Reviewerly/exHarmony huggingface dataset:
Odnn: An Automatic Optimized Deep Neural Network Based Multi-Document Summarization

As the information on the web is growing rapidly, automatic multi-document summarization plays the vital role for understanding the information in short about the documents. However, it is difficult to analyze about a reasonable and specific document related with a particular subject within a limited time period.  So, in this paper, we present an automatic optimized deep neural network (ODNN) based multi-document summarization. The proposed summarization approach follows the five phases that are i) pre-processing, ii) data conversion, iii) feature extraction, iv) sentence score calculation and v) rank based summarization. For sentence score calculation, we present ODNN in which weight parameters are selected optimally using pigeon optimization algorithm (POA). Depend on the score, the sentences are ranked and summarized. The performance of the proposed approach is analyzed using DUC 2002 dataset and is evaluated in terms of precision, recall and F-measure.
PERSEPSI PESERTA TERHADAP PENGGUNAAN METODE CERAMAH PADA DIKLAT PENYULUH AGAMA NON PNS ANGKATAN 74 DI BALAI DIKLAT KEAGAMAAN BANJARMASIN TAHUN 2019

Many methods can be used in learning in a training, one of which is the lecture method. Thelecture method is considered the conventional method and the oldest learning method; however, this method is still effective if used in the right way. The purpose of this study was to determine how participants' perceptions of the lecture method used by the trainers in teachingin the substantive technical training of Non PNS religious instructors’ class 74 in 2019. Thisresearch was a quantitative descriptive study. The population and sample of the study were34 training participants. Data were collected through a structured questionnaire totaling 11items. The data of the research were analyzed using descriptive statistics. The results showedthat the training participants' perceptions of the lecture method were very good, 35.3% saidthey really liked it, 47.1% said they liked it, and 17.6% said they sometimes liked the lecturemethod. The trainers conveyed the learning objectives, 38.2% stated always, 50% statedfrequently, and 11.8% stated sometimes. Delivering the benefits of learning materials, 55.9% stated always, 41.2% stated frequently, and 2.9% stated sometimes. Delivering the scope of learning, 47.1% stated always, 44.1% stated often, and 8.8% stated sometimes. Usinglanguage that is easy to understand, 67.6% states always, 29.4% states often, and 2.5% states sometimes. Complementing the explanation with examples, 58.8% stated always, 38.2% stated frequently, and 2.9% stated sometimes. Bringing out relevant humor, 55.9% stated always, 32.4% stated often, and 11.8% stated sometimes. Repeating key points, 17.6% stated always, 58.8% stated frequently, and 20.6% stated sometimes. Using sound volume and intonation are good, 61.8% stated always, 32.4% stated frequently, and 5.9% statedsometimes. Transition of material delivery from one point to another, 35.3% stated always,47.1% stated often, and 17.6% stated sometimes. Summing up the learning material, 41.2%stated always, 38.2% stated frequently, and 17.6% stated sometimes.
UDC LEADER AND LEADERSHIP IN SOCIAL AND PEDAGOGICAL WORK ACTIVITIES

This article discusses leadership in social and pedagogical activities and provides the author's concept of leadership types. The article focuses on the study of the theoretical and practical foundations of human leadership in a team. Questions of leadership and their role in management, leadership styles are considered.
Video scene detection based on link prediction using graph convolution network.

With the development of the Internet, multimedia data grows by an exponential level. The demand for video organization, summarization and retrieval has been increasing where scene detection plays an essential role. Existing shot clustering algorithms for scene detection usually treat temporal shot sequence as unconstrained data. The graph based scene detection methods can locate the scene boundaries by taking the temporal relation among shots into account, while most of them only rely on low-level features to determine whether the connected shot pairs are similar or not. The optimized algorithms considering temporal sequence of shots or combining multi-modal features will bring parameter trouble and computational burden. In this paper, we propose a novel temporal clustering method based on graph convolution network and the link transitivity of shot nodes, without involving complicated steps and prior parameter setting such as the number of clusters. In particular, the graph convolution network is used to predict the link possibility of node pairs that are close in temporal sequence. The shots are then clustered into scene segments by merging all possible links. Experimental results on BBC and OVSD datasets show that our approach is more robust and effective than the comparison methods in terms of F1-score.
Работа с популярными программами и приложениями

The mobile phone is very important in our lives, with the help of a mobile phone you can do everything: from reading ordinary cooking recipes to making deals on a large scale. The purpose of this work is to review mobile programs and applications, show the installation of programs on a mobile device, as well as what categories of programs can be installed on a mobile device
COVID-19-Related Predictions Using NER on News Headlines

