Reviewerly/exHarmony huggingface dataset:
Interactive Jupyter Notebooks for the visual analysis of critical choices in Global Sensitivity Analysis

&amp;lt;p&amp;gt;With earth system models growing ever more complex, a comprehensive, transparent and easily communicable analysis of the interacting model components is becoming increasingly difficult. Global Sensitivity Analysis (GSA) provides a structured analytical approach to tackle this problem by quantifying the relative importance of various model inputs and components on the variability of the model outputs. However, there are a number of critical choices needed to set up a GSA, such as selecting the appropriate GSA method for the intended purpose and defining the inputs to be tested and their variability space. In this work, we test the use of interactive visualization to analyze the impacts of such critical choices on GSA results and hence achieve a more robust and comprehensive understanding of the model behavior. To this end, we combine the Python version of the Sensitivity Analysis For Everybody (SAFE) toolbox, which is currently used by more than 2000 researchers worldwide, with the literate programming platform Jupyter Notebooks, and interactive visualizations. Unlike traditional static visualization, interacting visualizations allow the user to interrogate in real time the impact of user choices on the analysis result. &amp;amp;#160;Due to computational constraints of most earth system models, not all impacts can be visualized in real time (e.g. only those not requiring the model to be re-run). In those cases where a model needs to be re-run (e.g. to test the impact of the definition of the inputs space of variability), interactive visualizations still offer a useful tool, which allows to highlight only specific features of interest (e.g. behavior of the input/output samples for extreme values of the inputs or outputs). Jupyter Notebooks, by combining text, code and figures, enhance the transparency, transferability and reproducibility of GSA results. Interactive visualizations strengthen the understanding of the impacts of the choices made to run GSA and the robustness of GSA result (e.g. by being able to easily assess the impact of varying output metrics or the GSA method on the analysis results). In general, this work offers an example of how the use of notebooks and interactive visualizations can increase the transparency and communication of complex modelling concepts.&amp;lt;/p&amp;gt;
NLMS - NETWORK LOAD BASED QUEUE MONITORING AND SCHEDULING

In the load-based cloud network queue monitoring process, system busy mode and hibernate mode switching occurred depends on the network traffic, this is the most suitable examination area to validate the server load and processing queue on the network. The queue-based validations are used to extract the meaningful details of the attributes of the current queuing task. The feature extraction from the task is essential to generate a subset of the parameters about the task. Since the feature extraction (FE) process plays a primary role of the outcome, it is used to evaluate the efficiency of the system based on the FE. In this paper NLMS  network load based queue monitoring and scheduling, we presented the revision with an optimal feature extraction based node queuing allocation system. Here, to classify the aptest feature subsets from the queuing data, we used the whale optimization algorithm with minimum cardinality to gain better performances, later, the adaptive neuro-fuzzy FN computation was used to organize the queuing content.
Scientific Python Ecosystem and NumPy

In Chapter 3 , you learned how to create simple visualizations with Python 3 and the leather data visualization library. You also learned that only primitive visualizations can be prepared using the leather data visualization library. For more complex and elaborate visualizations, we need to use libraries with the advanced data handling and visualization capabilities.
A Synergy of Freezing and Dropout - A New Learning Strategy of Convolutional Networks

In this work, we explore training efficiency and generalization results on testing sets of a new learning strategy applied to convolutional neural networks. The proposed strategy is to limit the modification of the weights of the networks entering a randomly selected set of neurons (activation function). Randomizing a fragment of the network whose parameters are modified is carried out in parallel with the selection of a mini-batch based on which a stochastic gradient is determined. The strategy of combining dropout with random freezing allows for better generalization while shortening the duration of training in individual epochs. An interesting phenomenon is the widening of the generalization gap while reducing the error on the test set. Computational experiments were carried out on CNN networks with about 1–2 M trainable parameters and CIFAR-10 and CIFAR-100 benchmark data sets.
A Comparison of Spatio-temporal Filters for Color Video Denoising

Extensions of non linear image filtering techniques to color video filtering are presented and compared with well known non linear image filters proposed by other authors like VMF, GVDF, Marginal Median, etc, which are also modified to allow for 3D filtering. The proposed extensions and the other filters are tested against varying temporal window lengths to assess the benefits from the exploitation of temporal correlation on reduction of different noise types and their combinations. The filters are also tested on two video sequences, one with low spatial detail and low amount of movement and one with medium detail and amount of movement. Results show that the proposed extensions perform well under noisy conditions.
Policy Point-Counterpoint: Can Colleges and Universities Maintain High Quality Connections with Students through Virtual Platforms?

