Reviewerly/exHarmony huggingface dataset:
An analysis of educational objectives of alternative schools based on John Dewey's educational discourse

본 연구의 목적은 오늘날 새로운 패러다임의 형태로 자리 잡아가고 있는 대안학교의 교육체제 즉, 교육목적에 대해 고찰하고, 이를 통해 듀이의 실용주의 교육담론의 관점으로 조망하여, 듀이 교육담 론에서의 대안학교가 나아가야 할 방향을 탐색하고자 하는 데 그 목적이 있다. 듀이는 교육목적을 성장으로서의 내재적, 융통성, 능동적 목적으로 삼는다. 첫째, 목적은 현재 조건의 자연적인 산물로 나와야 하는 내재적인 목적이어야 한다. 둘째, 새로운 조건이 발생하면 수정 되어 나가는 융통성이 있는 목적이어야 한다. 셋째, 목적은 언제나 행위를 자유롭게 하는 능동적 목적이어야 한다. 듀이의 실용주의 철학의 교육담론을 대안교육과 관련지어 살펴볼 때, 교육은 인간의 경험 속에서 성장의 가능성을 실현시켜 주고자 하는 노력으로 이해되기 때문에 듀이의 실용주의 철학 안에서 구체적이고 체계적인 교육목적의 실천을 통해 대안교육은 계발되어질 것으로 판단되며, 결론적으로 이런 교육적 목적이 성취될 때 대안교육은 성장 발전할 수 있을 것이다.The purpose of this study is to explore and navigate the direction for alternative schools which are becoming a new paradigm today, through from the perspective in the pragmatic educational discourse of John Dewey and suggests the direction alternative schools in John Dewey s discourse should go forth. John Dewey suggest educatioal objectives an intrinsic, flaexible and active goal of growth. First, Dewey emphasize that educational objectives should be an intrinsic purpose that must be a natural product of the present conditions Second, Dewey, educational objectives shuold be flexible to vodifiable when new condition appear. Third, Dewey, educational objectives should be an active goal of freedom of action. As a result, Pairing pragmatic educational discourse with alternative education, Education is an effort to realize the possibility of growth in human experience, and in Dewey s pragmatic philosophy, alternative education will be developed through concrete and systematic educational purposes. And educational growth can be progressively transferred. when these educational goals are achieved, alternative education can grow and develop.
TEACHING QUALITY IN HIGHER EDUCATION: PREREQUISITES FOR ITS IMPROVEMENT

Engagement of students in the teaching process, joint project activities with teachers, their readiness for learning and initiative are the basic prerequisites for ensuring the quality of higher education process and outcomes. Searching the possibilities of improving university teaching quality, we examined students' attitudes (N = 174) in order to determine whether students recognize the importance of joint activities with teachers, project planning and implementation, in the context of their motivational readiness for more efficient learning and higher quality acquisition of knowledge. The research was performed using a descriptive method and a scaling process. Research results confirm that students recognize the importance of joint project activities they implement with teachers during instruction, as well as of step-by-step learning (by smaller units), further motivating them to learn. These results point the researchers to the directions of action aimed at developing a system of measures to improve the quality of contemporary university education.
Research Help: EDUC 9510: Capstone: Implementation of Best practices (Beyranevand): ILLiad: Interlibrary Loan & Document Delivery Service

ILLiad is a 2-part service. The Interlibrary Loan service lets you request items from another library If our library doesn't have a book, article, etc. that you want. The Document Delivery service lets you request that items be sent to you online when eve
Structure learning in graphical models by covariance queries.

We study the problem of recovering the structure underlying large Gaussian graphical models or, more generally, partial correlation graphs. In high-dimensional problems it is often too costly to store the entire sample covariance matrix. We propose a new input model in which one can query single entries of the covariance matrix. We prove that it is possible to recover the support of the inverse covariance matrix with low query and computational complexity. Our algorithms work in a regime when this support is represented by tree-like graphs and, more generally, for graphs of small treewidth. Our results demonstrate that for large classes of graphs, the structure of the corresponding partial correlation graphs can be determined much faster than even computing the empirical covariance matrix.
Customizing the LinGO Grammar Matrix Morphology

The LinGO Grammar Matrix is rapid development grammatical analyzer that is enabled by a customization system, including an online questionnaire. The questionnaire allows any linguist to quickly build a starter grammar for any language. However, its documentation is not easily navigable. This paper describes the process of customizing the morphological section of the grammar, using Lezgi case inflection as an example. It outlines how suggestions for helping potential users to gain confidence in using the Matrix.
Forecasting retail sales: The case of a medium enterprise of Taiwan

