Reviewerly/exHarmony huggingface dataset:
The 2005 National Research Council Database

The five agencies represented in the 2005 NRC database are the agencies with the largest SBIR programs as measured in terms of SBIR allocated dollars. The 2005 NRC database contains business and Phase II project information for awards made from 1992 through 2001. Table 4.1 shows that these five agencies accounted for about 96% of all Phase II awards over the period 1992 through 2001, and they accounted for almost 93% of all Phase II award obligations (in nominal dollars).
Hats off to new mobile voting tool

Top Hat, a new institutional mobile voting solution, is being implemented at Leeds. August 2019
NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning

Deep Neural Networks (DNNs) deliver state-of-the-art performance in many image recognition and understanding applications. However, despite their outstanding performance, these models are black-boxes and it is hard to understand how they make their decisions. Over the past few years, researchers have studied the problem of providing explanations of why DNNs predicted their results. However, existing techniques are either obtrusive, requiring changes in model training, or suffer from low output quality. In this paper, we present a novel method, NeuroMask, for generating an interpretable explanation of classification model results. When applied to image classification models, NeuroMask identifies the image parts that are most important to classifier results by applying a mask that hides/reveals different parts of the image, before feeding it back into the model. The mask values are tuned by minimizing a properly designed cost function that preserves the classification result and encourages producing an interpretable mask. Experiments using state-of-art Convolutional Neural Networks for image recognition on different datasets (CIFAR-10 and ImageNet) show that NeuroMask successfully localizes the parts of the input image which are most relevant to the DNN decision. By showing a visual quality comparison between NeuroMask explanations and those of other methods, we find NeuroMask to be both accurate and interpretable.
RANDOM FOREST DAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) UNTUK KLASIFIKASI KEMANDIRIAN PADA ANAK USIA DINI DI KECAMATAN BOJONEGORO

ABSTRACT Independence is a situation where someone tries to stand alone in the sense of not relying on others in a decision and is able to carry out life's tasks with full responsibility. Thereare so many factors that influence independence, including parenting, age of the child, order ofchildren, self-esteem of children, genes, status of mother workers, and so forth. In this studywanted to find out the factors that influence the independence of early childhood with the MARSmethod and improve the accuracy of classification using the resampling method namely randomforest and a combination of MARS and random forest. The formation stage of the MARS modelbegins by conducting trial and error on the maximum base function (BF), maximum interaction(MI), and minimum number of observations between knots (MO) to obtain optimal values withminimum GCV values. The next step is to take n samples with resampling techniques withreturns to obtain a new dataset. From the results of the analysis, the best MARS model wasobtained when the combination BF = 22, MI = 3 and MO = 1 with GCV values of 0.142. Of the11 variables analyzed, 5 variables entered into the best MARS model, namely parenting, orderof children in the family, habits of children, social characteristics of children, and maternalstatus. The accuracy of children's independence classification in Bojonegoro sub-district withthe MARS method is 77.39% with a random forest method of 95.95% and by the random forestMARS method of 89.86%. So that the best classification accuracy is obtained using the RandomForest method.Keywords : MARS, random forest, independence of children
DECONSTRUCTION OF SCENE OF FUTTERWACKEN DANCE IN ALICE IN WONDERLAND MOVIE BY TIM BURTON

The movie by Tim Burton entitled Alice in Wonderland is considered to have specialand distinctive characteristic from the Alice in Wonderland story in general. The premise that the Alice in Wonderland story is specialed for children tend to be described in cheerful, fantastic and joyful theme. But in Tim Burton movie, it seems like it has been transformed into something that does not emphasize the things of beauty, happiness and fantastic scene for the audience. With the camera angle technology and precision in the control of lighting, color, motion and expression, the description of happiness is delayed so that audience is herded to different uses in the meaning onmovie cinematography. Dark, gloomy or noir scene is also created at the end of the story that ideally should not be presented in explaining the happy ending of the story because of the defeat of evil. This thingis considered important to analyze, because someone must be able to explain the anomaly, so a deconstruction method of reading is taken which tends to be able to see the structure of the cinematography that is peculiar
Deep learning fuzzy classifier

It is considered a classification problem solution based on analysys of represented review. It’s shown that the neural networks have important advantages beside other methods, such as: classification using the nearest neighbor method, support vector classification, classification using decision trees, etc. Amount of artificial neural networks exists futher networks have the simplest structure, but the precision of the solution can be increased with help of deep learning approach, which is supposes the use of additional neural network for the solution of pretraining tasks(deep believe networks). It’s proposed new tophology which consist of: Takagi-Sugeno-Kang fuzzy classifier and Limited Boltzmann Machine neural network. Despite on this thopology was proposed early in this article it’s carried out enough researches that permited to specify the learning algorithm. An example of proposed algorithm implantation is represented.
Blockchain Meets Genomics: Governance Considerations for Promoting Food Safety and Public Health