The need to more deeply and creatively assess educational modalities has been amplified with the unexpected emergence of a global pandemic. In spring 2020, institutions of higher education quickly shifted to online formats with little time for pause to consider the pedagogies that were implemented as they more or less survival strategies. Entering into the 2020-21 academic year, institutions now must take responsibility for considering and implementing mechanisms for maintaining educational quality and students' connections to their professors and campus communities;in addition to deeply assessing health of the campus community. Preparation for distance education course delivery has likely been enhanced as many college students now have experience with utilizing online platforms, and faculty have developed a wider range of skills to deliver and facilitate high quality courses through technologically rich, remote formats.
Masheli: A Choctaw-English Bilingual Chatbot

We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to practice. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.
Concerns over mail-in voting could be driving ballot drop box numbers in Washington

96 percent of registered voters in King County have a ballot drop box within three miles of their home.
Exploring the Evolution and Source Properties of Injection-Induced Seismicity in Northern Oklahoma Using a Large-N Seismic Array

&amp;lt;p&amp;gt;The LArge-n Seismic Survey in Oklahoma (LASSO) array recorded local seismicity in 2016 in a region of active saltwater disposal. The month-long deployment of 1,833 vertical-component nodes had a nominal station spacing of 400 m and covered a 25 by 32 km&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; area. We estimate local event magnitudes and focal mechanisms of the induced seismicity using the vertical component waveforms from a catalog of 1375 earthquakes. Here we use the developed catalog to investigate the spatio-temporal evolution of seismicity and the source properties of the induced events.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The catalog is complete to a local magnitude of ~0.9, with a b-value of ~1.1. Focal mechanisms, which we determined using the HASH method, show a mix of strike-slip and normal faulting. The majority of the events are located at 1.5 &amp;amp;#8211; 5.0 km depth, where injection depths range from 0.1 &amp;amp;#8211; 2.0 km, and the basement contact is located at 1.5 &amp;amp;#8211; 2.5 km. Analysis of the coefficient of variation of interevent times suggests that the time evolution of seismicity is close to Poissonian, with minimal temporal clustering. We observe spatial clustering, with larger (M &amp;gt; 2) events occurring within dense clusters near the footprint of the array.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The dense station coverage of the array permits the exploration of variations in corner frequency and resulting stress drop estimates as a function of azimuth, i.e. radiation pattern. We calculate stress drops for the local catalog within 5 km of the array footprint from individual spectral and spectral ratio corner frequency values. Single spectra corner frequency estimates for events within the array footprint on individual nodes show evidence of variation related to radiation pattern, and vary as much as 100% from the mean for an individual event. Stress drop estimates from spectral ratio corner frequency estimations range between 10 &amp;amp;#8211; 100 MPa, show self-similar scaling, and fall within the typical range observed for intraplate (tectonic) earthquakes. Both single spectra and spectral ratio corner frequency estimates show a significant sample bias in the corner frequency estimation by using less than ~10 stations, and highlight the importance of azimuthal coverage for the stability of spectral estimates.&amp;lt;/p&amp;gt;
SISTEM KEAMANAN DATA DENGAN METODE CRYPTOGRAPHY

Abstract For data protection is important enough there is no other way but to use a special program protection / encryption of data. When this has been a lot of outstanding special program data protection either freeware, shareware, and commercial is very good. In general, these programs not only provide a single method alone, but some kind so that we can choose who we think is the most secure. One method is public key encryption criptography. Cryptography is a science or art of secure messaging and performed by criptographer. Criptography Public Key cryptography is done by combining the two related keys called a key pair of public and private keys. Both keys are created at the same time and relate it mathematically. Semetrik algorithm is a type of encryption algorithms are the most common. This algorithm is called symmetric because the same key used for encryption and decryption. Unlike the keys used in public key algorithms, symmetric key that is used on keys typically change often. When compared with the public key algorithms, symmetric key algorithms are very fast and therefore are more suitable when used to encrypt the data is very large. Modern cryptographic protocols that incorporate many current public key algorithm with a symmetric algorithm to obtain the advantages of each algorithm.
Exploring How Early Childhood Teachers Experience Instructional Materials: Focusing on Photovoice

