bluuebunny/crossref_metadata_2025 huggingface dataset:
EXPERIÊNCIA DE INTEGRALIZAÇÃO CURRICULAR DA EXTENSÃO EM UMA UNIVERSIDADE DO VALE DO ITAJAÍ (SC).
Authors: Haskel Igor Rodrigo , Fontoura Léia Viviane , Bueno Ednéia Casagranda , Oliveira Gabrielly Bos de , Domeciano Sabrina Regina , Bruttner Pietra Vettorato
Published: August 2019

A extensão universitária é compreendida como um processo acadêmico-pedagógico que promove o intercâmbio de saberes entre a Universidade e a comunidade. A integralização curricular da extensão, por sua vez, tem como objetivo a práxis por meio da inserção da extensão no currículo acadêmico. Este artigo objetiva analisar os resultados práticos e teóricos das ações de integralização curricular do Projeto de Extensão Discutindo a Relação (#D.R.) na formação de acadêmicos dos cursos de Biomedicina, Enfermagem e Psicologia da Universidade do Vale do Itajaí no primeiro semestre de 2018. Foram analisados os resultados obtidos das integralizações realizadas por meio de questionários semiestruturados. Destarte, foi realizado categorização a partir de palavras-chave identificadas nas respostas dos acadêmicos. Esta análise evidenciou que a participação no projeto de extensão oportunizou a aproximação do conhecimento teórico à prática.
PLANTANDO SORRISOS – MOMENTO II: SENSIBILIZAÇÃO AMBIENTAL COM GRUPOS DA TERCEIRA IDADE, EM BAURU - SP
Authors: Carlos Juliana Sanchez , Silverio Giovana Henrique , Tavares Beatriz Antoniassi , Siqueira Marcos Vinicius Bohrer Monteiro
Published: August 2019

O Plantando Sorrisos surgiu como um projeto de extensão com ações direcionadas a grupos da sociedade que sofrem algum tipo de discriminação. Desenvolvido pela comunidade universitária, o projeto teve por objetivo levantar questões sociais e ambientais e simultaneamente realizar o plantio de árvores nativas em áreas degradadas, ou em espaços urbanos que necessitavam de revitalização. O Momento II desse projeto ocorreu no abrigo para idosos Vila Vicentina (Bauru, SP), com a participação da Universidade Aberta à Terceira Idade – UATI, do Grupo de Ecologia Vegetal Aplicada (GEVA) e outros alunos e professores da Universidade do Sagrado Coração – USC (Bauru, SP). No evento, realizou-se uma componente de Educação Ambiental, ressaltando a importância das árvores. Foram plantadas vinte e uma mudas arbóreas frutíferas com o intuito de revitalizar uma área destinada ao pomar da instituição, bem como proporcionar uma atividade artística pela UATI. Conseguiu-se desta forma integrar diferentes públicos, exercendo-se o papel extensionista da universidade, e demonstrando a importância da inclusão social e do cuidado ao meio ambiente.
Place-Based Learning with Out-of-Place Species & Students: Teaching International Students about Biological Invasions
Authors: Barnes Matthew A. , Cox Robert D. , Spott Jessica
Published: August 2019

Place-based instruction allows students to explore learned concepts while building emotional connections with the location in which they are studying. Furthermore, the case for experiential science education continues to grow, and such pedagogy may be particularly beneficial to learning in ecology and environmental science. We present an experiential, place-based pedagogy aimed at introducing international high school or undergraduate students to the concept of biological invasions. Our lesson began by introducing our class, a group of Chinese high school students in a summer program in the United States, with examples of invasive species that had previously been introduced from China into the United States or vice versa. Guided discussion then focused on plant and animal species with which the students had some familiarity and covered concepts of biological invasions more generally. Next, students participated in a field activity exploring the ecology of the invasive tumbleweed Salsola tragus, a Eurasian (including much of China) species that has invaded the United States. Through classroom and field activity, students gained understanding of biological invasions, and we believe that internalization was enhanced by connecting the lesson with students' own experiences and participation in basic scientific methods and ecological fieldwork.
“He was unable to set aside the effeminate, and so was forgotten”: Masculinity, Its Fears, and the Uses of Falsetto in the Early Nineteenth Century
Authors: Crowe Robert
Published: August 2019

The male falsetto enjoyed a brief period of acceptance, even adulation, as it was wielded by tenors such as John Braham and Giovanni Rubini in the first four decades of the nineteenth century. At the same time, the last castrati to tread the stage were winding down their careers, while in Germany and Austria female impersonators such as Karl Blumenfeld, who possessed highly cultivated falsetto voices, were achieving a kind of fame of their own. These three kinds of falsetto—the castrato voice was heard at this time as having the same two registers standard for all voices, falsetto and chest voice—were, to a degree probably startling to modern readers, considered analogous to one another.

