Reviewerly/exHarmony huggingface dataset:
한중 학술논문 서론 텍스트 구조의 장르분석적 연구 ─ 언어교육 분야의 학술논문을 대상으로 ─

On the basis of the features in language education articles, by using the CARS model of Swales(1990), the paper conducts a contrastive study on the rhetorical structure of introduction in Korean and Chinese language education articles. The study shows, Korean and Chinese introductions have some differences in both macro-and micro-structures. Korean introductions have more diversified and flexible move-step structures, especially more frequent adoption in Move 3. Chinese authors usually stress the importance of the current research and choose a variety of ways in Move 1. The results can provide pedagogical implications for Korean and Chinese academic writers to improve their introduction writing.
MODELING OF ORE-MAGMATIC SYSTEM OF GOLD DEPOSITS IN EASTERN KAZAKHSTAN

The article is devoted to the results of using system-defined geological modeling for forecasting gold deposits on the territory of the oldest gold mining region in the Republic of Kazakhstan – the West Kalba gold belt (East Kazakhstan). Research is aimed at developing forecast criteria and searches for new ore objects in favorable geological structures. The main attention of the conducted research was focused on a system-defined analysis of geological factors, the interaction of which provides a synergy effect and reveals the patterns of formation of gold ore objects. To solve this problem, the authors made a quantitative assessment of the development sustainability of the ore-generating elements of the system according to S.V. Vasiliev methodology. A quantitative assessment of the stability of individual factors and their interaction made it possible to assess the qualitative state of individual sections and define the natural boundaries of the areas of the corresponding hierarchical level, promising for the search of certain mineral deposits. The specific work was carried out to define the areas promising for the search of deposits based on a quantitative assessment of the individual factors stability. Using geological materials, results of geophysical work, space-geological data, the localization of promising areas within the West Kalba structural-formation zone and the Zhumba ore-magmatic system in East Kazakhstan was done.
Faster Federated Learning With Decaying Number of Local SGD Steps

In Federated Learning (FL) client devices connected over the internet collaboratively train a machine learning model without sharing their private data with a central server or with other clients. The seminal Federated Averaging (FedAvg) algorithm trains a single global model by performing rounds of local training on clients followed by model averaging. FedAvg can improve the communication-efficiency of training by performing more steps of Stochastic Gradient Descent (SGD) on clients in each round. However, client data in real-world FL is highly heterogeneous, which has been extensively shown to slow model convergence and harm final performance when $K > 1$ steps of SGD are performed on clients per round. In this work we propose decaying $K$ as training progresses, which can jointly improve the final performance of the FL model whilst reducing the wall-clock time and the total computational cost of training compared to using a fixed $K$. We analyse the convergence of FedAvg with decaying $K$ for strongly-convex objectives, providing novel insights into the convergence properties, and derive three theoretically-motivated decay schedules for $K$. We then perform thorough experiments on four benchmark FL datasets (FEMNIST, CIFAR100, Sentiment140, Shakespeare) to show the real-world benefit of our approaches in terms of real-world convergence time, computational cost, and generalisation performance.
RETRACTED: Research on the Improvement of Students’ Intercultural Communication Competence through Electronic English Newspapers Based on Big Data Analysis under the Computer Internet

Abstract As you all know, in the international meeting, leaders will attend the meeting, many countries have their own language, in today’s world, English is widely receive language of all countries, is a language spoken by the largest country of 13th session of the National People’s Congress has held smoothly, many policies in China is the world’s attention, such as such as said in a policy. How to improve the intercultural communication ability of college students? Internet communication and electronic English newspapers and magazines are very good choices, which can be applied to people’s English learning and life. Such a method can overcome the shortcomings of traditional education, help teachers improve the effectiveness of cross-cultural communication teaching, and accelerate the cultivation of global trade talents.
Testbeds for Reinforcement Learning.

Experiments in classical conditioning show that animals such as rabbits, pigeons, and dogs can make long temporal associations that enable multi-step prediction. To replicate this remarkable ability, an agent must construct an internal state representation that summarizes its interaction history. Recurrent neural networks can automatically construct state and learn temporal associations. But the current training methods are prohibitively expensive for online prediction -- continual learning on every time step -- which is the focus of this paper. To facilitate research in online prediction, we present three new diagnostic prediction problems inspired by classical-conditioning experiments. The proposed problems test the learning capabilities that animals readily exhibit and highlight the current recurrent learning methods' limitations. While the proposed problems are nontrivial, they are still amenable to extensive testing and analysis in the small-compute regime, thereby enabling researchers to study issues in isolation carefully, ultimately accelerating progress towards scalable online representation learning methods.
LibGuides: Engineering Profession: Common First Year (FEBE1000A): Library Services

