Reviewerly/exHarmony huggingface dataset:
АВТОГРАФЫ ИЗ КНИЖНОГО СОБРАНИЯ А.Ф. ФОРТУНАТОВА (В ФОНДЕ ФЕДЕРАТИВНОГО ГОСУДАРСТВЕННОГО БЮДЖЕТНОГО НАУЧНОГО УЧРЕЖДЕНИЯ «ЦЕНТРАЛЬНАЯ НАУЧНАЯ СЕЛЬСКОХОЗЯЙСТВЕННАЯ БИБЛИОТЕКА»)

There are presented the results of studying the autographs from the personal library of Russian agrarian scientist A. F. Fortunatov in its part which was sent over to the CSAL collection. Based on the historical biographical method and use of modern technologies and equipment the information potential of 30 editions with autographs of the pre-revolutionary period has been studied. The autographs are considered as a source of data to study the personality of the prominent scientist. The study process comprises: identifying the owners of the autographs and revealing the maximum detailed information on them; describing the autographs and clarifying the circumstances that served a reason to describe them; determining the quantitative, chronological, subject-matter, geographical and type composition of autograph editions; creating illustrative material in the form of scans of covers and front pages of the editions on which autographs are placed; representing the obtained information in the form an electronic card catalogue. As a result of the work, a new group of historical sources has been described and introduced into the scientific circulation - the autographs from the personal library of Russian agrarian scientist A. F. Fortunatov. The presentation of the obtained information in the broad information communication space will make it accessible for the user beyond the territorial and time frame, initiate an interest in books and the personality of the autograph owner himself, increase the information support of research in agriculture. This research should be considered as the first stage of further work for creating, on the basis of modern information technologies, an illustrative card catalogue of autographs of all rare and valuable editions of the CSAL collection thereby launching new themes and subject-matters for researchers of agricultural science and agricultural book.
On Implicit Regularization in $\beta$-VAEs

While the impact of variational inference (VI) on posterior inference in a fixed generative model is well-characterized, its role in regularizing a learned generative model when used in variational autoencoders (VAEs) is poorly understood. We study the regularizing effects of variational distributions on learning in generative models from two perspectives. First, we analyze the role that the choice of variational family plays in imparting uniqueness to the learned model by restricting the set of optimal generative models. Second, we study the regularization effect of the variational family on the local geometry of the decoding model. This analysis uncovers the regularizer implicit in the $\beta$-VAE objective, and leads to an approximation consisting of a deterministic autoencoding objective plus analytic regularizers that depend on the Hessian or Jacobian of the decoding model, unifying VAEs with recent heuristics proposed for training regularized autoencoders. We empirically verify these findings, observing that the proposed deterministic objective exhibits similar behavior to the $\beta$-VAE in terms of objective value and sample quality.
LibGuides: Motion Pictures: Research Topics Browse: When Browse

An extensive grouping of prelinked Library of Congress Subject Headings for identify research topics focused on motion pictures. Scoped to the ASU Library Catalog.
LibGuides: Poll Everywhere: Polls (Responding)

Resources in support of your use of Poll Everywhere, a dynamic audience response system Describes the different ways in which participants respond
Force Fields and Jump Pads

In this chapter, we'll be implementing a configurable ForceField script that pairs with a trigger collider to apply force to Rigidbodies and/or the player. Based on a variable set in the Inspector determining the type of force to apply, we'll either apply it constantly while the trigger is touched or once when the trigger is first entered. Based on the force mode you choose, you could make an object either get pushed as if by a large fan or get shoved up like a “jump pad” thrusting them all at once.
NURSING SCIENCE STUDY PROGRAM STUDENTS’ PERCEPTION TOWARD THE USE OF EDMODO AS SUPPLEMENTARY TOOL IN LEARNING ENGLISH II AT STIK BINA HUSADA PALEMBANG 2019/2020

Due to the rapid advancement of technology, teachers are expected to integrate technology into the classroom to engage students who are considered as digital-natives. Thus, Edmodo as online learning platform is widely used by English teachers in teaching and learning. The online learning platforms are also used as a supplementary tool to promote independent learning. Edmodo is primarily a tool for within-class communication which provides several ways for teachers to connect with other teachers. This research focused on students’ perspective of the use of Edmodo as a supplementary tool in learning English. The researcher used triangulation approach in gathering data and survey questionnaire adapted from Enriquez (2014) used as the main research instrument. Forty four students joined the Edmodo class and filled the questionnaire completely. The results showed on the statement regarding the usefulness of reference materials posted by the teacher in Edmodo, a significant number 66.8% agree and 33.20% strongly agree or considered the reference materials as vital in understanding the course better. Participants also perceived that Edmodo is a good learning tool to supplement face to face discussion. One disadvantage in using Edmodo as perceived by 19.4% of the participants is the difficult procedure to understand and to follow for them. Overall, the findings affirmed that majority of the participants considered it as an effective supplementary tool in learning English.
Representing Unordered Data Using Multiset Automata and Complex Numbers

