bluuebunny/crossref_metadata_2025 huggingface dataset:
Desempenho dos Núcleos Ampliados de Saúde da Família na avaliação do PMAQ-AB
Authors: Pinto Alexandre Ramiro , Nascimento Débora Dupas Gonçalves , Nichiata Lúcia Yasuko Izumi
Published: August 2019

RESUMO Objetivo Analisar o resultado do desempenho dos Núcleos Ampliados de Saúde da Família do estado de São Paulo, na dimensão Organização do Processo de Trabalho, na Avaliação do PMAQ-AB, segundo o Índice Paulista de Responsabilidade Social. Método Estudo transversal, descritivo, exploratório, com abordagem quantitativa, baseado nos dados do 2º ciclo do Programa Nacional de Melhoria do Acesso e da Qualidade da Atenção Básica. Foram analisadas por frequência simples 149 equipes, de 47 municípios, distribuídas em cinco agrupamentos, de acordo com o Índice Paulista de Responsabilidade Social. Resultados As equipes do agrupamento quatro (municípios com baixa riqueza e indicadores sociais intermediários) alcançaram desempenho satisfatório e muito satisfatório (90,9%). As equipes dos municípios do agrupamento dois (alto índice de riqueza e indicadores sociais insatisfatórios) tiveram pior desempenho; as equipes do município de São Paulo obtiveram o maior percentual de desempenho satisfatório e muito satisfatório (95,8%). Conclusão As equipes do município de São Paulo (alto índice de riqueza e indicadores sociais insatisfatórios) e as equipes dos municípios do agrupamento quatro (baixa riqueza e indicadores sociais intermediários) foram as que alcançaram melhor desempenho.
Impacto das infecções relacionadas à assistência à saúde nos custos da hospitalização de crianças
Authors: Leoncio Jackeline Martins , Almeida Vanessa Fraga de , Ferrari Rosângela Aparecida Pimenta , Capobiango Jaqueline Dario , Kerbauy Gilselena , Tacla Mauren Teresa Grubisich Mendes
Published: August 2019

RESUMO Objetivo Avaliar o impacto das Infecções Relacionadas à Assistência à Saúde no custo da hospitalização de crianças. Método Estudo de coorte, prospectivo, quantitativo, envolvendo crianças admitidas nas Unidades de Internação e de Terapia Intensiva Pediátrica de um hospital universitário público. Os dados foram analisados por meio do software SPSS por distribuição de frequências, medidas de tendência central e dispersão. Para todas as análises foi considerado o nível de significância estatística de p<0,05. Resultados A amostra foi composta de 173 crianças, destas, 18,5% desenvolveram infecção relacionada à assistência à saúde, que aumentou em 4,2 vezes (p<0,001) o custo da hospitalização. Maior impacto de custo foi observado entre pacientes com dois ou mais sítios infecciosos (R$ 81.037,57, p=0,010) e sepse (R$ 46.315,63 p<0,001). Crianças colonizadas por microrganismos multirresistentes, com prevalência de E. coli e A. baumannii ESBL, geraram custos maiores, R$ 35.206,15 e R$ 30.692,52, respectivamente. Conclusão As infecções relacionadas à assistência à saúde aumentaram significativamente os custos da hospitalização de crianças, em especial entre aquelas com mais de dois sítios infecciosos, que desenvolveram sepse e colonizadas por microrganismos multirresistentes.
Auditoria de práticas de precauções-padrão e contato em Unidade de Terapia Intensiva
Authors: Castro Alaíde Francisca de , Rodrigues Maria Cristina Soares
Published: August 2019

RESUMO Objetivo Avaliar a estrutura e a adesão às medidas de precauções-padrão e específicas dos profissionais de saúde em Unidade de Terapia Intensiva de hospital de ensino, no Distrito Federal. Método Estudo descritivo, transversal e prospectivo. Utilizou-se de questionário estruturado mediante observações que registraram as práticas dos profissionais com Equipamentos de Proteção Individual e indicações de precauções. Foi aplicado o teste Qui-quadrado, e calculado o p-valor . Resultados Participaram do estudo 52 profissionais, e foram observados 445 procedimentos assistenciais em 36 sessões de auditoria. A média da taxa de adesão ao uso de equipamentos foi de 72,72%, sendo 94,91% às luvas, 91,43% ao avental, 80% à máscara e 24,56% aos óculos de proteção. Quando não havia indicação e não foi utilizado o Equipamento de Proteção Individual, a média da taxa foi de 68,01%, sendo 30,77% em relação às luvas, 87,58% ao avental, 57,58% à máscara, e 96,13% aos óculos. As precauções de contato foram indicadas desnecessariamente em 35% dos pacientes. Conclusão Verificou-se boa adesão ao uso de luvas, avental e máscara, baixa adesão ao uso de óculos de proteção e uso desnecessário de máscaras e precauções de contato admissionais.
Ações de extensão universitária para translação do conhecimento sobre desenvolvimento infantil em creches: relato de experiência
Authors: Costa Priscila , Palombo Claudia Nery Teixeira , Silva Letícia Soares , Silva Mariah Tomé , Mateus Letícia Verônica de Jesus , Buchhorn Soraia Matilde Marques
Published: August 2019

