goal is to see a picture of the motivation of the participants in following the pinhead embroidery training. This type of research is quantitative with a descriptive approach. The number of respondents in this study was 40 people consisting of training participants for pinhead embroidery training generation II and XIII. The data collection technique used a questionnaire. The data analysis technique uses the percentage formula. The results of this study indicate that the motivation to follow the training participants of the 3 in 1 head embroidery training is still low, namely 71% for 40 respondents.
Research on Intellectual Property Protection in Network Background

In today’s society, the Internet+ and the application of scientific information technology have gradually changed their transmission the way of information acquisition and dissemination makes the content of cultural communication in network environment more diverse. Original knowledge Intellectual property rights are impacted by the development of the times, the management needs have changed, and the network intellectual property protection has also entered public vision, become the focus in the design of the legal system. However, due to the openness and concealment of network information characteristic, make the existing property right management regulation and the network culture that develops rapidly have certain gap which the property rights law management lags behind, the legal mechanism exists the loophole and so on question.In order to better deal with the intellectual property legal disputes in the network environment, this paper will analyze the infringement of intellectual property protection in the network from the perspective of legislation and justice, and put forward countermeasures, and constantly improve the intellectual property legal management system in China under the network background.
LibGuides: EGL 101 - David Jakalski - Fall 2021: Keyword Search Vocabulary

This libguide assists students in Prof. Jakalski's EGL 101 class, familiarizing them with the library's resources and offering MLA 8 citation information and supports.
«THE LANGUAGE OF CLOTHES» IN THE ARTISTIC WORLD OF SEMUEL RICHARDSON’S NOVEL «PAMELA»

The research is devoted to exploring the features of reception of Henry Fielding’s dramatic heritage. The aim of the study is to analyze literary critics’ research of Henry Fielding’s creative work. We make the conclusion that Henry Fielding’s unprecedented success was due to the relevance of the topics and issues which the author focuses on. The characters, plots and themes were extremely close to the audience of that time and that is why they gained incredible popularity. In literary criticism, it was thought that the dramatic works served as a kind of start for Fielding to create his further fundamental works. But the writer should be considered in several guises: Fielding as a playwright, Fielding as a journalist, Fielding as a novelist, each of which deserves the detailed study and can help to provide a complete picture of the search that became the key to success in each new round of his creative evolution. We consider the analysis of Fielding’s plays, which can become a material for studying the genre preferences of the writer, the key to the study of his novels and Fielding’s impact on the next generations of playwrights.
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning

Spectral-based subspace clustering methods have proved successful in many challenging applications such as gene sequencing, image recognition, and motion segmentation. In this work, we first propose a novel spectral-based subspace clustering algorithm that seeks to represent each point as a sparse convex combination of a few nearby points. We then extend the algorithm to constrained clustering and active learning settings. Our motivation for developing such a framework stems from the fact that typically either a small amount of labelled data is available in advance; or it is possible to label some points at a cost. The latter scenario is typically encountered in the process of validating a cluster assignment. Extensive experiments on simulated and real data sets show that the proposed approach is effective and competitive with state-of-the-art methods.
Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions

Building invariance to non-meaningful transformations is essential to building efficient and generalizable machine learning models. In practice, the most common way to learn invariance is through data augmentation. There has been recent interest in the development of methods that learn distributions on augmentation transformations from the training data itself. While such approaches are beneficial since they are responsive to the data, they ignore the fact that in many situations the range of transformations to which a model needs to be invariant changes depending on the particular class input belongs to. For example, if a model needs to be able to predict whether an image contains a starfish or a dog, we may want to apply random rotations to starfish images during training (since these do not have a preferred orientation), but we would not want to do this to images of dogs. In this work we introduce a method by which we can learn class conditional distributions on augmentation transformations. We give a number of examples where our methods learn different non-meaningful transformations depending on class and further show how our method can be used as a tool to probe the symmetries intrinsic to a potentially complex dataset.
A War of the Words: Assessing Liability for Online Incitement of Terrorism

Social media posts and terrorism have become inextricably linked in public analysis of recent terror incidents. Terrorist organizations rely on the internet to recruit new members, incite violence, and publicize their actions. Despite the significant reliance of terrorist organizations on social media, social media companies have inconsistently and infrequently attempted to address this usage. At present, there is insufficient domestic regulation of terrorist presence on social media, and American law struggles to adapt from the print medium to a flexible and responsive First Amendment jurisprudence of the internet age. By taking inspiration from recent German and French regulation of online terrorist activity, the United States might be able to address some of these shortcomings. The government could limit terrorist organization presence online by (1) re-defining ‘hate speech’ to include language that incites imminent terrorist action and (2) instituting a compliance regime that regulates social media companies as internet platforms. This paper proposes a course of action that could result in an online environment that stifles, rather than fosters, terrorist organization activity moving forward.
The Impact of Parents’ Involvement Using Online Platforms in Improving Learners’ Reading Comprehension in English VI: an Action Reasearch

