Reviewerly/exHarmony huggingface dataset:
USING PICTURES AND GUIDED QUESTIONS TO IMPROVE STUDENTS’ ABILITY IN WRITING DESCRIPTIVE TEXT

This research is classroom action research. It is carried out at 3rd semester students PGSD study program in Panca Marga University Probolinggo, consisting of 32 students. The objective of the research is to find out how can pictures and guided questions be implemented to improve students’ ability in writing descriptive text. This research is conducted in 2 (two) cycles. The procedure of the research consist of: preliminary study, planning, action, observation, and reflection. Qualitative data are obtained from observation checklist, field notes and interview. While quantitative data are gained from the product of students’ descriptive text writing. The result of the research shows that using picture and guided questions can improve students’ ability in writing descriptive text. As the indicator of success, the researcher adapted scoring rubric consisting 4 level: Level 1 = Very Poor, Level 2 = Fair to Poor, Level 3 = Good to Average, and Level 4 = Excellent to Very Good. It is considered success if 63 % or more than 63% students have reached level 3. In preliminary study only 28% students belongs to level 3. In cycle 1 there a slight significant showing percentage improvement in level 3 (47% students reached level 3). In second cycle, the result of students’ writing finally shows a high improvement, 69% students have reached level 3. It indicates that using picture and guided questions can improve students’ ability in writing descriptive text.&#x0D; Keywords: picture, guided questions, writing, descriptive text
Weakly-supervised Video Anomaly Detection with Contrastive Learning of Long and Short-range Temporal Features.

In this paper, we address the problem of weakly-supervised video anomaly detection, in which given video-level labels for training, we aim to identify in test videos, the snippets containing abnormal events. Although current methods based on multiple instance learning (MIL) show effective detection performance, they ignore important video temporal dependencies. Also, the number of abnormal snippets can vary per anomaly video, which complicates the training process of MIL-based methods because they tend to focus on the most abnormal snippet -- this can cause it to mistakenly select a normal snippet instead of an abnormal snippet, and also to fail to select all abnormal snippets available. We propose a novel method, named Multi-scale Temporal Network trained with top-K Contrastive Multiple Instance Learning (MTN-KMIL), to address the issues above. The main contributions of MTN-KMIL are: 1) a novel synthesis of a pyramid of dilated convolutions and a self-attention mechanism, with the former capturing the multi-scale short-range temporal dependencies between snippets and the latter capturing long-range temporal dependencies; and 2) a novel contrastive MIL learning method that enforces large margins between the top-K normal and abnormal video snippets at the feature representation level and anomaly score level, resulting in accurate anomaly discrimination. Extensive experiments show that our method outperforms several state-of-the-art methods by a large margin on three benchmark data sets (ShanghaiTech, UCF-Crime and XD-Violence). The code is available at this https URL
Proposal of Autoregressive Assist for Bimanual Operation

Learners of machine operation need to learn the characteristics of the operating machine system in order to realize the intended operation. When machine operation is required for both hands, it is necessary to design a system that considers both hands. However, it is suggested that different operation characteristics appear depending on the dominant hand and the non-dominant hand. Therefore, when performing operation with both hands, we aim to acquire early operation by learning one-handed operation pattern and applying it as support to the other handed operation. In this paper, we suggested the support of bimanual operation system characteristics acquisition by using a two-dimensional targets tracking task. Results show bimanual operation support.
Study of institutional and procedural improvement of the forensic service for aspects of human rights protection

Since the results of the forensic science appraisal are used as evidence for criminal trials, credibility and validity must be guaranteed as they play an important role in determining a person s life. To this end, there is a way to ensure the reliability of the results by reducing errors in forensic science service. However, the number of emotions commissioned by the National Forensic Service increases exponentially every year, while the number of forensic scientist is overloaded due to problems such as not being met, which increases the likelihood of various errors in tests. So, it is necessary to improve the quality of emotional work, respectively, through institutional improvements such as increasing the workforce of workers and limiting the limits of forensic works per scientist. Next, due to its nature as a professional field of forensic science, forensic work is carried out, creating an imbalance between the people and investigative agencies, which could lead to a wide gap in criminal trials over whether or not they are guilty. Therefore, it is also necessary to narrow the inequality between the people and the investigative agencies by supplementing the number of people, as well as procedural and institutional supplementing them so that they can give expert witness in areas that include forensic science service, from the investigation stage to the trial stage, rather than expert witness at the trial stage. Finally, there is a lack of discussion on the ethics of forensic scientists as a special professional role compared to the profound impact of forensic science service on the lives of the people. Therefore, it is necessary to review the code of ethics abroad and establish a code of ethics that can impose practical sanctions, not just ethical norms, to raise the vigilance of forensic scientists against ethical violations.
Evaluating the Comfort Experience of a Head-Mounted Display with the Delphi Methodology