The decline of the ”legitimate” falsetto as an extension of the tenorial chest voice was concurrent with the phenomena of the disappearing castrati and the wildly over-the-top female impersonators—all of whom were both implicitly and explicitly compared to one another. Both the tenors and the falsettists bore an uncomfortable, even ridiculous, perceptual proximity to the epicene, effeminate, always/already maimed state of the castrato, under the regulation of an anxious version of the male gaze. This proximity played a large role in the rapid disappearance of the tenorial falsetto after 1840.
Peer Support Group as a Model for Improving Behavior of Hypertension Risk and Complication Control
Authors: Azinar Muhammad , Azam Mahalul , Sugiharto Sugiharto
Published: August 2019

Background: Based on Basic Health Research (Riset Kesehatan Dasar) 2013 data, the prevalence of hypertension in Indonesia was high (26.5%). It means that 1 in 4 people suffer from hypertension. Based on a preliminary study conducted by researchers in November 2017 at the Primary Healthcare Center (PHC) of Kalongan, it was found that 67% of hypertensive patients did not recognize risk factors, signs and symptoms of hypertension. Results of examinations and report at the PHC and Non-communicable Disease (NCD) Surveillance (Posbindu) in the working area of ​​ PHC of Kalongan in 2017, that Kawengen village was a village with the highest number of hypertension cases from 6 villages in their working area. Peer Support Group is a form of empowerment innovation that can be applied to. 

Methods: This study was pre-experimental with one group pretest-posttest design. The study population was all hypertensive patients in Kawengen village. Sample was determined purposively, obtained by 61 people. The research instrument was the pretest and posttest questionnaire. Data analysis was performed by McNemar test.

Results: Peer Support Group model can improve knowledge (p value 0.031), behavior (p value 0.008), risk and complication control practices (p value 0.012). Risk and complication control behavior is an effort that can be implemented to reduce hypertension cases in the community.

Conclusion: Peer Support Group is one of community empowerment models, especially for patients which can increase knowledge, change behavior, and improve risk and complication control practices among hypertensive patients as participant of Peer Support Group.
Central growth hormone signaling is not required for the timing of puberty
Authors: Bohlen Tabata M , Zampieri Thais T , Furigo Isadora C , Teixeira Pryscila D S , List Edward O , Kopchick John J , Donato Jose , Frazao Renata
Published: August 2019

Growth hormone (GH) is a key factor in the regulation of body growth, as well as a variety of other cellular and metabolic processes. Neurons expressing kisspeptin and leptin receptors (LepR) have been shown to modulate the hypothalamic-pituitary-gonadal (HPG) axis and are considered GH responsive. The presence of functional GH receptors (GHR) in these neural populations suggests that GH may regulate the HPG axis via a central mechanism. However, there have been no studies evaluating whether or not GH-induced intracellular signaling in the brain plays a role in the timing of puberty or mediates the ovulatory cycle. Toward the goal of understanding the influence of GH on the central nervous system as a mediator of reproductive functions, GHR ablation was induced in kisspeptin and LepR-expressing cells or in the entire brain. The results demonstrated that GH signaling in specific neural populations can potentially modulate the hypothalamic expression of genes related to the reproductive system or indirectly contribute to the progression of puberty. GH action in kisspeptin cells or in the entire brain was not required for sexual maturation. On the other hand, GHR ablation in LepR cells delayed puberty progression, reduced serum leptin levels, decreased body weight gain and compromised the ovulatory cycle in some individuals, while the lack of GH effects in the entire brain prompted shorter estrous cycles. These findings suggest that GH can modulate brain components of the HPG axis, although central GH signaling is not required for the timing of puberty.
KEBERTAHANAN PEREMPUAN SIMALANGGANG MENJADI SINGLE MOTHER
Authors: Utami Nofrianti Putri , Hanani Silfia
Published: August 2019

This study aims to determine and analyze the survival of women into a single mother. This was a qualitative research type. Single mothers are respondents in this study. Interview and observation carried out in data collection. The results showed that the task of women becomes more difficult if they become single parents. A woman should have a dual role in her family as a mother and father to her children. Mother is looking for a living for her family and their daily needs. It is concluded that a single mother at the same time must be able to overcome economic, psychological, and social problems in the society environment.
KONSEP PENDIDIKAN KELUARGA MENURUT HASAN LANGGULUNG DAN RELEVANSINYA DENGAN PENGEMBANGAN KESEHATAN MENTAL KELUARGA
Authors: Harahap Ahmad Muda
Published: August 2019

