Reviewerly/exHarmony huggingface dataset:
Opportunities from the COVID-19 Pandemic: Being Greener through Online and E-Learning Methods

To implement physical distancing during the COVID-19 pandemic, many universities around the world have applied online and E-learning methods. Although faced with several obstacles, online and E-learning methods also provide many opportunities. One of them include the reduction of paper usage, which in 2019 totaled to 394 million tons worldwide. To analyze such an opportunity, this study was conducted at Universitas Sumatera Utara (USU) by involving 232 students and 113 lecturers from 16 faculties. Data were obtained using a structured online questionnaire. The results show that paper usage at USU can be reduced through online and e-learning methods by 48,008,800 sheets per year or equivalent with approximately 4,003 trees. This amount is totalled from lecturer research activities, lecture materials, and student final projects, which are 240,000, 44,800,000 and 3,000,000 sheets, respectively. To realize this, USU is required to improve internet facilities and e-learning access, train lecturers and students to use and manage said facilities, and train lecturers to prepare materials specifically designed for online and e-learning.
Hermitian Rank-Metric Codes

In the space $$\mathrm {H}_n(q^2)$$ of Hermitian matrices over $$\mathbb {F}_{q^2}$$ of order n we can define a d-code as subset $$\mathrm {C}$$ of $$\mathrm {H}_n(q^2)$$ such that $$\mathrm {rk}(A-B)\ge d$$ for every $$A, B \in \mathrm {C}$$ with $$A\ne B$$ . In $$\mathrm {H}_n(q^2)$$ we give two possible equivalence definitions: As pointed out by Zhou in [15], there are examples of Hermitian d-codes $$\mathrm {C}_1$$ and $$\mathrm {C}_2$$ for which there not exist maps of form 2) sending $$\mathrm {C}_1$$ in $$\mathrm {C}_2$$ , but there exists a map $$\psi $$ of the form 1) such that $$\psi (\mathrm {C}_1)=\mathrm {C}_2$$ . We prove that when $$q>2$$ , $$d<n$$ and the codes considered are maximum additive d-codes and $$(n-d)$$ -designs, these two equivalence relations coincide. As a consequence, we get that the idealisers of such codes are not distinguishers, as it usually happens for rank-metric codes. The results rely on the paper [13].
IFR: Iterative Fusion Based Recognizer For Low Quality Scene Text Recognition

Although recent works based on deep learning have made progress in improving recognition accuracy on scene text recognition, how to handle low-quality text images in end-to-end deep networks remains a research challenge. In this paper, we propose an Iterative Fusion based Recognizer (IFR) for low quality scene text recognition, taking advantage of refined text images input and robust feature representation. IFR contains two branches which focus on scene text recognition and low quality scene text image recovery respectively. We utilize an iterative collaboration between two branches, which can effectively alleviate the impact of low quality input. A feature fusion module is proposed to strengthen the feature representation of the two branches, where the features from the Recognizer are Fused with image Restoration branch, referred to as RRF. Without changing the recognition network structure, extensive quantitative and qualitative experimental results show that the proposed method significantly outperforms the baseline methods in boosting the recognition accuracy of benchmark datasets and low resolution images in TextZoom dataset.
EXAMPLES NON EXAMPLES METHOD APPROACH WITH IMAGE MEDIA TO IMPROVE WRITING SKILLS OF GRADE 2 STUDENTS SDN 022 HARAPAN BARU INDRAGIRI HILIR

This form of research is a class action study conducted in grade 2 sdn 022 Harapan Baru Indragiri Hilir regency with 2 cycle cycleS I in the form of planning and cycle II in the form of actions using examples non examples approach with image media. The study subject consisted of 16 grade 2 students, the research instruments of observation sheets and daily replays. The purpose of the research was to improve the writing skills of grade 2 students at SDN 022 Harapan Baru. The results showed a significant improvement both in terms of teacher and student learning activities as well as students' writing skills. For the observation of teacher activity in the initial data only 55% , then after reflection of learning obtained a percentage of 85% with a good category. For the observation of student activity in the initial data obtained a percentage of 53% with a fairly good category, then after reflection of learning obtained a percentage of 92% with a good category. In the results of the student's writing ability found preliminary data with an average score of 60, then after reflection of learning obtained an average score of 88 students, based on the presentation, it can be concluded that the application of examples non examples learning methods using image media can improve the writing skills of grade 2 students at SDN 022 Harapan Baru.
APPLIED ASPECTS OF THE THEORY OF REDUCTION ANALYSIS OF STONE INDUSTRIES