RESUMO Objetivo Relatar a experiência de estudantes e docentes de um projeto de extensão universitária no planejamento e realização de oficinas educativas sobre desenvolvimento infantil com educadoras de creche. Método Relato de experiência alicerçado nos referenciais das Necessidades Essenciais das Crianças e na Teoria Bioecológica do Desenvolvimento Humano. Resultados Participaram das oficinas oito graduandas, duas docentes de Enfermagem de uma universidade pública e 24 educadoras de uma creche localizada no município de São Paulo, em região de alta vulnerabilidade social. As oficinas educativas abordaram os temas: fundamentos do desenvolvimento infantil, marcos do desenvolvimento nas áreas pessoal e social, linguagem, motor fino e grosseiro e promoveram o desenvolvimento infantil na creche. Como estratégias pedagógicas, foram utilizadas dinâmicas interativas, jogos didáticos e recursos midiáticos. As oficinas educativas favoreceram a translação do conhecimento sobre o desenvolvimento infantil e as necessidades essenciais das crianças. Conclusão As ações de extensão proporcionaram articulação entre a universidade e a comunidade por meio do compartilhamento de saberes entre profissionais da saúde e da educação visando à promoção do desenvolvimento infantil.
Atenção ao idoso: práticas de educação permanente do Núcleo de Apoio à Saúde da Família
Authors: Nogueira Iara Sescon , Acioli Sonia , Carreira Ligia , Baldissera Vanessa Denardi Antoniassi
Published: August 2019

RESUMO Objetivo Desvelar as práticas de Educação Permanente em Saúde desenvolvidas pelo Núcleo de Apoio à Saúde da Família na atenção ao idoso. Método Estudo qualitativo e exploratório-descritivo, desenvolvido em um município do estado do Paraná, com profissionais do Núcleo de Apoio à Saúde da Família. Os dados foram obtidos pela técnica de Grupo Focal e submetidos à Classificação Hierárquica Descendente utilizando o software IRaMuTeQ®. Os referenciais teórico-analíticos foram a Política Nacional de Educação Permanente em Saúde e a Teoria Dialógica. Resultados Participaram 46 profissionais. Surgiram cinco classes que permitiram desvelar que as práticas de educação permanente na atenção ao idoso ocorrem durante os momentos de discussão de casos, no matriciamento, nas visitas domiciliares, nos grupos operativos e no cotidiano do trabalho de modo informal. Conclusão As práticas de educação permanente desenvolvidas pelos profissionais na atenção ao idoso ocorrem em distintos momentos da atuação profissional e são permeadas pela prática.
Reconhecimento dos riscos no trabalho do Consultório na Rua: um processo participativo
Authors: Lima Ana Flávia Silva , Almeida Lenira Wanderley de Santos , Costa Laís de Miranda Crispim , Marques Emilly Souza , Lima Júnior Mário César Ferreira , Rocha Kely Regina da Silva Lima
Published: August 2019

RESUMO Objetivo Descrever o ambiente de trabalho e reconhecer os riscos ocupacionais a que a equipe do Consultório na Rua está exposta, bem como as medidas preventivas aplicáveis segundo a percepção dos profissionais integrantes da equipe. Método Pesquisa qualitativa, exploratória, descritiva, realizada com profissionais representantes das seis equipes de Consultório na Rua existentes em um estado do Nordeste brasileiro. As informações foram coletadas a partir da técnica de grupo focal, e os dados foram analisados à luz da análise temática. Resultados Participaram 14 profissionais. O ambiente de trabalho foi descrito como de tensões, dificuldades e vulnerabilidades, mas também de crescimento pessoal. Foram reconhecidos riscos ocupacionais ocasionados por fatores físicos, biológicos, químicos, de acidentes e psicossociais. As medidas de prevenção utilizadas foram predominantemente tecnologias leves. Sugeriu-se a inclusão de novos equipamentos de proteção individual. Conclusão A reflexão grupal sobre o ambiente e processo de trabalho permitiu o reconhecimento de seus riscos, dificuldades e desafios, demonstrando a necessidade de implementação de medidas de enfrentamento dos fatores identificados.
Sensibilização das mães de crianças com microcefalia na promoção da saúde de seus filhos
Authors: Santos Daniel Batista Conceição dos , Prado Lourivânia Oliveira de Melo , Silva Rivelaine Soares da , Silva Elaine Ferreira da , Cardoso Luana da Conceição Costa , Oliveira Cristiane da Costa Cunha
Published: August 2019

