Reviewerly/exHarmony huggingface dataset:
Intelligent recognition method of the typical pointer electric instrument based on machine vision

In order to solve the problem that existing recognition methods of typical pointer electric instrument which demand high image quality and are greatly influenced by the environment, an intelligent recognition method based on machine vision is proposed. In the process of image processing, an image transformation method is used to eliminate the influence of the shooting Angle. A dynamic threshold segmentation method is used to segment the image, which is not sensitive to the alteration of illumination. By setting different filter parameters, the regions of dashboard, calibration and pointer can be exactly extracted. The subpixel edge extraction method and the circle fitting method are used to extract the features of the dashboard contains radius and rotation center. The method of minimum external rectangle is used to extract the features of calibration and pointer, which contains the information of width, height and rotation angle. The fitted instrument is reconstructed by means of circle fitting and line fitting with the extracted features of dashboard, calibration and pointer. Based on the function between indicator number and the rotation angle of pointer, the indicator number can be calculated. This recognition method can be applied to the indoor and outdoor environment in the substation. Comparing the artificial reading with the indicator number recognized by this recognition method, it is proved that this method provides perfect identification accuracy.
THE IMPACT OF INFORMATIVE THREATS IN THE YOUNG MENTALITY

This  article  has  a  discussion  about  the  impact  of  informative  threats  in  the  young  mentality.  It  is  the  fact  that  an  overwhelming  majority  of  people  who  are  scholars,  scientists,  psychologists  or  pedagogies  and  educators  always  worry  about  almost  everything  which  may  be  innovative  information  technologies  or  else  global  informative  threats,  especially,  in  the  young  perspective  and  mentality  in  a  perceptible  manner.  Apart  from  them,  this  article  argues  about  psychological  effects  of  risks  for  young  ones  mind.  In  accordance  with  this  paper  readers  can  easily  comprehend  how  the  essence  of  cognitive  processing  in  education  today  as  well  as  a  wide  range  of  activities  for  enhancing  instruction  that  is  built  on  the  theories  of  discussed. Moreover,  author  attempt  to  prove  the  impact  of  informative  threats  by  calculating  in  the  percentage.
Regularization Cocktails for Tabular Datasets

The regularization of prediction models is arguably the most crucial ingredient that allows Machine Learning solutions to generalize well on unseen data. Several types of regularization are popular in the Deep Learning community (e.g., weight decay, drop-out, early stopping, etc.), but so far these are selected on an ad-hoc basis, and there is no systematic study as to how different regularizers should be combined into the best “cocktail”. In this paper, we fill this gap, by considering the cocktails of 13 different regularization methods and framing the question of how to best combine them as a standard hyperparameter optimization problem. We perform a large-scale empirical study on 40 tabular datasets, concluding that, firstly, regularization cocktails substantially outperform individual regularization methods, even if the hyperparameters of the latter are carefully tuned; secondly, the optimal regularization cocktail depends on the dataset; and thirdly, regularization cocktails yield the state-of-the-art in classifying tabular datasets by outperforming Gradient-Boosted Decision Trees.
포스트코로나 시대를 대비한 스마로그형 초등영어 독서 프로그램 개발

The COVID-19 pandemic is affecting educational systems worldwide, and teachers and educators encountered various challenges such as using different mode of teaching environments and methods. In response to the situation, this study aims to develop smarlogue (combination of smart and analogue) type primary English reading program in which classroom based reading instruction could occur both in an online and offline format. To accomplish the objective, the study set up two research tasks: to design a smarlogue type primary English reading program and to illustrate how to apply picture books selected for the program. The researcher started by selecting thirty-three picture books according to three different categories. To begin, twenty picture books were selected in accordance to English textbooks from 3rd to 6th grade. Then, science and art subjects were selected for content integrated reading, and four picture books in each subject were selected and analyzed briefly. Additionally, five picture books were selected and analyzed for narrow reading. The second task was achieved by providing some activities in detail at the initiation and sharing stages. For this purpose, eight picture books out of the thirty-three selected books in the first task were utilized. Some suggestions are provided for follow-up studies.
Anchor-free Detection Framework in Occlusion Scene

In traditional pedestrian detection framework, anchor is a very important part. However, anchor limits the scope of the selection box and brings many problems such as tedious parameter setting and increased computation. In this paper, we proposes a detection network based on anchor-free detection framework, which reduces the setting of anchors, and optimizes the label of positive samples and negative samples by dividing regions, which improves the accuracy of model training. In addition, the effectiveness of different detection frames is analyzed. Then, the visual part of dataset annotation is introduced and added to the training of the model as an additional loss. Experiments on Cityperson and Caltech pedestrian data sets show that the proposed method has certain competitiveness in improving the detection effect and alleviating the occlusion problem.
Warmer And Filler In Increasing Students Vocabulary Mastery