In the process of manufacturing of stone tools and their subsequent intended use, they were intensively transformed and subjected to reduction. They sequentially changed their shape and decreased in their size. This technologically determined process applied equally to both lithic cores and tools. Regardless of the goals, stadium splitting (with changes in the cleavage technique, according to E. Giria) and permanent splitting were used. During the reduction, morphology of stone tools changed significantly. In many cases, this led to appearance of new types of lithic cores and tools. The genetic relationship between these types is revealed on the base of morphometric analysis and refitting-model data. Based on empirical observations, it is possible to determine a steady trend of changes in the types of stone tools during their intensive processing. The type of reduction should be understood as a stable vector of changes in shape of stone tools during sequential splitting or cyclical renewal of their working properties. The method of reduction analysis is applicable to stone industries not only of the Stone Age, but also of the Early Iron Age. Reduction analysis of stone industries is associated with two levels of generalization. The first of them concerns reduction sequences in splitting of lithic cores and making-renewal of stone tools. The second one is related to the degree of transformation of structure of entire complex of stone tools, depending on the depth of processing of stone raw materials. The type of reduction reflects not only the technological, but also the cultural context, i.e. the stylistic features of stone industry. They appeared in the case when there is a variable choice of technology or technique for solving the task of stone tools producing. Reduction analysis of stone industry provides additional arguments for functional diagnostics of Stone Age sites.
Frustrated ex-Tesla employee agrees to $400K settlement in hacking case

A disgruntled former employee whose battle with Tesla and its founder, Elon Musk, played out in news outlets and on social media, has agreed to a settlement.
Burst Imaging for Light-Constrained Structure-From-Motion

Images captured under extremely low light conditions are noise-limited, which can cause existing robotic vision algorithms to fail. In this paper we develop an image processing technique for aiding 3D reconstruction from images acquired in low light conditions. Our technique, based on burst photography, uses direct methods for image registration within bursts of short exposure time images to improve the robustness and accuracy of feature-based structure-from-motion (SfM). We demonstrate improved SfM performance in challenging light-constrained scenes, including quantitative evaluations that show improved feature performance and camera pose estimates. Additionally, we show that our method converges more frequently to correct reconstructions than the state-of-the-art. Our method is a significant step towards allowing robots to operate in low light conditions, with potential applications to robots operating in environments such as underground mines and night time operation.
The State of SLIVAR: What's next for robots, human-robot interaction, and (spoken) dialogue systems?

We synthesize the reported results and recommendations of recent workshops and seminars that convened to discuss open questions within the important intersection of robotics, human-robot interaction, and spoken dialogue systems research. The goal of this growing area of research interest is to enable people to more effectively and naturally communicate with robots. To carry forward opportunities networking and discussion towards concrete, potentially fundable projects, we encourage interested parties to consider participating in future virtual and in-person discussions and workshops.
Creative Choreography Based on Digital Twins, the Voice and Breakthrough of Digital Performance

Performance creative is not only the core content of the performance creation stage, but also a key factor in gaining audience recognition. Performance creative is the artistic language for creators to express their aesthetic experience. Due to the limitation of time and space, the traditional creative process of performance has systemic problems such as low creative efficiency and unsatisfactory creative effect. In order to study and solve these problems, digital performance has opened up a new direction and concept for researchers. As an inter disciplinary subject, digital performance technology combines performance disciplines, psychology, graphics, optical technology, artificial intelligence and brain cognitive science, etc. It provides advanced scientific concepts and cutting-edge technologies for solving scientific problems such as performance creative, performance choreography and performance evaluation. I have every reason to believe that this is a top interdisciplinary subject in the world, and it will certainly attract enough attention from researchers.
AEGR: a simple approach to gradient reversal in autoencoders for network anomaly detection

Anomaly detection is referred to a process in which the aim is to detect data points that follow a different pattern from the majority of data points. With the rapid development of computer technology, protecting networks from various threats such as network intruders is becoming crucial. Traditional anomaly detection methods suffer from several well-known challenges that hinder their performance, such as the curse of high dimensionality. Autoencoders are unsupervised neural networks that have been used for the purpose of reducing dimensionality and also detecting network anomalies in large datasets. The performance of autoencoders debilitates when the training set contains noise and anomalies. In this paper, a new gradient-reversal method is proposed to overcome the influence of anomalies on the training phase for the purpose of detecting network anomalies. The method is different from other approaches as it does not require an anomaly-free training set and is based on reconstruction error. Once latent variables are extracted from the network, local outlier factor is used to separate normal data points from anomalies. A simple pruning approach and data augmentation are also added to improve performance further. The experimental results show that the proposed model can outperform other well-known approaches.
DEVELOPMENT OF SPECIAL CHEMISTRY SKILLS IN THE UNIVERSITY VOCATIONAL TRAINING SYSTEM OF THE STUDENTS

