Reviewerly/exHarmony huggingface dataset:
Pedagogy of cooperation of the 1980s as an innovative practice

Introduction. The relevance of the research topic lies in the possibility of using the axiological and didactic potential of the pedagogy of cooperation in modern domestic educational practice. The purpose of the article is to give a scientific assessment of the movement of innovative teachers that arose in the second half of the 1980s in the USSR on the wave of "perestroika" and the reform of the domestic general educational and vocational schools. Materials and methods. The author uses an axiological approach that allows identifying the most valuable content in the studied phenomenon that can enter the main fund of science. Methods – a retrospective and comparative historical analysis of the works of innovative teachers, a method of analytical grouping of the studied material, as well as a historical and typological method that allows you to define the main content lines of the pedagogical phenomenon under consideration. Results. The socio-political reasons that led to the emergence of the movement of innovative teachers in the 1980s are characterized. The most important of them were state resolutions that proclaimed the mandatory nature of general secondary education, which, in turn, stimulated the research search of the best teachers in the country in the direction of finding new forms and methods of educational practice. The main ideas of the pedagogy of cooperation are briefly described. These include the ideas of the content of the child's personality and personal approach to him, the development of his creative and performing abilities, the ideas of a difficult goal, support in learning, quick assessment of student work, free choice of the type of task by the child. The innovative teachers attached great importance to the implementation in practice of their work of the ideas of "reincarnation of a child into a teacher", advance in the study of educational material, teaching in large blocks, as well as the ideas of the children's half of the day, the selection of the appropriate form of the lesson, introspection, creating an intellectual background of the class, collective creative education, creative productive work, creative self-management, self-respect and self-regulation, the uniqueness of each child, teacher-student dialogue, cooperation with parents and colleagues. Conclusion. The scientific and practical heritage of the founders of the pedagogy of cooperation is of considerable value for education and pedagogy, and deserves further study.
Multi-view Scenario-based Service Resource Description Modeling and Application Method

In recent years, soft service robot(e-SBOT) that can exist in any intelligent terminal have received widespread attention. However, currently the services provided by e-SBOT are extremely limited. This defect mostly dues to two problems, one is that the inaccuracy of capturing user’s need caused by incomplete understanding, the other is that the richness, diversity and complexity of service resources make it difficult to select appropriate resources. The existing service resource library is mainly based on the digital world and lacks a unified description and management of service resources both in digital and physical world. In addition, it is mostly useless to recommend service resources that do not match the user’s current scenario, resulting in a worse experience. Therefore, it’s essential to describe the service resources in combination with the scene and provide the service resources that adapted to the scenario to users in a more accurate and rapid way. To achieve this, we propose a scenario-based service resource description model based on ontology modeling. A multi-view retrieval method is simultaneously developed for rapid retrieval of service resources. Experimental results show that our proposed model and method can improve the retrieval speed and accurately return service resources suitable for user scenarios.
THE ANALYSIS OF STUDENT S GRAMMATICAL ERROR IN WRITING SKILL OF RECOUNT TEXT AT THE ELEVENTH GRADE OF SMK NEGERI 17 WEST JAKARTA

The article investigated about Grammatical error in writing skill of recount text at the eleventh gradeof SMK Negeri 17 West Jakarta. The purpose of this study was to know the grammatical error of thestudents in part of speech and tense in recount text writing. The indicators are: (a) error in sentencepattern, (b) error in tense, (c) error in omission. From these indicators, the researcher would know thekinds of error that students made in their writings. To achieve this goal, this research uses descriptive qualitative methods. The population was class XI in SMK Negeri 17 West Jakarta. The samples were 10 students majoring in management. The researcher used total sampling to take the samples. The Instrument was writing test.
Types of Out-of-Distribution Texts and How to Detect Them.

Despite agreement on the importance of detecting out-of-distribution (OOD) examples, there is little consensus on the formal definition of the distribution shifts of OOD examples and how to best detect them. We categorize these examples as exhibiting a background shift or semantic shift, and find that the two major approaches to OOD detection, calibration and density estimation (language modeling for text), have distinct behavior on these types of OOD data. Across 14 pairs of in-distribution and OOD English natural language understanding datasets, we find that density estimation methods consistently beat calibration methods in background shift settings and perform worse in semantic shift settings. In addition, we find that both methods generally fail to detect examples from challenge data, indicating that these examples constitute a different type of OOD data. Overall, while the categorization we apply explains many of the differences between the two methods, our results call for a more explicit definition of OOD to create better benchmarks and build detectors that can target the type of OOD data expected at test time.
An Intensity Estimation Application Based on Website Microservice Logs