Today, all the newspapers and online news content are flooded with the news of coronavirus (i.e., COVID-19). The virus has spread across the globe at an alarming rate. Thus, people need to remain updated about news regarding the ongoing pandemic which has taken whole world by storm. Therefore, named entity recognition (NER) is applied to extract important information from these news headlines and articles and further used for more applications related to COVID-19 in India. This chapter uses the SpaCy module to categorize the tokens extracted from the news headlines database into various pre-defined tags. Further, four different machine learning models, namely CRF Model, LSTM Model, LightGBM Model, and AdaBoost Model, are applied for performing tagging. After that, these tags are used to predict different information regarding COVID-19. Some of these applications include finding nearby hospitals and pharmacies, predicting future potential hotspots in India, worst affected states of India, gender-based comparisons, age group-based comparisons, and area-based spreading of the virus.
Regulasi Diri Pada Pengurus Pramuka Di Sma X

Self-regulation is the ability of someone to plan and implement behaviour for achieving their goals. Self-regulation will help students of scout member managing their activities both in scout activity and academics. From this thing, purpose of this study is to determine the form of self-regulation applied by students. The method of data collection uses a semi-structured interview method with 6 subjects divided into 4 men and 2 women. The informant is a high school scout member at X High School. The results of this study conclude that students have their own way to do self-regulation. There are six forms of self-regulation managing time, activity planning, completion of school work, completion of scouting, balancing activities, and activities priority. Factors that influence students's self regulation are internal and external driving factors and internal and external inhibiting factors. The benefits that students get from self-regulation they can managing time well, get good grades, getting the trust from others, and assignments can be completed.
Android application on college ERP

In today’s era of Technology mobile has become an integral part of students and teachers so creating a convenient android app which helps students to communicate and learn with the help of staff members to improve their academics and also become the part of their daily routine. Now Introducing a Digitized Learning app which is an android app which will consist similar features as college ERP website or in other words creating vest-pocket android app of college ERP system. It will work as a management app which will be able to manage the student and staff members data and their activity through app admin /college can also circulate notice about some upcoming events and holidays. This app is also useful for students as well as the college faculty member and other academic staff. So that they can conduct their quiz using this app so that student will be able to know about academic's progress. There are many such activities such as Teacher can collect student's attendance through this application. Here individual profile is created for all student where student can directly communicate with teacher solve their doubt as well as discuss about Mini project, research paper and etc.
Hassle - Free and Secure e-KYC System Using Distributed Ledger Technology

The blockchain technology is a prominent, reliable and secure technology which is getting into almost every industry. The fundamental essence of blockchain technology offers features like transparency, decentralization, immutability, resilience, disintermediation, collaboration, security and trust. In this paper, we have focused onhow the present banking industry, especially the KYC document verification process, can be impacted after using blockchain to store and track the records. The current day banking KYC processes are highly reliable on paper which is an outworn process. It is utmost essential today to have an upgraded KYC system, embedded with a reliable and trustable technology like blockchain, that could withstand frauds, and resolve the scalability and security issues. In the proposed system, the use of blockchain in KYC process restricts the presence of middlemen. This results in a reduction of fraudulent activities and errors that may occur when there are a lot of manual activities involved. Furthermore, the document verification process is only conducted only one time, no matter what is the number of financial institutions with which the customer is working with. This system provides more efficiency, reduction in costs, enhanced customer rendezvous and end-to-end transparency during the process of integrating the customer documents into the bank database.Keywords: Blockchain, KYC, Smart Contracts, Decentralization, Security
Digital learning 2020 : reporting on practice in early learning and care, primary and post-primary contexts

This report presents the findings of an evaluation of digital learning, conducted by theInspectorate of the Department of Education and Skills, in a sample of early learning andcare (ELC) settings, primary schools and post-primary schools, during the period January toDecember 2019. One of the objectives of the Action Plan for Education 2016-2019 was to‘increase the use of ICT in teaching, learning and assessment so that learners are equipped withthe necessary skills to meet the challenges of a rapidly changing learning environment.
Correction to: Bayesian Topology Learning and noise removal from network data

A correction to this paper has been published: https://doi.org/10.1007/s43926-021-00013-8
EMface: Detecting Hard Faces by Exploring Receptive Field Pyraminds

