Reviewerly/exHarmony huggingface dataset:
Current Prevalence of Malaria Knowledge and Practices among Residents of a Rural Community in South India

Introduction: Malaria is a major public health problem in India. The prevention of Malaria is mainly dependenton knowledge and beliefs of people.The National Framework for Malaria Elimination (NFME) 2016-2030in India insists to eliminate malaria in India and for this to achieve the current malaria awareness on causesand protection in the region where malaria was a major problem about 7 years back is very important. In thisbackground this study was undertaken to know the current Knowledge of Malaria in this area.Objectives: To know the knowledge and practices of Malaria which was problem in the area about 7 yearsback.Methodology: Its a Cross-sectional study conducted in a rural community.Since the proportions of knowledgeand practices regarding malaria transmission in this setting was unknown, a 50% proportion was assumedto ensure the maximum sample size.The total number of houses visited was 110 to achieve the sample sizeof 384. The information of malaria with respect to Socio-demographic, knowledge and practices has beencollected from the adult respondent (aged &gt;18 years) of the households in the villages of field practice areaof the medical college. Data was collected by semistructred questionnaire method. Frequencies, proportionsand chi-square test are the test of significance.Results: Among the 384 subjects interviewed 73% (i.e. 280) knew about malaria and were assessed forKnowledge and practices regarding malaria. About 32.8% (i.e. 126) said that malaria is a communicabledisease, Majority 74.7% (i.e. 287) said that malaria is transmitted from mosquito bite. About 19% (i.e. 72)were not aware regarding mode of transmission of malaria. About 74.7% (i.e. 287) respondents informedthat mosquito nets were used in the night to protect from mosquito bites.Conclusions: The study revealed that knowledge and practices about malaria was satisfactory and therewas varied perception about causes, spread of disease, symptoms and protection from mosquitoes. Healthpersonnel and medical students were the major source of knowledge in this the population. Since the malariais not a problem from the past 5 years, but still the overall awareness regarding malaria was good in this area.
∂ is for Dialectica: Typing Differentiable Programming

Differentiable programming is a recent research area: its objective is to express differentiation as a modular algorithmic transformation on rich programming languages. It is in particular motivated by the various applications of automatic differentiation in machine learning or formal calculus. This work focuses on the typing system used to express differentiation. The first part of this paper is devoted to expressing the Dialectica transformation as a reverse automated differentiation transformation on a higher-order lambda-calculus with positive types. The second part builds on the intuitions provided by Dialectica to provide a lambda-calculus with an internal differentiation operator, with a typing system inspired by Differential Linear Logic, allowing to express backward automatic differentiation as a callby-name strategy and forward automatic differentiation as a call-by-value strategy. The target language of Dialectica is then given a semantics in smooth models of Differential Linear Logic.
Characteristic practice of science and engineering in liberal arts colleges

This paper narrates the origins of the environment of development and issues about engineering courses in our university. It analyzes issues about how to improve personal training and correct faultiness in the current situation. According to specific engineering areas, using TCP/IP protocol principle and network programming for example, this paper discusses the methods about accelerating the process of electronic information engineering and states the reason why it is necessary and significant to improve our teaching methods. Finally, it concludes with the processes and achievements of this decade.
StructSum: Summarization via Structured Representations

Abstractive text summarization aims at compressing the information of a long source document into a rephrased, condensed summary. Despite advances in modeling techniques, abstractive summarization models still suffer from several key challenges: (i) layout bias: they overfit to the style of training corpora; (ii) limited abstractiveness: they are optimized to copying n-grams from the source rather than generating novel abstractive summaries; (iii) lack of transparency: they are not interpretable. In this work, we propose a framework based on document-level structure induction for summarization to address these challenges. To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models. Our framework complements standard encoder-decoder summarization models by augmenting them with rich structure-aware document representations based on implicitly learned (latent) structures and externally-derived linguistic (explicit) structures. We show that our summarization framework, trained on the CNN/DM dataset, improves the coverage of content in the source documents, generates more abstractive summaries by generating more novel n-grams, and incorporates interpretable sentence-level structures, while performing on par with standard baselines.
Distribution Embedding Network for Meta-Learning with Variable-Length Input