The number of digital platforms that use cloud systems with microsystem architectures has increased day by day. By using public cloud systems efficiently, costs and expenses can be significantly reduced. This study tries to determine the necessary resource for the website by examining user activities for cloud resources management. A successful estimating system is essential for adjusting the price/performance balance of resource management. In this study, more than 1.5 million user logs with 18 different features were collected. SVM RBF and decision tree forest have been applied for this data. This study is shown that the SVM RBF method modeled the service rush time with an approximately 95% success rate. With the study, it has been revealed that a sound cloud resources management system can a significant economic benefit by adjusting the number of resources according to rush time prediction.
STUDI PERAMALAN BEBAN LISTRIK JANGKA PENDEKSISTEM JAWA-BALI MENGGUNAKAN METODE JARINGANSARAF TIRUAN DAN KOEFISIEN ENERGI

PT.PLN (Persero) UIP2B requires load forecasting to plan weekly and daily system operations and still uses the Energy Coefficient method to obtain daily and weekly load forecasting data where this method takes a long time and the level of accuracy is not good for short-term load forecasting. In this study, it discusses comparing short-term electrical load forecasting using the artificial neural network method with the Energy Coefficient method, where using this artificial neural network method only requires historical data of electrical loads to forecast and ignores the state of each area in the Java-Bali system. and the Energy Coefficient method is a method that has been used by PT PLN (Persero) UIP2B. To compare the two methods by looking for the smallest error percentagevalue and the best accuracy using the electric load that has been realized as a reference. In this study using historical data on the electric load of PT PLN (Persero) Load Control Center Main Unit in the Java-bali system from 27 October 2019 to 30 November 2019. The results show that the comparison error from Sunday is -0.48% with the value 101.81% accuracy for the neural network method and -1.81% with an accuracy value of 96.60% for the energy coefficient method, for Saturday the error is 0.64% with an accuracy of 99.36 for the artificial neural network method and -1, 81% with an accuracy value of 101.81% for the energy energy coefficient method, and the average error on weekdays is 0.28% with an accuracy of 99.72 for the artificial neural network method and 4.32% with an accuracy of 95.68% for the coefficient method energy.
The Construction and Development of Intelligent PE Classroom in Colleges and Universities

Students have a healthy physical quality and psychological quality is the basic requirements of education. Only with this premise, students can carry out normal learning and for the future into the society to lay a solid foundation. Physical education is an important course for students to keep fit. With the deepening of education reform, the original physical education model is less and less suitable for the new requirements of physical education reform. In order to further improve the quality of physical education, enhance students ' physical quality, integrate modern information technology into physical education and create a new physical education model--intelligent physical education, it is necessary to discuss the basic structure and development trend of this new physical education model.
Industrial Internet of Things and Industry 4.0

Digital Darwinism or Internet of things is the minute integration of Artificial intelligence and machine learning to transform the world into sensors where nothing will just “stop at a screen”. Internet of things refers to devices that normally do not require connectivity but are connected to each other via the internet and function smoothly without human action. Usage of this in businesses will depend on the implementation, efficiency and agility with which the systems are put into place. Industries 4.0 are adding sensors to their products so that the usage statistics can be reported back and any glitch can be cured before the object malfunctions and gives a bad name to the company. To have a reliable Internet of things network the most important thing is the compatibility standards which refers to the connected devices being able to talk and share data and recordings. In case all the devices run on different standards then they will be unable to match with each other and hence the interconnected system will break down at one or more links. Security is one of the key factors for its widespread use since the sensors collect extremely sensitive data. Once businesses adopt the advanced use of technology it might lead to critical attacks on the infrastructure or industrial espionage if there is no encryption done to sensors, gateways and company networks. The Internet of things bridges the gap between the digital world and the physical world, therefore hacking will lead to catastrophic real-world consequences.
KAPASITAS ADSORBSI LOGAM KROMIUM (VI) DENGAN ADSORBEN PEKTIN DARI KULIT LEMON