This study developed evaluation indicators for the comfort experience of virtual reality (VR) headsets by classifying, defining, and weighting cybersickness-causing factors using the Delphi research method and analytic hierarchical process (AHP) approach. Four surveys were conducted with 20 experts on VR motion sickness. The expert surveys involved the 1) classification and definition of cybersickness-causing dimensions, classification of sub-factors for each dimension, and selection of evaluation indicators, 2) self-reassessment of the results of each step, 3) validity revaluation, and 4) final weighting calculation. Based on the surveys, the evaluation indicators for the comfort experience of VR headsets were classified into eight sub-factors: field of view (FoV)-device FoV, latency-device latency, framerate-device framerate, V-sync-device V-sync, rig-camera angle view, rig-no-parallax point, resolution-device resolution, and resolution-pixels per inch (PPI). A total of six dimensions and eight sub-factors were identified; sub-factor-based evaluation indicators were also developed.
Rammed, or What RAM3S Taught Us

RAM3S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. Indeed, the use of Big Data platforms can give way to the efficient management and analysis of large data amounts, but they require the user to concentrate on issues related to distributed computing, since their services are often too raw. The use of RAM3S greatly simplifies deploying non-parallel techniques to platforms like Apache Storm or Apache Flink, a fact that is demonstrated by the four different use cases we describe here. We detail the lessons we learned from exploiting RAM3S to implement the detailed use cases.
Bridging Graph Network to Lifelong Learning with Feature Interaction

Graph neural networks (GNN) are powerful models for many graph-structured tasks. In this paper, we aim to bridge GNN to lifelong learning, which is to overcome the effect of ``catastrophic forgetting for continuously learning a sequence of graph-structured tasks. Although many lifelong learning techniques for convolutional neural networks (CNN) have been developed, lifelong learning for GNN is still underexplored and suffers from incomplete graph structure during learning. This is because in lifelong learning the nodes increase dynamically and can only be present to the model once, which makes many graph models and sampling strategies inapplicable. To solve this problem, we propose a new graph topology based on feature interaction, called the feature graph. It takes features as new nodes and turns nodes into independent graphs. This successfully converts the original problem of node classification to graph classification, in which the increasing nodes are turned into training samples. Therefore, the lifelong learning techniques developed for CNN become applicable to GNN for the first time. In the experiments, we demonstrate both the efficiency and effectiveness of feature graphs for lifelong learning tasks using a rehearsal method. We expect that it will have broad potential applications for graph-structured tasks in lifelong learning.
ПРОЄКТУВАННЯ УРОКУ БІОЛОГІЇ ЗА СЦЕНАРІЄМ ДОСЛІДНИЦЬКОГО НАВЧАННЯ В УМОВАХ ОЧНОЇ, ДИСТАНЦІЙНОЇ І ЗМІШАНОЇ ОСВІТИ

The article highlights the need to introduce research training into the educational process in accordance with the concept of “New Ukrainian School” and the State Standard of Basic Secondary Education. Based on the pedagogical ideas of great teachers, the essence of the concept “research teaching” as a pedagogical technology by which students form a new experience in the process of independent activity is determined. The author substantiates the stages of a biology lesson according to the scenario of research training on the basis of the international project Co-Lab, which allows to design a lesson in the conditions of full-time, distance and mixed education; to fill the educational content of the lesson with text documents, video materials; to import the developed materials by the international community; to have a discussion. The actions of the participants of the educational process are determined according to the stages of the basic cycle of the Go-Lab research: orientation, conceptualization, research, conclusion, discussion. Effective methods are presented – educational tools that promote the students’ self-educational activities, increase their cognitive activity: 1) research, which involves the study of biological objects or processes, the students’ work with text or handouts, problem solving, design or modeling, and 2) creative, based on the creation of something new. Considerable attention is paid to the methods of presentation of research results, including: Russell hexagons, digital artifacts, emoticons. Emphasis is placed on the discussion stage as a reflection of the lesson. The possibility of using Viber, Telegram, Instagram, WhatsApp, Google Docs Service, and Trello.com has been proved to organize teamwork.Identified further prospects in the development and formation of the bank In the future, we envisage the development and formation of a bank of STEM-tasks, as research training is related to STEM-education.
Text Region Detection and Recognition in Natural Scene Images Using MSER and Convolutional Neural Network