This article aims to describe and analyze the concept of family education according to Hasan Langgulung and its relevance to the development of family mental health. This study uses a character research method using descriptive qualitative approach. Hasan Lagulung’ concept is as data in this research. The results of the study shows that the six aspects of family education namely physical education and health, intellectual education, emotional education, religious education social education aspects can develop children's potential on cognitive, affective, psychomotor, and spiritual. It is concluded that the development of this potential impacts on family mental health. It implies that if the first aspect does not work well, it impacts on other aspects.
Interações entre Manutenção Produtiva Total e Gestão da Qualidade Total: Estudo de Caso em Uma Empresa do Setor Alimentício
Authors: Aguiar Marina Fernandes , Jugend Daniel , Souza Fernando Bernardi , Mariano Enzo
Published: August 2019

Para guiar as empresas na busca por alavancar seus resultados e, assim, alcançar a almejada vantagem competitiva, foram desenvolvidos conceitos, metodologias e sistemas de gestão. Neste contexto, programas como a Manutenção Produtiva Total (MPT) e a Gestão da Qualidade Total (GQT) têm ganhado destaque. A Manutenção Produtiva Total (MPT), mais conhecida pelo termo em inglês, Total Productive Maintenance (TPM), é um método de gestão que visa atingir perda zero por meio de melhorias contínuas no processo, contando com o envolvimento de todos. Já a GQT também atua sob a ótica do aprimoramento contínuo, reconhecendo que a satisfação do cliente é um fator determinante para a sobrevivência da empresa. Baseado nesta perspectiva, o presente trabalho teve como proposta apresentar um estudo de caso a respeito da aplicação integrada do TPM e do TQM em uma indústria alimentícia com unidade produtiva localizada no interior de São Paulo. A análise das evidências encontradas apresenta os benefícios advindos dos fatores em comum destas duas abordagens, dificuldades enfrentadas e possíveis oportunidades de melhoria. Dentre os resultados observados, notou-se que esta integração se mostra como uma importante ferramenta para contribuir com o bom desempenho da organização.
Avaliação da Qualidade dos Serviços Logísticos Prestados por Websites Utilizando o SERVQUAL
Authors: Oliveira Eliana Burgos Bernardo de , Lima Carlos Wilson Da Silva , Sant'Anna Carlos Henrique Michels de , Melo Fagner José Coutinho de , Júnior Djalma Silva Guimarães
Published: August 2019

Atualmente os consumidores estão cada vez mais exigentes em relação à escolha de um serviço, o que faz com que as empresas busquem um diferencial competitivo que supere as expectativas dos clientes, onde quando se trata do comércio eletrônico uma maneira de buscar esse diferencial é através da qualidade dos serviços logísticos. Portanto é fundamental saber o que os clientes esperam dos serviços, bem como sua percepção sobre eles. Neste trabalho foi utilizado o SERVQUAL para avaliar a qualidade dos serviços logísticos prestados pelos websites, através da identificação das lacunas entre as expectativas e a percepção dos serviços logísticos prestados pelos websites. Os resultados mostraram que a qualidade dos serviços prestados foi baixa na maioria dos casos, mostrando que as expectativas dos usuários não foram atendidas. Com base na análise de quartil foram propostas medidas de correção e/ou prevenção dos maiores gaps encontrados, visando melhorar a qualidade dos serviços logísticos prestados pelos websites.
Modelos de precificação para locação e venda de imóveis residenciais na cidade de João Monlevade-MG via regressão linear multivariada
Authors: Rosa Vanessa Silva , Oliveira Paganini Barcellos de , Pinto Rafael Lucas Machado
Published: August 2019

Os consumidores de bens e serviços de um determinado mercado são atraídos por um conjunto de atributos de valor que direta ou indiretamente os qualificam em relação aos seus concorrentes. Por outro lado, embora o “preço” seja apenas um dentre os vários atributos, ele muitas vezes é interpretado como uma variável resposta que reúne os demais, justamente pelo seu grau de relevância quanto à restrição orçamentária dos consumidores. Esse artigo objetiva entender o grau de influência e correlação de um conjunto de variáveis explicativas nos preços de imóveis residenciais ofertados para locação e venda na cidade de João Monlevade-MG, via modelos de regressão linear multivariada. A metodologia proposta se baseia em informações reais referentes aos preços ofertados na cidade e suas características estruturais e locacionais, a citar: quantidade de quartos e vagas na garagem; quantitativo de ocorrências policiais; proximidade com o centro comercial, postos de saúde e escolas estaduais mais próximas. Como resultado, foi possível obter um conjunto de equações matemática capazes de explicar o preço em função das variáveis preditoras, bem como entender a relação entre estas variáveis.
Índice de Sustentabilidade Empresarial (ISE): Diferença das Rentabilidades entre Grupos Formadores e Não Formadores
Authors: Thomaz Junior Nauro , Santos Marcos Antônio Rodrigues
Published: August 2019

