Reviewerly/exHarmony huggingface dataset:
텍스트 언어학 기반 한국어 교육을 위한 제언

This study aims to consider the reason why the results of textlinguistic research have not been reflected in the area of Korean language education and to investigate the way to solve the problem. In order to achieve this purpose, we will examine how the concepts, scope, and the themes of textlinguistics have been applied to Korean language education from a reflective and a critical point of view. Attention to the subject of textlinguistic research is gradually growing, and there is a great deal of empathy for the necessity of the applying textlinguistics to the teaching Korean language. Despite this situation, there are still many problems in Korean language education research and the field. This is interpreted as an important cause of the fact that the results of textlinguistic research were not properly reflected even in Korean literacy education. Some microscopic grammatical approaches are reflected in Korean language education as well as in Korean literacy education one after another. Otherwise the achievements of textual linguistics, that is, the results of macroscopic research, are not reflected in Korean literacy education.
Evolving State Machines as Robot Controllers

Automated generation of robot controllers using an Evolutionary Algorithm(EA) has received increasing attention in the last years as it has the potentialfor a reduction in the development time of a robot. Often these EAs generateNeural Networks (NNs) as robot controllers. Using a NN for automaticallygenerating robot controllers has two important downsides: 1.) A human isnot able to fully understand the inner working of a multi-layer NN, and 2.)a NN has only limited abilities to decompose a complex task into sub tasks.Both of these downsides can be addressed by using a State Machine (SM)instead of a NN as robot controller. Therefore, this thesis introduces an EAcalled Evolving State Machines As Controllers (ESMAC). ESMAC generatesSMs instead of NNs. A SM is understandable for humans because ofits modularity and allows for task decomposition by using a state for eachsub task. Furthermore, two extensions of ESMAC are proposed: adaptiveESMAC and selector ESMAC. Adaptive ESMAC aims to automatically determinesthe number of states with which the best tness for a task canbe achieved. Selector ESMAC replaces the transitions that are used in aSM to switch between states with a NN-based switching mechanism. This switching mechanism allows mutations to make more gradual changes to aSM's behaviours, which improves the performance of the EA. The performance of ESMAC is evaluated on two robotic tasks: come-and-go and phototaxis-with-obstacles. All three variants of ESMAC showequally good performance as a NN-based EA on the evaluated tasks. Thecontrollers generated with standard ESMAC and adaptive ESMAC hardlymake any state transitions and mainly use one state. However, controllers that do use multiple states appear to be more robust to changing scenarios and in noisy environments. Selector ESMAC is able to generate SMs-based controllers that have complementing states and, therefore, shows potentialfor decomposing a task into sub tasks.
El discurso profesional en la biblioteca: alternativas en red a la relación usuario-sistema

This work analyzes the relationship between librarians and users based on an approach that takes the text generated by these professionals, not as a window to a unique reality, but as an object of analysis. It is the analysis of the discourse, which allows to adopt a critical perspective, questioning widely accepted facts and reflecting on the object of study, in this case the users of the libraries.
Microsoft recommenders: tools to accelerate developing recommender systems

The purpose of this demonstration is to highlight the content of the Microsoft Recommenders repository and show how it can be used to reduce the time involved in developing recommender systems. The open source repository provides python utilities to simplify common recommender-related data science work as well as example Jupyter notebooks that demonstrate use of the algorithms and tools under various environments.
DEVELOP A COMPREHENSIVE SECURITY SYSTEM

The article describes the process of developing an integrated security system based on a single-board NVIDIA Jetson Nano computer. The main objective of the system is to provide profiled access to the room for trusted users. As a result of the work, a face recognition mechanism based on the convolutional neural network was designed and implemented. Face detection is implemented using histograms of directional gradients. Then, thе face is centered and processed by the trained convolutional neural network, out the characteristic features of the image supplied to the image classifier, which gives the percentage of image matching with the reference images. In the process of implementing the system, a framework was developed that allows implementing a complex security system, regardless of hardware components. To ensure the protection of the room, smoke detectors, opened doors and windows were added to the system. The scientific novelty of tht work is the encapsulation of all transmitted system traffic to overlay networks to ensure confidentiality.
Integrated Silicon Photonics Processing Elements for Neural Networks and switching applications

Two new classes of processing elements based on a generalized Mach-Zehnder architecture are proposed for neural networks applications in integrated optics. Both new processing elements are demonstrated experimentally on a silicon photonic platform and analyzed theoretically boosting further the toolbox for photonic neural networks.
예술통합 움직임프로그램에 참여한 지적장애 아동의 자기표현이 자기 효능감에 미치는 영향