RESUMO Objetivo Relatar as experiências educativas das mães ou cuidadoras de crianças com microcefalia, desenvolvidas por equipe acadêmica na temática da promoção da saúde dessas crianças. Método Trata-se de relato de experiência vivenciado por alunos do curso de graduação em enfermagem, discentes e docentes do programa de Pós-Graduação em Saúde e Ambiente da Universidade Tiradentes sobre intervenções educativas realizadas em três unidades de referência no atendimento à criança com microcefalia no estado de Sergipe. A amostra do estudo foi de conveniência. Resultados Participaram da pesquisa 70 mães ou cuidadoras principais de crianças com diagnóstico confirmado de microcefalia, durante os meses de setembro a dezembro. Os eixos temáticos-teóricos selecionados para descrever as atividades foram promoção da alimentação saudável, importância do vínculo mãe e filho e estimulação precoce de crianças com microcefalia. Conclusão A experiência relatada demostrou a importância das estratégias educativas na promoção da saúde de crianças com microcefalia, proporcionando capacitação adicional às mães/cuidadoras para que estas ofereçam um cuidado holístico e humanizado a essas crianças.
Percepção das lideranças de enfermagem sobre a luta contra a precarização das condições de trabalho
Authors: Dias Midian Oliveira , Souza Norma Valeria Dantas de Oliveira , Penna Lucia Helena Garcia , Gallasch Cristiane Helena
Published: August 2019

RESUMO Objetivo Descrever e analisar, sob a percepção das lideranças de enfermagem, questões de gênero e socioeconômicas dos trabalhadores da categoria que interferem na luta contra a precarização das condições de trabalho. Método Estudo descritivo, exploratório, com abordagem qualitativa, realizado na ABEn, Coren, sessões Rio de Janeiro e SindEnfRJ, com líderes de entidades de classe, profissionais de cargos de liderança, incluindo diretoria ou presidência. Realizaram-se entrevistas individuais, e os dados foram tratados por meio da técnica de análise de conteúdo. Resultados Participaram do estudo 17 líderes de entidades de classe. Se nas falas predominância feminina, dupla jornada de trabalho, origem social e cultural, desvalorização profissional, reduzida participação em espaços de luta e questões burocráticas do trabalho como justificativas para o baixo envolvimento em disputas trabalhistas. Conclusão São necessárias parcerias entre a categoria e as entidades de classe para maior mobilização e combate a esse processo perverso, por mudanças na legislação, imprescindíveis para viabilizar a manutenção do emprego, a qualidade de vida do trabalhador e, consequentemente, a excelência na assistência prestada à população.
Construcción social de la violencia obstétrica en mujeres Tének y Náhuatl de México
Authors: Flores Yesica Yolanda Range , Ledezma Alexia Guadalupe Martínez , Ibarra Luis Eduardo Hernández , Acevedo Claudia Elena González
Published: August 2019

RESUMEN Objetivo Explorar la construcción social que sobre violencia obstétrica han elaborado mujeres Tének y Náhuatl de México. Método Estudio cualitativo-sociocrítico, mediante gupos focales se profundizó en las experiencias de parto de quienes vivieron un parto en el periodo 2015-2016. Resultados Participaron 57 mujeres. Mediante análisis de discurso se identificó que las participantes no poseen suficiente información sobre violencia obstétrica y/o derechos sexuales y reproductivos, lo que las imposibilita para asociar sus experiencias negativas al término legal “violencia obstétrica”. Sus discursos corresponden en su mayoría a lo que desde el marco legal se ha denominado “violencia obstétrica”, sin embargo, experiencias como el ayuno prolongado o el uso de tecnologías para la invasión de su intimidad fueron narradas como algo que conciben violento y que no se ha incorporado dentro del término legal. Conclusión Múltiples acciones que atentan contra los derechos humanos de las mujeres tienen lugar dentro de las salas de parto, la mayor parte no son identificadas por las usuarias, puesto que no han construido socialmente la imagen de la violencia obstétrica, ello no las hace menos susceptibles sin embargo, a sentirse agredidas y denigradas en sus partos.
Multigrid Backprojection Super–Resolution and Deep Filter Visualization
Authors: Michelini Pablo Navarrete , Liu Hanwen , Zhu Dan
Published: August 2019

We introduce a novel deep–learning architecture for image upscaling by large factors (e.g. 4×, 8×) based on examples of pristine high–resolution images. Our target is to reconstruct high–resolution images from their downscale versions. The proposed system performs a multi–level progressive upscaling, starting from small factors (2×) and updating for higher factors (4× and 8×). The system is recursive as it repeats the same procedure at each level. It is also residual since we use the network to update the outputs of a classic upscaler. The network residuals are improved by Iterative Back–Projections (IBP) computed in the features of a convolutional network. To work in multiple levels we extend the standard back– projection algorithm using a recursion analogous to Multi– Grid algorithms commonly used as solvers of large systems of linear equations. We finally show how the network can be interpreted as a standard upsampling–and–filter upscaler with a space–variant filter that adapts to the geometry. This approach allows us to visualize how the network learns to upscale. Finally, our system reaches state of the art quality for models with relatively few number of parameters.
A Layer-Based Sequential Framework for Scene Generation with GANs
Authors: Turkoglu Mehmet Ozgur , Thong William , Spreeuwers Luuk , Kicanaoglu Berkay
Published: August 2019