O principal objetivo deste artigo é apresentar a diferença das rentabilidades, pelo sistema Dupont, entre os grupos formadores e não formadores do Índice de Sustentabilidade Empresarial (ISE), considerando as rentabilidades do ativo e do proprietário. Para tanto, acompanhou-se a evolução das métricas financeiras dos segmentos participantes do ISE, no período entre 2005 e 2014, passíveis de submissão a testes paramétricos e não paramétricos, no seu universo total e no universo das grandes corporações. Foram analisadas as empresas listadas na BM&FBOVESPA, dos segmentos de energia elétrica, bancos, papel e celulose e carnes. Os resultados, em grande parte, não apresentaram diferenciação de rentabilidade significativa entre os referidos grupos decorrente de aumento da rentabilidade do ativo, sendo as exceções relacionadas ao aumento da alavancagem. Em conclusão, evidenciou-se a necessidade de outros estudos referentes aos retornos de investimentos sustentáveis ligados ao valor econômico agregado às empresas participantes do ISE e à identificação dos riscos de perda de valor pelas organizações não participantes.
Variational Autoencoder with Implicit Optimal Priors
Authors: Takahashi Hiroshi , Iwata Tomoharu , Yamanaka Yuki , Yamada Masanori , Yagi Satoshi
Published: August 2019

The variational autoencoder (VAE) is a powerful generative model that can estimate the probability of a data point by using latent variables. In the VAE, the posterior of the latent variable given the data point is regularized by the prior of the latent variable using Kullback Leibler (KL) divergence. Although the standard Gaussian distribution is usually used for the prior, this simple prior incurs over-regularization. As a sophisticated prior, the aggregated posterior has been introduced, which is the expectation of the posterior over the data distribution. This prior is optimal for the VAE in terms of maximizing the training objective function. However, KL divergence with the aggregated posterior cannot be calculated in a closed form, which prevents us from using this optimal prior. With the proposed method, we introduce the density ratio trick to estimate this KL divergence without modeling the aggregated posterior explicitly. Since the density ratio trick does not work well in high dimensions, we rewrite this KL divergence that contains the high-dimensional density ratio into the sum of the analytically calculable term and the lowdimensional density ratio term, to which the density ratio trick is applied. Experiments on various datasets show that the VAE with this implicit optimal prior achieves high density estimation performance.
Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection
Authors: Tang Chang , Zhu Xinzhong , Liu Xinwang , Wang Lizhe
Published: August 2019

Multi-view unsupervised feature selection (MV-UFS) aims to select a feature subset from multi-view data without using the labels of samples. However, we observe that existing MV-UFS algorithms do not well consider the local structure of cross views and the diversity of different views, which could adversely affect the performance of subsequent learning tasks. In this paper, we propose a cross-view local structure preserved diversity and consensus semantic learning model for MV-UFS, termed CRV-DCL briefly, to address these issues. Specifically, we project each view of data into a common semantic label space which is composed of a consensus part and a diversity part, with the aim to capture both the common information and distinguishing knowledge across different views. Further, an inter-view similarity graph between each pairwise view and an intra-view similarity graph of each view are respectively constructed to preserve the local structure of data in different views and different samples in the same view. An l2,1-norm constraint is imposed on the feature projection matrix to select discriminative features. We carefully design an efficient algorithm with convergence guarantee to solve the resultant optimization problem. Extensive experimental study is conducted on six publicly real multi-view datasets and the experimental results well demonstrate the effectiveness of CRV-DCL.
Self-Paced Active Learning: Query the Right Thing at the Right Time
Authors: Tang Ying-Peng , Huang Sheng-Jun
Published: August 2019

Active learning queries labels from the oracle for the most valuable instances to reduce the labeling cost. In many active learning studies, informative and representative instances are preferred because they are expected to have higher potential value for improving the model. Recently, the results in self-paced learning show that training the model with easy examples first and then gradually with harder examples can improve the performance. While informative and representative instances could be easy or hard, querying valuable but hard examples at early stage may lead to waste of labeling cost. In this paper, we propose a self-paced active learning approach to simultaneously consider the potential value and easiness of an instance, and try to train the model with least cost by querying the right thing at the right time. Experimental results show that the proposed approach is superior to state-of-the-art batch mode active learning methods.
A Radical-Aware Attention-Based Model for Chinese Text Classification
Authors: Tao Hanqing , Tong Shiwei , Zhao Hongke , Xu Tong , Jin Binbin , Liu Qi
Published: August 2019