We propose Distribution Embedding Network (DEN) for meta-learning, which is designed for applications where both the distribution and the number of features could vary across tasks. DEN first transforms features using a learned piecewise linear function, then learns an embedding of the underlying data distribution after the transformation, and finally classifies examples based on the distribution embedding. We show that the parameters of the distribution embedding and the classification modules can be shared across tasks. We propose a novel methodology to mass-simulate binary classification training tasks, and demonstrate that DEN outperforms existing methods in a number of test tasks in numerical studies.
Medical image reconstruction using compressible latent space invertible networks

Many modern medical imaging systems are computed in nature and need computational reconstruction to form an image. When the acquired measurements are insufficient to uniquely determine the sought-after object, prior knowledge about the object needs to be utilized in order to successfully recover an image. An emerging line of research involves the use of deep generative models such as generative adversarial networks (GANs) as priors in the image reconstruction procedure. However, when GANs are employed, reconstruction of images outside the range of the GAN leads to errors that result in realistic but false image estimates. To address this issue, an image reconstruction framework is proposed that is formulated in the latent space of an invertible generative model. A novel regularization strategy is introduced that takes advantage of the architecture of certain invertible neural networks (INNs). To evaluate the performance of the proposed method, numerical studies of reconstructing images from stylized MRI measurements are performed. The method outperforms classical reconstruction methods in terms of traditional image quality metrics and is comparable to a state-of- the-art adaptive GAN based framework, while benefiting from a deterministic procedure and easier tuning of the regularization parameters.
Empower Your Team with NetSuite-Integrated Mobile Field Service

Get the power of NetSuite into the hands of your dispatchers and field techs with a NetSuite-integrated field service mobile app As a NetSuite ...
Library: Using the EBSCO Mobile App: Android Device Installation

Make progress on your research anytime, anywhere with EBSCO’s mobile app! This guide shows you how to find, install and start using the free EBSCO mobile app. How to install the free EBSCO mobile app on Apple devices.
Modelling Reflectance Spectra of Muscovite as Function of Aluminium Content and Grain Size Using Hapke Model

Muscovite is an important mineral commonly found in hydrothermal systems of Earth and Mars. Reflectance spectra of muscovite has several diagnostic absorption features in wavelength range 0.4-2.5 μm. The absorption feature near 2.20 μm is sensitive to alumina content variations. In this study, we use reflectance spectra of powdered muscovite and geochemical datasets to quantify the relationship between spectral shift and alumina content. Imaginary index of refraction (k) was derived from reflectance spectra and a linear model was proposed relating alumina content and k. Reflectance spectra of muscovite was modelled for varying alumina content and grain size using Hapke model. Modelled spectra shows shift in wavelength position of 2.20 μm with varying alumina content and deepening of absorption features with increase in grain size. The results shall be helpful in interpretation of reflectance spectra acquired from space borne and airborne platforms.
“Giving the Meaning” as a Social Practice on Pantelleria: The Metasemantics of Attunement

Author(s): La Mattina, Nicco Amedeo | Advisor(s): Duranti, Alessandro | Abstract: In this paper, I understand metasemantic practices (usually understood to give the meaning of an expression) as social practices that unfold interactionally according to pragmatic criteria. By examining verbal interactions centering on the toponymy and dialect of the Sicilian island Pantelleria, I show that metasemantic statements may (a) exhibit Pantelleria as an Orientalized “tourist place” through Arabic etymologies or (b) disclose it as a “place-world” through attunement to the singularity of what William James called the “tissue of experience”. By attending both formally and pragmatically to metasemantic constructions, I show that beyond their propositional content (expressing the equivalence of at least two expression-forms), translations may encode affective attunement and participate in place-making.
A short survey on face recognition

Face Recognition has received a great deal of attention over the past few years and has become one of the most researched and spoken topics. It is a kind of automated biometric distinguishing approach that recognizes an individual based on their facial characteristics. The main aim of face reorganization is to implement the system for a particular face and distinguish it from a large number of stored faces with some real-time variations as well. Face recognition is in trend these days, the main reason being its efficiency and vast applications in day to day life. *Most* all the Telecom companies provide an option to unlock the phones by recognizing the face, which is a time saver and gives protection from theft as well. There are many more such applications of this technique that will be discussed in this paper along with the methods used for face recognition.
GALILEO@UGA Subject Guides: Using Electronic Library Resources Remotely: Saving to View Later

Tips for using electronic library resources from remote locations and common solutions for access errors.
Setting up the Blog Site

