bluuebunny/crossref_metadata_2025 huggingface dataset:
Os sentidos da atenção domiciliar no cuidado ao idoso na finitude: a perspectiva humana do profissional do SUS
Authors: Marques Fernanda Pasquetti , Bulgarelli Alexandre Fávero
Published: June 2020

Resumo O acesso aos cuidados em saúde de usuários idosos que se encontram em processo de finitude e em necessidades paliativas no domicílio servem de alerta para programas e estratégias de cuidado em saúde. Objetiva-se com este estudo compreender os sentidos da atenção domiciliar no escopo das ações da atenção primária no cuidado a estes idosos pela perspectiva do profissional da saúde do SUS. Estudo de aproximação teórico-metodológica qualitativa, com desenho de pesquisa compreensivista partindo-se dos pensamentos do filósofo alemão Hans-Georg Gadamer, com doze profissionais. Os dados foram sistematizados e analisados segundo a Análise de Conteúdo e interpretados por meio da Hermenêutica filosófica. Compreende-se que a atenção domiciliar ao idoso como algo angustiante, porém efetivo e gerador de processos humanos de confiança e articulações coletivas para o cuidado em respeito a condição outro. O sentido da atenção domiciliar abarca na reflexão dialógica da representação do humano e da solidariedade no exercício do trabalho no SUS. Acredita-se que este estudo possibilitará um norte para que os gestores da atenção primária reflitam sobre o quão importante, necessário e angustiante é a realização de atenção domiciliar nas realidades brasileiras.
Contraceptive counseling lectures do not influence decision making in family planning services
Authors: Berardi Marília Cristina , Mendes-Rodrigues Clesnan , Paro Helena Borges Martins da Silva
Published: June 2020

Abstract Family planning services should provide women and men with information on contraceptive methods. This study aimed to evaluate the impact of multidisciplinary contraceptive counseling lectures related to the efficacy and individual choices of contraceptive methods. Sociodemographic variables, preferred methods and opinion about the efficacy of contraceptive methods of 101 participants were analyzed. After the lecture, a lower percentage of men declared that they did not know which contraceptive method was more efficacious (24.3% versus 0.0%; p=0.006). Also, a lower percentage of women (12.7% versus 1.8%; p=0.020) and men (25.5% versus 7.7%; p=0.003) reported that they did not know which contraceptive method was less efficacious. Number of children increased the likelihood of choosing an irreversible method in a 30.87-fold (95%CI, 5.503 to 173.168, p<0.001). The choice of irreversible methods did not change after the counseling lecture (p>0.05). The lecture impacted on participants’ opinions about the efficacy of contraceptive methods, making them have more informed choices. However, it did not influence their choice of contraception. Number of children was the most determining factor in choosing irreversible methods.
Study on hearing loss and its relationship with work in pesticide-exposed tobacco growers
Authors: França Denise Maria Vaz Romano , Lobato Diolen Conceição Barros , Moronte Elver Andrade , Albuquerque Guilherme Souza Cavalcanti de , Alcarás Patricia Arruda , Gonçalves Claudia Giglio de Oliveira , Lacerda Adriana Bender Moreira de
Published: June 2020

ABSTRACT The Purpose of this case report is to present four cases of tobacco growers with hearing loss due to occupational exposure to pesticides. A qualitative case study comprising three cases of sensorineural hearing loss with causal nexus (Cases 1, 2 and 4), and one (Case 3) of sensorineural hearing loss compatible with ototoxicity by pesticides, with causal nexus mainly based on minor neuropsychiatric disorders. The sample was composed of rural workers with health problems, in working age, having started working early in life, exposed to various pesticides, including organophosphates. The auditory and neurovegetative symptoms reported were noise discomfort (n = 2), speech perception difficulty (n = 3), dizziness (n = 2), and imbalance (n = 2). The pure-tone audiometry revealed a sensorineural hearing loss in one or more high frequencies, and one of the cases presented alteration in the brainstem auditory evoked potentials. There is evidence, in this study, of an association between hearing loss and work in tobacco growers exposed to pesticides, with peripheral auditory damage in four cases, and central damage in one of them. Thus, the need for a complete audiological evaluation of pesticide-exposed populations is highlighted.
Sustained auditory attention ability in children based on the breathing mode
Authors: Braga Junior Joel de , Blanco-Dutra Ana Paula , Pinheiro Maria Madalena Canina
Published: June 2020