Recent years, Chinese text classification has attracted more and more research attention. However, most existing techniques which specifically aim at English materials may lose effectiveness on this task due to the huge difference between Chinese and English. Actually, as a special kind of hieroglyphics, Chinese characters and radicals are semantically useful but still unexplored in the task of text classification. To that end, in this paper, we first analyze the motives of using multiple granularity features to represent a Chinese text by inspecting the characteristics of radicals, characters and words. For better representing the Chinese text and then implementing Chinese text classification, we propose a novel Radicalaware Attention-based Four-Granularity (RAFG) model to take full advantages of Chinese characters, words, characterlevel radicals, word-level radicals simultaneously. Specifically, RAFG applies a serialized BLSTM structure which is context-aware and able to capture the long-range information to model the character sharing property of Chinese and sequence characteristics in texts. Further, we design an attention mechanism to enhance the effects of radicals thus model the radical sharing property when integrating granularities. Finally, we conduct extensive experiments, where the experimental results not only show the superiority of our model, but also validate the effectiveness of radicals in the task of Chinese text classification.
Learning Competitive and Discriminative Reconstructions for Anomaly Detection
Authors: Tian Kai , Zhou Shuigeng , Fan Jianping , Guan Jihong
Published: August 2019

Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to determine whether a test instance is an outlier. Unfortunately, a good threshold is vital for the performance and it is really hard to find an optimal one. In this paper, we take the discriminative information implied in unlabeled data into consideration and propose a new method for anomaly detection that can learn the labels of unlabelled data directly. Our proposed method has an end-to-end architecture with one encoder and two decoders that are trained to model inliers and outliers’ data distributions in a competitive way. This architecture works in a discriminative manner without suffering from overfitting, and the training algorithm of our model is adopted from SGD, thus it is efficient and scalable even for large-scale datasets. Empirical studies on 7 datasets including KDD99, MNIST, Caltech-256, and ImageNet etc. show that our model outperforms the state-of-the-art methods.
Improving GAN with Neighbors Embedding and Gradient Matching
Authors: Tran Ngoc-Trung , Bui Tuan-Anh , Cheung Ngai-Man
Published: August 2019

We propose two new techniques for training Generative Adversarial Networks (GANs) in the unsupervised setting. Our objectives are to alleviate mode collapse in GAN and improve the quality of the generated samples. First, we propose neighbor embedding, a manifold learning-based regularization to explicitly retain local structures of latent samples in the generated samples. This prevents generator from producing nearly identical data samples from different latent samples, and reduces mode collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we propose a new technique, gradient matching, to align the distributions of the generated samples and the real samples. As it is challenging to work with high-dimensional sample distributions, we propose to align these distributions through the scalar discriminator scores. We constrain the difference between the discriminator scores of the real samples and generated ones. We further constrain the difference between the gradients of these discriminator scores. We derive these constraints from Taylor approximations of the discriminator function. We perform experiments to demonstrate that our proposed techniques are computationally simple and easy to be incorporated in existing systems. When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets, e.g. FID score of 30.80 for the STL-10 dataset. Our code is available at: https://github.com/tntrung/gan
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
Authors: Vahedian Amin , Zhou Xun , Tong Ling , Street W. Nick , Li Yanhua
Published: August 2019

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead, in this paper we argue that dispersal events follow a complex pattern of trips and other related features in the past, which can be used to predict such events. Therefore, we formulate the dispersal event prediction problem as a survival analysis problem. We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 20142016. Results show that DILSA can predict events in the next 5 hours with F1-score of 0:7 and with average time error of 18 minutes. It is orders of magnitude better than the state-of-the-art deep learning approaches for taxi demand prediction.
CAMO: A Collaborative Ranking Method for Content Based Recommendation
Authors: Wang Chengwei , Zhou Tengfei , Chen Chen , Hu Tianlei , Chen Gang
Published: August 2019

In real-world recommendation tasks, feedback data are usually sparse. Therefore, a recommender’s performance is often determined by how much information that it can extract from textual contents. However, current methods do not make full use of the semantic information. They encode the textual contents either by “bag-of-words” technique or Recurrent Neural Network (RNN). The former neglects the order of words while the latter ignores the fact that textual contents can contain multiple topics. Besides, there exists a dilemma in designing a recommender. On the one hand, we shall use a sophisticated model to exploit every drop of information in item contents; on the other hand, we shall adopt a simple model to prevent itself from over-fitting when facing the sparse feedbacks. To fill the gaps, we propose a recommender named CAMO 1. CAMO employs a multi-layer content encoder for simultaneously capturing the semantic information of multitopic and word order. Moreover, CAMO makes use of adversarial training to prevent the complex encoder from overfitting. Extensive empirical studies show that CAMO outperforms state-of-the-art methods in predicting users’ preferences.
Video Inpainting by Jointly Learning Temporal Structure and Spatial Details
Authors: Wang Chuan , Huang Haibin , Han Xiaoguang , Wang Jue
Published: August 2019