This LibGuide provides relevant information for all FEBE1000A Introduction to the Engineering Profession students.
Is It All Just Emojis and LOL

This chapter explores both the potential and limitations of social media as a quasi-networked citizenry, facilitating environmentally-focused learning and activism among young people. Online social networks have become embedded within most young people’s everyday lives. In fact, the increase in the use of social networking sites is unparalleled by any other activity on the web. Both the availability of online information and the rise of social networks has impacted how young people engage in knowledge production, communication, and creative expression. The open, collaborative nature of social networks as communication and organizational tools, along with shifts in the way that civic and political engagement unfolds, raises questions of how young people use these networks to learn about and take action on the environmental issues that concern them the most. This chapter draws upon data collected from an international survey of self-identified environmentalist youth and their usage of social media, including a 6-month observation of youth posts and usage of Facebook, as well as interviews that consider how they use social media for environmental learning and activism.
IMPROVING STUDENTS’ READING COMPREHENSION THROUGH PREVIEW, QUESTION, READ, SUMMARY, AND TEST METHOD OF VII GRADE AT MTs MADINATUL ILMI DDI SIAPO

This research is pre-experimental research design that aims to find whether or not the implementation of Preview, Question, Read, Summary, and Test method can improve the students’ reading comprehension of VII grade at MTs Madinatul Ilmi DDI Siapo. The subject of the research is the students of VII grade at MTs Madinatul Ilmi DDI Siapo which consists of 27 students. In this research used on Pre-test and Post-test. In the pre-test was used to measure the students’ previous improving students’ reading comprehension, the treatments were applying materials through Preview, Question, Read, Summary, and Test method , and the post-test was used to find out the students’ reading comprehension after given treatments. The results of the test show that the students’ mean score in pre-test is 76.19 while in the post-test improved up to 80.74. The data of this research were analyzed by using SPSS Program 16.0. Computation of the data T-counted result is 14.783, which means score was high than T-table result is 2.056. It means that the reading comprehension of VII grade at MTs Madinatul Ilmi DDI Siapo is improved. Keywords : Improving, Reading comprehension, Preview, Question, Read, Summary, and Test method, Descriptive text
Deep Learning Is Composite Kernel Learning

Recent works have connected deep learning and kernel methods. In this paper, we show that architectural choices such as convolutional layers with pooling, skip connections, make deep learning a composite kernel learning method, where the kernel is a (architecture dependent) composition of base kernels: even before training, standard deep networks have in-built structural properties that ensure their success. In particular, we build on the recently developed `neural path' framework that characterises the role of gates/masks in fully connected deep networks with ReLU activations.
A STUDY TO ASSESS THE EFFECTIVENESS OF SELF INSTRUCTIONAL MODULE ON KNOWLEDGE REGARDING PREVENTION OF CHILD ABUSE AMONG MOTHERS IN A SELECTED AREA OF INDORE M.P.

In this pre experimental design, sample consisted of 20 mothers, selected by Non probability Purposive sampling technique. Self structure knowledge questionnaire was used for assessing the knowledge of mothers. Pre test was conducted by using the same structured knowledge questionnaire and after 15 days post test was conducted using the same structured questionnaire for assessing the effectiveness of Self instructional module. Mean percentage of the knowledge score of  post test 23.56 was higher than  pre test 19.23 The ‘t’ value for total pre test and post test was 19.24 The data was analyzed in terms of descriptive and inferential statistics.
Innovative Learning: Mixed-Age Cross Curricular Learning

Tutors are trying to ensure that young people in the process of development and learning are as independent as possible. However, at the same time we are aware of how strong we still are in the majority of the components of the individual's development. We can see increase in the incompatibility and the dependent structure of the society in which the desire for knowledge is not common among students. Therefore, the need for external regulation in the learning process has increased. What can teachers do? is important to extend the control over learning from an adult to a child or to a whole learning community on different levels. The article present one of the important options, i.e. the way of introducing innovative learning: the mixed-aged cooperative learning (MACOOL). It describes the forms of MACCOL at Primary school Elvire Vatovec (Prade, Slovenia), its benefits and possible disadvantages. xed-age cross curricular learning is the need of modern society. We can no longer talk about forming permanent heterogeneous classes, as this would be a step back in the emotional and social interactions of the individual with peers. However, it is imperative that the school creates formal conditions for continuous mixed-age cross curricular education. If the primary purpose of education is to learn and acquire knowledge, then the problem has to be solved in the learning process, with a different understanding and evaluation of learning.
Sparse Binary Neural Networks