ABSTRACT Purpose: to analyze the auditory behavior and the performance in the Sustained Auditory Attention Ability Test (SAAAT) of children with different breathing modes. Methods: a total of 30 individuals (19 males and 11 females), ranging from seven to 11 years old, participated in the research. They were distributed into nasal-breathers, mouth-breathers, and oronasal-breathers groups. The Scale of Auditory Behaviors (SAB) questionnaire was applied to those responsible for the children, so as to characterize their auditory behavior. In the SAAAT, the following error patterns were analyzed: inattention, decreased vigilance, impulsivity, and total errors. For the inferential analysis, the Mann-Whitney, chi-square, and Kruskal-Wallis statistical tests were used. Results: the individuals with mouth- and oronasal-breathing modes had more complaints in the SAB questionnaire when compared with nasal-breathers. Regarding the SAAAT, it was found that the age group of seven to eight years was the one that had the worst performance in the test. The groups with oral and oronasal breathing had more errors, with a statistically significant difference between the groups for inattention and total errors. Conclusion: it can be concluded that individuals with oral and oronasal breathing had more complaints in the auditory behavior and worse performance in the SAAAT, especially for inattention and total errors.
Bioactive compounds, processing quality and yield of colored flesh potato clones
Authors: Cima Francieli Fatima , Pereira Elisa dos S , Schiavon Marina V , Munhoz Priscila C , Lenz Emerson A , Wolter Daiana D , Silva Tuane A da , Azevedo Fernanda Q , Vizzotto Márcia , Pereira Arione da S
Published: June 2020

ABSTRACT Colored flesh potatoes contain high concentrations of phytochemicals, such as phenolic acids, anthocyanins and carotenoids, which have therapeutic effects and promoters of human health. The objective of the study was to evaluate the concentration of bioactive compounds and industrial quality and yield traits of potato tubers. Four experiments were carried out in Pelotas-RS. In 2012 and 2013, tuber yield traits were evaluated and in 2014 and 2015, the bioactive compounds (phenolic compounds, anthocyanins and carotenoids), antioxidant activity and industrial quality traits [glucose content, dry matter (%) and frying color] were assessed. Clones C2715-01-09, C2721-22-09 and C2743-01-09, having fully pigmented purple flesh presented high concentrations of bioactive compounds and high antioxidant capacity and reasonable processing quality traits and yield potential. Clone C2743-09-09 with white flesh interspersed with purple was the most productive and had good processing attributes, but showed lower levels of bioactive compounds and moderate antioxidant activity when compared to completely purple clones.
Estigma e preconceito na atualidade: vivência dos portadores de tuberculose em oficinas de terapia ocupacional
Authors: Fernandes Thauana dos Santos , Pedrosa Noelle da Silva , Garcia Marcia Karolyne de Quadros , Silva Angela Maria Bittencourt Fernandes da
Published: June 2020

Resumo Objetivo: Avaliar qualitativamente o estigma e o preconceito vividos pelos portadores de tuberculose em seu contexto socioafetivo profissional, suas relações e subjetividades. Método: Estudo avaliativo-qualitativo, desenvolvido por meio da Avaliação de Quarta Geração, realizado com dois grupos de interesse (18 clientes e 11 familiares) atendidos pelo Sistema Único de Saúde da Zona Oeste do Rio de Janeiro. Para a coleta de dados, utilizaram-se a observação, a entrevista individual e os grupos de acolhimento da terapia ocupacional; a análise ocorreu pelo método hermenêutico-dialético-comparativo. Resultados: O processo avaliativo evidenciou que a sociedade percebe os portadores de tuberculose a partir da sua imagem virtual, desconsiderando sua imagem real, ou seja, predomina a identidade estigmatizante, gerando a ideia de pessoas não cidadãs, sem direito a um lugar social, ao trabalho, que devem ser excluídas, abolindo sua singularidade, potencializando situações de vulnerabilidade, sendo taxados como aqueles que não se encaixam no ideário da sociedade. Conclusão: É necessário desmitificar o imaginário social que exclui o portador de tuberculose, sendo pertinente o desenvolvimento de políticas públicas com ações de educação em saúde, prevenção, informação e combate ao estigma.
A accountability das Organizações Sociais no SUS: uma análise do papel institucional do Conselho Estadual de Saúde em Pernambuco
Authors: Pacheco Hélder Freire , Leal Eliane Maria Medeiros , Gurgel Júnior Garibaldi Dantas , Santos Francisco de Assis da Silva , Medeiros Kátia Rejane de
Published: June 2020