Product assortment and shelf-space allocation may have influence upon customer buying behavior and then induce demand (Yen-Liang Chen, Jen-Ming Chen and Ching-Wen Tung 2006). This study is aimed to understand customer buying behavior to offer a recommendation for the management department to rearrange product display. The research was conducted at a Taipei company that engaged in the sale of japan healthcare product that the company buys from the supplier. There are more than fifty sale stores across Taiwan for this medium enterprise. In this research, three-year data are collected by four retail stores of Taipei from 2015 to 2017. Motivated by the famous Heath’s beer-and-diapers discovery (Anonymous, 1998), this study uses data mining techniques to discover the undeclared, yet meaningful relationship between the relative spatial distance of displayed products and the items’ unit sales in a retailer’s store (C.W. Hubbard 1969). This paper use a dynamic programming language: PERL to analyze data. This paper proposes representation scheme and develops a robust algorithm based on association analysis.
LibGuides: NMSL Gov Docs Policies: State Government Documents

This is to ensure that the Public Service Bureau has openly expressed the expectations surrounding cataloging and processing of state government documents in our collection.
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation

We address the problem of speech act recognition (SAR) in asynchronous conversations (forums, emails). Unlike synchronous conversations (e.g., meetings, phone), asynchronous domains lack large labeled datasets to train an effective SAR model. In this paper, we propose methods to effectively leverage abundant unlabeled conversational data and the available labeled data from synchronous domains. We carry out our research in three main steps. First, we introduce a neural architecture based on hierarchical LSTMs and conditional random fields (CRF) for SAR, and show that our method outperforms existing methods when trained on in-domain data only. Second, we improve our initial SAR models by semi-supervised learning in the form of pretrained word embeddings learned from a large unlabeled conversational corpus. Finally, we employ adversarial training to improve the results further by leveraging the labeled data from synchronous domains and by explicitly modeling the distributional shift in two domains.
The State of the Uniform: Attacks on Encrypted Databases Beyond the Uniform Query Distribution.

Recent foundational work on leakage-abuse attacks on encrypted databases has broadened our understanding of what an adversary can accomplish with a standard leakage profile. Nevertheless, all known value reconstruction attacks succeed under strong assumptions that may not hold in the real world. The most prevalent assumption is that queries are issued uniformly at random by the client. We present the first value reconstruction attacks that succeed without any knowledge about the query or data distribution. Our approach uses the search-pattern leakage, which exists in all known structured encryption schemes but has not been fully exploited so far. At the core of our method lies a support size estimator, a technique that utilizes the repetition of search tokens with the same response to estimate distances between encrypted values without any assumptions about the underlying distribution. We develop distribution-agnostic reconstruction attacks for both range queries and k-nearest-neighbor (k-NN) queries based on information extracted from the search-pattern leakage. Our new range attack follows a different algorithmic approach than state-of-the-art attacks, which are fine-tuned to succeed under the uniformly distributed queries. Instead, we reconstruct plaintext values under a variety of skewed query distributions and even outperform the accuracy of previous approaches under the uniform query distribution. Our new k-NN attack succeeds with far fewer samples than previous attacks and scales to much larger values of k. We demonstrate the effectiveness of our attacks by experimentally testing them on a wide range of query distributions and database densities, both unknown to the adversary.
Library Guides: Research4Life Training: Evidence-based Practice Resources

This training is supported by the Elsevier Foundation 2014 New Scholars and Innovative Libraries in Developing Countries program
STORE: Security Threat Oriented Requirements Engineering Methodology

As we are continuously depending on information technology applications by adopting electronic channels and software applications for our business, online transaction and communication, software security is increasingly becoming a necessity and more advanced concern. Both the functional and non-functional requirements are important and provide the necessary needs at the early phases of the software development process, specifically in the requirement phase. The aim of this research is to identify security threats early in the software development process to help the requirement engineer elicit appropriate security requirements in a more systematic manner throughout the requirement engineering process to ensure a secure and quality software development. This article proposes the STORE methodology for security requirement elicitation based on security threats analysis, which includes the identification of four points: PoA, PoB, PoC and PoD for effective security attack analysis. Further, the proposed STORE methodology is also validated by a case study of an ERP System. We also compare our STORE methodology with two existing techniques, namely, SQUARE and MOSRE. We have shown that more effective and efficient security requirements can be elicited by the STORE methodology and that it helps the security requirement engineer to elicit security requirements in a more organized manner.
Empirical Analysis of K-means, Fuzzy C-means and Particle Swarm Optimization for Data Clustering