PURPOSE: The purpose of this study is to analyze how the self-expression of intellectually disabled children participating in movement programs applied at art integration education affects their self-efficacyMETHODS: To investigate the effects of the art-integrated movement program, 20 students with a second-grade intellectual disability were recruited from 1st grade to 4th grade of a special school in B metropolitan city. Ten students that applied for the dance program were assigned the art-integrated movement program, whereas the other students were who did not participate in the dance class but participated in a school program (music, art, and physical education class) were assigned the control group. Each of the classes were consists of 40 minutes sessions per week (two times/week) for a period of 12 weeks. Data were compiled and analyzed by SPSS window program (Ver. 21.0). Additionally, regression analysis was conducted to analyze the effects of self-expression on self-efficacy.RESULTS: Children with intellectual disabilities who participated in the art integration movement program had an impact on their confidence in contents self-expression (t=3.087. p<.01) and voice self-expression (t=2.766, p<.01). contents self-expression (t=3.940, p<.001) affected self-regulating efficacy. and contents self-expression (t=4.057, p<.001) had a positive effect on the preference to taskCONCLUSIONS: The movement program that applied art integrated education suggests that self-expression of children with intellectual disabilities is an effective education to increase self-efficacy. A multifaceted study that is practically applicable in the field of special education is needed.
Cross-Lingual Dependency Parsing via Self-Training

Recent advances of multilingual word representations weaken the input divergences across languages, making cross-lingual transfer similar to the monolingual cross-domain and semi-supervised settings. Thus self-training, which is effective for these settings, could be possibly beneficial to cross-lingual as well. This paper presents the first comprehensive study for self-training in cross-lingual dependency parsing. Three instance selection strategies are investigated, where two of which are based on the baseline dependency parsing model, and the third one adopts an auxiliary cross-lingual POS tagging model as evidence. We conduct experiments on the universal dependencies for eleven languages. Results show that self-training can boost the dependency parsing performances on the target languages. In addition, the POS tagger assistant instance selection can achieve further improvements consistently. Detailed analysis is conducted to examine the potentiality of self-training in-depth.
Social experiment of e-JIKEI Network Camera with greeting function

A social experiment using e-JIKEI Network Camera with "Greeting function" has been started. In this social experiment, the effect of the greeting function on citizens is confirmed by questionnaire survey at the start of the experiment. Similarly, the privacy protection function of the e-JIKEI Network Camera is also confirmed by the survey. The "Greeting function" is a function that gives a greeting to a pedestrian who is in the field of view of the e-JIKEI Network Camera. The e-JIKEI Network Camera is a network camera developed us with consideration of privacy. Since it has the function of double encryption function, it is possible to securely protect the privacy of pedestrians taken by the camera.
Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption.

The rolling shutter mechanism in modern cameras generates distortions as the images are formed on the sensor through a row-by-row readout process; this is highly undesirable for photography and vision-based algorithms (e.g., structure-from-motion and visual SLAM). In this paper, we propose a deep neural network to predict depth and camera poses for single-frame rolling shutter correction. Compared to the state-of-the-art, the proposed method has no assumptions on camera motion. It is enabled by training on real images captured by rolling shutter cameras instead of synthetic ones generated with certain motion assumption. Consequently, the proposed method performs better for real rolling shutter images. This makes it possible for numerous vision-based algorithms to use imagery captured using rolling shutter cameras and produce highly accurate results. Our evaluations on the TUM rolling shutter dataset using DSO and COLMAP validate the accuracy and robustness of the proposed method.
LibGuides: Library of Things: Fun & Learning

A guide to the non-traditional items available for checking out at the Milton Public Library
COS-Chronicles of Sites

This work is developed to increase the efficiency of users to access the information they require during a tour or travel, by providing them access to different features and to improve their tour or travelling experience. This technology is location-based service application. In this project, we are trying to provide the users, a fast access to information that they require, if they are in a given range of the location that they want to visit. This application also provides various other features that include preference feature that saves the preference of user and suggests the user, the locations that are near user according to user’s preference (ex: religious), bookmark feature allows users to bookmark the locations that they want to visit and have access to those locations information anytime they want.
Expansions of quantum group invariants