본 연구에서는 유아교육현장에서 유아교사가 경험한 교수매체의 의미가 어떠한지를 탐구하였다. 본 연구는 교수매체에 대한 관심이나 고민이 있는 유아교사 5인을 선정하여 포토보이스 연구방법을 활용하였다. 연구 기간은 2020년 4월 17일부터 5월 13일까지 이루어졌으며, 포커스 그룹 인터뷰, 개별면담을 통해 문서화한 전사기록과 사진을 자료로 수집하였다. 수집된 자료는 포토보이스 자료 분석과 주제 분석에 따라 선정, 맥락화, 범주화 작업을 거쳤으며, 범주화된 자료는 반복적으로 읽고 분석하여 최종 내용을 도출하고 의미를 해석하였다. 유아교사가 경험한 교수매체의 의미는 다름의 순간과 뜻밖의 사건의 마주함으로 열어가는 교수매체, 새롭게 다시 알아차리고 삶으로 이어지는 교수매체, 감응으로 이끌리는 교수매체로 드러났다. 이러한 연구 결과는 교수매체를 경험하는 유아교사들에게 교수매체를 새로운 시선으로 이해하고, 유아들이 교수매체를 접할 때 마주침을 알아차리는 것이 중요하다는 것을 강조한다. 나아가, 유아교사들은 다양하고 풍부한 교수매체로 유아들의 놀이와 배움을 지원해야한다는 시사점을 제시한다.This study is aimed to explore how the teachers experience instructional materials in the field of early childhood education. The method of photovoice was used, 5 early childhood teachers were selected. For 4 weeks from April to May 2020, data were collected through photographs and interviews, including pre-interviews, focus group interviews, and individual interviews. The collected data were analyzed through photovoice document analysis and thematic analysis, focusing on the key words of selecting, contextualizing, and categorizing. The data were repeatedly read, the themes were derived, and the meanings were interpreted. The meaning of the instructional materials experienced by the early childhood teachers was revealed as instructional material encountered in the moments of difference and unexpected events, instructional material that noticed the movements of exploration and experimentation of the children and leads to life, instructional material led by affect. This study indicates that teachers must understand instructional materials as new perspectives that materials constantly changes in the field of early childhood education. It also emphasizes the importance of noticing when children encounter materials. Furthermore, it offers implications on how teachers support children’s play and learning with diverse and rich materials.
Secret Algorithms, IP Rights, and the Public Interest

The secrecy surrounding the algorithms that play a central role in American life today is proving to have alarming effects. Judges and juries are convicting defendants based on secret evidence. Major advertisers like Facebook are discriminating against minorities seeking housing. And Russians may very well be hacking our voting machines to change election outcomes. The algorithm secrecy underlying these results obscures whether such legal outcomes are actually accurate and fair or whether they were based on faulty evidence, affected by bias, or manipulated by outside influences. These are just a handful of the public-interest perils of algorithm secrecy. This Article explains that the pervasive secrecy surrounding algorithms is not entirely by accident. The Supreme Court’s recent overhaul of intellectual property (IP) law has driven algorithm developers toward secrecy. By limiting patent protection for software, the Court’s new IP regime pushes developers away from the required disclosure of patent law and toward the obscurity of trade secret law. In doing so, the new regime neglects to take into account the many negative effects that this heightened secrecy has on the public interest. Accuracy, fairness, and good policy require a more careful consideration of the tradeoffs between secrecy and transparency. This includes not only exploring how to minimize these swelling public-interest concerns but also reexamining the Court’s new IP rules with these negative effects in mind.
Heterogeneous Weighted Voting-Based Ensemble (HWVE) for Root-Cause Analysis

Root-cause analysis has been one of the major requirements of the current information-rich world due to the huge number of opinions available online. This paper presents a heterogeneous weighted voting-based ensemble (HWVE) model for root-cause analysis. The proposed model is composed of an aspect extraction and filtering module, a model-based sentiment identification module, and a ranking module. Domain-based aspect ontologies are created using the available training data and is used for categorization. The input data is passed to the HWVE model for opinion identification and is in-parallel passed to the significance identification phase for aspect identification. The identified aspects are combined with their corresponding sentiments and ranked based on their ontological occurrence levels to provide the final categorized root-causes. Experiments were performed with the five-product dataset, and comparisons were performed with recent models. Results indicate that the proposed model exhibits improved performances of 5%-13% in terms of F-measure when compared to other models.
An Efficient Comparison Analysis of Scheduling Algorithms with a base of Genetic algorithm to Optimization technique of Ant colony and Artificial Bee colony

Day by Day so many Task scheduling algorithms are booming in the Cloud computing field. However they are having their own limitations. Now we are in the state to analyze all the algorithms and find out the better one for our efficient output. We know that Genetic Algorithm is one of the powerful metaheuristic Algorithms in task scheduling. But due to the random selection of parameters the process quality is not high. So we go for the combination of two or more optimization Algorithms counts on less execution time, maximum throughput, less makespan, full resource utilization, better quality of service, maximum energy consumption, Quick response time and less cost. In this paper we can analysis Genetic optimization hybrid ACO Algorithm, and genetic algorithm Hybrid with ABC Algorithm .Finally the analysis is tabulated for finding the better Algorithm.
O corpora, o mores

Abstract Abstract In this paper, I focus on strengths and weaknesses of corpus research in linguistics, and how it relates to intuition.
Knowledge and awareness among college students about the importance of iron blockage in tuberculosis - A survey