Chromium is a heavy metal that can pollute the aquatic environment. Tackling water pollution can be done by using the adsorption method using pectin from lemon peel. This study aims to determine the adsorption equation of chromium (VI) with pectin adsorbent from lemon peel and to determine the effect of adsorbent mass on chromium (VI) metal uptake. The lemon peel was extracted with a mass of 15 grams using the help of Microwave Assisted Extraction (MAE) with an extraction time of 26 minutes using 300 Watt power. The adsorption process was carried out with mass variations (1.2 gr; 1.3 gr; 1.4 gr; 1.5 gr and 1.6 gr) with a contact time of 1 hour and a stirring speed of 250 rpm. The analytical method used to measure the adsorption value is Atomic Absorption Spectrophotometry (AAS). The Cr (VI) adsorption equation follows the Langmuir equation. The results of this study indicate that the optimum conditions for adsorption, namely the pectin mass of 1.5 g, can reduce Cr (VI)  levels by 0.0452 mg / g (70.23%).
A Data-Driven Semi-Automatic Framenet Development Methodology

FrameNet is a lexical semantic resource based on the linguistic theory of frame semantics.A number of framenet development strategies have been reported previously and all of them involve exploration of corpora and a fair amount of manual work.Despite previous efforts, there does not exist a well-thought-out automatic/semi-automatic methodology for frame construction.In this paper we propose a data-driven methodology for identification and semi-automatic construction of frames.As a proof of concept, we report on our initial attempts to build a widerscale framenet for the legal domain (LawFN) using the proposed methodology.The constructed frames are stored in a lexical database and together with the annotated example sentences they have been made available through a web interface.
Spatial and Non-Spatial Information on Numerous Parcel Options Were Generated Exploitation RS Imageries

Geomorphologists have unremarkably printed conclusions regarding wearing and water movement supported experimental information obtained at the construction scale. Ways for crop phenology detection exploitation statistic analysis have provided correct info for giant agricultural areas in shorter process times, which may be helpful for science management and provide chain watching. Delineation of groundwater prospect zones has been meted out exploitation RS and GIS for Melattur grass punchayet in Malappuram district of Kerala.
Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to training data, we have partial structural knowledge of the shifted test distribution. We employ the principle of minimum discriminating information to embed the available prior knowledge, and use distributionally robust optimization to account for uncertainty due to the limited samples. By leveraging large deviation results, we obtain explicit generalization bounds with respect to the unknown shifted distribution. Lastly, we demonstrate the versatility of our framework by demonstrating it on two rather distinct applications: (1) training classifiers on systematically biased data and (2) off-policy evaluation in Markov Decision Processes.
Vr exposure in cbt is effective and efficacious treatment for simple phobia (flight phobia)

Introduction The virtual environment with realistically rendered fear-inducing stimuli is enough to conduct VR exposure therapy (VRE), although the total control over the virtual environment also enables presentation of stimuli, contexts, and tasks not possible in in vivo exposure therapy (i.e. flight etc.)30 randomized controlled trials revealing high efficacy and effect sizes comparable of VRE-CBT to in vivo exposure therapy. Aerophobia is a very frequent limitation and affect 25% of the population and 30% of the subjects who fly make habitual use of anxiolytics. Objectives The aims of this study is to show that conducting VR exposure in CBT for simple phobia (flight phobia) is effective and is an efficacious treatment for fear and anxiety,Vs other treatments. Methods Participants (n = 39; age between 19 and 60 years) in the active arms received individual CBT VR exposure for six sessions and outcome was assessed with questionnaires: MSPS;Rathus Assertiveness Scale (RAS); HAM-A; QMAV; QSAV – (Flying fear); QoL INDEX and a behaviour avoidance test (really take the plane). Wilcoxon tests was using for the statistical analysis. Results 36 subjects managed to take the plane at the end of treatment and the results obtained showed a significant difference between “before treatment (T0) and after (T1)” with the exception of the Rathus test. All the SF-36 scales show a significant difference between “before-after”. 3 subjects was dropped out Conclusions Using VR can be advantageous over standard CBT as a potential solution for treatment avoidance and as an efficient, cost-effective and practical medium of exposure. Disclosure No significant relationships.
Near Optimal Policy Optimization via REPS

Since its introduction a decade ago, \emph{relative entropy policy search} (REPS) has demonstrated successful policy learning on a number of simulated and real-world robotic domains, not to mention providing algorithmic components used by many recently proposed reinforcement learning (RL) algorithms. While REPS is commonly known in the community, there exist no guarantees on its performance when using stochastic and gradient-based solvers. In this paper we aim to fill this gap by providing guarantees and convergence rates for the sub-optimality of a policy learned using first-order optimization methods applied to the REPS objective. We first consider the setting in which we are given access to exact gradients and demonstrate how near-optimality of the objective translates to near-optimality of the policy. We then consider the practical setting of stochastic gradients, and introduce a technique that uses \emph{generative} access to the underlying Markov decision process to compute parameter updates that maintain favorable convergence to the optimal regularized policy.
Software Toolset for Localization and Control of Swarming Robots using Random Finite Set Statistics