Preparing students for work in the field of education contributes to the development of school education. The basis for teaching students is made up of techniques, methods, and forms of learning by practical activity and the implementation of professional actions. The use of information and communication technology (ICT) in higher education contributes to the disclosure, preservation, and development of individual interests; the motivation; the desire for self-improvement and self-realization of students; ensuring the complexity of studying the phenomena of reality, the inextricability of the relationship between science, technology, the humanities, and art; a constant dynamic update of the content, means, forms, and methods of training and education. This study aimed to identify the level of future chemistry teachers through ICT competence development. A survey was conducted among 24 teachers of general professional and subject-vocational training, who taught “Chemical Technology” to their students. The questionnaire was targeted; the questionnaires were used both in electronic format (Google form) and in print. The article shows how information competence for future chemistry teachers is developed. The practical significance of the study is determined by the need to match the level of school education with modern technological trends. It is shown that there is a necessity for the motivation of students in scientific activity. It was established that the acquisition of competencies and the experience in open training systems, according to all respondents, is a prerequisite for the implementation of further effective educational, cognitive, and professional activities. The ascertaining stage of the pedagogical experiment established the need to choose and introduce the disciplines of mobile, computer, and cloud technologies into the curriculum for solving professionally-oriented problems. The novelty of the study is determined by the necessity to increase the competence of the pedagogical university students in training how to teach chemical disciplines.
SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification

Multiple instance learning (MIL) is the preferred approach for whole slide image classification. However, most MIL approaches do not exploit the interdependencies of tiles extracted from a whole slide image, which could provide valuable cues for classification. This paper presents a novel MIL approach that exploits the spatial relationship of tiles for classifying whole slide images. To do so, a sparse map is built from tiles embeddings, and is then classified by a sparse-input CNN. It obtained state-of-the-art performance over popular MIL approaches on the classification of cancer subtype involving 10000 whole slide images. Our results suggest that the proposed approach might (i) improve the representation learning of instances and (ii) exploit the context of instance embeddings to enhance the classification performance. The code of this work is open-source at {github censored for review}.
Faktor Pengaruh Pembelajaran E-Learning Progdi S1 Akuntansi Universitas Pamulang

The Indonesian government implements Large-Scale Social Restriction known as PSBB which impacts the teaching and learning process at various levels of education to be disrupted as a result of school off in anticipation of the spread of COVID-19 in line with the implementation of the Regulation of the Minister of Health (Permenkes) RI Number 9 of 2020, the process of teaching and learning activities in various educational institutions have been temporarily suspended and replaced with online learning as imposed by Pamulang University by implementing E-Learning. The aim of the research is to explain the relevant aspects of the observed phenomenon by describing the effect of student motivation and interest in learning on e-learning methods by Pamulang University, Bachelor of Accounting Study Program, Academic Year of 2020/2021 through observation activities, as well as questionnaires followed by validity tests and reliability tests. The research was classified as a quantitative research. The data analysis used in this research was descriptive statistics and causality. The result showed that the effect of motivation and learning interest have positive and significant influence on e-learning in Bachelor of Accounting Program in Pamulang University. In other words, E-Learning activities will run optimally if students motivation and interest in learning can be maintained properly.
ALGORITHM FOR COMPLEXING MULTIPLE DATA SOURCES INTO A SINGLE OCCUPANCY MAP

The paper deals with the problem of constructing a passability model of environment witha large number of dynamic objects based on data from several different sensors. The aim of thework is to improve the algorithm for constructing the occupancy map by adding data from bothexisting algorithms for moving obstacles detection and from millimeter-wave automotive radar.The study solves the problem of combining data on static environment and dynamic objects intoone general passability model for further trajectory planning. The modification of the algorithmpresented in the article is able to combine data from both occupancy maps based on a threedimensional point cloud from any sensor such as lidar or radar, and arrays of bounding boxes ofobjects with known coordinates, sizes, and orientation. Data aggregation occurs at the level ofbuilding occupancy maps and does not impose requirements on the source of information aboutdynamic obstacles. The algorithm is able to refine the data on the position and size of the dynamicobject by speed from the radar, which allows to plan the trajectory taking into account the movementof dynamic objects. The parallel use of the classical approach allows to detect obstacles inthe event of an error in the output of the dynamic obstacle detection algorithm. The developedalgorithm works in real time on the Jetson AGX Xavier module, and is tested in real conditions ona mobile robotic platform in autonomous mode. Promising directions for further research to improvethe presented approach are formulated.
Performance Comparison of Monocrystalline PV Module Using Floating and Non-Floating Method