Tuberculosis threatens to destroy millions across the globe. There is an urgent need for new methods to prevent and treat the disease. Similar to most microorganisms, mycobacterium tuberculosis - the causative agent of tuberculosis, requires iron for important metabolic pathways. Since iron is not readily accessible in the host, pathogens must compete aggressively to create an infection for this metal, but they must also carefully regulate the acquisition of iron, as excess free iron can be highly toxic. The aim of the study was to create awareness of iron blockage and stop tuberculosis among college students. A descriptive survey was conducted among various college students using questionnaires in google forms. The questionnaire consists of 16 questions were framed based on the knowledge and awareness of iron knowledge to stop tuberculosis and receive a response from 100 participants. In this survey, 67.53% were aware of Mycobacterium tuberculosis cause tuberculosis in the human body and 59.74% were aware that bacteria which causes tuberculosis to need iron to survive but 27.27% were not aware. Majority of participants were aware that iron was capable of restarting replication and refractive to antibodies and trigger the state of persistence bacteria. Nearly 57.14% of participants Mycobacterium tuberculosis has a remarkable ability to persist in the absence of Fe, but 19.48% stated it does not survive. By this current survey, it can be evident that the majority of participants are educated about knowledge of tuberculosis associated with causes, main role and physiological mechanisms involved in it.
An Empirical Evaluation of Global Fitness Surrogate Models in Evolutionary Computation

In evolutionary computation, the fitness of an individual corresponds to its evaluation as a candidate solution. Fitness surrogate models are intended to achieve a reduction in the number of real fitness computations in situations where these computations are expensive. This paper evaluates the effectiveness of three global fitness surrogates: a quadratic model, the inverse distance weighting (IDW) interpolation algorithm and a variant from the later (IDWR). The evaluation is performed using four benchmark functions form the optimization literature. The IDWR algorithm was able to achieve a significantly lower number of real fitness evaluations until convergence when compared to the alternative models for most of the functions considered.
Application of “Cloud Class” App in the Integrated Course of Single-Chip Microcomputer in Technical Colleges

Single-chip microcomputer technology is an important part of electronic technology and occupies an important position in the development of science and technology. However, the current single-chip microcomputer course teaching mode in technical schools is greatly affected by the traditional mode, and the teaching effect needs to be improved. Adopting the integrated teaching mode of rationality and reality, and fully combining the current application of information technology, can greatly improve the quality of teaching. This article mainly introduces the application of the “Cloud Class” APP platform in the integrated course of single-chip microcomputers in technical colleges. First, it introduces the concept and characteristics of integrated teaching, and then analyzes the characteristics and current problems of single-chip microcomputer course in technical colleges. The information platform of “Cloud Class” APP is summarized. Finally, the specific application of the “Cloud Class” APP platform in the integrated course of single-chip computer of technical colleges is discussed. It is hoped that the research in this article can provide some valuable ideas for the teaching reform of technical schools.
Application of Improved U-Net and ResU-Net Based Semantic Segmentation Method for Digitization of Analog Seismograms

&amp;lt;p&amp;gt;Analog seismograms contain rich and valuable information over nearly a century. However, these analog seismic records are difficult to analyze quantitatively using modern techniques that require digital time series. At the same time, because these seismograms are deteriorating with age and need substantial storage space, their future has become uncertain. Conversion of the analog seismograms to digital time series will allow more conventional access and storage of the data as well as making them available for exciting scientific discovery. The digitization software, DigitSeis, reads a scanned image of a seismogram and generates digitized and timed traces, but the initial step of recognizing trace and time mark segments, as well as other features such as hand-written notes, within the image poses certain challenges. Armed with manually processed analyses of image classification, we aim to automate this process using machine learning algorithms. The semantic segmentation methods have made breakthroughs in many fields. In order to solve the problem of accurate classification of scanned images for analog seismograms, we develop and test an improved deep convolutional neural network based on U-Net, Improved U-Net, and a deeper network segmentation method that adds the residual blocks, ResU-Net. There are two segmentation objects are the traces and time marks in scanned images, and the goal is to train a binary classification model for each type of segmentation object, i.e., there are two models, one for trace objects and another for time mark objects, for each of the neural networks. The networks are trained on the 300 images of the digitizated results of analog seismograms from Harvard-Adam Dziewo&amp;amp;#324;ski Observatory from 1939. Application of the algorithms to a test data set results in the pixel accuracy (PA) for the Improved U-Net of 95% for traces and nearly 100% for time marks, with Intersection over Union (IoU) of 79% and 75% for traces and time marks, respectively. The PA of ResU-Net are 97% and nearly 100% for traces and time marks, with IoU of 83% and 74%. These experiments show that Improved U-Net is more effective for semantic segmentation of time marks, while ResU-Net is more suitable for traces. In general, both network models work well in separating and identifying objects, and provide a significant step forward in nearly automating digitizing analog seismograms.&amp;lt;/p&amp;gt;