We present a new data-driven video inpainting method for recovering missing regions of video frames. A novel deep learning architecture is proposed which contains two subnetworks: a temporal structure inference network and a spatial detail recovering network. The temporal structure inference network is built upon a 3D fully convolutional architecture: it only learns to complete a low-resolution video volume given the expensive computational cost of 3D convolution. The low resolution result provides temporal guidance to the spatial detail recovering network, which performs imagebased inpainting with a 2D fully convolutional network to produce recovered video frames in their original resolution. Such two-step network design ensures both the spatial quality of each frame and the temporal coherence across frames. Our method jointly trains both sub-networks in an end-to-end manner. We provide qualitative and quantitative evaluation on three datasets, demonstrating that our method outperforms previous learning-based video inpainting methods.
Orderly Subspace Clustering
Authors: Wang Jing , Suzuki Atsushi , Xu Linchuan , Tian Feng , Yang Liang , Yamanishi Kenji
Published: August 2019

Semi-supervised representation-based subspace clustering is to partition data into their underlying subspaces by finding effective data representations with partial supervisions. Essentially, an effective and accurate representation should be able to uncover and preserve the true data structure. Meanwhile, a reliable and easy-to-obtain supervision is desirable for practical learning. To meet these two objectives, in this paper we make the first attempt towards utilizing the orderly relationship, such as the data a is closer to b than to c, as a novel supervision. We propose an orderly subspace clustering approach with a novel regularization term. OSC enforces the learned representations to simultaneously capture the intrinsic subspace structure and reveal orderly structure that is faithful to true data relationship. Experimental results with several benchmarks have demonstrated that aside from more accurate clustering against state-of-the-arts, OSC interprets orderly data structure which is beyond what current approaches can offer.
An Efficient Approach to Informative Feature Extraction from Multimodal Data
Authors: Wang Lichen , Wu Jiaxiang , Huang Shao-Lun , Zheng Lizhong , Xu Xiangxiang , Zhang Lin , Huang Junzhou
Published: August 2019

One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation be-´ comes an appealing objective because of its operational meaning and desirable properties. However, the strict whitening constraints formalized in the HGR maximal correlation limit its application. To address this problem, this paper proposes Soft-HGR, a novel framework to extract informative features from multiple data modalities. Specifically, our framework prevents the “hard” whitening constraints, while simultaneously preserving the same feature geometry as in the HGR maximal correlation. The objective of Soft-HGR is straightforward, only involving two inner products, which guarantees the efficiency and stability in optimization. We further generalize the framework to handle more than two modalities and missing modalities. When labels are partially available, we enhance the discriminative power of the feature representations by making a semi-supervised adaptation. Empirical evaluation implies that our approach learns more informative feature mappings and is more efficient to optimize.
Scalable Distributed DL Training: Batching Communication and Computation
Authors: Wang Shaoqi , Pi Aidi , Zhou Xiaobo
Published: August 2019

Scalability of distributed deep learning (DL) training with parameter server architecture is often communication constrained in large clusters. There are recent efforts that use a layer by layer strategy to overlap gradient communication with backward computation so as to reduce the impact of communication constraint on the scalability. However, the approaches cannot be effectively applied to the overlap between parameter communication and forward computation. In this paper, we propose and design iBatch, a novel communication approach that batches parameter communication and forward computation to overlap them with each other. We formulate the batching decision as an optimization problem and solve it based on greedy algorithm to derive communication and computation batches. We implement iBatch in the open-source DL framework BigDL and perform evaluations with various DL workloads. Experimental results show that iBatch improves the scalability of a cluster of 72 nodes by up to 73% over the default PS and 41% over the layer by layer strategy.
Robustness Can Be Cheap: A Highly Efficient Approach to Discover Outliers under High Outlier Ratios
Authors: Wang Siqi , Zhu En , Hu Xiping , Liu Xinwang , Liu Qiang , Yin Jianping , Wang Fei
Published: August 2019

Efficient detection of outliers from massive data with a high outlier ratio is challenging but not explicitly discussed yet. In such a case, existing methods either suffer from poor robustness or require expensive computations. This paper proposes a Low-rank based Efficient Outlier Detection (LEOD) framework to achieve favorable robustness against high outlier ratios with much cheaper computations. Specifically, it is worth highlighting the following aspects of LEOD: (1) Our framework exploits the low-rank structure embedded in the similarity matrix and considers inliers/outliers equally based on this low-rank structure, which facilitates us to encourage satisfying robustness with low computational cost later; (2) A novel re-weighting algorithm is derived as a new general solution to the constrained eigenvalue problem, which is a major bottleneck for the optimization process. Instead of the high space and time complexity (O((2n)2)/O((2n)3)) required by the classic solution, our algorithm enjoys O(n) space complexity and a faster optimization speed in the experiments; (3) A new alternative formulation is proposed for further acceleration of the solution process, where a cheap closed-form solution can be obtained. Experiments show that LEOD achieves strong robustness under an outlier ratio from 20% to 60%, while it is at most 100 times more memory efficient and 1000 times faster than its previous counterpart that attains comparable performance. The codes of LEOD are publicly available at https://github.com/demonzyj56/LEOD.
SCNN: A General Distribution Based Statistical Convolutional Neural Network with Application to Video Object Detection
Authors: Wang Tianchen , Xiong Jinjun , Xu Xiaowei , Shi Yiyu
Published: August 2019