In my research, we developed a method to distinguish knots. A knot is a mathematical depiction of the everyday knot that occurs in ropes and cords. The distinguishing of knots is a computationally hard problem, which is equivalent with the untangling of knots. Often, knots are distinguished by a so called knot invariant. Such an invariant is a label that can be attributed to any knot, such that the label is identical for two identical knots. The theory of knots is closely related to quantum computing and lattices of atoms. The distinguishing of knots is a problem that can be solved in an exponential number of computations, in the number of crossings of a knot. Our invariants can be computed in polynomial time. This means that the number of computations is a power of the number of crossings of a knot. Another advantage of our method is that for the knots that have been calculated, the invariant was able to distinguish as the most effective methods combined. In fact, by removing unnecessary information from the conventional knot invariants, we obtained an invariant that can be computed much faster, and is as effective as the usual invariants.
Autonomous flight of the micro drone Crazyflie 2.1 through an obstacle course

A drone is an unmanned aerial vehicle with multiple forms of usage. Drones can be programmed to fly with different degrees of autonomous flight. Autonomous controlled flight makes it possible for t ...
Entropic Causal Inference: Identifiability and Finite Sample Results

Entropic causal inference is a framework for inferring the causal direction between two categorical variables from observational data. The central assumption is that the amount of unobserved randomness in the system is not too large. This unobserved randomness is measured by the entropy of the exogenous variable in the underlying structural causal model, which governs the causal relation between the observed variables. Kocaoglu et al. conjectured that the causal direction is identifiable when the entropy of the exogenous variable is not too large. In this paper, we prove a variant of their conjecture. Namely, we show that for almost all causal models where the exogenous variable has entropy that does not scale with the number of states of the observed variables, the causal direction is identifiable from observational data. We also consider the minimum entropy coupling-based algorithmic approach presented by Kocaoglu et al., and for the first time demonstrate algorithmic identifiability guarantees using a finite number of samples. We conduct extensive experiments to evaluate the robustness of the method to relaxing some of the assumptions in our theory and demonstrate that both the constant-entropy exogenous variable and the no latent confounder assumptions can be relaxed in practice. We also empirically characterize the number of observational samples needed for causal identification. Finally, we apply the algorithm on Tuebingen cause-effect pairs dataset.
The Application of Metaphor in Senior High School English Teaching

Metaphor is a universal linguistic rhetorical phenomenon whose essence is cognition. This kind of cognition can be reflected in the cultural level of vocabulary and idioms. It is rooted in language thinking and culture. abstract. concepts can be concretized through metaphor. Conceptual metaphor is a very effective auxiliary means for students to learn English words. If students can correctly understand the conceptual metaphor implied by vocabulary in specific context, they can fully acquire the connotation expressed by sentences, so that language can be understood and analyzed more effectively. In high school English classroom, teachers should use more teaching methods of conceptual metaphor to explain words, phrases and sentences so as to improve students' language perception ability.
Apoio online de comunidades virtuais ao portador de diabetes mellitus: revisão bibliométrica/ Online support from virtual communities towards diabetes mellitus patients: a bibliometric review/ Apoyo online de las comunidades virtuales al portador diabetes mellitus: revisión bibliométrica

Objective: The study's main purpose has been to analyze the international scientific production that describes the support of virtual communities for diabetes mellitus, available on the ISI Web of Knowledge/Web of ScienceTM database. Methods: It is a bibliometric study, in the time frame between 1945 and 2018, with descriptive analysis performed with the aid of HistCiteTM software. Results: There were identified 175 articles, published in 113 different journals, written by 817 authors linked to 322 research institutions located in 30 countries, 7,023 references were used, with an average of 40 references per article. Conclusion: Although the number of studies on social media and diabetes is growing, the literature still points to the lack of longitudinal studies that broaden the understanding of the meanings usually attributed to the disease and identify recurrent cognitive lapses in these spaces, as well as the quality of the information that is posted and shared online, thus highlighting the need for future studies that investigate their impact on users
LibGuides: TLA Edition - The Game is in There: Creating Gamified Library Instruction: Home

LibGuide to accompany workshop presentation of The Game is in There: Creating Gamified Library Instruction by Tricia Boucher & Megan Ballengee at the Texas Library Association Conference on April 18, 2019.
LibGuides: Free Resources for Home Use: Create

Free resources to keep yourself happy and busy while staying at home during the COVID-19 epidemic.
Knowledge-Experience Graph with Denoising Autoencoder for Zero-Shot Learning in Visual Cognitive Development