Resumo Este artigo tem como objetivo analisar a percepção dos integrantes dos órgãos de controles interno e externo da gestão pública, acerca do papel do Conselho Estadual de Saúde na accountability das Organizações Sociais em Saúde. Trata-se de estudo analítico, com abordagem qualitativa, baseado em entrevistas semiestruturadas, com amostra intencional. Dez atores-chave responsáveis pelos mecanismos dos controles interno e externo do Sistema Único de Saúde (SUS) foram selecionados. Os dados revelaram o papel do Conselho em relação às Organizações Sociais em Saúde, que consiste em fiscalizar a gestão dessas organizações como representantes da sociedade. Agir de forma transparente, bem como viabilizar a prestação de contas das Organizações Sociais com linguagem adequada para os integrantes do Conselho, além da independência política e liberdade para fiscalização das ações de saúde, são premissas que devem ser atendidas para que o processo de acompanhamento seja mais efetivo e propositivo na visão desses atores institucionais.
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature
Authors: Cheng Chaoran , Tan Fei , Wei Zhi
Published: June 2020

We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.
CONAN: Complementary Pattern Augmentation for Rare Disease Detection
Authors: Cui Limeng , Biswal Siddharth , Glass Lucas M. , Lever Greg , Sun Jimeng , Xiao Cao
Published: June 2020

Rare diseases affect hundreds of millions of people worldwide but are hard to detect since they have extremely low prevalence rates (varying from 1/1,000 to 1/200,000 patients) and are massively underdiagnosed. How do we reliably detect rare diseases with such low prevalence rates? How to further leverage patients with possibly uncertain diagnosis to improve detection? In this paper, we propose a Complementary pattern Augmentation (CONAN) framework for rare disease detection. CONAN combines ideas from both adversarial training and max-margin classification. It first learns self-attentive and hierarchical embedding for patient pattern characterization. Then, we develop a complementary generative adversarial networks (GAN) model to generate candidate positive and negative samples from the uncertain patients by encouraging a max-margin between classes. In addition, CONAN has a disease detector that serves as the discriminator during the adversarial training for identifying rare diseases. We evaluated CONAN on two disease detection tasks. For low prevalence inflammatory bowel disease (IBD) detection, CONAN achieved .96 precision recall area under the curve (PR-AUC) and 50.1% relative improvement over the best baseline. For rare disease idiopathic pulmonary fibrosis (IPF) detection, CONAN achieves .22 PR-AUC with 41.3% relative improvement over the best baseline.
Graduate Employment Prediction with Bias
Authors: Guo Teng , Xia Feng , Zhen Shihao , Bai Xiaomei , Zhang Dongyu , Liu Zitao , Tang Jiliang
Published: June 2020

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students' employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework.
Accurate Structured-Text Spotting for Arithmetical Exercise Correction
Authors: Hu Yiqing , Zheng Yan , Liu Hao , Jiang Dequang , Liu Yinsong , Ren Bo
Published: June 2020

Correcting arithmetical exercise is a labor intensive and time consuming task for primary school teachers all the time. To reduce their burdens, we propose Arithmetical Exercise Checker (AEC), which is the first system that automatically evaluates all arithmetical expressions (AEs) on exercise images. The major challenge is that AE is formed by printed and handwritten texts with particular arithmetical patterns (e.g., multi-line, fraction). Despite being part of AE, handwritten texts usually lead to zigzag boundaries and tangled rows. What's worse, AE may be arithmetical incorrect, which makes the contextual information less valuable for recognition. To tackle these problems, we introduce integrated detection, recognition and evaluation branches by leveraging AE's intrinsic features, namely 1) boundary indistinctive, 2) locally relevant patterns and 3) globally irrelevant symbols. Experimental results demonstrate that AEC yields a 93.72% correction accuracy on 40 kinds of mainstream primary arithmetical exercises. So far, the online service of AEC processes 75, 000 arbitrary exercises on average per day, and already reduced the burden of over 1, 000, 000 users. AEC shows the benefits for implementing an vision-based system as a way to aid teachers in reducing reduplicative tasks.
Generating Realistic Stock Market Order Streams
Authors: Li Junyi , Wang Xintong , Lin Yaoyang , Sinha Arunesh , Wellman Michael
Published: June 2020

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions to improve the generation task. We perform an ablation study to verify the usefulness of aspects of our network structure. We provide a mathematical characterization of distribution learned by the generator. We also propose statistics to measure the quality of generated orders. We test our approach with synthetic and actual market data, compare to many baseline generative models, and find the generated data to be close to real data.
SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-World Verification
Authors: Lai Songxuan , Jin Lianwen , Lin Luojun , Zhu Yecheng , Mao Huiyun
Published: June 2020

