Reviewerly/exHarmony huggingface dataset:
Deep Landscape Features for Improving Vector-borne Disease Prediction

The global population at risk of mosquito-borne diseases such as dengue, yellow fever, chikungunya and Zika is expanding. Infectious disease models commonly incorporate environmental measures like temperature and precipitation. Given increasing availability of high-resolution satellite imagery, here we consider including landscape features from satellite imagery into infectious disease prediction models. To do so, we implement a Convolutional Neural Network (CNN) model trained on Imagenet data and labelled landscape features in satellite data from London. We then incorporate landscape features from satellite image data from Pakistan, labelled using the CNN, in a well-known Susceptible-Infectious-Recovered epidemic model, alongside dengue case data from 2012-2016 in Pakistan. We study improvement of the prediction model for each of the individual landscape features, and assess the feasibility of using image labels from a different place. We find that incorporating satellite-derived landscape features can improve prediction of outbreaks, which is important for proactive and strategic surveillance and control programmes.
Thoreau’s “The Bean-Field”

Research Article| February 01 2019 Thoreau’s “The Bean-Field” John T. Price John T. Price John T. Price is the author of three memoirs, including Man Killed by Pheasant and Other Kinships, and is editor of the Tallgrass Prairie Reader. A recipient of an NEA fellowship, his nonfiction has most recently appeared in Orion, Fourth Genre, Brevity, Terrain.org, and How We Speak to One Another: An Essay Daily Reader. He teaches at the University of Nebraska at Omaha, where he directs the English Department’s Creative Nonfiction Writing Program. Search for other works by this author on: This Site Google Fourth Genre: Explorations in Nonfiction (2019) 21 (1): 195–200. https://doi.org/10.14321/fourthgenre.21.1.0195 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation John T. Price; Thoreau’s “The Bean-Field”. Fourth Genre: Explorations in Nonfiction 1 February 2019; 21 (1): 195–200. doi: https://doi.org/10.14321/fourthgenre.21.1.0195 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveMichigan State University PressFourth Genre: Explorations in Nonfiction Search Advanced Search The text of this article is only available as a PDF. © Michigan State University Board of Trustees Article PDF first page preview Close Modal You do not currently have access to this content.
A Model of Privacy Preserving in Dynamic Set-valued Data Re-publication

A model of privacy preserving in dynamic set-valued data re-publication is studied in this paper. Dynamic data in most practical applications may be re-published after updating, and the sensitive information of which may confront the risk of being exposed by adversary using historical publish results. A novel k-preserving model is proposed to protect data privacy from being exposed by continuing using transactional k-anonymity and maintaining the diversity and continuity of sensitive elements in the dataset during re-publication. An anonymous algorithm is also proposed to reduce information loss of the anonymous result by integrating local generalization with suppression technique. Real-world datasets are used in the experiment, the results and evaluations demonstrate that the approach in this paper can prevent privacy disclosure effectively and acquire publishing result with better availability.
R Guides to Statistics and Graphics

Abstract R. Allan Reese reviews three textbooks for users of the statistical programming language, R
Destination Korea: Abroad Study Preparation Trends of International Students

As a result of recent government policy, an increasing number of international students are attracted to South Korean universities (Kennedy, 2018). This mixed methods study examined how international students obtain information about the universities in South Korea, their motivations, priorities, and challenges, during their preparation for study abroad when they are in their home countries. We found that word of mouth has been the most used source (usually in the form of email or phone conversations). While university websites are used to obtain specific information such as programs for scholarships and admission guidelines, websites alone (without preceded by a personal recommendation)do not play a big role on a student's decision. Offerings of scholarships, financial aid and /or assistantships have had a positive effect in attracting students. Students are self-motivated to pursue higher education to obtain better professional opportunities. Implications of the results on admission strategies are offered at the end.
YANG Data Model for IS-IS SRv6

This document defines a YANG data model that can be used to configureand manage IS-IS SRv6 [I-D.bashandy-isis-srv6-extensions].
LibGuides: GOBI: How to request titles for purchase (For faculty): Details about the GOBI Notifications page

A guide for History & Political Science faculty on requesting books through GOBI. Faculty in other subject areas should consult with their liaison.
Constructing Inductive-Inductive Types in Cubical Type Theory