Warmer is a short activity used at the beginning of the lesson before starting the experience. The filler is a short activity that is used in the middle or in the end of the lesson. The research method used is quantitative. This study involved the first grade of State College of Law (STIH) Pengayoman Watampone, consisting of ± 200 students. However, only class A was used as research subjects. Data collection techniques consist of two instruments, namely vocabulary tests, and questionnaires. The results showed that warmer and filler activities were more effective in increasing students' mastery of English vocabulary. From the study results, the average value in the pre-test, 6.4, rose to 12.9 in the post-test. Therefore, it can conclude that warmer and filler are more effective in increasing students' mastery of English vocabulary. Hypothesis 0 is rejected, and Hypothesis 1 is accepted.
In-University Writing and Out-of-University Writing

The purpose of this thesis is to reflect on the present of ‘university writing’ education and to explore the future of ‘university writing’ education by reviewing writing textbooks of major universities and writing books by important authors. Since the 2000s, Korean universities have switched from reading-oriented education based on the existing “university Korean language” to writing-oriented education based on “university writing”. However, it is still questionable how much the institutional change to university writing and academic research on university writing have grown students" writing skills. Of course, there are many studies focusing on class satisfaction evaluation for students who have taken writing classes. However, class satisfaction assessment cannot objectively measure students" improvement in writing skills. Rather, it was time to fundamentally reflect on whether institutionalized writing education and standardized writing research are reducing students" interest and interest in writing. And in order to overcome this, it is necessary to carefully examine why writing, which is stagnating within universities, is reviving outside of universities, is the core thesis of this study. I hope that these considerations can contribute to university writing education.
Point Cloud Super Resolution with Adversarial Residual Graph Networks

Point cloud super-resolution is a fundamental problem for 3D reconstruction and 3D data understanding. It takes a low-resolution (LR) point cloud as input and generates a high-resolution (HR) point cloud with rich details. In this paper, we present a data-driven method for point cloud super-resolution based on graph networks and adversarial losses. The key idea of the proposed network is to exploit the local similarity of point cloud and the analogy between LR input and HR output. For the former, we design a deep network with graph convolution. For the latter, we propose to add residual connections into graph convolution and introduce a skip connection between input and output. The proposed network is trained with a novel loss function, which combines Chamfer Distance (CD) and graph adversarial loss. Such a loss function captures the characteristics of HR point cloud automatically without manual design. We conduct a series of experiments to evaluate our method and validate the superiority over other methods. Results show that the proposed method achieves the state-of-the-art performance and have a good generalization ability to unseen data.
An Improved Model of Mimic Defense Architecture adding Heterogeneous Evaluation

In the face of complex and changeable network environment, mimic defense technology has been developing continuously since it was proposed. Existing studies have demonstrated the gain of heterogeneous mechanisms to the security of mimic defense architectures, but heterogeneous selection will inevitably bring time loss. As a module, heterogeneous evaluation is added to the design of the engineering diagram of mimic defense architecture, and the scheduled tasks are decomposed - parallel simple scheduling and heterogeneous evaluation are used to reduce the time loss of the architecture. Experiments show that this design can effectively improve the efficiency of the mimic defense architecture.
Self-Supervised Map-Segmentation by Mining Minimal-Map-Segments.

In visual place recognition (VPR), map segmentation (MS) is a preprocessing technique used to partition a given view-sequence map into place classes (i.e., map segments) so that each class has good place-specific training images for a visual place classifier (VPC). Existing approaches to MS implicitly/explicitly suppose that map segments have a certain size, or individual map segments are balanced in size. However, recent VPR systems showed that very small important map segments (minimal map segments) often suffice for VPC, and the remaining large unimportant portion of the map should be discarded to minimize map maintenance cost. Here, a new MS algorithm that can mine minimal map segments from a large view-sequence map is presented. To solve the inherently NP hard problem, MS is formulated as a video-segmentation problem and the recently-developed efficient point-trajectory based paradigm of video segmentation is used. The proposed map representation was implemented with three types of VPC: deep convolutional neural network, bag-of-words, and object class detector, and each was integrated into a Monte Carlo localization algorithm (MCL) within a topometric VPR framework. Experiments using the publicly available NCLT dataset thoroughly investigate the efficacy of MS in terms of VPR performance.
МОДЕЛЬ МАЙКЛА ПОРТЕРА КАК ИНСТРУМЕНТ ИССЛЕДОВАНИЯ МИКРООКРУЖЕНИЯ ВУЗА