Significant morbidity and mortality from foodborne illness continues to burden the developing and industrialized worlds. The United States alone faces millions of foodborne illness cases per year, resulting in substantial public health and economic costs. Advances in two emerging technologies promise to bolster regulatory and private responses to foodborne pathogens. First, food regulators have begun implementing whole genome sequencing (WGS) to identify pathogens and their geographic locations of origin. Second, food distributors including Walmart have begun to report blockchain pilot projects to trace contaminated food products through their supply chains. If combined effectively, the nexus of blockchain and WGS could enhance the capacity of public health officials and industry actors to respond to and prevent foodborne illnesses. However, the convergence of these technologies raises novel technical, regulatory, and governance issues. For example, the structure and ownership of blockchain platforms will have implications on regulatory compliance, the ability to interface with government WGS programs, and competition amongst food distributors. Responding to regulatory challenges while promoting the public health benefits of blockchain and WGS will require flexible and agile governance approaches while these technologies continue to mature. Rather than rigid command-and-control regulation, “soft law” instruments including voluntary regulatory programs and technical standards should offer an appropriate, initial balance for these competing considerations.
Modern technology and its role in literary expression when students of the fifth grade literary.

Praise be to God who created man, and taught the statement, and and blessings on the best of his creation and the nobility of his prophets, our Prophet Muhammad peace be upon him and guided the gift to the day of religion, and after :    The written language is the principal axis of the human civilization, and distinguished from all other creatures. human can record their experiences through writing, so it is providing the opportunity for future generations to benefit from these experiences. The  importance of written expression as one of the most important means of communication, and the important point here is the  talking about literary expression and the role of modern technology in the expression of literature. In the opinion of the researcher there is a great responsibility rests on the teacher and his management of creative education by using modern technology, especially as it is based on the development of a social environment which  employs the skills of students. Solving the problem is based on using technology and modern teachers who responsible for the development of creativity. Therefore, in order teacher be able to play the right role in development of creativity, he must be trained in a specific technology that he adopts to nurture the  creativity and develop it among the students
A comparative study on generalizability of information extraction models on protest news

Information Extraction applications can help social scientists to obtain necessary information to understand the reasons behind certain social dynamics. Many recent state-of-the-art information extraction approaches are based on supervised machine learning which can recognize information that has similar patterns with previously shown ones. Recognizing relevant information with never-shown patterns, however, is still a challenging task. In this study, we design a Recurrent Neural Network (RNN) architecture employing ELMo embeddings and Residual Bidirectional Long-Short Term Memory layers to overcome this challenge in the context of CLEF 2019 ProtestNews shared task. Furthermore, we train a classical Conditional Random Fields (CRF) model as our strong baseline to display a contrast between a state-of-the-art classical machine learning approach and a recent neural network method both in performance and in generalizability. We show that RNN model outperforms classical CRF model and shows a better promise on generalizability.
일제 강점기 한국 기독교와 국제교류

This essay examines the intersection of Christianity and civic diplomacy under the constraints of colonial Korea. To identify Korean Christian engagement in international relations, this research investigates the interwar experiments of Christian internationalism, focusing on the Institute of the Pacific Relations, the Jerusalem World Missionary Conference, and the Commission on Appraisal of the Laymen’s Foreign Missions Inquiry, all of which operated as nodes of international cultural exchanges. The post-war period of the 1920s, in an effort to overcome the malaise of the First World War and rebuild a new world order, was marked by the emergence of modernist theology which promoted Christian egalitarianism over against paternalism among diverse nations, practicality of partnership between the West and East, and Christian internationalism against parochialism. Among such Christian scholars and statesmen as Daniel Johnson Fleming, William Earnest Hocking, and John D. Rockefeller, the vision for the new world order created a cultural idiosyncrasy, alternately a space of diplomacy, in which Koreans under the colonial jurisdiction and surveillance propounded Korean aspirations for self-government and the national capacity of independence. Through the cultural exchanges, Korean Christians debunked the Japanese government’s reports of the modern governance, unveiled the concomitant plight of Korean farmers, and asserted Koreans’ potency to unify both the progressives and the conservatives. Korean Christians appropriated the theological liberalism experimented in the U. S. and took initiative in endeavors of cultural reconstruction and self-government. More importantly, Korean Christian participation in international exchanges in the 1920s imparted the foundational knowledge for Asian studies in its embryonic stage and steered the interpretative direction of Korean information so as to influence shaping and understanding Korean issues during the Pacific War. The repository of Asian studies built through the cultural exchanges in the 1920s functioned as information agency for the American foreign policy in preparation for Korean liberation, underscoring the political effect of the civic diplomacy rendered through Christian channels.
Comparing EM with GD in Mixture Models of Two Components