Abstract Inductive-inductive types are a joint generalization of mutual inductive types and indexed inductive types. In extensional type theory, inductive-inductive types can be constructed from inductive types, and this construction has been conjectured to work in intensional type theory as well. In this paper, we show that the existing construction requires Uniqueness of Identity Proofs, and present a new construction (which we conjecture generalizes) of one particular inductive-inductive type in cubical type theory, which is compatible with homotopy type theory.
A Feature Based Methodology for Variable Requirements Reverse Engineering

In the past years, software reverse engineering dealt with source code understanding.Nowadays, it is levered to software requirements abstract level, supported by feature model notations, language independent, and simpler than the source code reading.The recent relevant approaches face the following insufficiencies: lack of a complete integrated methodology, adapted feature model, feature patterns recognition, and Graph based slicing.This work aims to provide some solutions to the above challenges through an integrated methodology.The following results are unique.Elementary and configuration features are specified in a uniform way by introducing semantics specific attributes.The reverse engineering supports feature pattern recognition and requirements feature model graph-based slicing.The slicing criteria are rich enough to allow answering questions of software requirements maintainers.A comparison of this proposed methodology, based on effective criteria, with the similar works, seems to be valuable and competitive: the enrichment of the feature model and feature pattern recognition were never approached and the proposed slicing technique is more general, effective, and practical.
Defogging Kinect: Simultaneous Estimation of Object Region and Depth in Foggy Scenes

Three-dimensional (3D) reconstruction and scene depth estimation from 2-dimensional (2D) images are major tasks in computer vision. However, using conventional 3D reconstruction techniques gets challenging in participating media such as murky water, fog, or smoke. We have developed a method that uses a time-of-flight (ToF) camera to estimate an object region and depth in participating media simultaneously. The scattering component is saturated, so it does not depend on the scene depth, and received signals bouncing off distant points are negligible due to light attenuation in the participating media, so the observation of such a point contains only a scattering component. These phenomena enable us to estimate the scattering component in an object region from a background that only contains the scattering component. The problem is formulated as robust estimation where the object region is regarded as outliers, and it enables the simultaneous estimation of an object region and depth on the basis of an iteratively reweighted least squares (IRLS) optimization scheme. We demonstrate the effectiveness of the proposed method using captured images from a Kinect v2 in real foggy scenes and evaluate the applicability with synthesized data.
A Framework for Merging Possibilistic Knowledge Bases

Knowledge base merging is one of active research fields with a large range of applications in Artificial Intelligence. Most of the works in this research field has a lot of restrictions such as in the centralized approach, drowning effect, it is difficult to apply to interactive systems such as multi-agent systems as well as omitting knowledge in the merging process. The purpose of this paper is to focus on the integration of possibilistic knowledge bases in the propositional sense in propositional language. The integration is done through the special possibility distribution of possibilistic knowledge bases. To solve that problem, we introduce a new powerful argumentation framework for merging knowledge bases. In order to model this argument, firstly, the source knowledge bases of each agent are ranked in order of priority and the merging of prior knowledge bases into a priority knowledge base and then infer the final knowledge. An axiomatic model, including a set of rational and intuitive postulates is interested and discussed so that the merging result of knowledge bases needs to be satisfied.
How Blockchain-based Solutions Impact the Healthcare Sector

The Startup Digest section introduces startups and grassroots initiatives from around the world that push the boundaries of emerging technologies.Most conversations around emerging technologies are stuck in silos and are quite hyped, making it hard to understand their actual impact on businesses, society and governance.The Startup Digests aim to demystify what is happening on the ground by establishing a discourse via case studies and interviews with startups and grassroots initiatives.Each edition will take a critical look on how these movements apply emerging technologies to achieve a specific purpose -facilitating a discourse that makes the (new) thinking, the approach and potential impact become more tangible.This edition takes a closer look at how blockchain-based solutions affect the healthcare industry.Three case studies have been selected to exemplify how blockchain can potentially transform healthcare supply chain, patient data management, and access to health care.Each case study will be critically analysed to identify the potentials, challenges and risks.
Smart glove for hand gesture recognition