Unordered, variable-sized inputs arise in many settings across multiple fields. The ability for set- and multiset-oriented neural networks to handle this type of input has been the focus of much work in recent years. We propose to represent multisets using complex-weighted multiset automata and show how the multiset representations of certain existing neural architectures can be viewed as special cases of ours. Namely, (1) we provide a new theoretical and intuitive justification for the Transformer model's representation of positions using sinusoidal functions, and (2) we extend the DeepSets model to use complex numbers, enabling it to outperform the existing model on an extension of one of their tasks.
Public Education Information Management System

Home  |  Legacy Data Standards |  Student Attendance Accounting Handbook  | Request TEASE Access to PEIMS EDIT+ | TEASE Logon |  PID | EDIT+   | TREx  | Compatible Software Systems The Public Education Information Management System (PEIMS) encompasses all data requested and received by TEA about p
Robust Autonomous Landing of Fixed-Wing UAVs in Wind

Fixed-wing UAVs are today used in many different areas, from agriculture to search and rescue operations. Through various research efforts, they are becoming more and more autonomous. However, the ...
Classifications Using Artificial Neural Network Method In Protecting Credit Fitness

Classification is information that has the closest relationship with data, we make a prediction in providing customer eligibility to get a loan from a financial service institution . In this study, we use the Artificial Neural Network (NN) method in combination with the Particle Swarm Optimization method. It is known that the method has excellent generalizations to solve a problem in increasing accuracy. However, some of the attributes in the data can reduce accuracy and increase the complexity of the Artificial Neural Network (ANN) algorithm. Therefore, attribute selection is very necessary, the attribute selection method used in this study is the Particle swarm optimization (PSO) method. This method can be used for proper attribute selection in determining lending to customers, therefore the Particle Swarm Optimization (PSO) method can increase the value of higher accuracy weights in determining attribute selection.
Design and implementation of an onscreen keyboard for Yoruba language

Language is essential for human existence; it enhances understanding between individuals whilst aiding communication. The Yorùbá language is one of the major Nigerian languages, that is gradually being eroded due to different reasons such as westernization, colonial legacy, modernization and economic development, advancement of technology amongst other reasons, it is therefore necessary for Africans especially of Yorùbá descent to save Yorùbá language. In this paper, an Onscreen Keyboard model is presented with an attempt to save Yorùbá language by prolonging its’ use through technology as it evolves. Data was collected on Yorùbá alphabets and a computational model was formulated (using unified modelling language) based on the output of the collected data. The On screen keyboard’s model was implemented using visual C#, and the system was evaluated to test its effectiveness. The model allows user input into the computer using Yorùbá language whilst predicting text as the user types thereby enhancing easy typing and learning experience. Although, the model was tested on Windows operating system, the model design can easily be adapted to other operating systems environments. In this study, we have successfully localized the typical English Language keyboard to allow input in Yorùbá language.Keywords: Language, Yorùbá, Endangered, Onscreen keyboardVol. 26 No 1, June 2019
Deep Learning Requires Explicit Regularization for Reliable Predictive Probability

From the statistical learning perspective, complexity control via explicit regularization is a necessity for improving the generalization of over-parameterized models, which deters the memorization of intricate patterns existing only in the training data. However, the impressive generalization performance of over-parameterized neural networks with only implicit regularization challenges this traditional role of explicit regularization. Furthermore, explicit regularization does not prevent neural networks from memorizing unnatural patterns, such as random labels, that cannot be generalized. In this work, we revisit the role and importance of explicit regularization methods for generalizing the predictive probability, not just the generalization of the 0-1 loss. Specifically, we present extensive empirical evidence showing the versatility of explicit regularization techniques on improving the reliability of the predictive probability, which enables better uncertainty representation and prevents the overconfidence problem. Our findings present a new direction to improve the predictive probability quality of deterministic neural networks, unlike the mainstream of approaches concentrates on building stochastic representation with Bayesian neural networks, ensemble methods, and hybrid models.
Increasing the trust between automotive actors using a Hyperledger Fabric blockchain