Visual cognitive development is vital for intelligent robots to handle various types of visual tasks rather than predefined ones. It can transfer the classification ability from an original model to a novel task. However, the high reliance on large amounts of data hinders its development. The energy it costs to adjust to the novel tasks is also a tough problem. Thus we propose a model called knowledge-experience graph (KEG) to imitate the mechanisms of human brains. With the help of social knowledge stored in the knowledge graph, the novel classes can be easily added. The combination of the experience via denoising autoencoder (DAE) also takes the relationship in the visual space into account. With the propagation of information among the graph by graph convolutional network (GCN), KEG generates the classifier of the novel tasks effectively. Experiments show that KEG improves the classification accuracy of novel categories on zero-shot learning and accomplishes visual cognitive development to a certain extent.
Spending Money Wisely: Online Electronic Coupon Allocation based on Real-Time User Intent Detection

Online electronic coupon (e-coupon) is becoming a primary tool for e-commerce platforms to attract users to place orders. E-coupons are the digital equivalent of traditional paper coupons which provide customers with discounts or gifts. One of the fundamental problems related is how to deliver e-coupons with minimal cost while users' willingness to place an order is maximized. We call this problem the coupon allocation problem. This is a non-trivial problem since the number of regular users on a mature e-platform often reaches hundreds of millions and the types of e-coupons to be allocated are often multiple. The policy space is extremely large and the online allocation has to satisfy a budget constraint. Besides, one can never observe the responses of one user under different policies which increases the uncertainty of the policy making process. Previous work fails to deal with these challenges. In this paper, we decompose the coupon allocation task into two subtasks: the user intent detection task and the allocation task. Accordingly, we propose a two-stage solution: at the first stage (detection stage), we put forward a novel Instantaneous Intent Detection Network (IIDN) which takes the user-coupon features as input and predicts user real-time intents; at the second stage (allocation stage), we model the allocation problem as a Multiple-Choice Knapsack Problem (MCKP) and provide a computational efficient allocation method using the intents predicted at the detection stage. Long Short Term Memory (LSTM) and a special attention mechanism are applied on IIDN to better describe temporal dependencies of sequential features. And we manage to solve the imbalanced label problem for the user intent detection task with a brand new perspective by using the logical relationship between multiple user intents. We conduct extensive online and offline experiments and the results show the superiority of our proposed framework, which has brought great profits to the platform and continues to function online.
Offering Simulation Services Using a Hybrid Cloud/HPC Architecture

We outline the design and implementation of a system which implements the Simulation as a Service (SMaaS)Simulation as a Service (SaaS) model. SMaaS is based on the Software as a Service (SaaS)Simulation as a Service (SaaS) model, extending SaaS to include High-Performance Computing (HPC)-hosted applications. Simulations in an HPC context can be expensive, complex, and lengthy. The use of a cloud to provide and manage simulations as a service on an HPC cluster provides greater flexibility for users, in particular smaller businesses and educational institutions that might otherwise struggle to use simulation in their work. Adding a cloud to a standard HPC setup allows the HPC component to specialize in its strengths (e.g., performing calculations, storing Big Data), while the cloud can provide its own capabilities. We show how a cloud’s ability to scale up/down and support heterogeneous environmentsHeterogeneous environments provides support for all phases of simulation workflow—education, prototyping, and production. This chapter covers several different systems built at the Ohio Supercomputer Center. All systems have been deployed to production and used by paying customers. In addition to strictly technical concerns issues related to payment, licensing, and other business topics are covered.
Conceptualizing the impact of machines, science and technologies on the sociocultural environment that shapes the teacher's paradigm of values and views of life

Subject. In this article we analyze how machinery, science and technologies influence the sociocultural environment that engenders the teacher's paradigm of values and views of life. Objectives. We herein outline guidance to predict the way teachers' views of life might evolve in corresponding sociocultural periods more precisely. The article analyzes making more precise forecasts of oncoming economic crises, which will cause some changes in teachers' mindset. Methods. The study involves learning methodologies, methods of prediction and forecasting, including foresight. Results. We propose and analyze the theory holding that the human civilization passes cycles during its sociocultural development in terms of a new set of values in contemporary teachers' views of life. The article sets forth our recommendations on innovation-driven views of life, mindset and thinking and, consequently, the development of intellectual qualities, knowledge, skills, cognitive activity, positive motivation to the professional activity of a teacher and alumni during more elevated periods, which requires to more precisely predict the way teachers’ mindset may change in certain sociocultural periods. Conclusions and Relevance. As the human civilization enters the innovation-driven sociocultural period, teachers and social relationships should demonstrate more innovative and environmentally-friendly attitudes and views of life.
Research Guides: Writing 100 Library Guide: Our Search System

This guide supports the Writing 100 course with information on use of library search tools and resources, as well as providing guidance for MLA and APA formatting.