Various convolutional neural networks (CNNs) were developed recently that achieved accuracy comparable with that of human beings in computer vision tasks such as image recognition, object detection and tracking, etc. Most of these networks, however, process one single frame of image at a time, and may not fully utilize the temporal and contextual correlation typically present in multiple channels of the same image or adjacent frames from a video, thus limiting the achievable throughput. This limitation stems from the fact that existing CNNs operate on deterministic numbers. In this paper, we propose a novel statistical convolutional neural network (SCNN), which extends existing CNN architectures but operates directly on correlated distributions rather than deterministic numbers. By introducing a parameterized canonical model to model correlated data and defining corresponding operations as required for CNN training and inference, we show that SCNN can process multiple frames of correlated images effectively, hence achieving significant speedup over existing CNN models. We use a CNN based video object detection as an example to illustrate the usefulness of the proposed SCNN as a general network model. Experimental results show that even a nonoptimized implementation of SCNN can still achieve 178% speedup over existing CNNs with slight accuracy degradation.
Hyperbolic Heterogeneous Information Network Embedding
Authors: Wang Xiao , Zhang Yiding , Shi Chuan
Published: August 2019

Heterogeneous information network (HIN) embedding, aiming to project HIN into a low-dimensional space, has attracted considerable research attention. Most of the exiting HIN embedding methods focus on preserving the inherent network structure and semantic correlations in Euclidean spaces. However, one fundamental problem is that whether the Euclidean spaces are the appropriate or intrinsic isometric spaces of HIN? Recent researches argue that the complex network may have the hyperbolic geometry underneath, because the underlying hyperbolic geometry can naturally reflect some properties of complex network, e.g., hierarchical and power-law structure. In this paper, we make the first effort toward HIN embedding in hyperbolic spaces. We analyze the structures of two real-world HINs and discover some properties, e.g., the power-law distribution, also exist in HIN. Therefore, we propose a novel hyperbolic heterogeneous information network embedding model. Specifically, to capture the structure and semantic relations between nodes, we employ the meta-path guided random walk to sample the sequences for each node. Then we exploit the distance in hyperbolic spaces as the proximity measurement. The hyperbolic distance is able to meet the triangle inequality and well preserve the transitivity in HIN. Our model enables the nodes and their neighborhoods have small hyperbolic distances. We further derive the effective optimization strategy to update the hyperbolic embeddings iteratively. The experimental results, in comparison with the state-of-the-art, demonstrate that our proposed model not only has superior performance on network reconstruction and link prediction tasks but also shows its ability of capture hierarchy structure in HIN via visualization.
Multiple Independent Subspace Clusterings
Authors: Wang Xing , Wang Jun , Domeniconi Carlotta , Yu Guoxian , Xiao Guoqiang , Guo Maozu
Published: August 2019

Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it’s still a challenge for users to analyze and understand the distinctive structure of each output clustering. To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering. To this end, we provide a two-stage approach called MISC (Multiple Independent Subspace Clusterings). In the first stage, MISC uses independent subspace analysis to seek multiple and statistical independent (i.e. non-redundant) subspaces, and determines the number of subspaces via the minimum description length principle. In the second stage, to account for the intrinsic geometric structure of samples embedded in each subspace, MISC performs graph regularized semi-nonnegative matrix factorization to explore clusters. It additionally integrates the kernel trick into matrix factorization to handle non-linearly separable clusters. Experimental results on synthetic datasets show that MISC can find different interesting clusterings from the sought independent subspaces, and it also outperforms other related and competitive approaches on real-world datasets.
Unified Embedding Alignment with Missing Views Inferring for Incomplete Multi-View Clustering
Authors: Wen Jie , Zhang Zheng , Xu Yong , Zhang Bob , Fei Lunke , Liu Hong
Published: August 2019

Multi-view clustering aims to partition data collected from diverse sources based on the assumption that all views are complete. However, such prior assumption is hardly satisfied in many real-world applications, resulting in the incomplete multi-view learning problem. The existing attempts on this problem still have the following limitations: 1) the underlying semantic information of the missing views is commonly ignored; 2) The local structure of data is not well explored; 3) The importance of different views is not effectively evaluated. To address these issues, this paper proposes a Unified Embedding Alignment Framework (UEAF) for robust incomplete multi-view clustering. In particular, a locality-preserved reconstruction term is introduced to infer the missing views such that all views can be naturally aligned. A consensus graph is adaptively learned and embedded via the reverse graph regularization to guarantee the common local structure of multiple views and in turn can further align the incomplete views and inferred views. Moreover, an adaptive weighting strategy is designed to capture the importance of different views. Extensive experimental results show that the proposed method can significantly improve the clustering performance in comparison with some state-of-the-art methods.
The effect of soil–vegetation–atmosphere interaction on slope stability: a numerical study
Authors: Jamalinia Elahe , Vardon Philip J , Steele-Dunne Susan C
Published: August 2019