An open research problem in automatic signature verification is the skilled forgery attacks. However, the skilled forgeries are very difficult to acquire for representation learning. To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures. First, a neuromotor inspired signature synthesis method is proposed to synthesize signatures with different distortion levels for any template signature. Then, given the templates, we construct a lightweight one-dimensional convolutional network to learn to rank the synthesized samples, and directly optimize the average precision of the ranking to exploit relative and fine-grained signature similarities. Finally, after training, fixed-length representations can be extracted from dynamic signatures of variable lengths for verification. One highlight of our method is that it requires neither skilled nor random forgeries for training, yet it surpasses the state-of-the-art by a large margin on two public benchmarks.
Robust Low-Rank Discovery of Data-Driven Partial Differential Equations
Authors: Li Jun , Sun Gan , Zhao Guoshuai , Lehman Li-wei H.
Published: June 2020

Partial differential equations (PDEs) are essential foundations to model dynamic processes in natural sciences. Discovering the underlying PDEs of complex data collected from real world is key to understanding the dynamic processes of natural laws or behaviors. However, both the collected data and their partial derivatives are often corrupted by noise, especially from sparse outlying entries, due to measurement/process noise in the real-world applications. Our work is motivated by the observation that the underlying data modeled by PDEs are in fact often low rank. We thus develop a robust low-rank discovery framework to recover both the low-rank data and the sparse outlying entries by integrating double low-rank and sparse recoveries with a (group) sparse regression method, which is implemented as a minimization problem using mixed nuclear norms with ℓ1 and ℓ0 norms. We propose a low-rank sequential (grouped) threshold ridge regression algorithm to solve the minimization problem. Results from several experiments on seven canonical models (i.e., four PDEs and three parametric PDEs) verify that our framework outperforms the state-of-art sparse and group sparse regression methods. Code is available at https://github.com/junli2019/Robust-Discovery-of-PDEs
Privacy-Preserving Gradient Boosting Decision Trees
Authors: Li Qinbin , Wu Zhaomin , Wen Zeyi , He Bingsheng
Published: June 2020

The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT while preserving the strong guarantee of differential privacy. Sensitivity and privacy budget are two key design aspects for the effectiveness of differential private models. Existing solutions for GBDT with differential privacy suffer from the significant accuracy loss due to too loose sensitivity bounds and ineffective privacy budget allocations (especially across different trees in the GBDT model). Loose sensitivity bounds lead to more noise to obtain a fixed privacy level. Ineffective privacy budget allocations worsen the accuracy loss especially when the number of trees is large. Therefore, we propose a new GBDT training algorithm that achieves tighter sensitivity bounds and more effective noise allocations. Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds. Furthermore, we design a novel boosting framework to allocate the privacy budget between trees so that the accuracy loss can be further reduced. Our experiments show that our approach can achieve much better model accuracy than other baselines.
PSENet: Psoriasis Severity Evaluation Network
Authors: Li Yi , Wu Zhe , Zhao Shuang , Wu Xian , Kuang Yehong , Yan Yangtian , Ge Shen , Wang Kai , Fan Wei , Chen Xiang , Wang Yong
Published: June 2020

Psoriasis is a chronic skin disease which affects hundreds of millions of people around the world. This disease cannot be fully cured and requires lifelong caring. If the deterioration of Psoriasis is not detected and properly treated in time, it could cause serious complications or even lead to a life threat. Therefore, a quantitative measurement that can track the Psoriasis severity is necessary. Currently, PASI (Psoriasis Area and Severity Index) is the most frequently used measurement in clinical practices. However, PASI has the following disadvantages: (1) Time consuming: calculating PASI usually takes more than 30 minutes which poses a heavy burden on dermatologists; and (2) Inconsistency: due to the complexity of PASI calculation, different or even the same dermatologist could give different scores for the same case. To overcome these drawbacks, we propose PSENet which applies deep neural networks to estimate Psoriasis severity based on skin lesion images. Different from typical deep learning frameworks for image processing, PSENet has the following characteristics: (1) PSENet introduces a score refine module which is able to capture the visual features of skin at both coarse and fine-grained granularities; (2) PSENet uses siamese structure in training and accepts pairwise inputs, which reduces the dependency on large amount of training data; and (3) PSENet can not only estimate the severity, but also locate the skin lesion regions from the input image. To train and evaluate PSENet, we work with professional dermatologists from a top hospital and spend years in building a golden dataset. The experimental results show that PSENet can achieve the mean absolute error of 2.21 and the accuracy of 77.87% in pair comparison, outperforming baseline methods. Overall, PSENet not only relieves dermatologists from the dull PASI calculation but also enables patients to track Psoriasis severity in a much more convenient manner.
Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-Like Molecules
Authors: Pathak Yashaswi , Laghuvarapu Siddhartha , Mehta Sarvesh , Priyakumar U. Deva
Published: June 2020