Clustering is a fundamental task in data mining technique which puts more similar data objects into one group and dissimilar objects into another group. The aim of this paper is to compare the quality of clusters produced by K-Means, Particle swarm optimization (PSO) and Fuzzy C-Means (FCM) for data clustering. The k-means algorithm is the most widely used partitional clustering algorithm technique in the industries and academia. The algorithm is simple and easy to implement. The main drawback of the K-Means algorithm is that it is sensitive to the selection of the initial cluster centers and it may converge to local optima. Fuzzy C-means algorithm is a popular algorithm in the field of fuzzy clustering. Fuzzy clustering using FCM can provide a data partition that is both better and more meaningful than hard clustering approaches. Particle Swarm Optimization (PSO) is an evolutionary computational technique which was motivated by the organism’s behavior such as schooling of fish and flocking of birds. The quality of the clusters produced by above three algorithms is estimated using Silhouette Coefficient. The experimental results show that the performance of PSO clustering is better than FCM & K-Means clustering. The difference in time taken by the algorithms for execution is negligible
The Influence of Flash Card Learning Media toward Students’ Writing Skill of Explanatory Text

Abstract This study aimed at finding out the influence of flash card learning media toward students’ writing skill of explanatory text. There were 72 students which 36 experimental class students (XI.TAV.1) and 36 control class students (XI.TITL.2). This research was a quantitative experimental design. The data were collected by using test and documentation. The results of data analysis using the t-test formula obtained t-count = 11.48, and t-table with a real level of 5% and dk = 70 obtained t-table = 1.668. Thus, it turns out that t-count> t-table (I-α) (11.48> 1.668). So, the hypothesis was accepted. This proves that the learning outcomes of writing explanatory text using Flash Card learning media is more influential. Keywords: Flash Card Learning Media; Students’ Writing Skill; Explanatory Text.
Modeling Uncertainty in Group Recommendations

In many settings, it is required that items are recommended to a group of users instead of a single user. Most often, when the decision criteria and preferences of the group as a whole are not known, the gold standard is to aggregate individual member preferences or recommendations. Such techniques typically presuppose some process under which group members reach consensus, e.g., least misery, maximum satisfaction, disregarding any uncertainty on whether this presumption is accurate. We propose a different approach that explicitly models the system's uncertainty in the way members might agree on a group ranking. The basic idea is to quantify the likelihood of hypothetical group rankings based on the observed member's individual rankings. Then, the systems recommends a ranking that has the highest expected reward with respect to the hypothetical rankings. Experiments with real and synthetic groups demonstrate the superiority of this approach compared to previous work based on aggregation strategies and to recent fairness-aware techniques.
Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training

Adversarial training has shown impressive success in learning bilingual dictionary without any parallel data by mapping monolingual embeddings to a shared space. However, recent work has shown superior performance for non-adversarial methods in more challenging language pairs. In this work, we revisit adversarial autoencoder for unsupervised word translation and propose two novel extensions to it that yield more stable training and improved results. Our method includes regularization terms to enforce cycle consistency and input reconstruction, and puts the target encoders as an adversary against the corresponding discriminator. Extensive experimentations with European, non-European and low-resource languages show that our method is more robust and achieves better performance than recently proposed adversarial and non-adversarial approaches.
Blockchain y su impacto en la evolución del modelo de negocio del sector asegurador

espanolel sector asegurador es, sin lugar a dudas, un sector estrategico para el desarrollo de cualquier pais. muchos de los avances, sociales, economicos o incluso tecnologicos que se han producido a lo largo de la historia no hubieran sido posibles sin su existencia. sin embargo, nos encontramos en un momento especialmente singular, en el que la tecnologia desafia cualquier modelo de negocio existente y nos obliga a reflexionar sobre si modelos tradicionales siguen teniendo sentido en un mundo en constante cambio, donde nuevas inquietudes, habitos y necesidades emergen casi a diario. en este contexto el sector asegurador vive su propia revolucion de la mano del Insurtech, pero la llegada, casi inesperada, de Blockchain, ha hecho que esta reflexion se tenga que hacer de forma mucho mas profunda si cabe. si Blockchain es capaz de avanzar y cumplir con muchas de las bonanzas que se le atribuyen, podemos afirmar que el mundo esta en la antesala de uno de los cambios mas disruptivos de su historia, y el sector asegurador tambien. Englishthe insurance industry is strategic for the development of any country. many of the social, economic or even technological advances that have occurred throughout history have not been possible without its existence. However, currently, we are in a singular moment, in which technology is challenging the business models of any industry, and it forces us to reflect on whether traditional models continue to make sense in a world in constant change, where new concerns, habits and needs emerge almost daily. in this context, the insurance sector lives its own revolution called Insurtech, but the arrival, almost unexpected, of the Blockchain, has made this reflection is carried out in the deepest way, if possible. if Blockchain is able to advance and meet many of the bonanzas that are attributed to it, we can affirm that the world is in the lead-up to one of the most disruptive changes in its history, and the insurance industry as well.
3D object position retrieval with the light-field camera and microscope

Light-field camera ray-trace simulations show that for sparse samples, the location, symmetry and size of the point-source response can provide lateral positions to a detector pixel size, and defocus to a lenslet focal length.
The Virtual Tour as a Tool for the Preservation of the Traditional Knowledge at Kasepuhan Palace