Text detection and recognition in natural scene images is a computer vision problem that remained a challenge for computer engineers for quite a long time. The new advancements in deep learning have revolutionized the world of computer vision. This paper attempts to build a Deep Learning (DL) based Text detection and recognition model for interpreting the text in natural scene images. The proposed model consists of three stages namely candidate text region detection, text region extraction, and text recognition. The natural scene image is first fed to the candidate text region detection mechanism which extracts potential regions containing text characters. The regions containing non-text which are introduced in the first stage of processing are filtered in the second stage. The set of text regions resulted from the second stage is then recognized in the final stage. Maximally Stable Extremal Region (MSER) algorithm is used in the candidate text region detection. Two convolutional neural networks, one in the text region extraction stage and the other one in the text recognition stage, are used in the proposed model. Text detection in natural scenes is not an easy problem as it appears. The complexity of detection and recognition of text characters in natural scene images is mainly due to the diversity of the textual characters and the natural scene, presence of various disturbances, different illumination conditions, different color, size, and area of the text. ICDAR-2011, ICDAR-2013, CHARS-74K, and CIFAR-100 datasets are used for training and validating our models.
Practical Distributed Control for VTOL UAVs to Pass a Tunnel.

Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to amateur and commercial users alike. An air traffic management (ATM) system is needed to help ensure that this newest entrant into the skies does not collide with others. In an ATM, airspace can be composed of airways, intersections and nodes. In this paper, for simplicity, distributed coordinating the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass an airway is focused. This is formulated as a tunnel passing problem, which includes passing a tunnel, inter-agent collision avoidance and keeping within the tunnel. Lyapunov-like functions are designed elaborately, and formal analysis based on invariant set theorem is made to show that all UAVs can pass the tunnel without getting trapped, avoid collision and keep within the tunnel. What is more, by the proposed distributed control, a VTOL UAV can keep away from another VTOL UAV or return back to the tunnel as soon as possible, once it enters into the safety area of another or has a collision with the tunnel during it is passing the tunnel. Simulations and experiments are carried out to show the effectiveness of the proposed method and the comparison with other methods.
IMPROVING VOCABULARY MASTERY THROUGH GROUP A VERSUS GROUP B TO THE SEVENTH GRADE STUDENTS OF SMP N 2 PARANGINAN IN ACADEMIC YEAR 2018/2019

The research was aimed at improving the students’ vocabulary mastery through Group A versus group B to the seventh grade students of SMP Negeri 2 Paranginan in Academic Year 2018/2019. They were 28 students, consisted of 18 males and 9 females. The study intended to answer the research question: can vocabulary mastery of the seventh grade students of SMP Negeri 2 Paranginan in Academic Year 2018/2019 be improved through Group A versus group B? The present study used a classroom action research design. There were two cycles in which every cycle consisted of two sessions which involved four steps namely planning, action, observation and reflection. There were three instruments used to collect the data in this study such as: pre-test, post-test and questionnaire. The undertaking of the present study was based on the preliminary observation, research which showed that the students’ vocabulary mastery in the seventh grade students especially class B of SMP Negeri 2 Paranginan in Academic Year 2018/2019 relatively low. In addition, the result of pre-test revealed that students’ vocabulary mastery was unsatisfying, the score only 4.2. The finding showed that there were continuous improvements on the students’ mean score after they were taught by Group A versus group B. In Cycle I, the mean score of session 1 was 5.7 and session 2 was 6.3 then the grand mean score in Cycle I was 6.0. It showed that students’ vocabulary mastery was considered fairly satisfactory. Moreover, the mean score of Cycle II in session 3 was 7.7 and session 4 was 8.3, the grand mean score in Cycle II was 8.0. It meant that the increasing score in every session showed that vocabulary mastery of the students’ was considered very well. The difference score between pre-test (IR) and cycle I was 1.8, while the differences score in cycle I and cycle II was 2.0. Therefore, the difference score from IR to Cycle II was 3.8. The result of the analysis of questionnaires score showed the comparative percentage chosen by students’ such as: 62% for item A, 28% for item B, 11% for item C and 0% for item D. These resulted comparative percentage figures clearly showed the students’ attitudes and motivation in learning vocabulary mastery through Group A versus group B. The result of the present study showed that the use of Group A versus group B could improve the vocabulary mastery of the seventh grade students of SMP Negeri 2 Paranginan in Academic Year 2018/2019. It could be seen from the significance improvement of the grand mean score they got in IR was 4.2 then it improved in cycle I became 6.0 and the last improved in cycle II became 8.0. Based on the fact above it could be concluded that Group A versus group B were effective to improve the students’ vocabulary mastery especially in seventh grade students.
Evaluating Federated Learning for human activity recognition