The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities. Therefore, it is a very challenging task to generate vivid scenes of similar complexity using computers. In this work, we present a scene generation framework based on Generative Adversarial Networks (GANs) to sequentially compose a scene, breaking down the underlying problem into smaller ones. Different than the existing approaches, our framework offers an explicit control over the elements of a scene through separate background and foreground generators. Starting with an initially generated background, foreground objects then populate the scene one-by-one in a sequential manner. Via quantitative and qualitative experiments on a subset of the MS-COCO dataset, we show that our proposed framework produces not only more diverse images but also copes better with affine transformations and occlusion artifacts of foreground objects than its counterparts.
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Authors: Zhou Hang , Liu Yu , Liu Ziwei , Luo Ping , Wang Xiaogang
Published: August 2019

Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech. This is a challenging task because face appearance variation and semantics of speech are coupled together in the subtle movements of the talking face regions. Existing works either construct specific face appearance model on specific subjects or model the transformation between lip motion and speech. In this work, we integrate both aspects and enable arbitrary-subject talking face generation by learning disentangled audio-visual representation. We find that the talking face sequence is actually a composition of both subject-related information and speech-related information. These two spaces are then explicitly disentangled through a novel associative-and-adversarial training process. This disentangled representation has an advantage where both audio and video can serve as inputs for generation. Extensive experiments show that the proposed approach generates realistic talking face sequences on arbitrary subjects with much clearer lip motion patterns than previous work. We also demonstrate the learned audio-visual representation is extremely useful for the tasks of automatic lip reading and audio-video retrieval.
Dynamic Capsule Attention for Visual Question Answering
Authors: Zhou Yiyi , Ji Rongrong , Su Jinsong , Sun Xiaoshuai , Chen Weiqiu
Published: August 2019

In visual question answering (VQA), recent advances have well advocated the use of attention mechanism to precisely link the question to the potential answer areas. As the difficulty of the question increases, more VQA models adopt multiple attention layers to capture the deeper visual-linguistic correlation. But a negative consequence is the explosion of parameters, which makes the model vulnerable to over-fitting, especially when limited training examples are given. In this paper, we propose an extremely compact alternative to this static multi-layer architecture towards accurate yet efficient attention modeling, termed as Dynamic Capsule Attention (CapsAtt). Inspired by the recent work of Capsule Network, CapsAtt treats visual features as capsules and obtains the attention output via dynamic routing, which updates the attention weights by calculating coupling coefficients between the underlying and output capsules. Meanwhile, CapsAtt also discards redundant projection matrices to make the model much more compact. We quantify CapsAtt on three benchmark VQA datasets, i.e., COCO-QA, VQA1.0 and VQA2.0. Compared to the traditional multi-layer attention model, CapsAtt achieves significant improvements of up to 4.1%, 5.2% and 2.2% on three datasets, respectively. Moreover, with much fewer parameters, our approach also yields competitive results compared to the latest VQA models. To further verify the generalization ability of CapsAtt, we also deploy it on another challenging multi-modal task of image captioning, where state-of-the-art performance is achieved with a simple network structure.
Calibrated Stochastic Gradient Descent for Convolutional Neural Networks
Authors: Zhuo Li’an , Zhang Baochang , Chen Chen , Ye Qixiang , Liu Jianzhuang , Doermann David
Published: August 2019

In stochastic gradient descent (SGD) and its variants, the optimized gradient estimators may be as expensive to compute as the true gradient in many scenarios. This paper introduces a calibrated stochastic gradient descent (CSGD) algorithm for deep neural network optimization. A theorem is developed to prove that an unbiased estimator for the network variables can be obtained in a probabilistic way based on the Lipschitz hypothesis. Our work is significantly distinct from existing gradient optimization methods, by providing a theoretical framework for unbiased variable estimation in the deep learning paradigm to optimize the model parameter calculation. In particular, we develop a generic gradient calibration layer which can be easily used to build convolutional neural networks (CNNs). Experimental results demonstrate that CNNs with our CSGD optimization scheme can improve the stateof-the-art performance for natural image classification, digit recognition, ImageNet object classification, and object detection tasks. This work opens new research directions for developing more efficient SGD updates and analyzing the backpropagation algorithm.
A Genetic Algorithm for Finding a Small and Diverse Set of Recent News Stories on a Given Subject: How We Generate AAAI’s AI-Alert
Authors: Eckroth Joshua , Schoen Eric
Published: August 2019