Guidance, navigation, and control of robotic multi-agent vehicle systems is currently a topic of major focus for a variety of industrial, commercial, and space applications. Collective vehicles within these systems operating independently or cooperatively are often referred to as swarms, as they mimic the behavior of many natural systems. The multi-agent swarm localization problem can be formulated as estimating the local intensity function of agents in the near field and developing probabilistic control strategies to track an expected per-agent location state space configuration. The interrelated localization and control problem of multi-agent swarms can be structured as maneuvering a swarm collective as a whole, as well as individual control of constituent agents, using either centralized or decentralized strategies. To address the simulation and direct control of evolving large-number systems, along with a growing need to accurately model the behaviors and actions of a swarm and its individual agents, a new software toolset based on Random Finite Set statistics and a variety of control methods were developed. The basis for the toolset and its functionality is provided, along with sample output solutions, as well as planned further developments.
Dataset Distillation with Infinitely Wide Convolutional Networks

The effectiveness of machine learning algorithms arises from being able to extract useful features from large amounts of data. As model and dataset sizes increase, dataset distillation methods that compress large datasets into significantly smaller yet highly performant ones will become valuable in terms of training efficiency and useful feature extraction. To that end, we apply a novel distributed kernel based meta-learning framework to achieve state-of-the-art results for dataset distillation using infinitely wide convolutional neural networks. For instance, using only 10 datapoints (0.02% of original dataset), we obtain over 64% test accuracy on CIFAR-10 image classification task, a dramatic improvement over the previous best test accuracy of 40%. Our state-of-the-art results extend across many other settings for MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and SVHN. Furthermore, we perform some preliminary analyses of our distilled datasets to shed light on how they differ from naturally occurring data.
PENGARUH MODEL PEMBELAJARAN SNOWBALL THROWING TERHADAP HASIL BELAJAR MATEMATIKA MATERI PEMBELAJARAN BANGUN RUANG KUBUS SISWA KELAS V SDN 104181 MEDAN TAHUN AJARAN 2018/2019

This research to find out how students' Mathematics learning outcomes before and after using the Snowball Throwing learning model Against Mathematics Learning Results Class V SDN 104181 Medan. The sampling technique is done by purposive sampling. The sample in this study were students of class V-A and V-B of 104181 Medan Elementary School with 56 students, with details of class V-A totaling 27 students and class V-B totaling 29 students. The research instrument used in data collection is a test in the form of an Essay Test. The number of tests consists of 10 questions that have already been tested for validity and reliability. Then the questions are divided into two parts with details, 5 questions in the pretest and 5 questions in the posttest. From the results of data analysis in the experimental class using the Snowball Throwing Learning Model obtained an average value of the pre test X = 64.07, S2 = 77.85 and S = 8.82 and the average value of post test X = 94, 44, S2 = 78.39 and S = 8.85. An increase in experimental class learning outcomes by 47%. While the analysis of data in the control class using conventional learning obtained the average value of the pre test X = 82.07, S2 = 116.41 and S = 10.79 and the average value obtained posttest (X) = 86.03 , S2 = 85.13 and S = 9.23. An increase in control class learning outcomes by 4.8%. From the results of data analysis that there is a significant effect between the Snowball Throwing learning model rather than conventional learning on mathematics learning outcomes. Judging from the average scores and posttests there was an increase in learning outcomes marked by an increase in the student's average grade.
PDTX: A novel local explainer based on the Perceptron Decision Tree

Artificial Intelligence (AI) approaches that achieve good results and generalization are often opaque models and the decision-maker has no clear explanation about the final classification. As a result, there is an increasing demand for Explainable AI (XAI) models, whose main goal is to provide understandable solutions for human beings and to elucidate the relationship between the features and the black-box model. In this paper, we introduce a novel explainer method, named PDTX, based on the Perceptron Decision Tree (PDT). The evolutionary algorithm jSO is employed to fit the weights of the PDT to approximate the predictions of the black-box model. Then, it is possible to extract valuable information that explains the behavior of the machine learning method. The PDTX was tested in 10 different datasets from a public repository as an explainer for three classifiers: Multi-Layer Perceptron, Random Forest and Support Vector Machine. Decision-Tree and LIME were used as baselines for comparison. The results showed promising performance in the majority of the experiments, achieving 87.34% of average accuracy, against 64.23% from DT and 37.44% from LIME. The PDTX can be used for black-box classifier explanations, for local instances and it is model-agnostic.