The article uses the Michael Porter model to assess the forces of competitive pressure of the department as a structural unit of the university. The results of scoring the influence of suppliers, new and existing competitors, con-sumers and substitute services are presented. The threat from existing and new competitors, substitute services is of a high level; the threat from suppliers and consumers is medium. Based on the analysis results, proposals for the strategic development of the department were formulated.
A note on the representation and learning of quantificational determiners

There is a tight, bidirectional connection between the formalism that defines how linguistic knowledge is stored and how this knowledge can be learned. In one direction, the formalism can be mapped onto an evaluation metric that allows the child to compare competing hypotheses given the input data. In the other direction, an evaluation metric can help the linguist to compare competing hypotheses about the formalism in which linguistic knowledge is written. In this preliminary note we explore this bidirectional connection in the domain of quantificational determiners (e.g., ‘every’ and ‘some’). We show how fixing an explicit format for representing the semantics of such elements – specifically, a variant of semantic automata – yields an evaluation metric, based on the principle of Minimum Description Length (MDL), that can serve as the basis for an unsupervised learner for such denotations. We then show how the MDL metric may provide a handle on the comparison of semantic automata with a competing representational format.
Aprendizaje autónomo en Moodle

The objective of this research is to determine the effectiveness of autonomous learning through the Moodle platform, with the field of study in the Third year of high school students of the Rey David Educational Unit that has 648 users, 189 courses and 15167 activities in Moodle. The research approach is quantitative and of the correlational and experimental type. It starts from the application of analysis-synthesis and inductivedeductive methods to define the influential variables in order to study autonomous learning and the essential characteristics of Moodle platforms. These studies are complemented with the application of the UCINET software for the analysis of the presence of the variables with respect to the authors and the VOSviewer in terms of density according to the frequency of appearance and their distribution in the cluster. It shows an analysis of the results of the academic performance of the scores when using Moodle based on existing records and it accompanies the collection of information by instruments such as the survey and observation. The results show favorable values because of autonomous learning. It concludes that autonomous learning was effective with the implementation of interactive activities in Moodle.
Discussion on Rational Mind and Heuristics in Medical Diagnostic Decision Making

The medical diagnostic label is the most important process in patient management. Diagnostic decisions are not easy but very crucial. The diagnosis is a vital part in patient care as it not only affects the patient but the society at large. The decision process involves the thinking process of the caregiver which operates differently in emergencies and outpatient clinic. The heuristics aspect of the mind is more efficient in dealing with emergencies and the slower analyzing mind in outpatient setting. The heuristic approach is useful in rapid decisions and management of emergencies whereas the slower stepwise approach in outpatient treatment will help in reducing investigations, medical errors and cost of care. A properly equipped and trained system1 needs to play a dominant role in the emergency setting. The activated and time consuming system2 needs to be more involved in the outpatient setting .The combination of system1 and system can probably give the most likely diagnosis in both outpatient and emergency setting. The article also gives an evidence based template for predicting the disease process based on positive and negative likelihood ratios.
Distributed Ledgers, Cryptography, and Smart Contracts

The advent of distributed ledger technology has challenged the legal community to learn how technology will change law, finance, and commerce in coming years. Blockchain technology, a method for storing data in cryptographically secure distributed ledgers, has spread all over the world and into nearly every vertical as global commerce searches for secure tools that support ecosystem-wide growth. The World Economic Forum estimates that by 2026, 10% of global commerce will be conducted on blockchain systems.1 Blockchain technology promises to bring more legal relationships into the realm of data and code, so there is a clear need for legal minds to help guide this development.
ЕВОЛЮЦІЯ СТАНДАРТІВ ПІДГОТОВКИ КВАЛІФІКОВАНОГО ВЧИТЕЛЯ СПЕЦІАЛЬНОЇ ОСВІТИ У США