Scale variation is one of the most challenging problems in face detection. Modern face detectors employ feature pyramids to deal with scale variation. However, it might break the feature consistency across different scales of faces. In this paper, we propose a simple yet effective method named the receptive field pyramids (RFP) method to enhance the representation ability of feature pyramids. It can learn different receptive fields in each feature map adaptively based on the varying scales of detected faces. Empirical results on two face detection benchmark datasets, i.e., WIDER FACE and UFDD, demonstrate that our proposed method can accelerate the inference rate significantly while achieving state-of-the-art performance. The source code of our method is available at \url{https://github.com/emdata-ailab/EMface}.
Shifting traditional undergraduate software engineering instruction to a DevOps focus

Classical Software Engineering education often includes traditional methodologies that do not adequately describe today's industry practice. DevOps is a culture that promotes fast delivery, continu...
코로나바이러스감염증-19(COVID-19)에 대한 대학생의 지식, 교육요구도 및 예방행위 수행도

Purpose : The first case of coronavirus disease-2019 (COVID-19) disease outbreak in Korea occurred in January 2020, and the cumulative number of confirmed cases by the Central Defense Response Headquarters of the Quality Management Administration as of November 30 was 34,201. Looking at the incidence trend of domestic confirmed cases by age, the incidence rate of COVID-19 in the age group of 20-29 years, which corresponds to college students, was 27.4 %, the highest compared by age group. Considering the current status of the infection rate, universities will become the centers of new COVID-19 cases if face-to-face lectures are recommenced without preparatory measures and analysis of infection prevention (e.g., infection awareness and education for university students). Thus, this study intended to investigate the level of knowledge, educational needs, and preventive behavior performance of college students about COVID-19 and provide basic data for the development of an education program for the prevention of COVID-19 for college students.Methods : This study is a descriptive correlational study aimed to investigate the correlation between knowledge about COVID-19, educational needs, and degree of preventive behavior performance in 407 college students attending one University in Gyeonggi-do.Results : The subjects’ knowledge score about COVID-19 was 12.46±1.39 points, average educational needs score was 29.16±3.14 points, and prevention behavior performance survey result was 35.50±3.61 points. Moreover, positive correlation was observed between the knowledge about COVID-19 and educational needs (r=.203, p<.001) and knowledge and preventive behavior performance (r=.140, p=.005). Further, educational needs and preventive behavior performance demonstrated a statistically significant positive relationship (r=.311, p<.001).Conclusion : Therefore, organizing an educational program to acquire accurate knowledge is necessary to make it a habit for college students to practice preventive behavior against COVID-19.
Youth Empowerment Based on Participation Through Graphic Design Training

This study investigates youth empowerment using participatory training methods and adult education strategies through graphic design training.The empowerment of young people is a learning approach that adapts to the community's needs.In attempt to optimise youth capacity, it then reacts globally as an action technique in social services.This presents study aims to describe and analyze the participatory methods used for youth empowerment and adult education techniques through graphic design training at PKBM Sukamulya.This research used descriptive method with a qualitative approach.Data was taken by interview, observation and documentation study.To test the validity of the data is done by triangulating data sources.This research took place in Sukamulya City Bandung PKBM.The research subjects consisted of managers, instructors and activity participants.This research found that participatory training methods can encourage youth to identify their individual needs with the training process using adult education techniques.The process of participatory training methods can be taken through several stages, namely the identification of needs, learning contracts, the learning process, evaluation and rediagnosis of community needs.
Low-Memory Implementations of Ridge Solutions for Broad Learning System with Incremental Learning

The existing low-memory BLS implementation proposed recently avoids the need for storing and inverting large matrices, to achieve efficient usage of memories. However, the existing low-memory BLS implementation sacrifices the testing accuracy as a price for efficient usage of memories, since it can no longer obtain the generalized inverse or ridge solution for the output weights during incremental learning, and it cannot work under the very small ridge parameter that is utilized in the original BLS. Accordingly, it is required to develop the low-memory BLS implementations, which can work under very small ridge parameters and compute the generalized inverse or ridge solution for the output weights in the process of incremental learning. In this paper, firstly we propose the low-memory implementations for the recently proposed recursive and square-root BLS algorithms on added inputs and the recently proposed squareroot BLS algorithm on added nodes, by simply processing a batch of inputs or nodes in each recursion. Since the recursive BLS implementation includes the recursive updates of the inverse matrix that may introduce numerical instabilities after a large number of iterations, and needs the extra computational load to decompose the inverse matrix into the Cholesky factor when cooperating with the proposed low-memory implementation of the square-root BLS algorithm on added nodes, we only improve the low-memory implementations of the square-root BLS algorithms on added inputs and nodes, to propose the full lowmemory implementation of the square-root BLS algorithm. All the proposed low-memory BLS implementations compute the ridge solution for the output weights in the process of incremental learning, and most of them can work under very small ridge parameters.
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data