This paper describes the genetic algorithm used to select news stories about artificial intelligence for AAAI’s weekly AIAlert, emailed to nearly 11,000 subscribers. Each week, about 1,500 news stories covering various aspects of artificial intelligence and machine learning are discovered by i2k Connect’s NewsFinder agent. Our challenge is to select just 10 stories from this collection that represent the important news about AI. Since stories and topics do not necessarily repeat in later weeks, we cannot use click tracking and supervised learning to predict which stories or topics are most preferred by readers. Instead, we must build a representative selection of stories a priori, using information about each story’s topics, content, publisher, date of publication, and other features. This paper describes a genetic algorithm that achieves this task. We demonstrate its effectiveness by comparing several engagement metrics from six months of “A/B testing” experiments that compare random story selection vs. a simple scoring algorithm vs. our new genetic algorithm.
Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI
Authors: Mandal Atri , Malhotra Nikhil , Agarwal Shivali , Ray Anupama , Sridhara Giriprasad
Published: August 2019

Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution.The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of a classifier that is accurate is one of the main challenges, we also need to address the need of designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources.Our system has been deployed in production of three major service providers and currently assigning over 90,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of more than 50000 man-hours of effort per annum. Till date, our deployed system has already served more than 700,000 tickets in production.
Early-Stopping of Scattering Pattern Observation with Bayesian Modeling
Authors: Asahara Akinori , Morita Hidekazu , Mitsumata Chiharu , Ono Kanta , Yano Masao , Shoji Tetsuya
Published: August 2019

This paper describes a new machine-learning application to speed up Small-angle neutron scattering (SANS) experiments, and its method based on probabilistic modeling. SANS is one of the scattering experiments to observe microstructures of materials; in it, two-dimensional patterns on a plane (SANS pattern) are obtained as measurements. It takes a long time to obtain accurate experimental results because the SANS pattern is a histogram of detected neutrons. For shortening the measurement time, we propose an earlystopping method based on Gaussian mixture modeling with a prior generated from B-spline regression results. An experiment using actual SANS data was carried out to examine the accuracy of the method. It was confirmed that the accuracy with the proposed method converged 4 minutes after starting the experiment (normal SANS takes about 20 minutes).
Querying NoSQL with Deep Learning to Answer Natural Language Questions
Authors: Blank Sebastian , Wilhelm Florian , Zorn Hans-Peter , Rettinger Achim
Published: August 2019

Almost all of today’s knowledge is stored in databases and thus can only be accessed with the help of domain specific query languages, strongly limiting the number of people which can access the data. In this work, we demonstrate an end-to-end trainable question answering (QA) system that allows a user to query an external NoSQL database by using natural language. A major challenge of such a system is the non-differentiability of database operations which we overcome by applying policy-based reinforcement learning. We evaluate our approach on Facebook’s bAbI Movie Dialog dataset and achieve a competitive score of 84.2% compared to several benchmark models. We conclude that our approach excels with regard to real-world scenarios where knowledge resides in external databases and intermediate labels are too costly to gather for non-end-to-end trainable QA systems.
Probabilistic-Logic Bots for Efficient Evaluation of Business Rules Using Conversational Interfaces
Authors: Bockhorst Joseph , Conathan Devin , Fung Glenn M
Published: August 2019

We present an approach for designing conversational interfaces (chatbots) that users interact with to determine whether or not a business rule applies in a context possessing uncertainty (from the point of view of the chatbot) as to the value of input facts. Our approach relies on Bayesian network models that bring together a business rule’s logical, deterministic aspects with its probabilistic components in a common framework. Our probabilistic-logic bots (PL-bots) evaluate business rules by iteratively prompting users to provide the values of unknown facts. The order facts are solicited is dynamic, depends on known facts, and is chosen using mutual information as a heuristic so as to minimize the number of interactions with the user. We have created a web-based content creation and editing tool that quickly enables subject matter experts to create and validate PL-bots with minimal training and without requiring a deep understanding of logic or probability. To date, domain experts at a well-known insurance company have successfully created and deployed over 80 PLbots to help insurance agents determine customer eligibility for policy discounts and endorsements.
Anomaly Detection Using Autoencoders in High Performance Computing Systems
Authors: Borghesi Andrea , Bartolini Andrea , Lombardi Michele , Milano Michela , Benini Luca
Published: August 2019

Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states).We propose a novel approach for anomaly detection in HighPerformance Computing systems based on a Machine (Deep) Learning technique, namely a type of neural network called autoencoder. The key idea is to train a set of autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes and, after training, use them to identify abnormal conditions. This is different from previous approaches which where based on learning the abnormal condition, for which there are much smaller datasets (since it is very hard to identify them to begin with).We test our approach on a real supercomputer equipped with a fine-grained, scalable monitoring infrastructure that can provide large amount of data to characterize the system behaviour. The results are extremely promising: after the training phase to learn the normal system behaviour, our method is capable of detecting anomalies that have never been seen before with a very good accuracy (values ranging between 88% and 96%).
A Fast Machine Learning Workflow for Rapid Phenotype Prediction from Whole Shotgun Metagenomes
Authors: Carrieri Anna Paola , Rowe Will PM , Winn Martyn , Pyzer-Knapp Edward O.
Published: August 2019