Human beings have a natural ability to see, listen and interact with their external world. Unfortunately, there are some people who are differently abled and do not have the ability to use their senses to the best extent possible. Such people depend on other means of communication like sign language. This presents a major roadblock for people in the hearing and speech impairment communities when they try to engage in interaction with others who are unfamiliar with the sign language, especially in their educational, social and professional environments. Therefore, it is necessary to have a gesture recognition or sign language detection system to bridge this communication gap. The main aim of the proposed system is to develop a smart glove for real-time gesture recognition using IoT. A mobile application is used as an interface to convert gestures into speech or voice output.
Fake Event Detection Using Web Resources

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
EFFORTS TO IMPROVE THE ABILITY OF READING EARLY AGE CHILDREN THROUGH THE METHOD OF SINGING

Language is a very important tool in the child's life, because with the language of children can communicate with others, the language aspect developed is reading in early childhood. Early reading can be said of the early ability to pass the child in the process of mastering the ability to read thoroughly. This encourages researchers to conduct a study on efforts to improve early childhood reading skills in Paud Sri Rejeki in Rw 06 Rt 03 Setiamanah village, Cimahi district, Central Cimahi city. The research is based on the problems found in Paud Sri Rejeki Rw 06 group B that is the ability to read the children who mostly are in the less category. The purpose of this research is to know the ability of children to read early before, during learning process, and result of reading ability of early child after using method of singing. The method used is descriptive study with qualitative approach. Subjects in this study were students Paud Sri Rejeki Rw 06 Cimahi which amounted to 5 people. The data collected is data of observation, interview and documentation study. The results showed that the use of singing method can improve reading ability of early child so that children can recognize letters, recognize words, and know the sentence. In other words, the method of singing can increase interest in reading early childhood.
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.
Prototype Pemblokir Gambar Pornografi Menggunakan Algoritma Viola and Jones

The spread of pornographic content extends very quickly through internet media. Various types of porn sites increase dramatically every day. Various prevention efforts to overcome the spread of pornographic content have been carried out. Starting from URL blocking, text filtering to be more specific directly to pornographic images through skin detection. However, all of these methods have various weaknesses. An effective step to reduce the spread of pornographic images is to block the breast area that is characteristic of the pornographic images. In this study a breast area blocking prototype was developed on pornographic images using the Viola and Jones algorithm.. From the test results obtained a precision value of 82.61%, 77.55% recall and accuracy of 78.26%.
Experimental Realization of an Innovative Phase-Stable Bulk-Optic Scheme for Quantum Walks

We present a bulk-optics setup for discrete quantum walks, based on a novel multipass displaced Sagnac geometry. It is phase stable, reconfigurable and allows to meausure the output radiation at each step. The experimental results of both ordered and disordered one-particle evolutions are reported.
KEMAS ULANG KRITERIA DAN PEMANFAATAN SISTEM PENDUKUNG KEPUTUSAN DALAM MELAKUKAN PEMILIHAN PUSTAKAWAN BERPRESTASI (STUDI KASUS: UPT PERPUSTAKAAN UNIVERSITAS ANDALAS)

&lt;p class="Pa9"&gt;The best user award is one of the awards that given to increase visitors interest to visit library. During this time, UPT Central Library Andalas University, and most of the other libraries only used instrument of the transaction number of borrowing book as the criteria for selection. The development of library services and the utilization of information technology has demanded a review of the best selection system. In addition to the number of lending transactions, it is necessary to add the criteria of number of visits, delay returning of borrowing and the contribution of the librarian towards the development of the library. The system involved 20 candidate based on the number of borrowing transaction and the visitation number. They were observed by the criteria delay of returning number, and scientific writing about library. Result of assessment from each alternative which were converted into Likert Scale in decision matrix form. The MCDM model was used for deciding the assessment result. This model is used to determine the final value of each alternative with the help of TOPSIS method. This method is used to determine the criteria weight and calculate the value of alternative preferences. The best user is awarded by the highest references value.&lt;/p&gt;
25th Workshop on Logic, Language, Information and Computation (WoLLIC 2018)