This study aimed to examine the impact of parental involvement upon learner’s reading achievement. The study investigated whether learners with difficulty would improve reading comprehension when parents became actively involved in their child’s reading activities. Together with their parents, selected learners of Grade Six of Laong-Laan Elementary School participated in this reading intervention program. They were identified as struggling readers who were having difficulty in comprehension. Prior to the start of the reading program, learners were individually administered a pre-reading inventory using PHIL-IRI passage to assess the reading comprehension level of the learners. Each learner read and answered 8 written questions in an online platform using Google form about the story without parental support. Learners took a pre-test and classified their reading level as frustration, instructional or independent readers. There were 21 or 45.65% students under the frustration, 20 or 43.48% students in the instructional, and 5 or 10.87% students in the independent. Their parents were involved to read with their child at home during this time of pandemic. The learners read and discussed books or short story once a week with their parents at home and answered written comprehension questions about the story. The intervention lasted for 8 weeks. The post-test result showed, from the 21 learners under the frustration level decreased into 9 or 19.57%, 20 learners in the instructional increased into 22 or 47.83%, and from 5 learners in the independent increased into 15 or 32.60%. The outcomes of the study reveals that learner’s reading comprehension level increased as a result of parental involvement in their child’s reading activities. Meanwhile. the participating learners have also experienced a heightened enjoyment of reading and the parents have become more confidant in providing reading support to their children. The school should come up training sessions and a reading manuals to guide parents on meaningful indulgence in their children’s reading at home especially in this time of pandemic due to COVID-19 virus. Future and more in-depth research is recommended to identify what aspects of parent involvement have the most positive effect on diverse students’ achievement in reading and in other subjects.
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds

Conditional GANs (cGAN), in their rudimentary form, suffer from critical drawbacks such as the lack of diversity in generated outputs and distortion between the latent and output manifolds. Although efforts have been made to improve results, they can suffer from unpleasant side-effects such as the topology mismatch between latent and output spaces. In contrast, we tackle this problem from a geometrical perspective and propose a novel training mechanism that increases both the diversity and the visual quality of a vanilla cGAN, by systematically encouraging a bi-lipschitz mapping between the latent and the output manifolds. We validate the efficacy of our solution on a baseline cGAN (i.e., Pix2Pix) which lacks diversity, and show that by only modifying its training mechanism (i.e., with our proposed Pix2Pix-Geo), one can achieve more diverse and realistic outputs on a broad set of image-to-image translation tasks. Codes are available at this https URL.
Design of a fuzzy controller for open architecture quadrotor

The use of quadrotor helicopters has now increased, especially in civilian applications such as maintenance tasks related to power line or large construction status control, surveillance, crop control in agriculture, work processes in the logistics sector, among others. One of the main problems with some of the conventional designs is the lack of stability. This paper presents the design of a controller using an intelligent control technique to achieve the stability of a quadrotor, to experiment an open architecture quadcopter helicopter was developed, the controller was programmed using LabVIEW software and the data acquisition system is based on the NI PCI 6251 card.
Neighborhood Rough Set based Multi-document Summarization

This research paper proposes a novel Neighbourhood Rough Set based approach for supervised Multi-document Text Summarization (MDTS) with analysis and impact on the summarization results for MDTS. Here, Rough Set based LERS algorithm is improved using Neighborhood Rough Set which is itself a novel combination called Neighborhood-LERS to be experimented for evaluations of efficacy and efficiency. In this paper, we shall apply and evaluate the proposed Neighborhood-LERS for Multi-document Summarization which here is proved experimentally to be superior to the base LERS technique for MDTS.
Data Duplication Removal Using File Checksum

Data duplication uses file checksum technique to identify the duplicate or redundant data rapidly and accurately. There may be the chance of inaccurate result which can be avoided by comparing the checksum of already exiting file with newly uploaded file. The file can be stored using multiple attributes such as file name, date and time, checksum, user id, and so on. When the user uploads the new files the system will generates the checksum of the file and compare it with the check of file that has already been stored. If the match is found then it will update the old entry otherwise new entry will be created into the database.
KOMBINASI SISTEM BERBASIS WEB DAN ANDROID SEBAGAI APLIKASI PRESENSI KEGIATAN MENGGUNAKAN QR CODE