Research on the microbiome is an emerging and crucial science that finds many applications in healthcare, food safety, precision agriculture and environmental studies. Huge amounts of DNA from microbial communities are being sequenced and analyzed by scientists interested in extracting meaningful biological information from this big data. Analyzing massive microbiome sequencing datasets, which embed the functions and interactions of thousands of different bacterial, fungal and viral species, is a significant computational challenge. Artificial intelligence has the potential for building predictive models that can provide insights for specific cutting edge applications such as guiding diagnostics and developing personalised treatments, as well as maintaining soil health and fertility. Current machine learning workflows that predict traits of host organisms from their commensal microbiome do not take into account the whole genetic material constituting the microbiome, instead basing the analysis on specific marker genes. In this paper, to the best of our knowledge, we introduce the first machine learning workflow that efficiently performs host phenotype prediction from whole shotgun metagenomes by computing similaritypreserving compact representations of the genetic material. Our workflow enables prediction tasks, such as classification and regression, from Terabytes of raw sequencing data that do not necessitate any pre-prossessing through expensive bioinformatics pipelines. We compare the performance in terms of time, accuracy and uncertainty of predictions for four different classifiers. More precisely, we demonstrate that our ML workflow can efficiently classify real data with high accuracy, using examples from dog and human metagenomic studies, representing a step forward towards real time diagnostics and a potential for cloud applications.
DeBGUer: A Tool for Bug Prediction and Diagnosis
Authors: Elmishali Amir , Stern Roni , Kalech Meir
Published: August 2019

In this paper, we present the DeBGUer tool, a web-based tool for prediction and isolation of software bugs. DeBGUer is a partial implementation of the Learn, Diagnose, and Plan (LDP) paradigm, which is a recently introduced paradigm for integrating Artificial Intelligence (AI) in the software bug detection and correction process. In LDP, a diagnosis (DX) algorithm is used to suggest possible explanations – diagnoses – for an observed bug. If needed, a test planning algorithm is subsequently used to suggest further testing. Both diagnosis and test planning algorithms consider a fault prediction model, which associates each software component (e.g., class or method) with the likelihood that it contains a bug. DeBGUer implements the first two components of LDP, bug prediction (Learn) and bug diagnosis (Diagnose). It provides an easy-to-use web interface, and has been successfully tested on 12 projects.
Automatic Generation of Chinese Short Product Titles for Mobile Display
Authors: Gong Yu , Luo Xusheng , Zhu Kenny Q. , Ou Wenwu , Li Zhao , Duan Lu
Published: August 2019

This paper studies the problem of automatically extracting a short title from a manually written longer description of Ecommerce products for display on mobile devices. It is a new extractive summarization problem on short text inputs, for which we propose a feature-enriched network model, combining three different categories of features in parallel. Experimental results show that our framework significantly outperforms several baselines by a substantial gain of 4.5%. Moreover, we produce an extractive summarization dataset for Ecommerce short texts and will release it to the research community.
A Machine Learning Suite for Machine Components’ Health-Monitoring
Authors: Hasani Ramin , Wang Guodong , Grosu Radu
Published: August 2019

This paper studies an intelligent technique for the healthmonitoring and prognostics of common rotary machine components, with regards to bearings in particular. During a run-to-failure experiment, rich unsupervised features from vibration sensory data are extracted by a trained sparse autoencoder. Then, the correlation of the initial samples (presumably healthy), along with the successive samples, are calculated and passed through a moving-average filter. The normalized output which is referred to as the auto-encoder correlation based (AEC) rate, determines an informative attribute of the system, depicting its health status. AEC automatically identifies the degradation starting point in the machine component. We show that AEC rate well-generalizes in several run-tofailure tests. We demonstrate the superiority of the AEC over many other state-of-the-art approaches for the health monitoring of machine bearings.
Novelty Detection for Multispectral Images with Application to Planetary Exploration
Authors: Kerner Hannah R , Wellington Danika F , Wagstaff Kiri L , Bell James F , Kwan Chiman , Ben Amor Heni
Published: August 2019

In this work, we present a system based on convolutional autoencoders for detecting novel features in multispectral images. We introduce SAMMIE: Selections based on Autoencoder Modeling of Multispectral Image Expectations. Previous work using autoencoders employed the scalar reconstruction error to classify new images as novel or typical. We show that a spatial-spectral error map can enable both accurate classification of novelty in multispectral images as well as human-comprehensible explanations of the detection. We apply our methodology to the detection of novel geologic features in multispectral images of the Martian surface collected by the Mastcam imaging system on the Mars Science Laboratory Curiosity rover.
Robust Multi-Object Detection Based on Data Augmentation with Realistic Image Synthesis for Point-of-Sale Automation
Authors: Koturwar Saiprasad , Shiraishi Soma , Iwamoto Kota
Published: August 2019