The expectation-maximization (EM) algorithm has been widely used in minimizing the negative log likelihood (also known as cross entropy) of mixture models. However, little is understood about the goodness of the fixed points it converges to. In this paper, we study the regions where one component is missing in two-component mixture models, which we call one-cluster regions. We analyze the propensity of such regions to trap EM and gradient descent (GD) for mixtures of two Gaussians and mixtures of two Bernoullis. In the case of Gaussian mixtures, EM escapes one-cluster regions exponentially fast, while GD escapes them linearly fast. In the case of mixtures of Bernoullis, we find that there exist one-cluster regions that are stable for GD and therefore trap GD, but those regions are unstable for EM, allowing EM to escape. Those regions are local minima that appear universally in experiments and can be arbitrarily bad. This work implies that EM is less likely than GD to converge to certain bad local optima in mixture models.
La produzione linguistica del razzismo ovvero la costruzione di frontiere nell’interazione sociale

This article aims at describing the production of “races” as the result of categorization processes. During the social interactions of everyday life, in its different spheres, borders are linguistically and continuously realised and, in that way, value-related assumptions and moral pre-conceptions – on which discriminatory behaviours of different intensity are sometimes based – are imported. To examine the mechanisms of linguistic production of the border is exactly what will allow us to point out the need of knowing how to maintain ourselves able to abandon our categories, to think the “between” and to perform the crossing.
Άνίχνευση αντικειμένων με matlab & simulink

The present thesis concerns the development of algorithms-models onMatlab and Simulink for multi-object detection.The following methods that took place for developing this project are basedon Artificial Inteligence (Machine Learning) and also the application of multiplemathematical algorithms and techniques such as (Gaussian Mixture Model,Morphological Opening, Foreground Detection, Bayesian Inference, KalmanFilter) etc. On alpha stage we will apply the algorithms on Objection C and forthe application of the same purpose and the improvement of the algorithm’saccuracy with zero potential error.
Learning Body Shape and Pose from Dense Correspondences

In this paper, we address the problem of learning 3D human pose and body shape from 2D image dataset, without having to use 3D dataset (body shape and pose). The idea is to use dense correspondences between image points and a body surface, which can be annotated on in-the wild 2D images, and extract and aggregate 3D information from them. To do so, we propose a training strategy called ``deform-and-learn where we alternate deformable surface registration and training of deep convolutional neural networks (ConvNets). Unlike previous approaches, our method does not require 3D pose annotations from a motion capture (MoCap) system or human intervention to validate 3D pose annotations.
The Needs of Assistive Technology on Students’ EFL Writing

Nowadays generally, technology encompasses all aspects of modern life and particularly, the writing by using English language. The challenges of learning English writing in Indonesia are plenty as English is perceived as a Foreign Language. This paper aims to reveal tertiary level students’ perception and attitude towards their technology aptitude: usage models, noticed benefits of and challenges to employ technology in learning English as Foreign Language (EFL) writing. This is a qualitative research, a case study, and used a self-structured questionnaire and interview to collect the data. Two participant lecturer observers and one hundred and sixty-seven students in a private college in Tangerang were purposively selected thirty students for questionnaire and interview. The data were carried out to reveal the lecturers and students’ perspective of assistive technology in EFL writing. The results revealed that assistive technology such as Webbing/Blogs, Computer software, Social Mediaapps, YouTubechannel, smart phone and tablet apps are found to be used by EFL learners to learn EFL Writing. Yet, most of the students had constraint at bad Internet connection. The outcome of this study offers thinkable instructional suggestions for students’ EFL Writing and creates base for further studies in this area.Keywords: Students’ EFL Writing, Learning EFL Writing, Assistive Technology
The Impression That I Get: Reference & Instruction Uses/Perceptions of Primo in a Consortial Environment