This is the first in a series of eight chapters in which you will learn how to create a personal blog site with MDX, a format that lets you use React components in markdown files. Here, we’ll complete the basic setup for the project, and create different pages, the navbar, and a footer with styling.
Anytime Diagnosis for Reconfiguration

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FlexDiag helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.
A Study on Performance of Solar Thermal System for Domestic Hot Water According to the Weather Conditions and Feedwater Temperatures at Different Locations in Korea

The purpose of this study is to analyze the performance of solar thermal system according to regional weather conditions and feedwater temperature. The performance analysis of the system was carried out for the annual and winter periods in terms of solar fraction, collector efficiency and it’s optimal degree. The system is simulated using TRNSYS program for 6 cities, Seoul, Incheon, Gangneung, Mokpo, Gwangju, and Ulsan. Simulation results prove that the solar fraction of the system varies greatly from region to region, depending on weather conditions and feedwater temperatures. Monthly average solar fraction for winter season from November to February, a time when heat energy is most required, indicated that the highest is 73.6% in Gangnueng and the lowest is 56.9% in Seoul. This is about 30% relative difference between the two cities. On the other hand, the collector efficiency of the system for all six cities was analyzed in the range between 40% and 42%, indicating small difference compare to the solar fraction. The annual average solar fraction is rated the highest at 40 collector degree, while monthly average solar fraction during winter season is rated at 60 degree.
Pengaruh Model Pembelajaran Kooperatif Tipe Course Review Horay (CRH) Berbantuan Multimedia Presentasi Terhadap Hasil Belajar Siswa Kelas VIII Di SMP

Learning putcomes are low affected by several problems conducted in learning activities, namely the lows of enthusiasm the students to participate in learning in the classroom, lack to variation learning model in teaching and learning activities, the use of media that is rarely used, the lack of activity and the absence of chance for learners to participate resulting in less active students in conducting learning activities. The goal is to determine the effect of the cooperative model type course review horay (CRH) powered by multimedia presentations on student learning outcomes. This research uses literature study. Where The type of data used is secondary data. Data collection techniques are carried out by analyzing and analyzing books and journals with literary rules that are closely related to the course review horay model. The data analysis technique is qualitative analysis. These results indicate that the model CRH aided by multimedia presentations is effective for increasing student activity, student collaboration, enthusiastic students, the competence of the students’ knowledge, confidence and learning achievement, and creating a class atmosphere becomes lively and fun so that with this will have a significant influence on student learning outcomes.
THE USE U-DICTIONARY TO INCREASING THE STUDENT’ VOCABULARY

The lack of speaking competence is influenced by several factors. One of them is the English teachers haven’t used the modern media in teaching process which can motivate the students to learn English actively. The students of SMA Negeri 1 Sei Kepayang Kiri have low motivation in speaking English whether during the English learning proses or speaking in public. Therefore, the researcher conducted a study in which the researchers apply the use of U-Dictionary as a learning media to increase the students’ vocabulary in teaching speaking. U- Dictionary is one of English offline dictionary that can be downloaded by every student in every grade. It can be found at playstore, one of application in android phone. It is easy to use either to increase the vocabulary or to improve the ability in pronouncing English word because U- Dictionary not only provides the meaning of a word but also giving the spell of a word. With U-Dictionary, Everyone not only can translate words and short phrases, but also practice the skills in any language. U- Dictionary can be used as an effective learning media to increase the students’ vocabulary. From the main tab, everyone can quickly access short tests and mini games to practice any language wanted. By using U- Dictionary as a learning media in teaching speaking particularly increasing the vocabulary, the students’ vocabulary has increased significantly. Based on the pre-test result, it was showed that the students only master about fifty words for the four categories; noun, adjective, verb and adverb. It is not a good condition where the senior high schools students only master about fifty words. It is equal to primary students’ vocabulary. During the research, the students got many vocabularies at least one hundred new words based on the category; noun, adjective, verb and adverb
Hallucinating Scenes

The goal of this work is to synthesize high-resolution panoramic images of a scene. We propose a system that takes low-resolution panoramic video frames as the input, extracts geometric information as the reference, and produces high-resolution panoramas of the scene as the output. The deep learning module of our system benefits from the structure-from motion geometry and learns to decouple the content and the style of an input low-resolution patch for hallucinating its high-resolution version. We show that the high-resolution panoramas generated by our system achieve a better quality of detail and color enhancement than those produced by existing super-resolution and style-transfer methods.
Ship Target Image Segmentation Algorithm Based on Fuzzy Level Set