As an alternative to bar-code scanning, we are developing a real-time retail product detector for point-of-sale automation. The major challenge associated with image based object detection arise from occlusion and the presence of other objects in close proximity. For robust product detection under such conditions, it is crucial to train the detector on a rich set of images with varying degrees of occlusion and proximity between the products, which fairly represents a wide range of customer tendencies of placing products together. However, generating a fairly large database of such images traditionally requires a large amount of human effort. On the other hand, acquiring individual object images with their corresponding masks is a relatively easy task. We propose an realistic image synthesis approach which uses individual object images and their corresponding masks to create training images with desired properties (occlusion and congestion among the products). We train our product detector over images thus generated and achieve a consistent performance improvement across different types of test data. With the proposed approach, detector achieves an improvement of 46.2% (from 0.67 to 0.98) and 40% (from 0.60 to 0.84) over precision and recall respectively, compared to using a basic training dataset containing one product per image.
Feature Isolation for Hypothesis Testing in Retinal Imaging: An Ischemic Stroke Prediction Case Study
Authors: Lim Gilbert , Lim Zhan Wei , Xu Dejiang , Ting Daniel S.W. , Wong Tien Yin , Lee Mong Li , Hsu Wynne
Published: August 2019

Ischemic stroke is a leading cause of death and long-term disability that is difficult to predict reliably. Retinal fundus photography has been proposed for stroke risk assessment, due to its non-invasiveness and the similarity between retinal and cerebral microcirculations, with past studies claiming a correlation between venular caliber and stroke risk. However, it may be that other retinal features are more appropriate. In this paper, extensive experiments with deep learning on six retinal datasets are described. Feature isolation involving segmented vascular tree images is applied to establish the effectiveness of vessel caliber and shape alone for stroke classification, and dataset ablation is applied to investigate model generalizability on unseen sources. The results suggest that vessel caliber and shape could be indicative of ischemic stroke, and sourcespecific features could influence model performance.
Building Trust in Deep Learning System towards Automated Disease Detection
Authors: Lim Zhan Wei , Lee Mong Li , Hsu Wynne , Wong Tien Yin
Published: August 2019

Though deep learning systems have achieved high accuracy in detecting diseases from medical images, few such systems have been deployed in highly automated disease screening settings due to lack of trust in how well these systems can generalize to out-of-datasets. We propose to use uncertainty estimates of the deep learning system’s prediction to know when to accept or to disregard its prediction. We evaluate the effectiveness of using such estimates in a real-life application for the screening of diabetic retinopathy. We also generate visual explanation of the deep learning system to convey the pixels in the image that influences its decision. Together, these reveal the deep learning system’s competency and limits to the human, and in turn the human can know when to trust the deep learning system.
Ensemble Machine Learning for Estimating Fetal Weight at Varying Gestational Age
Authors: Lu Yu , Zhang Xi , Fu Xianghua , Chen Fangxiong , Wong Kelvin K. L.
Published: August 2019

Obstetric ultrasound examination of physiological parameters has been mainly used to estimate the fetal weight during pregnancy and baby weight before labour to monitor fetal growth and reduce prenatal morbidity and mortality. However, the problem is that ultrasound estimation of fetal weight is subject to populations’ difference, strict operating requirements for sonographers, and poor access to ultrasound in low-resource areas. Inaccurate estimations may lead to negative perinatal outcomes. We consider that machine learning can provide an accurate estimation for obstetricians alongside traditional clinical practices, as well as an efficient and effective support tool for pregnant women for self-monitoring. We present a robust methodology using a data set comprising 4,212 intrapartum recordings. The cubic spline function is used to fit the curves of several key characteristics that are extracted from ultrasound reports. A number of simple and powerful machine learning algorithms are trained, and their performance is evaluated with real test data. We also propose a novel evaluation performance index called the intersectionover-union (loU) for our study. The results are encouraging using an ensemble model consisting of Random Forest, XGBoost, and LightGBM algorithms. The experimental results show an loU of 0.64 between predicted range of fetal weight at any gestational age from the ensemble model and that from ultrasound. Comparing with the ultrasound method, the estimation accuracy is improved by 12%, and the mean relative error is reduced by 3%.
Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation
Authors: Martin Stéphane , Faltings Boi , Schickel Vincent
Published: August 2019