Quantized neural networks are gaining popularity thanks to their ability to solve complex tasks with comparable accuracy as full-precision Deep Neural Networks (DNNs), while also reducing computational power and storage requirements and increasing the processing speed. These properties make them an attractive alternative for the development and deployment of DNN-based applications in Internet-Of-Things (IoT) devices. Among quantized networks, Binary Neural Networks (BNNs) have reported the largest speed-up. However, they suffer from a fixed and limited compression factor that may result insufficient for certain devices with very limited resources. In this work, we propose Sparse Binary Neural Networks, a novel model and training scheme that allows to introduce sparsity in BNNs by using positive 0/1 binary weights, instead of the -1/+1 weights used by state-of-the-art binary networks. As a result, our method is able to achieve a high compression factor and reduces the number of operations and parameters at inference time. We study the properties of our method through experiments on linear and convolutional networks over MNIST and CIFAR-10 datasets. Experiments confirm that SBNNs can achieve high compression rates and good generalization, while further reducing the operations of BNNs, making it a viable option for deploying DNNs in very cheap and low-cost IoT devices and sensors.
The Effect of Android Studio Application to Increase Adolescent Nutrition Knowledge

The number of Corona Virus patients in various parts of the world, including Indonesia, increases every day because the spread is relatively fast and difficult to detect. Optimal body defense is needed to prevent COVID-19. One of them is eating a balanced nutritious diet.Â  The purpose of this study was to see the effect of the android studio application on adolescent knowledge about nutrition in preventing COVID-19â€”the research method used pre-experimental using One Group Pretest-Posttest Design. The population in this study were grade XI IPA SMA Negeri 12 Semarang students. The sampling technique used purposive sampling. The results of the statistical analysis with the Wilcoxon test found that the p-value was 0.000 â‰¤ 0.05.Â  So it can be concluded that there is a significant influence before and after the intervention using the android studio application media. Therefore, this study proposes a technology based on the Android Studio application that can be used anywhere and anytime
Ensemble-based Adversarial Defense Using Diversified Distance Mapping

We propose an ensemble-based defense against adversarial examples using distance map layers (DMLs). Similar to fully connected layers, DMLs can be used to output logits for a multi-class classification model. We show in this paper how DMLs can be deployed to prevent transferability of attacks across ensemble members by adapting pairwise (almost) orthogonal covariance matrices. We also illustrate how DMLs provide an efficient way to regularize the Lipschitz constant of the ensemble's member models, which further boosts the resulting robustness. Through empirical evaluations across multiple datasets and attack models, we demonstrate that the ensembles based on DMLs can achieve high benign accuracy while exhibiting robustness against adversarial attacks using multiple white-box techniques along with AutoAttack.
Avtalsrättsliga aspekter av smarta kontrakt - I vilken utsträckning kan smarta kontrakt automatisera avtals ingående?

The general principles of contract law has never been a barrier to parties entering into agreements electronically. Digitization has developed and given rise to new ways to streamline the processes for entering into agreements, e.g. the possibility, for parties to enter into agreements without the involvement of any legal representative. The latest stage in this development is called smart contracts the application of so-called blockchain technology for automating the process leading up to a legally binding contract. The purpose of this thesis is to investigate thgrough a lega scientific method, to which extent a smart contract can automate the process leading up to a legally binding contract. To assess to which extent the process leading up to a legally binding contract can be automated via smart contract (technology), the technology will be related to basic principles and rules on contract, e.g. as set out in the Swedish Contracts Act. The thesis describes and analyses som challenges with applying basic contractual principles to smart contracts. (Less)
On the Importance of Distraction-Robust Representations for Robot Learning

Representation Learning methods can allow the application of Reinforcement Learning algorithms when a high dimensionality in a robot's perceptions would otherwise prove prohibitive. Consequently, unsupervised Representation Learning components often feature in robot control algorithms that assume high-dimensional camera images as the principal source of information. In their design and performance, these algorithms often benefit from the controlled nature of the simulation or laboratory conditions they are evaluated in. However, these settings fail to acknowledge the stochasticity of most real-world environments. In this work, we introduce the concept of Distraction-Robust Representation Learning. We argue that environment noise and other distractions require learned representations to encode the robot's expected perceptions rather than the observed ones. Our experimental evaluations demonstrate that representations learned with a traditional dimensionality reduction algorithm are strongly susceptible to distractions in a robot's environment. We propose an Encoder-Decoder architecture that produces representations that allow the learning outcomes of robot control tasks to remain unaffected by these distractions.
Methodological Fundamentals of Humanization of Chemistry Teaching in Teachers’ Training Universities