Solubility of drug molecules is related to pharmacokinetic properties such as absorption and distribution, which affects the amount of drug that is available in the body for its action. Computational or experimental evaluation of solvation free energies of drug-like molecules/solute that quantify solubilities is an arduous task and hence development of reliable computationally tractable models is sought after in drug discovery tasks in pharmaceutical industry. Here, we report a novel method based on graph neural network to predict solvation free energies. Previous studies considered only the solute for solvation free energy prediction and ignored the nature of the solvent, limiting their practical applicability. The proposed model is an end-to-end framework comprising three phases namely, message passing, interaction and prediction phases. In the first phase, message passing neural network was used to compute inter-atomic interaction within both solute and solvent molecules represented as molecular graphs. In the interaction phase, features from the preceding step is used to calculate a solute-solvent interaction map, since the solvation free energy depends on how (un)favorable the solute and solvent molecules interact with each other. The calculated interaction map that captures the solute-solvent interactions along with the features from the message passing phase is used to predict the solvation free energies in the final phase. The model predicts solvation free energies involving a large number of solvents with high accuracy. We also show that the interaction map captures the electronic and steric factors that govern the solubility of drug-like molecules and hence is chemically interpretable.
DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series
Authors: Tan Qingxiong , Ye Mang , Yang Baoyao , Liu Siqi , Ma Andy Jinhua , Yip Terry Cheuk-Fung , Wong Grace Lai-Hung , Yuen PongChi
Published: June 2020

Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly and different physiological variables are examined at each visit, producing large amounts of irregular multivariate time series (IMTS) data with missing values and varying intervals. Existing methods process IMTS into regular data so that standard machine learning models can be employed. However, time intervals are usually determined by the status of patients, while missing values are caused by changes in symptoms. Therefore, we propose a novel end-to-end Dual-Attention Time-Aware Gated Recurrent Unit (DATA-GRU) for IMTS to predict the mortality risk of patients. In particular, DATA-GRU is able to: 1) preserve the informative varying intervals by introducing a time-aware structure to directly adjust the influence of the previous status in coordination with the elapsed time, and 2) tackle missing values by proposing a novel dual-attention structure to jointly consider data-quality and medical-knowledge. A novel unreliability-aware attention mechanism is designed to handle the diversity in the reliability of different data, while a new symptom-aware attention mechanism is proposed to extract medical reasons from original clinical records. Extensive experimental results on two real-world datasets demonstrate that DATA-GRU can significantly outperform state-of-the-art methods and provide meaningful clinical interpretation.
Finding Minimum-Weight Link-Disjoint Paths with a Few Common Nodes
Authors: Tao Binglin , Xiao Mingyu , Zhao Jingyang
Published: June 2020

Network survivability has drawn certain interest in network optimization. However, the demand for full protection of a network is usually too restrictive. To overcome the limitation of geographical environments and to save network resources, we turn to establish backup networks allowing a few common nodes. It comes out the problem of finding k link-disjoint paths between a given pair of source and sink in a network such that the number of common nodes shared by at least two paths is bounded by a constant and the total link weight of all paths is minimized under the above constraints. For the case k = 2, where we have only one backup path, several fast algorithms have been developed in the literature. For the case k > 2, little results are known. In this paper, we first establish the NP-hardness of the problem with general k. Motivated by the situation that each node in a network may have a capability of multicasting, we also study a restricted version with one more requirement that each node can be shared by at most two paths. For the restricted version, we build an ILP model and design a fast algorithm by using the techniques of augmenting paths and splitting nodes. Furthermore, experimental results on synthetic and real networks show that our algorithm is effective in practice.
Robust Adversarial Objects against Deep Learning Models
Authors: Tsai Tzungyu , Yang Kaichen , Ho Tsung-Yi , Jin Yier
Published: June 2020