Pervasive computing promotes the integration of connected electronic devices in our living environments in order to deliver advanced services. Interest in machine learning approaches for engineering pervasive applications has increased rapidly. Recently federated learning (FL) has been proposed. It has immediately attracted attention as a new machine learning paradigm promoting the use of edge servers. This new paradigm seems to fit the pervasive environment well. However, federated learning has been applied so far to very specific applications. It still remains largely conceptual and needs to be clarified and tested. Here, we present experiments performed in the domain of Human Activity Recognition (HAR) on smartphones which exhibit challenges related to model convergence.
STUDI KOMPARASI PENGEMBANGAN WEBSITE DENGAN FRAMEWORK CODEIGNITER DAN LARAVEL

Abstract:In developing web-based applications the designers need a framework to facilitate the work process. In the framework of its development has been made with many features that simplify the application process, therefore we need an analysis of the selection of the right framework. The framework to be analyzed in this study is Codeigniter and Laravel, testing analysis using Load Test with a file size of 3401 KB. The Codeigniter and Laravel frameworks have different time and speed performance results. Codeigniter has an average value of time and speed greater than Laravel. On the average time value, Codeingiter has a greater time duration than Laravel with a difference of 4.2 ms and at an average value of speed, Codeigniter is still superior with a difference of 40.08 Kbit / s from Laravel
Online Document Verification System

In this modern era, there has been a need for a reliable and fastest mode to verify the certificate to reduce the level of duplicity. It also reduces the certificate verification time which has been done manually till now. Document or certificate verification plays an important role, whereas a document or certificate issued by an authority will be verified for its authenticity. In a typical system, it will be performed through interchange of mails or post, and it is not effective and efficient process due to human intervention. So in our system, we organize a common database for admin access and for user access so that it can be operated more frequently and it will be more secured as there is no need of any third party to involve in between as it is a direct communication between client and user, and by which we can also get to know which document is altered or real or fake by checking it and verifying it at our admin side and then only one can upload their document to the portal.
Education system and scientometrics in the information society

The article substantiates the need for expanding scientometric research, highlights the essence of the methodology for evaluating its effectiveness and for the implementation of scientific potential. The author also presents the existing methods of scientometric research and scientometric indicators in use throughout the world.
Large-scale Simulated Dataset for Aerial Image Deblurring based on DOTA Dataset

Due to the vibration of the aerial imaging platform or the interference of atmospheric transmission, aerial images may be blurred and the image quality may be affected. However, the current traditional image deblurring algorithms are limited in processing capabilities and difficult to solve complex blur problems. Although deep learning methods provide inspirations for aerial image deblurring, large-scale dataset is needed to facilitate the training of deep networks. In this paper, a deblurring dataset generation method for aerial images is designed based on the DOTA dataset, which considers multiple degrading factors and efficiently generates a large number of simulated blurred images. The proposed method can handle the lack of training data and help to recover blur aerial images.
Displaying open cultural collections. Interface characteristics for effective cultural content aggregators

In recent years, cultural heritage institutions, such as museums and libraries, are introducing several strategies to enhance access to their collections. The digitization of extensive cultural collections and their release under open licences are fostering the creation of cultural content aggregators, namely Web platforms funded by non-profit organizations to converge digitized cultural collections from multiple cultural institutions. Cultural content aggregators, such as Europeana and Wikimedia Commons, aim to support scholars, writers and artists in their research, dissemination, and artistic activities.Although cultural content aggregators are adopting performative technologies and rigorous sharing methods, their user interfaces have several usability issues. Thus, part of the digitized heritage seems invisible to the end-user, as if it were a sort of digital depot.This paper adopts a research through design approach to investigate interface solutions that may foster access, navigation and use of digitized cultural objects within cultural content aggregators.
Curriculum and pandemic: Time of crisis and opportunity for disruption

The virtual homeschooling strategy is discussed in terms of prevailing inequality and the transmissive approach adopted Likewise, it talks about some paths of disruptive innovation in the field of the school curriculum © Los articulos de la Revista Electronica Educare del Centro de Investigacion y Docencia en Educacion de la Universidad Nacional, Costa Rica, se comparten bajo terminos de la Licencia Creative Commons: Reconocimiento, No Comercial, Sin Obra Derivada 3 0 Costa Rica Las autorizaciones adicionales a las aqui delimitadas se pueden obtener en el correo: educare@una cr