The research is aimed at scientific substantiation of such methods for teaching the chemical disciplines that ensure the chemistry knowledge and skills and they will favor the formation of professional competences of the future chemistry teacher, his/her proactive approach to life. As the theoretical basis, the humanization methodology, namely, humanitarian, competency-based and environmental approaches, is used. By a comparison and grouping method, the research analyzed a series of essays made by 324 students of from 17-20 years.It was detected that among the students, during teaching of whom, the methods, which are based on the humanization methodology, were used, 90.63% reached a “sufficient” level, showing the results in mastering the content as well as in    application of the received knowledge to the chemistry teacher profession, in a value-based attitude to the received education, the professional self-development, while in the control group only 40.76 % reached the same results.The chemistry teaching humanization is considered through the priority of humanistic values in organizing the teaching and implementing the methods turned to the student personality.  The authors described four groups of the chemistry teaching methods, which are based on the humanization methodology: methods for creating a positive motivation, methods for organizing the interactive educational cognitive and practical activities of the student, reflective-estimating methods, methods for developing the personal educational environment of the teaching.
LibGuides Home: Remote Online Library Support: Home

This guide will support you while you are working remotely and will direct you to Library resources and support.
An introduction to quantum computing for high energy physics

Author(s): Provasoli, Davide Livio | Advisor(s): Lin, Tongyan | Abstract: Applications of quantum computing to high energy physics (HEP) is a relatively new field of research. As such, the relevant literature is not well organized in one place and a clear road-map for people approaching the field is not currently available. Addressing this issue was the main motivation for this article, which is intended a pedagogical introduction to the field and specifically to our research direction, i.e. application of quantum computing to parton shower event generators, with the hope of more people becoming interested in exploring this path. Our paper on the subject, “A quantum algorithm for high energy physics simulations” [8], is the subject of chapter 4. I hope this article can provide a direct path for people familiar with quantum mechanics and quantum field theory to start reading papers or conducting research in this area of quantum computing applications in HEP. In this regard, the quantum algorithm we present in Chapter 4, can serve as an example of development of a novel efficient quantum algorithm to solve a problem in a particle physics model.
IMPLEMENTASI PERATURAN GUBERNUR NO.54 TAHUN 2017 TENTANG PENERAPAN DISIPLIN JAM KERJA DI BIRO PEMERINTAHAN DAN PERBATASAN SEKRETARIAT DAERAH PROVINSI KEPULAUAN RIAU DALAM PENGGUNAAN MESIN PEMINDAI SIDIK JARI (FINGERPRINT SCANNER)

Working hour discipline is an activity that must be carried out by all employees. The application of the discipline of working hours is regulated in Governor Regulation No.54 of 2017. The application of fingerprint attendance is made to make it easier for superiors to see the level of discipline for the attendance of each employee. So far, in manual attendance, superiors or other employees who see attendance cannot see the level of discipline of employee attendance, because in manual attendance there is no information about when the employee comes and goes home, employees can join other days or leave absences to other employees. This makes it difficult for superiors to impose sanctions in accordance with Governor Regulation No.54 of 2017 concerning Working Hours Discipline.The purpose of this study is to determine the implementation of PERGUB No.54 of 2017 concerning the Application of Working Hours Discipline at the Government Bureau and Border Regional Secretariat of the Riau Islands Province in the Use of Fingerprint Scanner.The population will be taken from The Government Bureau and Border Regional Secretariat of the Riau Islands Province with total population 45 employees. The Research type is a qualitative research with descriptive qualitative analysis technique. Sources and types of data are using primary and secondary data, with data collection techniques and tools using interview techniques.The results of this study are from the Governor’s Regulation Number 54 on 2017 about Implementation of working time discipline at the Government Bureau and Border Regional Secretariat of the Riau Islands Province is considered Effective because the Employees are able to do and implemented the Governor’s Regulation Number 54 as stated in the Regulation itself.
Symmetry Control Neural Networks

This paper continues the quest for designing the optimal physics bias for neural networks predicting the dynamics of systems when the underlying dynamics shall be inferred from the data directly. The description of physical systems is greatly simplified when the underlying symmetries of the system are taken into account. In classical systems described via Hamiltonian dynamics this is achieved by using appropriate coordinates, so-called cyclic coordinates, which reveal conserved quantities directly. Without changing the Hamiltonian, these coordinates can be obtained via canonical transformations. We show that such coordinates can be searched for automatically with appropriate loss functions which naturally arise from Hamiltonian dynamics. As a proof of principle, we test our method on standard classical physics systems using synthetic and experimental data where our network identifies the conserved quantities in an unsupervised way and find improved performance on predicting the dynamics of the system compared to networks biasing just to the Hamiltonian. Effectively, these new coordinates guarantee that motion takes place on symmetry orbits in phase space, i.e.~appropriate lower dimensional sub-spaces of phase space. By fitting analytic formulae we recover that our networks are utilising conserved quantities such as (angular) momentum.
LibGuides: Comic Books for Information Literacy: Home

A guide to understanding how comic books and graphic novels can be used to help students improve their Literacy skills. This guide offers theories, lesson ideas, how to assess comics, and many other tips to help your student succeed in Information Literac