This project explains the concept of comparative performance of monocrystalline PV modules in grasslands and floating lakes, where the performance efficiency of the two types of methods installed are being analyzed. The main objectives of this paper are to design a floating lake PV system to float on the water surface, to develop an algorithm that can automatically agitate a proper reading of the parameter required, and to design a prototype for the development of the floating lake PV system where the water is used as natural cooling effects. In this project, Arduino UNO is selected to program the system as a microcontroller, while sensors such as a voltage sensor, a current sensor, and a temperature sensor are used to initiate data reading for system data logger 1 and system data logger 2. The system has been built accordingly and the performance of the system is tested. The result shows that the system can run with the aid of solar energy and the overall concept of the system can be implemented for the real application system.
Deduction with Uncertain Conditionals (Revised &amp; Simplified, with Examples)

Section 1 of this paper provides an introduction to this new “algebra of conditionals”, addresses various plausibility tests for such an algebra, deconstructs a supposed counter-example, and answers various other objections raised in the literature about the efficacy of this algebraic extension of logic and conditional probability. Section 2 greatly simplifies the calculation of the implications of a set of conditional propositions or conditional events.  These results depend on defining a deductive relation for conditionals (actually two have been found) with the property that the conjunction of two conditionals implies each of its components. That seemingly innocuous property assures that the deductively closed set implied by a finite set of n conditionals with respect to the deductive relation is implied by the single conditional formed by conjoining all n of them. The results are illustrated by solving several examples of deduction with several uncertain conditionals.
THE EFFECT OF USING CLOZE PASSAGE TECHNIQUE TOWARD STUDENTS’ READING COMPREHENSION IN NARRATIVE TEXT AT THE EIGHTH GRADE OF SMPN 8 KOTA SERANG

The aim of this research was to find out whether the Cloze Passage Technique has effect on students’ reading comprehension in narrative text. This research was conducted at the eighth grade of SMPN 8 Kota Serang in academic year 2019/2020 during the COVID-19 pandemic. This research used quantitative method and true experimental design. The populations of this research were all classes in class VIII. The sample of this research were class VIII B which consisted 0f 20 students as the experimental class and class VIII F which consisted of 20 students as the control class. The experimental class was taught by using Cloze Passage Technique and control class without using Cloze Passage Technique. The research instrument was a test which consisted pre-test and post-test. The pre-test was given to students before treatment and the post-test was given to students after treatment. After collected and calculated the data, it was found that there was any effect of using Cloze Passage Technique. This was indicated by the value of tcount was higher than ttable (5,131 ≥ 2,024) at the level of significance of α 0,05 and degree of freedom is 38. It means that the alternative hypothesis (Ha) was accepted. For those findings, this research showed that there is any effect of using Cloze Passage Technique on students’ reading comprehension in narrative text.
An Efficient Novel Load Balancing Algorithm to Improve the Performance of the System in Cloud Environment

Distribution of workload in a balanced manner is a main challenge in cloud computing system. It distributes workload among multiple nodes, hence resources are properly utilized. This is an optimization problem and a good load balancer should be involved for this strategy to the types of tasks and dynamic environment. To overcome load balancing problem here a Novel Load balancing Algorithm is develop i.e. Dragonfly Algorithm is design and developed, to execute the entire task with shortest completion time and load balanced. Our algorithm will be presented with efficient solution representation, derivation of efficient fitness function (or multi-objective function) along with the usual Dragonfly operators. The performance of the algorithm will be analyzed based on the different evaluation measures. The algorithms like particle swarm optimization (PSO) and Genetic algorithm (GA) will be taken for the comparative analysis.
복수전공자의 학습사회화과정에 대한 연구 : SNS 기반 학습공동체를 중심으로

This study explores the process of using mobile discussion as an interactive channel in the context of English education to overcome teaching and learning difficulties and to adapt to a new learning community. This study was conducted as a classroom study, and data was collected through various methods such as open survey, personal interview, and mobile discussion. Data analysis was conducted using a continuous comparison method that combined qualitative and quantitative analysis. The results of the study are as follows. First, pursuing double majors in the department of English education was found to increase the inability to adapt to the new educational environment in areas such as learning activities, cultural shock and social activities. Second, SNS-based community discussion (Kakao Talk) provided opportunities for among members to facilitate the learning socialization process for dual majors. Specifically, the complete sequence of interaction led to meaning negotiations nearly tripling over the course of a semester for group members. Third, dual majors actively induced support behaviors in existing members from a beginner's point of view, and under the most common discussion pattern, 'novice Question-Proficient Response', assistance activities were actively carried out for multidisciplinary majors. This study suggests that in order for dual majors to successfully settle in a new learning community, further research is needed in instructional models that incorporate mobile channels into classroom teaching in various ways.
YouTube to remove content that alleges widespread voter fraud in 2020 election

YouTube will remove videos and content that allege widespread voter fraud in the 2020 presidential election starting Wednesday.