Abstract The effective recognition of ship target is very important for the protection of ocean rights, and the image segmentation is the precondition of the ship target recognition. To solve the two problems existing in the traditional level set graph, namely the increase of image segmentation and the increase of computation, the Fuzzy C-Means (FCM) and level set are introduced. A new algorithm for image segmentation based on fuzzy level set is proposed in this paper. The algorithm can use spatial fuzzy clustering to realize the parameter estimation and evolutionary adjustment. The algorithm is applied to optical remote sensing ship target image segmentation in complex sea environment, and the effectiveness of the proposed algorithm is verified by experiment results.
Hard-label Manifolds: Unexpected advantages of query efficiency for finding on-manifold adversarial examples

Designing deep networks robust to adversarial examples remains an open problem. Likewise, recent zeroth order hard-label attacks on image classification tasks have shown comparable performance to their first-order alternatives. It is well known that in this setting, the adversary must search for the nearest decision boundary in a query-efficient manner. State-of-the-art (SotA) attacks rely on the concept of pixel grouping, or super-pixels, to perform efficient boundary search. It was recently shown in the first-order setting, that regular adversarial examples leave the data manifold, and on-manifold examples are generalization errors. In this paper, we argue that query efficiency in the zeroth-order setting is connected to the adversary's traversal through the data manifold. In particular, query-efficient hard-label attacks have the unexpected advantage of finding adversarial examples close to the data manifold. We empirically demonstrate that against both natural and robustly trained models, an efficient zeroth-order attack produces samples with a progressively smaller manifold distance measure. Further, when a normal zeroth-order attack is made query-efficient through the use of pixel grouping, it can make up to a two-fold increase in query efficiency, and in some cases, reduce a sample's distance to the manifold by an order of magnitude.
EU-Citizen.Science Platform launched

The new EU-Citizen.Science digital platform for anyone interested in citizen science has just been launched. It gathers best practice resources from the ...
Geometric and topological inference by Jean-Daniel Boissonnat, Frédéric Chazal and Mariette Yvinec, pp. 233, £28.99 (paper), ISBN 978-1-10841-089-2, Cambridge University Press (2018)

Geometric and topological inference by Jean-Daniel Boissonnat, Frédéric Chazal and Mariette Yvinec, pp. 233, £28.99 (paper), ISBN 978-1-10841-089-2, Cambridge University Press (2018) - Volume 105 Issue 562
Universal Education

Widespread education has been linked to lower levels of corruption. This chapter shows a surprisingly strong correlation between levels of mass education in 1870 and corruption levels in 2010 for 78 countries. This correlation remains strong when controlling for change in the level of education, GDP/capita, and democracy. A model for the causal mechanism between universal education and control of corruption is presented. Firstly, societies with more equal education gave citizens more opportunities and power for opposing corruption. Secondly, universal education leads to other factors that seem to promote decreased corruption, such as a high level of generalized trust, and a sense nationalism leading people to identify with an entire country rather than with specific sects, clans, or ethnic/religious groups. Thirdly, losing wars seem to have led elites to strive for increased state capacity and was a strong motivation for the introduction of universal education in many countries.
예비과학교사의 정체성에 대한 내러티브 탐구: 이공계와 인문계라는 두 문화를 중심으로

This study sought to understand the biology education major students in a narrative way, focusing on two cultures, science and the humanities. Seventeen students majoring in biology education participated in this study. Participants had time to describe and discuss their lived experiences related with their academic track choice (Science vs Humanities) and current identity as STEM students. Their descriptions were analyzed by two researchers. In their narrative, individual backgrounds for choosing the science track appeared to be diverse. In common, however, two cultures played a role as a very solid system in their experiences, and for this reason, rather than critically thinking about the two academic track systems, they thought about which was more suitable among the two cultures. After choosing the science track, they had been thinking and defining themselves as a science person without doubt, but after studying various fields that they experienced as a biology education major and critical consideration of the two cultures system made them reflect on their thoughts of their identity within the two cultures. As a result, the participants shared their lived stories together and re-constructed their current identities. Based on the results, we suggest the necessity of education for science teachers to help pre-service science teachers construct diverse identities with flexible perspectives of the two cultures.