We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SSAGD, is a novel variant of the Matrix-Factorization technique (MF), which is considered state-of-the-art in the recommender literature.We discuss the offline tests we carried out to select the best MF variant, and present the results of two A/B tests performed on live ecommerce websites after the deployment of the new algorithms. Comparing the new recommenders and Prediggo’s proprietary algorithm of Ontology Filtering, we show that the BVMM significantly outperforms the two others in terms of CTR and prediction speed, and leads to a strong increase in recommendation-mediated sales. Although MF exhibits reasonably good accuracy, the BVMM is still significantly more accurate and avoids the high memory requirements of MF. This scalability is essential for its application in online businesses.
Tagging Address Queries in Maps Search
Authors: Mokhtari Shekoofeh , Mahmoody Ahmad , Yankov Dragomir , Xie Ning
Published: August 2019

Map search is a major vertical in all popular search engines. It also plays an important role in personal assistants on mobile, home or desktop devices. A significant fraction of map search traffic is comprised of “address queries” - queries where either the entire query or some terms in it refer to an address or part of an address (road segment, intersection etc.). Here we demonstrate that correctly understanding and tagging address queries are critical for map search engines to fulfill them. We describe several recurrent sequence architectures for tagging such queries. We compare their performance on two subcategories of address queries - single entity (aka single point) addresses and multi entity (aka multi point) addresses, and finish by providing guidance on the best practices when dealing with each of these subcategories.
Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications
Authors: Nordon Galia , Koren Gideon , Shalev Varda , Horvitz Eric , Radinsky Kira
Published: August 2019

We present a system that jointly harnesses large-scale electronic health records data and a concept graph mined from the medical literature to guide drug repurposing—the process of applying known drugs in new ways to treat diseases. Our study is unique in methods and scope, per the scale of the concept graph and the quantity of data. We harness 10 years of nation-wide medical records of more than 1.5 million people and extract medical knowledge from all of PubMed, the world’s largest corpus of online biomedical literature. We employ links on the concept graph to provide causal signals to prioritize candidate influences between medications and target diseases. We show results of the system on studies of drug repurposing for hypertension and diabetes. In both cases, we present drug families identified by the algorithm which were previously unknown. We verify the results via clinical expert opinion and by prospective clinical trials on hypertension.
Discovering Temporal Patterns from Insurance Interaction Data
Authors: Qazi Maleeha , Tunuguntla Srinivas , Lee Peng , Kanchinadam Teja , Fung Glenn , Arora Neeraj
Published: August 2019

In the insurance industry, timely and effective interaction with customers are at the core of everyday operations and processes that are key for a satisfactory customer experience. These interactions often result in sequences of data derived from events that occur over time. Such recurrent patterns can provide valuable information that can be used in a variety of ways to improve customer related work-flows. In this paper we demonstrate the application of a recently proposed algorithm to uncover such time patterns that takes into account the time between events to form such patterns. We use temporal customer data generated from two different use-cases (satisfaction and fraud) to show that this algorithm successfully detects patterns that occur in the insurance context.
Enhancing Evolutionary Conversion Rate Optimization via Multi-Armed Bandit Algorithms
Authors: Qiu Xin , Miikkulainen Risto
Published: August 2019

Conversion rate optimization means designing web interfaces such that more visitors perform a desired action (such as register or purchase) on the site. One promising approach, implemented in Sentient Ascend, is to optimize the design using evolutionary algorithms, evaluating each candidate design online with actual visitors. Because such evaluations are costly and noisy, several challenges emerge: How can available visitor traffic be used most efficiently? How can good solutions be identified most reliably? How can a high conversion rate be maintained during optimization? This paper proposes a new technique to address these issues. Traffic is allocated to candidate solutions using a multi-armed bandit algorithm, using more traffic on those evaluations that are most useful. In a best-arm identification mode, the best candidate can be identified reliably at the end of evolution, and in a campaign mode, the overall conversion rate can be optimized throughout the entire evolution process. Multi-armed bandit algorithms thus improve performance and reliability of machine discovery in noisy real-world environments.
Inferring Concept Prerequisite Relations from Online Educational Resources
Authors: Roy Sudeshna , Madhyastha Meghana , Lawrence Sheril , Rajan Vaibhav
Published: August 2019

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.
Forecasting Intra-Hour Imbalances in Electric Power Systems
Authors: Salem Tárik S. , Kathuria Karan , Ramampiaro Heri , Langseth Helge
Published: August 2019

Keeping the electricity production in balance with the actual demand is becoming a difficult and expensive task in spite of an involvement of experienced human operators. This is due to the increasing complexity of the electric power grid system with the intermittent renewable production as one of the contributors. A beforehand information about an occurring imbalance can help the transmission system operator to adjust the production plans, and thus ensure a high security of supply by reducing the use of costly balancing reserves, and consequently reduce undesirable fluctuations of the 50 Hz power system frequency. In this paper, we introduce the relatively new problem of an intra-hour imbalance forecasting for the transmission system operator (TSO). We focus on the use case of the Norwegian TSO, Statnett. We present a complementary imbalance forecasting tool that is able to support the TSO in determining the trend of future imbalances, and show the potential to proactively alleviate imbalances with a higher accuracy compared to the contemporary solution.