Previous work has shown that Deep Neural Networks (DNNs), including those currently in use in many fields, are extremely vulnerable to maliciously crafted inputs, known as adversarial examples. Despite extensive and thorough research of adversarial examples in many areas, adversarial 3D data, such as point clouds, remain comparatively unexplored. The study of adversarial 3D data is crucial considering its impact in real-life, high-stakes scenarios including autonomous driving. In this paper, we propose a novel adversarial attack against PointNet++, a deep neural network that performs classification and segmentation tasks using features learned directly from raw 3D points. In comparison to existing works, our attack generates not only adversarial point clouds, but also robust adversarial objects that in turn generate adversarial point clouds when sampled both in simulation and after construction in real world. We also demonstrate that our objects can bypass existing defense mechanisms designed especially against adversarial 3D data.
Incorporating Expert-Based Investment Opinion Signals in Stock Prediction: A Deep Learning Framework
Authors: Wang Heyuan , Wang Tengjiao , Li Yi
Published: June 2020

Investment messages published on social media platforms are highly valuable for stock prediction. Most previous work regards overall message sentiments as forecast indicators and relies on shallow features (bag-of-words, noun phrases, etc.) to determine the investment opinion signals. These methods neither capture the time-sensitive and target-aware characteristics of stock investment reviews, nor consider the impact of investor's reliability. In this study, we provide an in-depth analysis of public stock reviews and their application in stock movement prediction. Specifically, we propose a novel framework which includes the following three key components: time-sensitive and target-aware investment stance detection, expert-based dynamic stance aggregation, and stock movement prediction. We first introduce our stance detection model named MFN, which learns the representation of each review by integrating multi-view textual features and extended knowledge in financial domain to distill bullish/bearish investment opinions. Then we show how to identify the validity of each review, and enhance stock movement prediction by incorporating expert-based aggregated opinion signals. Experiments on real datasets show our framework can effectively improve the performance of both investment opinion mining and individual stock forecasting.
HDK: Toward High-Performance Deep-Learning-Based Kirchhoff Analysis
Authors: Wang Xinying , Tawose Olamide Timothy , Yan Feng , Zhao Dongfang
Published: June 2020

The Kirchhoff law is one of the most widely used physical laws in many engineering principles, e.g., biomedical engineering, electrical engineering, and computer engineering. One challenge of applying the Kirchhoff law to real-world applications at scale lies in the high, if not prohibitive, computational cost to solve a large number of nonlinear equations. Despite recent advances in leveraging a convolutional neural network (CNN) to estimate the solutions of Kirchhoff equations, the low performance is still significantly hindering the broad adoption of CNN-based approaches. This paper proposes a high-performance deep-learning-based approach for Kirchhoff analysis, namely HDK. HDK employs two techniques to improve the performance: (i) early pruning of unqualified input candidates and (ii) parallelization of forward labelling. To retain high accuracy, HDK also applies various optimizations to the data such as randomized augmentation and dimension reduction. Collectively, the aforementioned techniques improve the analysis speed by 8× with accuracy as high as 99.6%.
Accelerating and Improving AlphaZero Using Population Based Training
Authors: Wu Ti-Rong , Wei Ting-Han , Wu I-Chen
Published: June 2020

AlphaZero has been very successful in many games. Unfortunately, it still consumes a huge amount of computing resources, the majority of which is spent in self-play. Hyperparameter tuning exacerbates the training cost since each hyperparameter configuration requires its own time to train one run, during which it will generate its own self-play records. As a result, multiple runs are usually needed for different hyperparameter configurations. This paper proposes using population based training (PBT) to help tune hyperparameters dynamically and improve strength during training time. Another significant advantage is that this method requires a single run only, while incurring a small additional time cost, since the time for generating self-play records remains unchanged though the time for optimization is increased following the AlphaZero training algorithm. In our experiments for 9x9 Go, the PBT method is able to achieve a higher win rate for 9x9 Go than the baselines, each with its own hyperparameter configuration and trained individually. For 19x19 Go, with PBT, we are able to obtain improvements in playing strength. Specifically, the PBT agent can obtain up to 74% win rate against ELF OpenGo, an open-source state-of-the-art AlphaZero program using a neural network of a comparable capacity. This is compared to a saturated non-PBT agent, which achieves a win rate of 47% against ELF OpenGo under the same circumstances.
Scalable and Generalizable Social Bot Detection through Data Selection
Authors: Yang Kai-Cheng , Varol Onur , Hui Pik-Mai , Menczer Filippo
Published: June 2020