We develop a distribution-free, unsupervised anomaly detection method called ECAD, which wraps around any regression algorithm and sequentially detects anomalies. Rooted in conformal prediction, ECAD does not require data exchangeability but approximately controls the Type-I error when data are normal. Computationally, it involves no data-splitting and efficiently trains ensemble predictors to increase statistical power. We demonstrate the superior performance of ECAD on detecting anomalous spatio-temporal traffic flow.
Public Cloud Integration

This chapter explains how NSX-T is used inside the commonly used VMware public cloud offerings (hyperscalers) and the commonly used native public cloud providers.
Interacto: A Modern User Interaction Processing Model

Since most software systems provide their users with interactive features, building user interfaces (UI) is one of the core software engineering tasks. It consists in designing, implementing and testing ever more sophisticated and versatile ways for users to interact with software systems, and safely connecting these interactions with commands querying or modifying their state. However, most UI frameworks still rely on a low level model, the bare bone UI event processing model. This model was suitable for the rather simple UIs of the early 80's (menus, buttons, keyboards, mouse clicks), but now exhibits major software engineering flaws for modern, highly interactive UIs. These flaws include lack of separation of concerns, weak modularity and thus low reusability of code for advanced interactions, as well as low testability. To mitigate these flaws, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interacto</i> as a high level user interaction processing model. By reifying the concept of user interaction, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interacto</i> makes it easy to design, implement and test modular and reusable advanced user interactions, and to connect them to commands with built-in undo/redo support. To demonstrate its applicability and generality, we briefly present two open source implementations of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interacto</i> for Java/JavaFX and TypeScript/Angular. We evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interacto</i> interest (1) on a real world case study, where it has been used since 2013, and with (2) a controlled experiment with 44 master students, comparing it with traditionnal UI frameworks.
Improving face recognition in surveillance video with judicious selection and fusion of representative frames.

Face recognition in unconstrained surveillance videos is challenging due to the different acquisition settings and face variations. We propose to utilize the complementary correlation between multi-frames to improve face recognition performance. We design an algorithm to build a representative frame set from the video sequence, selecting faces with high quality and large appearance diversity. We also devise a refined Deep Residual Equivariant Mapping (DREAM) block to improve the discriminative power of the extracted deep features. Extensive experiments on two relevant face recognition benchmarks, YouTube Face and IJB-A, show the effectiveness of the proposed method. Our work is also lightweight, and can be easily embedded into existing CNN based face recognition systems.
Towards Annotation-Free Evaluation of Cross-Lingual Image Captioning

Cross-lingual image captioning, with its ability to caption an unlabeled image in a target language other than English, is an emerging topic in the multimedia field. In order to save the precious human resource from re-writing reference sentences per target language, in this paper we make a brave attempt towards annotation-free evaluation of cross-lingual image captioning. Depending on whether we assume the availability of English references, two scenarios are investigated. For the first scenario with the references available, we propose two metrics, i.e., WMDRel and CLinRel. WMDRel measures the semantic relevance between a model-generated caption and machine translation of an English reference using their Word Mover's Distance. By projecting both captions into a deep visual feature space, CLinRel is a visual-oriented cross-lingual relevance measure. As for the second scenario, which has zero reference and is thus more challenging, we propose CMedRel to compute a cross-media relevance between the generated caption and the image content, in the same visual feature space as used by CLinRel. The promising results show high potential of the new metrics for evaluation with no need of references in the target language.
Identify Potential Breaking News Based on Social Media Posts Using Machine Learning

Social media consists of interactive applications and platforms for creating and exchange of user-generated contents. Due to its ease of use, speed and reach, social media is changing the public discourse in society and setting trends and agendas in topics that range from the healthcare and educational industry to politics and the technology. In recent days, the use of Social Media (Twitter) has tremendously increased. People are 24x7 available on Social Media and post and share a lot of data. The aim and goal of this project is to use the data from Social Media for predicting the news from it and calling for help in crisis. Sometimes it happens like there is a disaster or tragedy in some area and no emergency services are called. The proposed work classifies Twitter Trending Topics into categories such as disease, healthcare and calamities. Implementation conducted on twitter dataset shows effectiveness of the proposed approach.