Kasepuhan palace is the oldest palace in Cirebon and has been designated a cultural heritage building. As a place of the ruler or king, Kasepuhan palace embodies knowledge about the activities and culture of the local community, and the elements and layout of the palace buildings have both aesthetic and symbolic functions. Libraries can play a major role in knowledge preservation in heritage buildings by utilizing information technology. Using a qualitative research method, this paper describes the efforts made by the National Library of the Republic of Indonesia to preserve the traditional knowledge in the Kasepuhan palace by developing a virtual tour. It was concluded that virtual tours can be valuable cultural heritage building preservation tools if supported by appropriate knowledge selection and identification and adequate photo shooting techniques and image recording technology. The addition of maps, sounds, images, and other information features can enrich virtual tours, better disseminate the knowledge, and achieve the preservation goal.
Novel Computational Methods for Bayesian Hierarchical Modeling in the Biomedical Domain

Author(s): Pourzanjani, Arya Alexander | Advisor(s): Petzold, Linda R | Abstract: The recent growth in the availability of biomedical data promises to reshape healthcare by ushering in an era of personalized medicine where data can be used to diagnose and treat patients with pinpoint accuracy. Truly realizing this goal requires building statistical models that individually model patient variations such as age, sex, and genetic makeup, which leads to a combinatorial growth in the number of parameters in a model and noisy estimates. Fortunately, Bayesian hierarchical models, along with recent computational advances, provide a solution to this issue. By naturally embedding the hierarchical structure that many datasets exhibit into the model, these models allow for separate estimates that capture population-level variation and simultaneously avoid noise via regularization to a population mean.In this thesis, we describe novel models and computational methods for Bayesian hierarchical modeling of biomedical data. We begin by describing our contributions to various areas of Bayesian modeling, along with the problems from our applied work that motivated these contributions.Specifically, we describe our disease progression model, which hierarchically models patient disease trajectories. The model, which was motivated by our applied work on Alzheimer's Disease, utilizes I-splines to capture the characteristic monotonic shape of dementia disease trajectories, along with Dirichlet distributions over the coefficients of these I-splines to hierarchically model these trajectories. Next we describe our work on using the Givens Representation of orthogonal matrices to infer models with orthogonal matrix parameters, such as factor models, in a general Bayesian framework. We describe the innovations in our method along with our motivating hierarchical example based on the analysis of protein biomarkers of coagulopathic trauma patients. Next, we describe a mechanistic model of coagulopathy that relates clotting assay data to protein concentrations, effectively providing a fast and convenient way for clinicians to understand key protein markers involved in clotting.Next we transition specifically to Hamiltonian Monte Carlo (HMC) and elucidate the connection between multiscale posterior distributions and the efficiency of HMC. We describe the issue of numerical stability inside HMC and present our implicit HMC algorithm for efficiently sampling non-Gaussian posterior distributions.Lastly, we provide a summary of our contributions along with ideas for future work.
Language use in the social media and national integration: current trends and challenges

The focus of this paper is on language use in the social media. It demonstrated the current trends in the computer mediated discourses as seen on Facebook handle. The alarming rate of ‗nationism‘ and its possible effect as seen in these discourses which are capable of causing chaos necessitated the need for this study. It explored these trends with the view to ascertaining its challenges to national integration. About forty linguistic properties of the utterances in response to three particular excerpts from events in Nigeria gotten from Facebook accounts constitute the data with which this current trend of language use is demonstrated. The paper discovered that in the midst of this social media revolution, prevalent issues such as crime, violence, conflict, elections and insurgency are discussed online. Their examination revealed that there are predominantly hate speeches especially in reaction to issues of national interest. Most of such discourses are laced with inappropriate use of language exposing the lack of communicative competence amongst users. Judging from the basic components of speech act theory, the paper foresees possible perlocutionary acts which might be in the form of war and possible disintegration. As parts of its recommendations, the paper brought to limelight the need to put in check the destructive use of language on the social media. Keywords: Social media, Language use, Speech act theory, Perlocutionary, National integration
Developing an Approach to Evaluate and Observe Sentiments of Tweets

Social media are computer- mediated tools that allow people or companies to create, share or exchange information, career interests, ideas and the form of text, audio, video, image in virtual communities and networks. Twitter is a trendy microblogging service where many users procreate various status messages called tweets. Twitter is the fastest way to get real time information from the around the world. Tweets themselves are short and compact, like newspaper headlines. Analysis of sentiment is widely observed on numerous social networking websites. Nowadays, microblogging sites deed as a base to perceive the actual social opinion. The task of sentimental analysis is often known by many other names as opinion extraction, opinion mining, sentiment mining, subjectivity analysis.