Efficient and reliable social bot classification is crucial for detecting information manipulation on social media. Despite rapid development, state-of-the-art bot detection models still face generalization and scalability challenges, which greatly limit their applications. In this paper we propose a framework that uses minimal account metadata, enabling efficient analysis that scales up to handle the full stream of public tweets of Twitter in real time. To ensure model accuracy, we build a rich collection of labeled datasets for training and validation. We deploy a strict validation system so that model performance on unseen datasets is also optimized, in addition to traditional cross-validation. We find that strategically selecting a subset of training data yields better model accuracy and generalization than exhaustively training on all available data. Thanks to the simplicity of the proposed model, its logic can be interpreted to provide insights into social bot characteristics.
MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control
Authors: Zang Xinshi , Yao Huaxiu , Zheng Guanjie , Xu Nan , Xu Kai , Li Zhenhui
Published: June 2020

Using reinforcement learning for traffic signal control has attracted increasing interests recently. Various value-based reinforcement learning methods have been proposed to deal with this classical transportation problem and achieved better performances compared with traditional transportation methods. However, current reinforcement learning models rely on tremendous training data and computational resources, which may have bad consequences (e.g., traffic jams or accidents) in the real world. In traffic signal control, some algorithms have been proposed to empower quick learning from scratch, but little attention is paid to learning by transferring and reusing learned experience. In this paper, we propose a novel framework, named as MetaLight, to speed up the learning process in new scenarios by leveraging the knowledge learned from existing scenarios. MetaLight is a value-based meta-reinforcement learning workflow based on the representative gradient-based meta-learning algorithm (MAML), which includes periodically alternate individual-level adaptation and global-level adaptation. Moreover, MetaLight improves the-state-of-the-art reinforcement learning model FRAP in traffic signal control by optimizing its model structure and updating paradigm. The experiments on four real-world datasets show that our proposed MetaLight not only adapts more quickly and stably in new traffic scenarios, but also achieves better performance.
Spatio-Temporal Graph Structure Learning for Traffic Forecasting
Authors: Zhang Qi , Chang Jianlong , Meng Gaofeng , Xiang Shiming , Pan Chunhong
Published: June 2020

As an indispensable part in Intelligent Traffic System (ITS), the task of traffic forecasting inherently subjects to the following three challenging aspects. First, traffic data are physically associated with road networks, and thus should be formatted as traffic graphs rather than regular grid-like tensors. Second, traffic data render strong spatial dependence, which implies that the nodes in the traffic graphs usually have complex and dynamic relationships between each other. Third, traffic data demonstrate strong temporal dependence, which is crucial for traffic time series modeling. To address these issues, we propose a novel framework named Structure Learning Convolution (SLC) that enables to extend the traditional convolutional neural network (CNN) to graph domains and learn the graph structure for traffic forecasting. Technically, SLC explicitly models the structure information into the convolutional operation. Under this framework, various non-Euclidean CNN methods can be considered as particular instances of our formulation, yielding a flexible mechanism for learning on the graph. Along this technical line, two SLC modules are proposed to capture the global and local structures respectively and they are integrated to construct an end-to-end network for traffic forecasting. Additionally, in this process, Pseudo three Dimensional convolution (P3D) networks are combined with SLC to capture the temporal dependencies in traffic data. Extensively comparative experiments on six real-world datasets demonstrate our proposed approach significantly outperforms the state-of-the-art ones.
Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction
Authors: Zhang Weijia , Liu Hao , Liu Yanchi , Zhou Jingbo , Xiong Hui
Published: June 2020

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting city-wide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of \hmgnn outperforms seven state-of-the-art baselines.
A Novel Learning Framework for Sampling-Based Motion Planning in Autonomous Driving
Authors: Zhang Yifan , Zhang Jinghuai , Zhang Jindi , Wang Jianping , Lu Kejie , Hong Jeff
Published: June 2020

Sampling-based motion planning (SBMP) is a major trajectory planning approach in autonomous driving given its high efficiency in practice. As the core of SBMP schemes, sampling strategy holds the key to whether a smooth and collision-free trajectory can be found in real-time. Although some bias sampling strategies have been explored in the literature to accelerate SBMP, the trajectory generated under existing bias sampling strategies may lead to sharp lane changing. To address this issue, we propose a new learning framework for SBMP. Specifically, we develop a novel automatic labeling scheme and a 2-Stage prediction model to improve the accuracy in predicting the intention of surrounding vehicles. We then develop an imitation learning scheme to generate sample points based on the experience of human drivers. Using the prediction results, we design a new bias sampling strategy to accelerate the SBMP algorithm by strategically selecting necessary sample points that can generate a smooth and collision-free trajectory and avoid sharp lane changing. Data-driven experiments show that the proposed sampling strategy outperforms existing sampling strategies, in terms of the computing time, traveling time, and smoothness of the trajectory. The results also show that our scheme is even better than human drivers.
OF-MSRN: Optical Flow-Auxiliary Multi-Task Regression Network for Direct Quantitative Measurement, Segmentation and Motion Estimation
Authors: Zhao Chengqian , Feng Cheng , Li Dengwang , Li Shuo
Published: June 2020