It is a well-established phenomenon that blockchain technology can be applied to reach a consensus between entities which do not trust one another. Moreover, blockchain technology also allows these ...
LibGuides: HIST2 - Primary Source Research Guide: Primary Source Collections

This guide provides resources and suggestions for locating both primary source materials and secondary sources that provide context for understanding those primary sources.
Research Guides: Streaming Video Guide: How to Request

This guide was created to provide information about streaming videos here at the Marquette University Raynor Memorial Libraries
LibGuides: TeenSpace: Summer Reading Challenge

Your guide to all things teen at the Huntington Beach Public Library! 2020 Teen Summer Reading Challenge
Central-Eastern European Loess Sources : Central- och östeuropeiska lössursprung

Climate change is today one of our highest priority challenges. But to understand the change in climate and to be able to make predictions about the future, knowledge about past climate is of subst ...
Mitigating health care’s cybersecurity risks in the era of hyperconnectivity

For an increasingly sophisticated, global community of cybercriminals, health care pays — both figuratively and literally.
El impacto del uso de mundos virtuales en los estudiantes de la carrera de inglés de la modalidad a distancia

The present study has the purpose of researching on the impact of Imprudence as a virtual environment on English pre-service teachers at a distance program. It took place at an Ecuadorian private university and the sample was comprised of 62 students of the English major. The results were analyzed quantitatively and qualitatively, which allowed us to conclude that Imprudence has a positive incidence on students’ participation; likewise, they perceived it as a useful tool for their learning.
Using machine learning on feature selection

Feature selection, or variable selection, is an important task that is needed within a lot of disciplines. This study applies deep learning on the selection of features using a proposed model. This study contributes to existing literature by examining the performance of a recurrent neural network in a model where both knockoffs of features, or almost identical copies of variables, and the true features are combined within one model framework. This model is used for variable selection purposes where a controlled false discovery rate is used. A simulation study shows that adding additional information with a recurrent neural network does not seem to improve a selection of a subset of features and that combining both knockoff features and true features does only give an advantage to subset selection in case of a feedforward neural network.
Research Guides: Downloading e-books from the library collection: Downloading on an iPad or other iOS device

This guide provides instructions on how you can download e-books in the library collection from several different platforms.
Determining the Effectiveness of the Developed Prototype That Translate Pakistan Sign Language into English text and speech.

The present research aims to evaluate the effectiveness of the developed prototype, which translates Pakistan sign language into text and speech while using convolutional neural networking. Due to the lack of sign language teaching, unimpaired teachers face problems while teaching deaf students. The development of a translation tool can fill this communication gap. Research indicates that the gap was highlighted and to bridge the communication gap among impaired and impaired people, ta prototype was conceived, which can translate the Pakistan sign language into English text and speech. The study employed a qualitative approach. Eight teachers were selected as sample using purposive sampling technique who were teaching deaf students. The present research incorporates the practical application of the sign translation tool in the separate article. Data was collected through focus group interviews. Data were analyzed using Nvivo. Findings of the results revealed that the tool proved to be effective in overcoming the communication barrier between deaf learners and unimpaired teachers. Moreover, the sign to speech translation option is more effective because it facilitates visually blind learners. This tool helps the teachers and other people to teach and communicate with deaf people, efficiently and quickly.
LibGuides: *Curbside Pick - Up / Return: Getting Help

Curbside pick up and returns is available for Technology and Library books. Please call us at 636-584-6560
Continual imitation learning: Enhancing safe data set aggregation with elastic weight consolidation

The field of machine learning currently draws massive attention due to ad- vancements and successful applications announced in the last few years. One of these applications is self-driving vehicles ...
What Are the Qualifications for CISA Professionals

CISA, (Certified Information Systems Auditor) is a globally recognized credential for professionals that audit information systems.
Improving Students' Ability to Write Descriptive Text By Using Numbered Heads Together (NHT)

The purpose of this research is to determine whether using Numbered Heads Together (NHT) can improve students’ ability to write descriptive texts at SMPN 6 Toili Barat. This research uses quasi-experimental research that requires a post-test for both groups. The research population was eighth grade students of SMP 6 Toili Barat. The sample of this research was class VIII A as an experimental class and consisted of 26 students while class VIII B was a control class and consisted of 27 students. The test consisted of pre-test and post-test. Data were analyzed statistically. After being analyzed, the result of t-counted was 6,744 with a degree of freedom 51, and a significance level of 0,05, t-table was 1,675. Therefore, the t-test result is higher than the t-table. It means that the hypothesis of this research can be accepted. The result showed that there was a significant influence in using Numbered Heads Together to improve students’ writing abilities in descriptive text in eighth grade at SMPN 6 Toili Barat Keywords:Improving; Writing; Descriptive Text; NHT.
Bio-Inspired Hashing for Unsupervised Similarity Search