The Discovery and User Experience Team in the Orbis Cascade Alliance (Alliance) conducted an open-ended survey designed to gather information about how Primo works within the context of the daily work of patron-centered staff and librarians. We felt that this information and feedback was important because it provided the only avenue in which reference and instruction librarians could give direct input to the Alliance about how improve our current discovery interface. The survey asked reference and instruction librarians about their specific experiences with Primo with the goal of identifying ways that the Discovery and User Experience Team could better support these users. We plan on covering the following topics in our presentation:Brief BackgroundMethod of data gathering and analysisOverview of FindingsRecommendations madeNext StepsThis survey opened our eyes to many issues and misconceptions about Primo that would be of interest to the Primo Community. This presentation will allow us to share our findings with you but also give you an opportunity to ask questions about our findings.
Towards closing the gap between the theory and practice of SVRG

Among the very first variance reduced stochastic methods for solving the empirical risk minimization problem was the SVRG method (Johnson & Zhang 2013). SVRG is an inner-outer loop based method, where in the outer loop a reference full gradient is evaluated, after which $m \in \mathbb{N}$ steps of an inner loop are executed where the reference gradient is used to build a variance reduced estimate of the current gradient. The simplicity of the SVRG method and its analysis have led to multiple extensions and variants for even non-convex optimization. We provide a more general analysis of SVRG than had been previously done by using arbitrary sampling, which allows us to analyse virtually all forms of mini-batching through a single theorem. Furthermore, our analysis is focused on more practical variants of SVRG including a new variant of the loopless SVRG (Hofman et al 2015, Kovalev et al 2019, Kulunchakov and Mairal 2019) and a variant of k-SVRG (Raj and Stich 2018) where $m=n$ and where $n$ is the number of data points. Since our setup and analysis reflect what is done in practice, we are able to set the parameters such as the mini-batch size and step size using our theory in such a way that produces a more efficient algorithm in practice, as we show in extensive numerical experiments.
Development of an Information System for Organizing and Monitoring the Educative Process of Students on the Basis of Cloud Technologies

The article considers the development of an information system for organizing and monitoring the educative process of students on the basis of cloud technologies. In the course of the research, the analysis of the requirements of the standards for the information and educational environment, the analysis of factors affecting the quality of the learning process, the analysis of the elements of the process of organizing and monitoring the students' education, analysis of popular cloud services, SWOT-analysis of the information system were carried out. The main results of the research are presented in the article. As a result of the research, an information system for organizing and monitoring the educative process of students on the basis of cloud technologies has been developed. This system meets all the requirements mentioned above. It includes an electronic journal of students' progress and attendance records, methodological materials and tasks for self-study of disciplines, a testing system, communication with teachers, a calendar of events and other elements
Clustering methods with variable selection for data with mixed variable types or limits of detection

Clustering has emerged as one of the most essential and popular techniques for discovering patterns in data. However, challenges exist in application of clustering. First, many of the existing clustering methods are only useful for data with either all continuous or all categorical variables, despite the abundance of data with mixed variable types. Second, clustering algorithms typically require complete data. But measurements for clinical biomarkers are often subject to limits of detection (LOD). In addition, researchers are getting more interest in knowing variable importance due to the increasing number of variables that become available for clustering. To overcome aforementioned challenges, this dissertation proposes clustering methods for mixed data with the ability of variable selection and handling censored biomarker variables.In the first section, we propose a hybrid density- and partition-based (HyDaP) algorithm for mixed data. The HyDaP algorithm involves two steps: variable selection step and clustering step. In the first step, variables that have much contribution to clustering will be selected; in the second step, a novel dissimilarity measure will be applied on those selected variables and obtain final results. Simulations and real data analysis were conducted to compare the performance of the HyDaP algorithm with other commonly used clustering algorithms.In the second section, we propose a Bayesian finite mixture model to simultaneously conduct variable selection, account for biomarker LOD and obtain clustering results. We put a spike-and-slab type of prior on each variable to obtain variable importance. To account for LOD, we added one more step in Gibbs sampling that iteratively fills in censored biomarker values. The same simulation settings and real data were used to evaluate its clustering performance.PUBLIC HEALTH SIGNIFICANCE: This dissertation proposes clustering algorithms that can be applied to any mixed data with or without censored biomarkers like electronic health record (EHR) data and other clinical data. The identified patient subgroups could provide medical experts more knowledge of patient heterogeneity and the selected important variables could let them better know where the heterogeneity comes from. Thus these information could help develop precision medicine for better patient care.
Dark Channel Applied for Reduction of the Effects of Non-uniform Illumination in Image Binarization

Non-uniform illumination is a common issue in images acquired in uncontrolled environments. Elimination or reduction of the non-uniform illumination problem is required in order to get an accurate image binarization. This paper introduces the combination of the dark channel and the atmospheric scattering model along with the k-means segmentation to reduce the effects of non-uniform illumination conditions in image binarization. The results show the effectiveness and robustness of this approach.