Comprehensively analyzing the carotid artery is critically significant to diagnosing and treating cardiovascular diseases. The object of this work is to simultaneously achieve direct quantitative measurement and automated segmentation of the lumen diameter and intima-media thickness as well as the motion estimation of the carotid wall. No work has simultaneously achieved the comprehensive analysis of carotid artery due to three intractable challenges: 1) Tiny intima-media is more challenging to measure and segment; 2) Artifact generated by radial motion restrict the accuracy of measurement and segmentation; 3) Occlusions on diseased carotid walls generate dynamic complexity and indeterminacy. In this paper, we propose a novel optical flow-auxiliary multi-task regression network named OF-MSRN to overcome these challenges. We concatenate multi-scale features to a regression network to simultaneously achieve measurement and segmentation, which makes full use of the potential correlation between the two tasks. More importantly, we creatively explore an optical flow auxiliary module to take advantage of the co-promotion of segmentation and motion estimation to overcome the restrictions of the radial motion. Besides, we evaluate consistency between forward and backward optical flow to improve the accuracy of motion estimation of the diseased carotid wall. Extensive experiments on US sequences of 101 patients demonstrate the superior performance of OF-MSRN on the comprehensive analysis of the carotid artery by utilizing the dual optimization of the optical flow auxiliary module.
MaskGEC: Improving Neural Grammatical Error Correction via Dynamic Masking
Authors: Zhao Zewei , Wang Houfeng
Published: June 2020

Grammatical error correction (GEC) is a promising natural language processing (NLP) application, whose goal is to change the sentences with grammatical errors into the correct ones. Neural machine translation (NMT) approaches have been widely applied to this translation-like task. However, such methods need a fairly large parallel corpus of error-annotated sentence pairs, which is not easy to get especially in the field of Chinese grammatical error correction. In this paper, we propose a simple yet effective method to improve the NMT-based GEC models by dynamic masking. By adding random masks to the original source sentences dynamically in the training procedure, more diverse instances of error-corrected sentence pairs are generated to enhance the generalization ability of the grammatical error correction model without additional data. The experiments on NLPCC 2018 Task 2 show that our MaskGEC model improves the performance of the neural GEC models. Besides, our single model for Chinese GEC outperforms the current state-of-the-art ensemble system in NLPCC 2018 Task 2 without any extra knowledge.
RiskOracle: A Minute-Level Citywide Traffic Accident Forecasting Framework
Authors: Zhou Zhengyang , Wang Yang , Xie Xike , Chen Lianliang , Liu Hengchang
Published: June 2020

Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. Specifically, we first transform the zero-risk values in labels to fit the training network. Then, we propose the Differential Time-varying Graph neural network (DTGN) to capture the immediate changes of traffic status and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and region selection schemes to highlight citywide most-likely accident subregions, bridging the gap between biased risk values and sporadic accident distribution. Extensive experiments on two real-world datasets demonstrate the effectiveness and scalability of our RiskOracle framework.
ITE Reform at the University of Glasgow: Principles, Research-basis and Implications
Authors: Dickson Beth
Published: June 2020

The Faculty of Education at the University of Glasgow's reform of initial teacher education was undertaken on the basis of current research within a mature educational infrastructure. Within the university research knowledge was utilized in two ways: research on teacher education indicated
that enquiry could become a key aspect of teacher identity; and it indicated the need for a curriculum for pre-service teachers in schools. Thus enquiry learning was embedded in schools and the new school-based curriculum had three elements: seminars; peer learning through learning
rounds; joint-assessed visits. These innovations were positively reinforced by Teaching Scotland's Future (Donaldson, 2011). This series of reforms has implications for Wales and can be usefully analysed against the binary thinking which dominates discourses in teacher education; and
Williams's thought on the vulnerability of emergent culture. Four binaries are identified and re-conceptualised: binaries of time, space, content and persons. The binary of time (initial and continuing teacher education is conceptualised a career-long process; the binary of space (school and
university) is recast as a third space; the binary of content (theory and practice) is recast as different forms of knowledge permeating space and time; and the binary of persons is recast as a (university-based teacher educator and pre-service teacher) is recast as a triad which sets all
three in dialogue. Implications include the deeper consideration of career-long teacher learning; and the role of the teacher educator. This emergent practice may be vulnerable to dominant practice.