Workshop on Logic, Language, Information and Computation (WoLLIC) was held in Bogotá, Colombia, 24–27 July 2018, in the Departamento de Matemáticas of the Universidad de los Andes. WoLLIC (http://wollic.org) is a series of workshops, which started in 1994 with the aim of fostering interdisciplinary research in pure and applied logic. The idea is to have a forum that is large enough in the number of possible interactions between logic and the sciences related to information and computation. and yet is small enough to allow for concrete and useful interaction among participants. Previous versions were held at Recife (Pernambuco, Brazil) in 1994 and 1995, Salvador (Bahia, Brazil) in 1996, Fortaleza (Ceará, Brazil) in 1997, São Paulo (Brazil) in 1998, Itatiaia (Rio de Janeiro, Brazil) in 1999, Natal (Rio Grande do Norte, Brazil) in 2000, Brasília (Distrito Federal, Brazil) in 2001, Rio de Janeiro (Brazil) in 2002, Ouro Preto (Minas Gerais, Brazil) in 2003, Fontainebleau (France) in 2004, Florianópolis (Santa Catarina, Brazil) in 2005, Stanford (CA, USA) in 2006, Rio de Janeiro in 2007, Edinburgh in 2008, Tokyo in 2009, Brasília in 2010, Philadelphia in 2011, Buenos Aires in 2012, Darmstadt in 2013, Valparaiso in 2014, Bloomington (IN) in 2015, Puebla in 2016, London in 2017. It is planned that the meeting will take place in: 2019 in Utrecht, 2020 in Arequipa, 2021 in Chennai (tbc).
Predicting Early Reviews for Effective Product Marketing on E-Commerce Websites

The percentage of purchasing products by the user has been increased drastically through web. Users even have the facility of sharing their thoughts about the particular product on web in the form of reviews, blogs, comments etc. Many users read review information given on web to take decisions for buying products. Some users may give the reviews for hyping the sale of the product or to decrease the sale. This may confuse the customers who rely on the reviews to buy a product. So, there is a need to find the honest reviews and remove fake reviews that are added by malicious or fraud user. The proposed system comes up with the solution for this problem. Leading events has been used to find the time interval between the reviews. The proposed system mines the active periods such as leading sessions to accurately locate the hierarchical fraud. These leading sessions can be useful for detecting the local anomaly instead of global anomaly of product reviews. After this to analyze the rating, reviews and hierarchy of the product we examine three facts, they are rating based facts, review based facts and hierarchy facts. In addition, we propose an optimization-based aggregation method to integrate all the facts for fraud detection. The evaluations of this optimization are done on synthetic dataset that are collected. The classified and summarized product review information helps web users to understand review contents easily in a short time.
Manifold Optimization Assisted Gaussian Variational Approximation

Gaussian variational approximation is a popular methodology to approximate posterior distributions in Bayesian inference especially in high dimensional and large data settings. To control the computational cost while being able to capture the correlations among the variables, the low rank plus diagonal structure was introduced in the previous literature for the Gaussian covariance matrix. For a specific Bayesian learning task, the uniqueness of the solution is usually ensured by imposing stringent constraints on the parameterized covariance matrix, which could break down during the optimization process. In this paper, we consider two special covariance structures by applying the Stiefel manifold and Grassmann manifold constraints, to address the optimization difficulty in such factorization architectures. To speed up the updating process with minimum hyperparameter-tuning efforts, we design two new schemes of Riemannian stochastic gradient descent methods and compare them with other existing methods of optimizing on manifolds. In addition to fixing the identification issue, results from both simulation and empirical experiments prove the ability of the proposed methods of obtaining competitive accuracy and comparable converge speed in both high-dimensional and large-scale learning tasks.
Do Fundamentals Drive Cryptocurrency Prices

We examine if two fundamental blockchain characteristics affect cryptocurrency prices. They are computing power (hashrate) and network (number of users), which are related to blockchain trustworthiness and transaction benefits. We find a significant long-run relationship between these characteristics and the prices of prominent cryptocurrencies. We also document that cryptocurrency returns are exposed to fundamental-based risk factors related to aggregate computing power and network, even after controlling for Bitcoin return and cryptocurrency momentum. In out-of-sample tests, the computing power and network factors can explain the return variation of a broad set of cryptocurrencies, whose fundamentals are not included in the factors.