The stability of a dike is influenced strongly by its water content, by way of changes in effective stress and weight. While flow through porous media is relatively well understood, water flux in and out of a dike through a vegetated surface is not as well understood. This paper presents a numerical study of the soil–vegetation–atmosphere interaction and discusses how it influences the stability of dikes covered with grass. A crop model was used to simulate vegetation growth and infiltration in response to meteorological forcing. The PLAXIS finite-element method model was used to simulate the impact of this infiltration on hydromechanical behaviour and dike stability. Results from a 4-year analysis indicated a strong correlation between root zone water content (WC rz) and factor of safety, although the relationship is not unique. The leaf area index (LAI) was also found to have a strong, lagged correlation with the water flux into the dike. This suggests that monitoring LAI could be a useful tool to identify vulnerable locations along dikes. It is therefore proposed that vegetation and root zone water content could be used as an indication to detect vulnerable dikes in the early stage.
Improved stud layouts for steel-plate–concrete walls subject to blast load
Authors: Lim Jin-Sun , Jeong Young-Do , Yi Seong-Tae
Published: August 2019

An analytical study using a non-linear finite-element method was carried out to evaluate the effect of the shape and spacing of shear studs on the behaviour of steel-plate–concrete walls subject to blast loads. The size of the analytical model was determined by reference to an existing literature review and code requirements (Kepic-SNG). The analytical model was validated against experimental results. The behaviour of a wall subject to blast loading was then analysed for various stud spacings and shapes. As found in previous research, the maximum deflection increased as the spacing of studs increased and the time when the maximum deflection occurred was delayed. Improved studs were found to be effective in reducing displacement when arranged at lengthwise spacings of 100 mm or more.
Variance Disparity and Market Frictions
Authors: Park Yang-Ho
Published: August 2019

This paper introduces a new model-free approach to measuring the expectation of market variance using VIX derivatives. This approach shows that VIX derivatives carry different information about future variance than S&P 500 (SPX) options, especially during the 2008 financial crisis. I find that the segmentation is associated with frictions such as funding illiquidity, market illiquidity, and asymmetric information. When they are segmented, VIX derivatives contribute more to the variance discovery process than SPX options. These findings imply that VIX derivatives would offer a better estimate of expected variance than SPX options, and that a measure of segmentation may be useful for policymakers as it signals the severity of frictions.
Bleach synchronisation of C. elegans v1
Authors: Barlow Ida
Published: August 2019

This protocol is for obtaining synchronised populations of C. elegans. Eggs are harvested from gravid hermaphrodites and allow to grow to L1 arrest for 12-120 hours (0.5-5days). L1 larvae are then fed and left to develop to the appropriate developmental stage
Measure feeding during large population swarming with bioluminescent bacteria v1
Authors: Ding Serena
Published: August 2019

Protocol uses bioluminescently labelled bacteria (Gregor et al, 2008, Addgene Plasmid #107879, https://www.pnas.org/content/115/5/962) to measure population feeding during large-scale C. elegans swarming.
Immunofluorescence staining of paraffin embedded cell block sections v1
Authors: Nissim Baruch Erez , Ortenberg Rona
Published: August 2019

This protocol is designated for Immunofluorescence staining of paraffin embedded cell block sections.
Using Learner-centered Instructional Approach to Foster Students’ Performances
Authors: Jaiswal Preeti
Published: August 2019

This study aims to delineate the significance of using student centered instructional approaches for enhancing learners’ competency in vocabulary learning, in higher education. The working group for this study comprised of 26 students studying the Foundation Year Program in the English Language Centre at University of Bahrain during the academic year 2018-2019. Student Centered “instructions designed according to the model consisting of nine steps of instructional events based on the Condition of Learning theory by Robert. M. Gagné” was used for this purpose. Gagne's model of instructional design provides valuable guidelines to organize lessons as a systematic instructional design process. It focuses on the instructional learning outcomes and how to organize specific instructional events to facilitate effective learning experiences in order to accomplish the intended learning objectives. Applying Gagne's nine stages of learning administers a systematic learning program as it presents a structured framework for lesson planning and a holistic view to the teaching learning process. Prior to using this series of nine levels of learning, students were given a pretest, in order to understand the stratum of active and passive vocabulary they utilize in language learning. At the end of the study, students were evaluated by a post test. The results demonstrated that learner centered instructional approach was successful in augmenting learners ’retention and transfer of productive and receptive vocabulary knowledge in language learning therefore, such learner centred instructional approaches are cogent for paving higher educational academic accomplishments and promoting learner autonomy.