The fruit fly Drosophila's olfactory circuit has inspired a new locality sensitive hashing (LSH) algorithm, FlyHash. In contrast with classical LSH algorithms that produce low dimensional hash codes, FlyHash produces sparse high-dimensional hash codes and has also been shown to have superior empirical performance compared to classical LSH algorithms in similarity search. However, FlyHash uses random projections and cannot learn from data. Building on inspiration from FlyHash and the ubiquity of sparse expansive representations in neurobiology, our work proposes a novel hashing algorithm BioHash that produces sparse high dimensional hash codes in a data-driven manner. We show that BioHash outperforms previously published benchmarks for various hashing methods. Since our learning algorithm is based on a local and biologically plausible synaptic plasticity rule, our work provides evidence for the proposal that LSH might be a computational reason for the abundance of sparse expansive motifs in a variety of biological systems. We also propose a convolutional variant BioConvHash that further improves performance. From the perspective of computer science, BioHash and BioConvHash are fast, scalable and yield compressed binary representations that are useful for similarity search.
Evaluating player performance and usability of graphical FPS interfaces in VR

Background. When designing video games for Virtual Reality, graphical user interfaces (GUIs) cannot always be designed as they have been for traditional video games. An often recommended approach i ...
LibGuides: Motion Pictures: Research Topics Browse: Genre Browse

An extensive grouping of prelinked Library of Congress Subject Headings for identify research topics focused on motion pictures. Scoped to the ASU Library Catalog.
Blind Quantum Computation by Circuit Based Quantum Computer

When universal quantum computer is used by public, it is assumed that it will be used as a quantum cloud server that exists in a few bases because the quantum computer is expensive. When it will be realized, a quantum universal computer is thought to work as a quantum cloud server. In the cloud server the privacy is a crucial issue. That is, we need a blind quantum computation (BQC) protocol so that each user can use the server without showing the details of the calculation. It is also important to be able to verify that the server is performing calculations as instructed by the user, since quantum calculations cannot be verified by classical computation. In this paper, we give a protocol for the blindness by using a quantum one-time pad encryption and T-like gate, and for verifying computation by using trap qubits.
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis

To recognize the unseen classes with only few samples, few-shot learning (FSL) uses prior knowledge learned from the seen classes. A major challenge for FSL is that the distribution of the unseen classes is different from that of those seen, resulting in poor generalization even when a model is meta-trained on the seen classes. This class-difference-caused distribution shift can be considered as a special case of domain shift. In this paper, for the first time, we propose a domain adaptation prototypical network with attention (DAPNA) to explicitly tackle such a domain shift problem in a meta-learning framework. Specifically, armed with a set transformer based attention module, we construct each episode with two sub-episodes without class overlap on the seen classes to simulate the domain shift between the seen and unseen classes. To align the feature distributions of the two sub-episodes with limited training samples, a feature transfer network is employed together with a margin disparity discrepancy (MDD) loss. Importantly, theoretical analysis is provided to give the learning bound of our DAPNA. Extensive experiments show that our DAPNA outperforms the state-of-the-art FSL alternatives, often by significant margins.
Developing Software for Law Enforcement

© 2019 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
진로결정 자기효능감이 대학생활적응에 미치는 영향에 있어 교수·학생 상호작용의 매개효과: 4년제 대학교 항공서비스학과 학생을 중심으로

University life adaptation is a factor which can predict from the graduation to the adulthood adaptation, and the successful college life is very important in the social aspect as well as the personal aspect. The students who want to enter the department related to the airline service seem to constantly increase as the students who want to work for the airlines continue to increase. Accordingly, this study was to investigate the necessary factors for successful employment and college life adaptation to the students majoring in the airline service. Based on such research findings, the career exploration had a positive effect on all sub-factors of college life adaptation. In addition, this study has found out that the career decision had a positive effect on all sub-factors except the social adaptation. However, this study has shown that the problem-solving did not influence all sub-factors of college life adaptation. And, the student-faculty interaction was mediated in the effects of career decision-making on the college life adaptation. To conclude, this researcher considers it as it needs to develop a program to improve the career decision-making self-efficacy for the successful college life adaptation, and as it needs to maintain the emotional sense of connectedness in that the teachers guide the students. In particular, the lower the grade is, the lower the college life adaptation is. Accordingly, the management is much needed with great interest.