The systemic transformation of educational forms for children with special educational needs in the United States is considered one of the basic categories of the evolution of American education system and the corresponding changes in the system of teacher training for such children. The public awareness of the need to teach all children resulted in changes in the federal and state legislation, which directly affects the development of the education system, especially the availability and quality of education for all children, training teachers to work with them and implemented in various forms of interaction between students and teachers. Educators should be able to interact effectively with students with special educational needs, to self-actualize in the profession, and at the same time to find jobs in the profession easily, as their qualifications meet the requirements of the labor market, so developing corresponding standards becomes extremely important. The study of the American experience in the training of special education teachers provides a historical and comparative analysis of relevant standards. As a result of the analysis, conclusions were made about the dependence of special education teachers’ training standards on social, political, economic and other factors that determined the characteristics of care and education for people with disabilities, and thus directly influenced the formation of special education teachers’ training system in the United States. The analysed standards were adopted in different eras of special education teachers’ preparation: categorical orientation era (until late 1970s), noncategorical (1980-1990), and inclusive ones. The current content and organization of special education teacher training and inclusive education in the United States comply with the special teacher code of ethics, professional standards, and standards for the training, retraining, and certification of special education teachers according to their specialization. The purpose of this work is to study the specifics of standards for preservice special education teachers’ preparation in the USA as well as comparing the experience in Ukraine and formulating proposals for the domestic educational system reformation.
BIG DATA PRIVACY ANONYMIZATION ALGORITHMS: A REVIEW

This paper provides an overview of big data privacy challenges, variety and classification of privacy algorithms.Information of each individual wish that his private information is not revealed in some or the other way.Privacy preservation plays a vital role in preventing individual private data.Anonymization is a process of removing personally identifiable information from the data set.Under anonymization category k-anonymity, Ldiversity and T-closeness algorithms is discussed in detail.The comparison of anonymization algorithms is carried out for eight parameters.
Perancangan Aplikasi Input Data Dalam Pelayanan Jasa Seksi Tata Kelola Dan Pelayanan Usaha Di Tempat Pelelangan Ikan (Tpi) Pelabuhan Perikanan Nusantara Sibolga

Services at the Fish Auction Place at the Sibolga Archipelago Fishing Port, it wasfound weaknesses, the method of inputting data on fishery port service receipts atthe Fish Auction Place which was still done manually. This study examines thedesign of data input applications to facilitate the input and calculation process atthe fish auction. This application is used for the purpose of compiling a data inputapplication for service acceptance at fishing ports using the Visual Basic ForApplication found in Microsoft Excel. This research was conducted in February2020 at the Sibolga Archipelago Fishing Port. The method used is a survey bybuilding an application in the form of a Graphical User Interface in accordancewith field data. The result is that the data input process provides a work timeefficiency of around 90% compared to the manual method. Another advantage isthat the data that is executed is stored automatically in the database.Keywords: Services, Fish Auction Services for Sibolga Archipelago Fishing Port,Visual Basic For Applications, Data Entry Applications1) Students of the Faculty of Fisheries and Marine, University of Riau2) Lecturer at the Faculty of Fisheries and Marine, University of Riau
AOL: Adaptive Online Learning for Human Trajectory Prediction in Dynamic Video Scenes.

We present a novel adaptive online learning (AOL) framework to predict human movement trajectories in dynamic video scenes. Our framework learns and adapts to changes in the scene environment and generates best network weights for different scenarios. The framework can be applied to prediction models and improve their performance as it dynamically adjusts when it encounters changes in the scene and can apply the best training weights for predicting the next locations. We demonstrate this by integrating our framework with two existing prediction models: LSTM [3] and Future Person Location (FPL) [1]. Furthermore, we analyze the number of network weights for optimal performance and show that we can achieve real-time with a fixed number of networks using the least recently used (LRU) strategy for maintaining the most recently trained network weights. With extensive experiments, we show that our framework increases prediction accuracies of LSTM and FPL by ~17% and 28% on average, and up to ~50% for FPL on the worst case while achieving real-time (20fps).
A Simulative Study on the Performance of Load Balancing Techniques over Varying Cloud Infrastructure Using Cloudsim

Cloud computing is a recent evolution in computer technology. A cloud server has to deal with a lot of workload. So, the process of distributing load over the virtual machines or among the servers in a distributed cloud system is very important to get better performance. Before launching a cloud based application or a new load balancing policy in the cloud server, it is very difficult to know whether the performance will be good or bad. Because testing an application or load balancing policy’s performance in Cloud for different workload in a repeatable manner under varying system and user configurations and requirements are very complex and expensive to achieve. So simulating the performance of an application or a load balancing policy is a good alternate. This paper is about simulating some existing load balancing approaches as well as a proposed improved load balancing approach and the comparison of the performance using CloudSim simulator.