Registration and list of attendees are an important factor to be done in formal and informal activities. The purpose is to know the attendees’ personal data and presence. Registration and presence using the manual method such as form filling in a piece of paper produce problem, among them is over excess of paper and time consuming presence activity. Therefore, a new efficient and effective system that take advantage of Android Smartphone and Quick Response Code (QR Code) as Smart Attendance System is needed. The use of Android and QR Code technology simplify registration and presence process so that the proses would be much faster and efficient. The design of QR Code presence application based on Android is using System Development Life Cycle (SDLC) Waterfall model and designed using Use Case Diagram and Entity Relationship Diagram. The presence application is made using combination of two platform, that is Android and web. On the Android Smartphone there are scanner and administrator profile. Whilst at the web platform contain participant, event, and admin database. The existence of the design and Smart Attendance system implementation, registration and list of attendees process will be faster, efficient, and used as on of the smart system to support the smart city is expected.
Pengembangan Lembar Kegiatan Peserta Didik (LKPD) Berbasis Higher Order Thinking Skills (HOTS) Perbankan Dasar Kelas X SMK

This development study aims to produce teaching materials in the form of a worksheet based on Higher Order Thinking Skills in basic banking topics for grade X SMK accounting expertise. There are several problems in the curriculum, namely the low competence of teachers brought by the average set by the government, low readiness of student competencies, and limited relevant teaching materials. This research was conducted to solve problems related to very limited teaching materials and will be developed through the Higher Order Thinking Skills (HOTS) Basic Banking Student Activity Sheet (LKPD). The test subjects to be used in this study were 20 students of class X Accounting at SMK Negeri 1 Sambeng Lamongan. The development model that used in this study is 4D Thiagarajan with some stages, they are: define, design, develop, disseminate. But, the was not carried out. The results of this study show that material eligibility 80,5%, evaluation eligibility 81%, language eligibility 77% with the overall eligibility rate is 80% that can be said “feasible”. That teaching materials in the form of the worksheet based on Higher Order Thinking Skills can be used in learning activities.
Rule-Based Bibliographic Computer Science Indexing Review with COVID-19 Disease

A researcher in conducting his research usually uses a search through the homepage of the publication, based on expertise, collaboration in research, and research interests. Today, the COVID-19 pandemic is becoming a trending topic for researchers from various scientific fields. The study classified the case based on publications located in the homepage sources such as Scopus, Crossref, IEEE Xplore, and Google Scholar, by analyzing the following topics, namely Artificial Intelligence, Data Mining, Deep Learning, Machine Learning and the Internet of Things by using Named Entity Recognition to detect and classify named entities in text and using occurence and link strength methods. Based on this study, the results were obtained that Scopus has the most equitable percentage, which has a good occurrence and link strength among the five scientific fields, namely Artificial Intelligence 33.33%, Machine Learning 15.38%, Deep Learning 23.08%, Data Mining 12.82% and IoT 15.38%. The second-best are Google Scholar, then IEEE Xplore, and Crossref.
Towards a Federated Learning Framework for Heterogeneous Devices of Internet of Things

Federated Learning (FL) has received a significant amount of attention in the industry and research community due to its capability of keeping data on local devices. To aggregate the gradients of local models to train the global model, existing works require that the global model and the local models are the same. However, Internet of Things (IoT) devices are inherently diverse regarding computation speed and onboard memory. In this paper, we propose an FL framework targeting the heterogeneity of IoT devices. Specifically, local models are compressed from the global model, and the gradients of the compressed local models are used to update the global model. We conduct preliminary experiments to illustrate that our framework can facilitate the design of IoT-aware FL.
Opportunities to use project-based teaching technology in the development of students’ research competence

The article highlights the essence of student’s research competence, the features of project-based learning in its development, as well as the possibility of using project-based learning technology in the process of theoretical training and qualification practice.
Temporally coherent video anonymization through GAN inpainting

This work tackles the problem of temporally coherent face anonymization in natural video streams.We propose JaGAN, a two-stage system starting with detecting and masking out faces with black image patches in all individual frames of the video. The second stage leverages a privacy-preserving Video Generative Adversarial Network designed to inpaint the missing image patches with artificially generated faces. Our initial experiments reveal that image based generative models are not capable of inpainting patches showing temporal coherent appearance across neighboring video frames. To address this issue we introduce a newly curated video collection, which is made publicly available for the research community along with this paper. We also introduce the Identity Invariance Score IdI as a means to quantify temporal coherency between neighboring frames.
The Icon and Interface of the Journaling Design of the Ace Diary Mobile App

The Ace Diary is an online counseling app that provides art therapy features, receptive music therapy, journaling, a chat room, emergency calling, loving quotes, and scheduling based on quadrant-time of 4th generation time management information architecture. Furthermore, counselors can monitor activities on the app. These features had not been integrated before, which is why a basic design was developed for this purpose by using the M. Asimov model to design this app. The visual strategy used for showing a typical journaling diary is lines with brushstroke and calming pastel colors. Ace Diary has six primary pages: drawing, venting, scheduling, room chat and calling, quotes, and record tracking. The different group pages have different background colors so that audiences can distinguish the page’s functions. Students’ picture profiles are created using an illustration in the same design style to keep identities confidential.&#x0D; Keywords: strategic journaling design, quadrant-time of 4th generation of time management, pastel colors, brushstroke